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5th World Conference on Information Systems for Business...
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Monday, October 19
 

9:28am PDT

Opening Remarks
Monday October 19, 2026 9:28am - 9:30am PDT
Virtual Room A
Monday October 19, 2026 9:28am - 9:30am PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 7A, Virtual Room A

9:28am PDT

Opening Remarks
Monday October 19, 2026 9:28am - 9:30am PDT
Virtual Room B
Monday October 19, 2026 9:28am - 9:30am PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 7B, Virtual Room B

9:28am PDT

Opening Remarks
Monday October 19, 2026 9:28am - 9:30am PDT
Virtual Room C
Monday October 19, 2026 9:28am - 9:30am PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 7C, Virtual Room C

9:28am PDT

Opening Remarks
Monday October 19, 2026 9:28am - 9:30am PDT
Virtual Room D
Monday October 19, 2026 9:28am - 9:30am PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:28am PDT

Opening Remarks
Monday October 19, 2026 9:28am - 9:30am PDT
Virtual Room E
Monday October 19, 2026 9:28am - 9:30am PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 7E, Virtual Room E

9:28am PDT

Opening Remarks
Monday October 19, 2026 9:28am - 9:30am PDT
Virtual Room F
Monday October 19, 2026 9:28am - 9:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 7F, Virtual Room F

9:28am PDT

Opening Remarks
Monday October 19, 2026 9:28am - 9:30am PDT
Virtual Room G
Monday October 19, 2026 9:28am - 9:30am PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 7G, Virtual Room G

9:30am PDT

A Comparative Review of Employee Engagement and Retention Strategies in Public and Private Sector Banks
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A
Authors - Sapna B. Warghane, Ajay Pethe
Abstract - Modern banking institutions recognize employee engagement and retention as critical to achieving long-term goals. These elements influence productivity, innovation, and customer satisfaction. Public and private sector banks adopt different strategies shaped by political culture, regulatory environments, and competitive dynamics. Public Sector Banks (PSBs) offer job security, structured career growth, and welfare benefits, fostering stability and long-term com-mitment. These factors contribute to employee motivation and consistent performance but may limit agility and innovation. In contrast, Private Sector Banks (PvSBs) emphasize performance-based incentives, career mobility, and a dynamic work environment. While these strategies enhance engagement and drive results, they can also lead to stress and higher employee turnover. Organizational culture, compensation, career development, work-life balance, and employee well-being play significant roles in shaping these outcomes. This review compares engagement and retention strategies across both sec-tors, identifying the strengths and challenges of each. A hybrid approach—combining financial rewards, career development opportunities, and wellness initiatives—is recommended to enhance satisfaction and commitment across the board. By refining these strategies, both PSBs and PvSBs can develop a more engaged and resilient workforce, ultimately supporting long-term organizational success and stability.
Paper Presenter
avatar for Sapna B. Warghane
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 7A, Virtual Room A

9:30am PDT

A SYSTEMATIC LITERATURE REVIEW: SAAS MARKETING STRATEGY
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A
Authors - Jannat Khair Chowdhury, S. M. Saikat Hossain, Farzana Sadia, Mahady Hasan, M. Rokonuzzaman, Marufa Akter
Abstract - Developing an effective marketing strategy is crucial for the success of Software-as-a-Service (SaaS) businesses, given the dynamic and competitive nature of the market. This systematic literature review looks at major tactics including pricing structures, client acquisition and retention, and digital technologies. It emphasizes the problems of transferring academic findings to industry, with an emphasis on data-driven solutions such as hybrid pricing, freemium models, and tailored content. The report provides recommendations for optimizing SaaS marketing to promote scalable, long-term growth.
Paper Presenter
avatar for Jannat Khair Chowdhury
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 7A, Virtual Room A

9:30am PDT

Advanced Supply Chain Planning Model to Boost Resilience and Sustainability: Evidence of a Pharmaceutical Supply Chain
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A
Authors - Juan Mejia, Valentino Galindo, Edgar Ramos, Donovan Fuqua
Abstract - This study proposes a conceptual model that integrates advanced supply chain planning to enhance supply chain resilience and sustainability. Drawing from previous literature, the study explores how such planning approaches contribute to strengthening supply chains and improving long-term operational stability. The model is developed through a structured review of academic sources and expert perspectives, with an emphasis on theoretical alignment and systemic relevance. Using the Best-Worst Method (BWM) as a decision-making framework, the paper outlines key factors that may influence effective resource allocation and disruption management. While no empirical validation is conducted, the study highlights potential criteria for improving operations, particularly in terms of reducing disruptions and optimizing resource use. This work offers a foundation for future empirical research and supports ongoing discussions on resilient and sustainable supply chain strategies.
Paper Presenter
avatar for Valentino Galindo
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 7A, Virtual Room A

9:30am PDT

BERT-Based Text Classification Pipeline for Categorising Mental Health Statements
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A
Authors - Divya Saleela, Reshma Lucos, Reji R, Teena Joseph, Shyna A, Chinchu M S, Hashna Rasheed, Arjun Rajesh Panicker
Abstract - The automatic classification of mental health-related textual data is essential for enabling scalable and proactive psychological support. This study employs a fine-tuned BERT-base transformer model categorise statements into seven clinically relevant mental health conditions. The model was evaluated using a publicly available dataset from Kaggle and assessed with standard performance metrics, including accuracy, precision, recall, specificity, and F1-score. Achieving an overall accuracy of 90.00 per cent, the model demonstrated strong generalisability across a linguistically diverse and imbalanced corpus. The highest classification performance was achieved in the Normal, Depression, and Suicidal categories, with satisfactory results obtained for less frequent classes. These findings highlight the effectiveness of transformer-based architectures in mental health text classification and support their potential application in digital screening and early intervention systems.
Paper Presenter
avatar for Divya Saleela

Divya Saleela

United Kingdom
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 7A, Virtual Room A

9:30am PDT

Driving change: Analyzing visual advertisements of Tata EV cars through Archetypal Branding through sustainable advertising
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A
Authors - Gokul B, Arumugam Balasubramanian
Abstract - Climate change and sustainable ecosystem are gaining momentum in the public domain. Innovative technology is steering towards environmental protection enabled products and processes. Tata motors have captured the public imagination by lining up green vehicles with clean fuel. In order to accelerate the adoption of this innovative product they have embarked on an advertising campaign that not only caters to product marketing but also satisfies their inner desire towards contributing their might in the green initiatives. To this end, content analysis is used as a methodological tool to examine whether archetypal advertising strategies align with Tata Motors’ goal of promoting innovation in a sustainable manner. Analysis has come out quite clearly that archetypal advertising strategy are put to effective use to successfully kindle their desire and satisfy the consumers motivation towards adopting automotive innovation in the context of green commerce.
Paper Presenter
avatar for Arumugam Balasubramanian
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 7A, Virtual Room A

9:30am PDT

Empowering Women in Business Leadership Challenges, Opportunity and Strategies for Advancement
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A
Authors - Asra Fatema Zakir Baig, Amol Mashankar
Abstract - Women in business leadership must be empowered through promotion of diversity, innovation, and sustainable growth. Conceding that there have been noticeable gains, women remain way behind in their quest for leadership positions, due to multifaceted barriers, such as systemic biases, limited access to their networks, and societal expectation; this paper discusses the organizational culture and its gender-based stereotypes and the lack of mentorship, which hold back women's advancement in business leadership. Further, it goes on discussing the various opportunities that now come forth from women's leadership empowerment, further focusing on the advantages of a diverse leadership team: better decision-making and improved performance for companies. Through real examples of best practices, the authors demonstrate which organizations have applied some of the best diversity and inclusion strategies followed by positive results from investing in women's leadership development. The paper speaks to actionable strategies an organization can employ in addressing the challenges and seizing the opportunities available. It focuses first on creating an inclusive workplace culture followed by starting out with mentorship and sponsorship programs, targeted training and development. And finally work arrangements and others that also involve policies that support work-life balance and equal pay and works continually in amplifying the need for systemic change in creating an environment that avails women's advancement. Finally, women's empowerment in business leadership is more about equity and strategic advantage as a group for organizations to leverage benefits. In the end, embracing diversity in leadership can help unlock potential, drive innovation, and develop a resilient workforce for businesses. This paper, therefore, calls for collective efforts from stakeholders across all levels to advance women's leadership and pave the way for unlocking the silos of barriers in the business world.
Paper Presenter
avatar for Asra Fatema Zakir Baig
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 7A, Virtual Room A

9:30am PDT

Harnessing Social Media to Advance Social Entrepreneurship in Bangladesh: A Qualitative Study
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A
Authors - Sazzad Hussain Farhaan, Tanvir Hussain Himel, Ahsanul Kaiser, Md. Nayon Ali
Abstract - Utilising digital technology is more crucial than ever, especially social media, to address urgent societal issues and encourage creative solutions. Since traditional infrastructures and resources may be few in underdeveloped nations like Bangladesh, social media provides an affordable and easily accessible avenue for impact, creativity, and collaboration. This strategy continues the heritage of Grameen Bank and Dr. Muhammad Yunus, who pioneered creative social entrepreneurship through microfinance. The use of social media to promote social entrepreneurship in Bangladesh is examined in this qualitative study. Based on 20 in-depth interviews with people involved in social entrepreneurship, the study explores how digital platforms may support social transformation, especially in environments with limited resources. Purposive sampling was used to choose participants, guaranteeing that they had at least a year of experience utilising social media for social entrepreneurship. Social media is a vital tool for outreach, community involvement, fundraising, and creating awareness, according to the findings. By overcoming conventional obstacles, it enables business owners to reach a larger audience and rally support for social causes. Future research can be done on digital infrastructure, and community participation affect social media’s ability to promote social entrepreneurship.
Paper Presenter
avatar for Tanvir Hussain Himel
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 7A, Virtual Room A

9:30am PDT

Impact Analysis of Incubates Funded Under Government Scheme
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A
Authors - Radhika V. Kulkarni, Spandan Papade, Yash Paryani, Saniya Patil, Rahul Madhawani, Prakash Sharma
Abstract - Governments worldwide actively support entrepreneurs tackling pressing social issues, aiming to boost economic opportunities for all, generate employment, and accelerate tech-driven progress. Unfortunately, some recipients exploit these funds for personal gain, sidelining projects that could otherwise drive fair governance, community welfare, and environmentally conscious industries. Manually tracking thousands of funded startups is inefficient, allowing dis-honest actors to slip through the cracks. This study introduces an AI-powered solution to bridge this gap. The system scans public data—such as LinkedIn activity and online footprints—to identify red flags, like founders holding jobs un-related to their funded ventures. Advanced language analysis and pattern recognition techniques assess how closely a founder’s current work aligns with their original proposal, simplifying oversight while fostering synergy between public programs and modern technology platforms. By promoting fairness, transparency, and accountability, the system supports several United Nations Sustainable Development Goals, including SDG 8: Decent Work and Economic Growth, SDG 9: Industry, Innovation, and Infrastructure, and SDG 16: Justice, and Strong Institutions. It enables governments to channel resources more effectively to innovators committed to creating lasting social value.
Paper Presenter
avatar for Rahul Madhawani
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 7A, Virtual Room A

9:30am PDT

Instagram motivations towards sustainable fashion apparel purchase: understanding the dynamics of consumer identities and sustainability related psychological and behavioural constructs
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A
Authors - Ashiqha Sultana, Kalyani Suresh
Abstract - Sustainable fashion apparel purchase on Instagram has utmost importance in recent times, as Indian consumers are currently influenced by many factors that finally lead them to purchase intention. This paper tries to examine and explore the Indian consumer mindset towards sustainable fashion apparel on Instagram and the connection between demographics and consumer identity of the consumers with the sustainable constructs. Responses were taken from 100 respondents using snowball sampling from different parts of India. Results of the study show that there is a significant association between demographics, patterns of interacting with sustainable fashion apparel on Instagram, and Consumer Identity. The study also reveals the strong connection between Indian consumers and purchase intention, consumer identity, sustainable constructs, purchase behaviour, and Instagram motivation.
Paper Presenter
avatar for Ashiqha Sultana
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 7A, Virtual Room A

9:30am PDT

The Impact of Artificial Intelligence on Business Decision-Making: Opportunities, Challenges, and Strategic Implications
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A
Authors - Sapna B. Warghane, Ajay Pethe
Abstract - Artificial Intelligence (AI) has had a significant effect on decision-making in business by providing data-driven decision-making, improving efficiencies and decision-making accuracy. This literature review will elaborate on the impact of Artificial Intelligence on decision-making in business and its various components, such as predictive analytics, automation, risk management, marketing personalization, strategy, etc. It will also explore issues, ethics, and future prospects of AI in business-related decision-making. AI has already been adopted in various industries, including finance, healthcare, retail, and manufacturing, to increase efficiencies and optimize operations while attempting to optimize the customer experience. On the other side of AI, increasing utilization creates other challenges about bias, security, and regulatory compliance must be considered in the sustainable growth of AI in business.
Paper Presenter
avatar for Sapna B. Warghane
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 7A, Virtual Room A

9:30am PDT

A Novel Design of Miniaturized Heptagonal Koch Fractal Wide Band Antenna for 5G mmWave Applications
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B
Authors - J. Silamboli, Divya Rani M S, G Dhivyasri, Nagesh H, J Ajayan, Manaswini R, Kalyandurg Rafeeq Ahmed, M Manikandan
Abstract - This article uses Koch fractals to demonstrate a compact heptagonal wideband antenna for 5G mm-wave applications. This antenna is designed with defective ground planes to enhance impedance matching and employs Koch fractal geometry for wideband characteristics. In terms of performance and wideband features, fractal antennas prove to be more effective than other antennas. This antenna is designed to achieve greater gain and improved bandwidth characteristics while also minimizing interference at higher frequencies. A peak realized gain of 5dBi at 27.58 GHz is observed for the antenna, which offers a bandwidth between 26.5 and 40 GHz. Millimeter-wave integration is well suited to the proposed flexible antenna. A similar response can be found both in the measured results and in the simulated results. It is a compact antenna with a size of 9 mm by 9 mm. With a bandwidth of 7.42GHz, the VSWR for the whole frequency spectrum is less than 2 is achieved with the HFSS Tool. Experiments and simulations yield a good radiation pattern.
Paper Presenter
avatar for J. Silamboli
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 7B, Virtual Room B

9:30am PDT

A Novel Financial Market Prediction Framework Based on Deep Learning for Accurate Trend Forecasting
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B
Authors - Priya Shirley Muller, Yadala Sucharitha, Shilpa Kottapally, Gulshodakhon Ismoilova, Lola Rakhimova, Pundru Chandra Shaker Reddy
Abstract - Many scholars worry about the stock market, a key economic component. Many experts are exploring how to forecast stock prices and market trends. Previous prediction methods centered on geometric and neural network models, which have become admired. Although not typically utilized in financial time series, deep learning offers excellent learning abilities and is ideal for complex timeseries. Due to its cyclic structure, the LSTM network has long-term memory, making it perfect for financial timeseries anticipation. A new stock closing price anticipation framework yields better predictions than standard models in the study. Data pre and postprocessing use empirical-wavelet-transform (EWT) and outlier-resilient-extreme learning-machine (ORELM) models. Dropout strategy and particle swarm optimization (PSO) method optimize the mixed frame's primary component, an LSTM predictor. Each hybrid framework algorithm can maximize its functionalities to improve prediction ac-curacy. Three hard datasets are used for predicting trials to test the model. To demonstrate the framework's efficacy, some comparison models are chosen. Investigational results reveal that the hybrid approach proposed in the study has the best anticipation accuracy and may be used for stock market data investigation.
Paper Presenter
avatar for Yadala Sucharitha
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 7B, Virtual Room B

9:30am PDT

Enhancing Personal Financial Decision-Making in the Digital Era: Awareness, Adoption, and Influence of Digital Personal Finance Tools
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B
Authors - Chandana M C, Roshna Rahim, Aarathy K Sudhakaran
Abstract - In the current situation where technology is getting an important part of our day-to-day life, digital money management is an important necessity and convenience. This study examines the role of digital personal finance literacy and how it influences people’s financial behaviors, decision-making, and overarching stability. It looks at how aware people are of digital financial tools, how they feel about digital versus traditional methods, and the outcomes of certain investing techniques. The research highlights how digital tools can make people financially empowered but also acknowledgeable hurdles such as lack of access and know-how. Skills, to create economically successful and knowledgeable communities and in this way fosters social inclusion. It focuses on enhancing digital finance literacy to contribute to building economically strong and sustainable communities in this dynamically evolving digital period. The study will require 156 people to gain factual results, including those from various backgrounds and locations. A structured questionnaire will be filled through a google form to collect data. The questions will cover areas such as awareness of digital financial services, changes in behavior due to these services, preference for digital or traditional platforms, and long-term economic impacts. The questionnaire will be a set of close-ended questions comprising mostly multiple-choice options, with a few examples using a Likert scale so that it can be analyzed in an easy and evident way.
Paper Presenter
avatar for Chandana M C
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 7B, Virtual Room B

9:30am PDT

HomeCookSL: A Digital Approach to Addressing Food Insecurity through Empowering Home-Based Food Businesses with focus to Nutrition, Hygiene and Business Sustainability
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B
Authors - Achala Fernando, Shyani Siriwardene, Salitha Dinushika
Abstract - "Food Insecurity" is a widespread global challenge that significantly impacts individuals, particularly students and working professionals living away from home. This issue extends beyond mere access to food, encompassing the quality, nutrition, and hygiene of the available options. Home-based food (HBF) vendors often face difficulties in establishing and sustaining their presence in the competitive food market, further complicating consumers' access to nutritious and hygienic meals. This research aims to address these challenges through HomeCookSL, a digital platform designed to empower HBF vendors by enabling them to effectively showcase their recipes while providing users with comprehensive nutritional information and meals tailored to their dietary preferences. By facilitating informed meal decisions and promoting healthy eating habits, HomeCookSL not only enhances consumer satisfaction but also fosters the growth and sustainability of local HBF businesses, contributing to a more resilient food ecosystem.
Paper Presenter
avatar for Achala Fernando
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 7B, Virtual Room B

9:30am PDT

MasquerAge: Anonymous Age Verification Using the Modular Open Source Identity Platform
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B
Authors - Ivan Cassidy A. Cadiang, Jared L. Hechanova, Wilson M. Tan
Abstract - Traditional identity verification methods pose significant challenges to user privacy and security, often requiring individuals to disclose more personal information than necessary for manual verification. As the country advances toward a digital economy, the Philippine Identification System (PhilSys) provides an opportunity to shift from physical document checks to a unified and secure digital identity framework. This paper introduces MasquerAge, an age verification system leveraging the Modular Open Source Identity Platform (MOSIP). The system utilizes MOSIP-based IDs, containing QR codes aligned with PhilSys specifications, to perform secure age verification. A web application was developed to facilitate scanning QR codes and authenticating users via MOSIP APIs. Performance testing showed the system to be generally efficient; however, cryptographic processes required by MOSIP introduced noticeable latency. The results highlight the viability of a privacy-preserving age verification system while identifying areas for optimizations and security enhancements in real-world implementations.
Paper Presenter
avatar for Wilson M. Tan

Wilson M. Tan

philippines
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 7B, Virtual Room B

9:30am PDT

Optimizing banks' decision-making architecture in the context of corporate and business-oriented BI Models
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B
Authors - Efrem MBAKI LUZAYISU
Abstract - The aim is to analyze the possibilities of optimizing decision-making systems in financial institutions, essentially based on a comparison of two models: centralized (corporate) BI and decentralized (business) BI under four do-mains — decision-making efficiency, data governance, agility and user satisfaction — The article evaluates the advantages and limitations of each approach. The study is based on a qualitative methodology combining semi-structured interviews and documentary analysis, applied to two major European banks. Based on concrete indicators (reporting time, adoption of BI tools, GDPR compliance, satisfaction), the article puts into perspective the trade-offs between centralization and autonomy, and proposes avenues towards hybrid architectures reconciling rigor, flexibility and innovation.
Paper Presenter
avatar for Efrem MBAKI LUZAYISU

Efrem MBAKI LUZAYISU

Democratic Republic of the Congo
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 7B, Virtual Room B

9:30am PDT

Psychological Capital and Entrepreneurial Passion: The Moderating Role of Environmental Dynamism in Chinese Nascent Entrepreneurs
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B
Authors - Mo Xiao, Chatchai Chatpunyakulr, Sarist Gulthawatvichai
Abstract - This study investigates how psychological capital (PsyCap) influences nascent entrepreneurial behavior in China's dynamic economic environment, with a focus on passion mediation and environmental contingencies. Drawing on social cognitive theory and planned behavior frameworks, we propose an integrated model testing (1) PsyCap direct effects on passion and venture activity, (2) passion's mediating role, and (3) environmental dynamism's moderating effects. Using stratified sampling across three Chinese innovation hubs (N=387), we employ mixed methods combining SEM with fuzzy-set qualitative analysis. Findings reveal industry-specific boundary conditions where regulatory turbulence amplifies PsyCap impact in tech sectors but attenuates effects in policy-sensitive domains. The study contributes a cognition-anchored PsyCap measurement model while informing adaptive training protocols for nascent entrepreneurs facing institutional voids. Future research should examine cultural contingencies in emerging economies through longitudinal designs.
Paper Presenter
MX

Mo Xiao

Thailand
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 7B, Virtual Room B

9:30am PDT

Smart Business: AI Applications in Entrepreneurship
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B
Authors - Nitin Prakash, Kajal P. Salampuriya
Abstract - Significant progress has been made in integrating artificial intelligence (AI) with entrepreneurship, which has changed conventional business structures. AI is used by entrepreneurs to improve automation, predictive analytics, and decision- making, which increases operational effectiveness and fosters creativity. This abstract examines the various applications of artificial intelligence (AI) in entrepreneurship, emphasising how these applications can revolutionise strategic planning, consumer involvement, and the development of flexible and adaptable businesses. The adoption of AI technology by entrepreneurs confers a competitive advantage in navigating intricate markets and promoting sustainable growth, hence highlighting the critical role of AI in defining the trajectory of entrepreneurial endeavours.
Paper Presenter
avatar for Nitin Prakash
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 7B, Virtual Room B

9:30am PDT

The Psychology of Digital Brand Engagement: Understanding Consumer Responses to UGC, BGC and AIGC
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B
Authors - Ioseb Gabelaia
Abstract - In the digital attention economy, brands are increasingly utilizing diverse content strategies, including user-generated content (UGC), brand-generated content (BGC), and AI-generated content (AIGC), to engage consumers and shape their brand perception. This research investigates the psychological mechanisms that influence consumer engagement with these three content forms, utilizing cognitive processing theory (CPT). The research insights are based on a survey of 291 respondents, which measured engagement behaviors, emotional responses, and perceived authenticity. Moreover, 17 marketing professionals, through semi-structured interviews, provided insights into content reception, trust dynamics, and platform-specific interaction patterns. Results indicate that UGC produced the highest levels of perceived authenticity and emotional resonance, positively influencing engagement and brand trust. BGC was associated with consistent brand identity but yielded moderate emotional impact. AIGC, while efficient and scalable, generated uncertain responses, valued for personalization yet questioned for credibility. Thematic analysis of the interviews underscored the growing strategic integration of AIGC in marketing while also highlighting consumer sensitivity to transparency and ethical content sourcing. This research contributes to the knowledge of digital marketing by examining how psychological responses to different content origins influence digital brand engagement. Moreover, the practical implications highlight the need for content hybridization strategies that strike a balance between authenticity, control, and scalability.
Paper Presenter
avatar for Ioseb Gabelaia
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 7B, Virtual Room B

9:30am PDT

The Role of Agriculture in the Food Industry and Global Trade: Challenges and Opportunities
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B
Authors - Kajal P. Salampuriya, Deepak S. Sharma
Abstract - Agriculture forms the foundation of the global food industry, ensuring food security and contributing significantly to economies worldwide. The agriculture contributes half of the india’s population it is the backbone of the nation’s economy. Agriculture encomposses of different activities like crop cultivation, livestrock breeding, poultry farming, cattle husbandry and aquaculture. This paper explores the interrelation between agriculture, the food industry, and international trade, highlighting the challenges such as climate change, supply chain disruptions, and trade barriers. It also investigates opportunities for growth through technological advancements, sustainable practices, and global market expansion. This study concludes by emphasizing policy recommendations for enhancing agricultural productivity and export capabilities to meet the growing global food demand. With a diverse climate supporting cultivation of various crops, India ranks among the world’s top producers of agricultural and food products in financial year 2021-22, the agricultural sector grew at an estimate rate of 3.9%, exceeded the previous year’s growth rate 3.6% respectively. Globalization has amplified the necessity for the countries to actively engage in international trade, merging the national market into a competative global economy. Rapid technological advancements and scientific progress continue to redefine agricultural practices, including machinization and chemical innovations, it helps to increase productivity and international competitiveness. This article highlights the challenging role of agriculture in economy and the growing importance of technical advancements, globalization, trade and the different opportunities.
Paper Presenter
avatar for Kajal P. Salampuriya
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 7B, Virtual Room B

9:30am PDT

A Study on The Adoption of UPI Applications in Kerala Among Generation X
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C
Authors - Syamanarayanaprabha S S, Abymon A, Aarathy K Sudhakaran
Abstract - Digital transactions in India have increased significantly since the National Payments Coperation of India introduced the Unified Payments Interface (UPI) in 2016. This study focuses on Generation X (ages 45-60) in Kerala, a state known for its high level of digital literacy, whereas adoption studies have mostly focused on younger generations like Millennials and Gen Z. The results, which are based on responses from 132 participants, show that security, transaction speed and convenience of use are important element that promote UPI adoption within this demographic. However, technological difficulties and security-related worries still serve as obstacles. To increase UPI acceptance in this group, the study emphasizes the need of more user-friendly application designs, focused digital education initiatives, and calculated interventions.
Paper Presenter
avatar for Syamanarayanaprabha S S
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 7C, Virtual Room C

9:30am PDT

Accelerating AI Agent Development through a Domain-Specific Programming Language
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C
Authors - Salvatore Vella, Fatima Hussain, Salah Sharieh, Murrium Zaheer, Alex Ferworn
Abstract - Artificial Intelligence (AI) using Large Language Models (LLMs) is now used to automate simple knowledge worker tasks such as creating communications and summarizing documents. This paper presents a domain-specific language for creating agents, which we call ContentCreator (CC), to perform common knowledge worker tasks and mimic the behaviour of knowledge workers and their workflows. An interpreter has been built to execute and automate these workflows. In the same way that computer languages have progressed from assembly language to increasingly higher-level languages, CC enables the high-level programming of workflows that utilize large language models. CC is a simple language that non-programmers can use and understand. We use several use cases to demonstrate the ability of ContentCreator to automate workflows across multiple tasks.
Paper Presenter
avatar for Salvatore Vella
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 7C, Virtual Room C

9:30am PDT

Adopting smart tools in the mango leagile supply chain for enhancing the circular supply chain
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C
Authors - Bryan Atunca, Henry Coico, Edgar Ramos, Araceli Macassi, Donovan Fuqua
Abstract - The adoption of digital technologies and intelligent systems is significantly enhancing efficiency and safety across the global agri-food industry. Integrating Industry 4.0 objectives with circular economy principles offers effective solutions to challenges such as resource scarcity and environmental impact. This study proposes a leagile supply chain model—combining lean and agile method-ologies—to optimize mango supply chains worldwide. By leveraging Cyber-Physical Systems (CPS) in postharvest processes such as drying and storage, the model enables remote monitoring and control of key environmental parameters. CPS technologies, through sensors, actuators, and data-driven algorithms, provide real-time feedback, minimize human error, and support precision in decentralized processing units. Findings show a reduction in total supply chain time from 41 to 30 days (26.8%), with efficiency gains of 25% in harvesting and 28.5% in storage. The integration of CPS strengthens circular supply chain practices, reduces losses, and enhances sustainability, profitability, and system resilience across global mango supply chains.
Paper Presenter
avatar for Bryan Atunca
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 7C, Virtual Room C

9:30am PDT

An Attention-Based Deep Learning Framework for Gold Price Prediction using Time Series Data
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C
Authors - Abhijit T. Somnathe, Parameshwari M, Sampurnima Pattem, Sridhar N Koka, Shokhida Abdurakhmanova, Pundru Chandra Shaker Reddy
Abstract - Gold prices are non-linear, unexpected, volatile, and unregulated, making prediction difficult. Numerous studies have anticipated gold prices because they affect international economic and monetary systems. However, linear relationship studies rarely explain gold price changes. Gold price time series data is unpredictable, nonlinear, and volatile, making prediction difficult. Classical statistics and machine-learning (ML) approaches like Random-Forests, CNNs, and RNNs offer excellent accuracy but limits. A model that combines Temporal-Convolutional-Networks(TCN) with Query (Q) and Keys (K) attention-mechanisms (TCN-QV) is presented to improve gold price forecasts. The model extracts temporal properties from sequence data using stacked dilated causal convolution layers in the TCN architecture. To adapt weight distribution to information features, an attention mechanism is introduced. Finally, dense layers give projected results. This approach predicts Shanghai gold price time-series. The optimised model reduces Mean Absolute Error (MAE) by 5.47% in the least favorable case and 33.69% in the most favorable case across four investigational datasets compared to the baseline model.
Paper Presenter
avatar for Pundru Chandra Shaker Reddy
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 7C, Virtual Room C

9:30am PDT

ArogyaMind-NER: Benchmarking a Mental Health NER Dataset for Marathi
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C
Authors - Pooja Anil Patil, Yashodhara Haribhakta
Abstract - Named Entity Recognition (NER) is a core task in Natural Language Processing (NLP) with applications in conversational AI, healthcare analytics, and search systems. It enables structured extraction of key entities from text, supporting clinical decision-making and automated diagnosis. While NER resources for high-resource languages like English are widely available, low-resource languages such as Marathi lack domain-specific datasets, particularly in the mental health sector. This work introduces ArogyaMINDNER, a Marathi mental health-specific NER dataset, designed to enhance entity recognition for diseases, symptoms, and treatments. The dataset is manually annotated using BIO tagging, ensuring linguistic accuracy and contextual consistency across formal and conversational text. Annotation policies account for psychiatric terminology nuances, accommodating Marathi’s morphological richness and linguistic diversity. To assess performance, ArogyaMIND-NER is benchmarked using classical machine learning models (SVM, Naïve Bayes, XGBoost, LightGBM, Random Forest), deep learning architectures (CNN, BiLSTM, BiLSTM-CRF), and transformer-based models (mBERT, XLMRoBERTa, IndicBERT, MahaBERT, MahaMarathi-7B). Comparative evaluation reveals MahaMarathi-7B and Marathi-Social-NER as topperforming models, achieving higher precision and recall in disease recognition, while classical models struggle with contextual understanding. This dataset and trained models establish benchmarks for Marathi NLP research, contributing to mental health analytics, clinical applications, and low-resource language processing. The findings advocate for finetuning transformer architectures for domain-specific NER, ensuring better entity identification in Marathi mental health texts. The dataset and models will be made available for further research and development.
Paper Presenter
avatar for Pooja Anil Patil
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 7C, Virtual Room C

9:30am PDT

Design of E-Learning System for College Student
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C
Authors - Navin Barde, Deepak S. Sharma
Abstract - Good E-Learning system for colleges would ensure student learning to be flexible, accessible, and interactive for being remote learning option in the Post COVID-19 times. The most important aspects are an intuitive and easy to use GUI to easily browse on any device, the interactive content such as video lectures, simulations, quizzes, forum to improve the engagement. Customized learning paths, progress tracking, grade management, and instant communication features enhance learning. We take security very seriously, we encrypt all of your data while storing it and ensure that students privacy is maintained with strong user authentication. Laying particular emphasis on usability, interactivity, personalization, and security, such systems create a convincing and flexible learning environment, which encourages life long learning.
Paper Presenter
avatar for Navin Barde
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 7C, Virtual Room C

9:30am PDT

Enhancing Full-Stack Application Development with Generative AI-Powered Conversational User Interfaces
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C
Authors - Mohit Menghnani
Abstract - The increasing expenses and shortage of qualified developers have driven many to look to software development with an eye toward the incorporation of Artificial Intelligence (AI). Full-stack development facilitates the web application process, from the interface to the server-side functionality that powers it. AI has changed the tasks for many software developments services, and full-stack is one of them. The integration of Generative AI (GAI) into full-stack applications presents a transformative opportunity for healthcare assistance by enhancing conversational user interfaces. This study explores the development of an AI-powered healthcare assistant using a React.js frontend, a Node.js/Express.js backend, and Google GAI for symptom analysis and response generation. The aim is to assess the feasibility, performance, and effectiveness of Generative AI in providing real-time medical suggestions. A process of repeating development occurs and the system continuously improves through testing alongside user engagement. Users can maintain safe communication between systems through Jason Web Token (JWT) authentication and HyperText Transfer Protocol Secure (HTTPS) encryption features in addition to multi-language compatibility. Test results show that symptoms processed by the AI-enabled chatbot require 1.5 to 2.5 seconds before providing recommendations with 85-90% accuracy which surpasses traditional rule-based chatbots. The obtained results indicate how GAI shows promise to expand healthcare availability to wider populations.
Paper Presenter
avatar for Mohit Menghnani
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 7C, Virtual Room C

9:30am PDT

Ensemble Deep Learning Framework for Pneumonia Detection in Chest X-rays Using the RSNA Dataset
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C
Authors - Aneri Pandya, Killol Pandya, Hemant Yadav
Abstract - Convolutional Neural Networks (CNNs) play a crucial role in medical image analysis, particularly in the detection of pneumonia, by facilitating accurate diagnosis and treatment planning. This paper presents a robust approach to pneumonia classification using the publicly available RSNA Pneumonia Detection Challenge dataset. The proposed method employs an ensemble-based CNN framework that integrates multiple pre-trained architectures, specifically MobileNetV2, InceptionV3, and ResNet152V2, through a combination of stacking techniques and soft voting. This ensemble strategy aims to leverage the individual strengths of each model for improved performance. All models are trained and evaluated exclusively on the RSNA dataset, and their effectiveness is assessed using AUC, accuracy, and F1-score metrics. The results demonstrate that the ensemble consistently outperforms individual models in terms of prediction stability and classification accuracy. This work highlights the potential of deep model aggregation techniques to enhance diagnostic reliability in clinical pneumonia screening systems.
Paper Presenter
avatar for Aneri Pandya
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 7C, Virtual Room C

9:30am PDT

From Algorithms To Assurance: Understanding The Link Between AI-Based Personalization And Consumer Trust In Beauty Products
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C
Authors - Sana S Santhosh, Ashitha Mohan, Vishnu N
Abstract - Artificial Intelligence (AI) has transformed the cosmetics market by providing tailored suggestions based on client interests and behaviors. This study investigates the correlation between AI-driven personalization and consumer trust, emphasizing critical elements such as the precision of AI recommendations, transparency, and the degree of customization. Research indicates that AI-driven customisation markedly improves client trust, therefore influencing consumer pleasure and brand loyalty. Trust development in AI-driven recommendations is contingent upon perceived reliability, security, and transparency. The review emphasizes that AI transparency techniques, including explainable AI (XAI), enhance customer confidence, whereas excessive personalization may provoke privacy issues. Moreover, the precision of AI recommendations is strongly linked to consumer happiness and intents to repurchase, enhancing perceived expertise and diminishing decision-making anxiety. Brands utilizing AI-generated cosmetic advice must equilibrate customisation, openness, and security to cultivate enduring consumer trust. Future research ought to investigate the ethical ramifications of AI personalization and analyze the evolution of confidence in AI-driven cosmetic brands over time. With the ongoing advancement of AI technology, it is imperative to optimize algorithms for precision and ethical concerns to maintain client confidence in the beauty sector.
Paper Presenter
avatar for Sana S Santhosh
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 7C, Virtual Room C

9:30am PDT

Towards Bioacoustic Insect Recognition: Spectro-Temporal Attention Networks for Fine-Grained Insect Classification
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C
Authors - Amarnath K R, Nandini Nayakudi, Lekha S Nair
Abstract - Accurate and timely identification of insect species is a foundational requirement for precision agriculture, ecological monitoring, and integrated pest management. This paper presents a novel deep learning architecture for fine-grained acoustic classification of insect species using multichannel wingbeat recordings. Targeting critical agricultural use cases, the model is designed to differentiate between two morphologically and acoustically similar pest species, Halyomorpha halys and Nezara viridula, and a beneficial pollinator, Episyrphus balteatus. The proposed approach utilizes log-Mel and Per-Channel Energy Normalization (PCEN) spectrograms as input features and integrates a residual convolutional backbone with Squeeze and Excite blocks and Transformer inspired self-attention modules to enhance spectro-temporal representation learning. Evaluation on a curated subset of the InsectSound1000 dataset demonstrates a classification accuracy of 88.73% and a macro averaged F1 score of 88.78%. Class wise analysis reveals that the model effectively captures subtle differences in wingbeat harmonics, achieving an AUC of 1.00 and AP of 0.99 for the pollinator class. The results suggest strong discriminative power across ecologically distinct classes and highlight the model’s potential for deployment in automated, non-invasive insect monitoring systems for real-time agricultural decision-making.
Paper Presenter
avatar for Nandini Nayakudi
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 7C, Virtual Room C

9:30am PDT

A Study on the Commercialization Path and Sustainable Development Model of Integrating Health Qigong with the Modern Health Management Industry
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D
Authors - XIAOLIN HE, CHUANCHEN BI
Abstract - With the rapid development of the global health management industry and the growing awareness of personal well-being, traditional wellness practices are provided with significant opportunities for modern transformation. As an important component of traditional Chinese health culture, Health Qigong—featuring the integration of body movement, breath regulation, and mental focus—has demonstrated notable advantages in chronic disease prevention, psychological regulation, and overall health promotion. However, integration of Health Qigong into the modern health management industry continues to encounter multiple obstacles, such as insufficient standardization, limited market recognition, and a lack of diversified business models. These challenges constrain its commercialization and sustainable development. This study adopts a qualitative research approach based on secondary data and case analysis to explore emerging trends, practical models, and key barriers in the commercialization of Health Qigong. By applying Business Model Canvas (BMC) and Creating Shared Value (CSV) framework, the study proposes sustainable business pathways for Health Qigong, including course standardization, digital dissemination, membership subscriptions, corporate health partnerships, and integration with health tourism. The findings indicate that establishing standardized curricula, utilizing digital platforms, and fostering cross-sectoral cooperation can support the formation of viable commercialization models that align with contemporary health industry demands. This, in turn, may enable the global expansion of Health Qigong as a sustainable health intervention. The results not only offer theoretical support for the modern promotion of Health Qigong but also provide practical insights for the commercialization of traditional cultural products within the health sector.
Paper Presenter
avatar for XIAOLIN HE

XIAOLIN HE

Thailand
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am PDT

AI Powered Text to Video Generation
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D
Authors - Aditya Dere, Bharati Dixit
Abstract - The world of AI is has gone through tremendous advancements in over last decade. AI journey started from Algorithmic Intelligence and Linguistic Intelligence, and is moving towards Imaginative intelligence. This generative AI’s historical journey is focused on generating realistic texts and images through the means of early models like autoencoders and variational autoencoders (VAEs). From creating simple and coherent images from basic textual descriptions using Generative Adversarial Networks (GANs) and models like DALL-E in text to image generation, the AI leaped towards generating coherent frames which aligned for improving video quality, length, and the ability to generate more complex scenes in video with natural transitions in the field of text to video generation. The T2V models made use of models such as diffusion model, transformer model and the latest and the best one which is diffusion transformer model (DiT model). This paper basically gives us the overview of text to image and text to video generation models and their advancement in the field of generative AI.
Paper Presenter
avatar for Aditya Dere
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am PDT

Assessing The Degree of Contribution of IoT Integration in the Hotel Supply Chain: 4Rs Framework
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D
Authors - Lyndon Avendano
Abstract - The Philippine tourism sector significantly contributes to the national GDP, hence continuous improvements is necessary to achieve competitive advantage within the region, evidently the hotel industry plays an integral role in this objective. Several studies posits that effective Supply Chain Management (SCM) is crucial for competitive advantage and customer satisfaction in the hotel industry. Given today's technological advancements, integrating technology into SC operations is imperative. This study investigates the nuances of Internet of Things (IoT) integration within Hotel Supply Chain and its perceived contribution to supply chain performance from the perspective of various SC functions. This research posits that IoT integration can foster competitive advantage in hotel supply chains, enhancing their reliability, responsiveness, realignment, and resilience against disruptions. The analysis revealed a consistent, moderate perceived contribution of IoT integration across all four SC performance dimensions: Responsiveness, Reliability, Realignment, and Resilience. Further analysis indicated: Logistics demonstrated the highest overall perceived IoT contribution particularly strong in resilience, highlighting IoT's role in tracking and disruption management. Demand Planning showed the highest mean for realignment suggesting IoT's strength in data analytics for market adaptation. A significant difference was also noted for resilience across functions. These findings emphasize understanding each supply chain function's unique needs for effective IoT implementation. The results offer valuable insights for hotel operations aiming to strategically leverage IoT to optimize supply chain performance and gain a sustained competitive advantage in the dynamic hotel industry. Future research should delve deeper into factors driving these functional variations and explore tailored IoT applications.
Paper Presenter
avatar for Lyndon Avendano

Lyndon Avendano

Philippines
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am PDT

From Awareness to Enterprise: A Participatory Innovation Framework for Vulture Safe Zones in the Nilgiri Biosphere
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D
Authors - Bharathidasan, Dhivyalakshumi, Vasanthi
Abstract - This paper investigates an innovative, community-driven model of vulture conservation developed by Arulagam, an environmental NGO operating within the Nilgiri Biosphere Reserve in Tamil Nadu, India. Declination of vulture population due to the misuse of non-steroidal anti-inflammatory drugs (NSAIDs) in veterinary care, has been observed in recent decades. This study examines how Arulagam's interdisciplinary approach combines scientific outreach, cultural engagement, and stakeholder collaboration has evolved into a replicable model for sustainable ecological entrepreneurship. Using qualitative methods, including in-depth interviews, field observation, and campaign-based ethnography, this study documents how interventions such as pharmacist sensitisation, ethno-veterinary advocacy, school programs, and mobile awareness drives have contributed to the formation of Vulture Safe Zones (VSZs). It highlights how creative mediums like shadow puppetry, murals, and mythological storytelling have shifted negative public perceptions of vultures and introduced conservation ethics within local communities. This paper also explores entrepreneurial pathways emerging from the conservation framework, including eco-tourism, ethical livestock branding, and native plant nurseries. Emphasizing on the principles of nature-based solutions and aligned with SDG 15 (Life on Land), this study proposes a measurable intervention model that merges biodiversity protection with livelihood generation, offering policy-relevant insights for governing conservation in other biodiversity hotspots.
Paper Presenter
avatar for Dhivyalakshumi
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am PDT

Impact of Social Media Marketing on Customer Behaviour Outcomes in Bangladesh
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D
Authors - Md Shahim Uddin Saba, Mohitul Shafir, Md Zahid Hasan, Md Asfaqur Rahman, Farhan Khan, Mahady Hasan, Farzana Sadia
Abstract - Social media has reshaped customer engagement, leading to widespread use of digital marketing. However, the effects of specific social media marketing practices on purchase decisions, participation, and brand loyalty remain underexplored. This study investigates the impact of social media marketing activities on consumer behaviour in Bangladesh’s technology product sector. Based on a quantitative survey and Structural Equation Modeling (SEM) using SmartPLS 4, the results show that both social media marketing and customer experience significantly influence behavioural outcomes. The findings provide strategic guidance for brands that seek to strengthen engagement in competitive digital markets.
Paper Presenter
avatar for Md Shahim Uddin Saba
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am PDT

Integral Model for the Management and Mitigation of Credit Risk in the Colombian Solidarity Sector
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D
Authors - Jasleidy Astrid Prada Segura, Efren Danilo Ariza, Maria del Pilar Corredor Garcia, Eduard Ricardo Espinosa Armero
Abstract - The objective of this research is to design a comprehensive model aimed at reducing credit risk in the solidarity sector. This effort seeks a substantial improvement in the efficiency of the recovery of non-performing loans, considering that this process involves a significant level of risk. It is an applied and mixed research in terms of its sources of information, the scope is exploratory and explanatory, and the approach is quantitative. The model contains crucial parameters for effective risk mitigation. It should be noted that, as additional support, information from the sectoral analysis of the cooperatives in the report of the Superintendencia Solidaria and portfolio balances by city and entity with cutoff to 2021 and 2022 generated by the Superintendencia Solidaria will be used to support the current situation of the portfolio in the solidarity sector in Colombia. The methodology developed in this research can be extrapolated and applied in other regional contexts and economic sectors, offering a valuable tool to address credit risk and strengthen the solidarity economy in Colombia.
Paper Presenter
avatar for Jasleidy Astrid Prada Segura
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am PDT

Medical Ethics and Medico-Legal Issues for Storing Electronic Health Records in India Using Blockchain Technology
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D
Authors - Neha A. Samsir, Arpit A. Jain
Abstract - With the pandemic taking a serious impact, it is becoming progressively clearer that India needs to boost funding and develop its healthcare system. The integration of Information and Communication Technology (ICT) into the healthcare sector is transforming healthcare systems worldwide by improving patient access to and availability of healthcare services. A basic requirement for utilizing ICT in healthcare is having an electronic health record. It is a digital record that combines patient health information and is utilized for handling data, educational purposes, research, and reference purposes. Cloud-based electronic health records are constantly evolving and have transparency, trust, and confidentiality, they are vulnerable to several security and privacy threats. In the new Digital India, digital health (DH) plays a big role in healthcare. Utilizing technology, the audacious goal of offering universal health coverage—first to 500 million citizens with low incomes— is being accomplished. The widespread adoption of electronic health records (EHRs) has presented several obstacles, even though they have transformed healthcare by facilitating rapid access to patient information. The implementation of EHRs raises ethical and legal concerns, which are examined in this study with a specific emphasis on the Indian healthcare system. The implementation of EHRs should be guided by ethical issues, including medical beneficence, fairness, autonomy for patients, confidentiality, and beneficence. Strong regulations and security measures are essential considering legal difficulties such as medical errors, data breaches, and billing discrepancies. This paper aims to analyze all the legalities and medical issues of storing EHR using Blockchain Technology.
Paper Presenter
avatar for Neha A. Samsir
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am PDT

Optimizing LAN Performance: Key Determinants and Algorithmic Solutions
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D
Authors - Sudhir B.Agarmore, Hemant S.Mahalle
Abstract - This research paper analyses the key determinants affecting LAN performance and articulates algorithmic solutions for improving the efficiency of such networks. Elaborating on factors like bandwidth, latency, packet loss, and congestion, against the backdrop of cutting-edge algorithmic interventions, inclusive of dynamic routing, congestion control, and traffic engineering, this current research offers a comprehensive insight into the augmentation of LAN performance. A local area network is a major part of any modern computing infrastructure. The user experience and productivity depend to a great degree on their performance; at the same time, the overall network efficiency depends on them. This paper explores the main factors of LAN performance and examines algorithmic solutions to optimize these factors. Such factors that will be discussed are the impact of network topology, traffic patterns, hardware capabilities, software configuration, and their effective combinations on LAN performance. We will also investigate some higher-level algorithms, such as those for congestion control, routing protocols, and QoS mechanisms, which help increase network throughput, reduce latency, and deal with reliability in data transmission. Proper understanding of these determining factors and implementing the relevant algorithmic solutions significantly improve performance and reliability in organizational LANs.
Paper Presenter
avatar for Sudhir B.Agarmore
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am PDT

Smart Hormone Monitoring: Sensors, Wearables and AI Powered Detection Techniques
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D
Authors - Shaily Sharma, Disha Shah
Abstract - Hormone level fluctuation is connected with several health problems affecting human beings. To properly managing and diagnosing hormone levels, it is essential to understand hormonal imbalance and its detection. Commonly the levels of hormones were detected with the help of laboratory tests like chromatographic methods and immunoassays which gives accurate results but are very slow process and there is no real time detection proficiency. Hormone monitoring is crucial and wearable devices are progressing very effectively with the help of biosensors to monitor hormones to provide better understanding of health control. This review paper is focusing on the theory of hormones, hormonal imbalance indicators, and detection approaches which are available. Several tests for diagnosing hormone level imbalance were investigated including chromatographic techniques (HPLC, GC-MS), biosensors and immunoassays (ELISA, RIA) while also focusing on machine learning including deep learning algorithms, pattern recognition, regression, classification to provide customized treatment. Deep learning approaches like CNN (Convolutional Neural Networks) and Supervised learning approaches like SVM (Support Vector Machine) and Random Forests have demonstrated encouraging uses in real-time monitoring and the prediction of hormone related diseases. The review also highlights wearable devices available for hormonal levels monitoring, comparison of wearable sensors like electrochemical, optical and microfluidic systems. By empowering real-time surveillance of hormone level imbalances and promoting early detection, diagnosing and illness prevention, these advancements have improved conveniency. According to the research, combining Machine learning (ML) algorithms with wearable biosensors can transform monitoring of hormone levels may result in better results and can provide early medication.
Paper Presenter
avatar for Shaily Sharma
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am PDT

TRENDS IN VENTURE CAPITAL: A BIBLIOMETRIC REVIEW
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D
Authors - Sneha Sarah Alex, Jains P Chacko
Abstract - This study explores the evolution of venture capital (VC) research through a bibliometric analysis of 2,577 journal articles from 1972 to 2022, using Scopus, VOSviewer, and Biblioshiny. It identifies five key research clusters: private equity and IPOs, entrepreneurship, innovation, human capital, and institutional factors. Highly cited works highlight VC’s impact on firm performance, governance, and innovation. Emerging themes include syndication, decision-making dynamics, globalization, and sectoral shifts like biotech. Grounded in agency, human capital, and institutional theories, this study provides insights for researchers, investors, and policymakers, while also suggesting broader future research directions.
Paper Presenter
avatar for Sneha Sarah Alex
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am PDT

A Machine Learning and Data-Driven Analysis of English Anxiety Among Rural High School Students in Bangladesh
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E
Authors - Md. Najmus Sakib Sourov, Md. Ataur Rahman, Urmi Ghosh, Sabera Sultana, Mahady Hasan, Md. Tarek Habib
Abstract - English Language Anxiety represents a prevalent issue among learners, significantly affecting their capacity to communicate and acquire knowledge effectively. The study uses machine learning models to offer insights into how anxiety affects students' general well-being and academic performance. English language anxiety among rural high school students in Bangladesh is investigated using machine learning techniques to determine its sources and effects on aca-demic performance. Machine learning techniques like k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, and Gradient Boosting were used to analyze data from several rural schools, including student scores and qualitative insights on their perceptions of English. The results showed that SVM was the most accurate model, with an accuracy of 99.43%. The study reveals major anxiety patterns and predictions, with a focus on the interaction between academic pressure, English complexity, and student mental health. The findings will guide educators and policymakers in designing focused interventions to relieve English subject anxiety and establish a supportive learning environment.
Paper Presenter
avatar for Md. Najmus Sakib Sourov
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 7E, Virtual Room E

9:30am PDT

CHARACTER RECOGNITION OF NEPALI NUMBER PLATE
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E
Authors - Satyasa Khadka, Sandhya Baral, Sudip Tiwari, Sharad Kumar Ghimire
Abstract - This paper presents a robust Automatic Number Plate Recognition (ANPR) system tailored for Nepali license plates written in Devanagari script. In this paper, a pipelined model was used that integrates YOLO-based models for license plate and character detection, followed by a CNN classifier trained on 34 Devanagari characters. Two publicly available data sets were used that incorporate diverse lighting, fonts, and structural variations. Data augmentation and additional training on embossed plates enhanced the generalizability of the model. The system achieved a recognition accuracy of up to 93%, demonstrating strong performance under real-world conditions and providing a scalable solution for traffic management in Nepal.
Paper Presenter
avatar for Satyasa Khadka
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 7E, Virtual Room E

9:30am PDT

CHARACTERIZATION AND ANALYSIS OF BADMINTON RACKET USING SINGLE WALLED AND MULTIWALLED CARBON NANOTUBES
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E
Authors - K. Vidyasagar, G. Lakshminarayana, B. Surya Prasada Rao, K. Vijaychandra, P. Suresh babu
Abstract - Nanoscience is the emerging technology to strengthen the devices that are more essential to human life. This chapter discussed structural design considerations of a badminton racket. The stress-strain relationship, modulus of elasticity, bending modulus, and moment of inertia are studied to improve the shaft deflections while smashing the shuttle with high force. Carbon nanotubes are elected for badminton rackets to withstand both rapid forward and side deflections. This paper discussed the physical characteristics of armchairs, Zig-Zag Single-walled carbon nanotubes and double-walled carbon nanotubes (DWCNT). The length of the carbon nanotube is fixed to 200 A. The C-C bond length is considered as 1.4210 A. The stretch factor is considered as ‘1’ for both single-walled and carbon nanotubes (SWCNT). And the ellipse ratio is also considered as ‘1’ for both SWCNT and DWCNT. The screw distortion and bending angle are varied from 00 to 900 for single-walled carbon nanotubes, and the physical elastic properties of the SWCNT and DWCNT are investigated. Finite element analysis is used to analyse SWCNT and DWCNT structures. Nano tube modeler is used to simulate the SWCNT and DWCNT structures.
Paper Presenter
avatar for K. Vidyasagar
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 7E, Virtual Room E

9:30am PDT

Cross-Modal Attention Transformer for Multi-SKU Demand Forecasting in Retail Environments
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E
Authors - Sabar Aritonang Rajagukguk, Eileen Heriyanni, Ahmad Fikron Maulida, Enina Putri
Abstract - Multi-SKU demand forecasting poses considerable challenges for re-tail operations due to intricate interdependencies among products, seasonal fluctuations, and external market influences. Conventional forecasting approaches often fail to model these complexities effectively, resulting in poor inventory management and financial inefficiencies. This paper proposes a novel Cross-Modal Attention Transformer (CMAT) architecture for addressing multi-SKU demand prediction in retail contexts. The model employs a dual-attention mechanism to capture temporal patterns within individual SKU time series as well as cross-SKU relationships via an inter-product attention layer. It further integrates multimodal external variables such as promotional campaigns, weather data, and economic indicators using dedicated feature encoders. Evaluation on a large-scale retail dataset comprising 15,847 SKUs across 12 product categories shows a 23.4% reduction in Mean Absolute Percentage Error (MAPE) compared to standard transformer models and a 31.2% improvement over traditional statistical methods. The results indicate potential for improving supply chain efficiency and reducing inventory-related costs through more accurate multi-horizon demand predictions.
Paper Presenter
avatar for Eileen Heriyanni
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 7E, Virtual Room E

9:30am PDT

Detection of Advanced Persistent Threats Using Swift-KNN
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E
Authors - Kshitij Gupte, Kunj Patel, Krisha Desai, Dhrumi Patel, Anjali Jivani
Abstract - Advanced Persistent Threats (APTs) pose a serious challenge to cybersecurity due to their stealthy and prolonged nature. Detecting these attacks early is critical, yet traditional methods often fail to catch subtle patterns or require extensive resources. This study investigates the effectiveness of several machine learning algorithms including Logistic Regression, Random Forest, Naive Bayes, and K-Nearest Neighbors (KNN) to identify APT behavior from system and network activity data. A significant focus was placed on reducing the complexity of the detection system without compromising accuracy. Dimensionality reduction using Linear Discriminant Analysis (LDA) was applied to transform high-dimensional data into a more manageable format, and data cleansing techniques were used to handle null and infinite values. The results showed that the KNN algorithm not only achieved the highest detection accuracy but also maintained performance even when trained on just 10% of the dataset. This approach offers a lightweight, high-precision method for APT detection that can minimize processing overhead while improving real-time responsiveness helping organizations enhance their security posture without sacrificing efficiency.
Paper Presenter
avatar for Kshitij Gupte
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 7E, Virtual Room E

9:30am PDT

Integrating Climate Action into Business Operations: Perspectives from City SMEs in the Philippines
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E
Authors - Justin Q. Cabellon, Ashley MJ A. Andrade, Dave P. Bautista, Kenshin Z. Gutierrez, Dorothy Joy B. Nabo, Angel Anne R. Nuarin, Manuel J. Logatoc, Rexieden N. Barrera
Abstract - This study examines the perceptions of small and medium enterprises (SMEs) toward United Nations Sustainable Development Goal 13 (Climate Action). Employing a quantitative survey across multiple industries, it captures numeric data to identify patterns in SMEs’ attitudes and evaluates whether demo-graphic factors— length of operation, nature of business, number of employees, or type of ownership—influence these perceptions. Respondents rated statements on a five-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree) across five business dimensions: product, service, process, network, and model. Results reveal high overall support for climate-action initiatives, with mean scores indicating positive engagement in each dimension. Statistical analysis shows no significant variation in perceptions among SMEs grouped by any demographic criterion, suggesting a shared recognition of climate action’s importance irrespective of firm characteristics. These findings underscore the uniform commitment within the SME sector to integrate climate considerations into core operations. By illuminating SMEs’ supportive stance and the absence of demographic dis-parities, this research highlights the sector’s potential contributions to global cli-mate objectives. Insights from this study can inform policymakers and practitioners aiming to tailor interventions that bolster SME participation in sustainable development and foster resilient economic growth.
Paper Presenter
avatar for Rexieden N. Barrera
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 7E, Virtual Room E

9:30am PDT

Model Confidentiality in AI-as-a-Service: Challenges and Cryptographic Solutions
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E
Authors - Parikshit Mahalle, Pradnya H Desai, Pankaj Chandre
Abstract - With the widespread adoption of AI-as-a-Service (AIaaS), enterprises increasingly rely on cloud platforms to deploy and access powerful machine learning models. This shift offers scalability and cost-efficiency but also introduces significant security and privacy concerns. Protecting the confidentiality of AI models and user data has become a critical requirement in this outsourced computing paradigm. However, AIaaS platforms face numerous threats, including model inversion, membership inference, extraction attacks, and data poisoning. Traditional security mechanisms are insufficient to address the unique confidentiality risks associated with remote AI model hosting and inference. Furthermore, balancing performance, privacy, and deployment feasibility remains a core challenge. This paper proposes a comprehensive confidentiality-focused architecture for AIaaS, integrating multiple cryptographic techniques and secure hardware layers. The design leverages Homomorphic Encryption, Secure Multiparty Computation, Trusted Execution Environments, and Federated Learning with Secure Aggregation, supported by a centralized Management & Compliance layer. Threat vectors are explicitly addressed across client, cloud, and edge layers, while performance and auditability are maintained. In conclusion, our architecture provides a layered and resilient defense against modern AI threats while maintaining functionality and scalability. It offers a blueprint for deploying privacy-preserving AIaaS infrastructures. Future work will focus on optimizing hybrid models and advancing quantum-resistant protections.
Paper Presenter
avatar for Pradnya H Desai

Pradnya H Desai

United States of America
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 7E, Virtual Room E

9:30am PDT

Multi-Label Fake News Detection with Transformer: A Study on the LIAR Dataset
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E
Authors - Mridul Das Joshe, Shyja Rafeek S, Aji Sivanandan
Abstract - Fake news has become a critical research area due to the expanding spread of misinformation on digital platforms. As social media becomes a main news source for millions, confirming the authenticity of online material is crucial. This research addresses the problem using a multi-label text classification method, which better identifies the complex nature of misinformation that may fall into multiple categories. In our research, we used the LIAR dataset, a bench-mark collection of 12.8k short political statements labeled with six fine-textured truthfulness classes. A transformer-based architecture, notably a fine-tuned BERT model, is proposed including both textual context and metadata to enhance better classification output. The model achieved an accuracy of 0.7351, presenting superior results than those reported earlier research using the LIAR dataset. Confirming the robustness of the approach across different classes with evaluation metrics and per-label precision and recall. The findings illustrate the potency of transformer-based architectures in identifying different degrees of falsehood. This research adds an extensible and practical solution for real-time fake news detection to support information.
Paper Presenter
avatar for Mridul Das Joshe
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 7E, Virtual Room E

9:30am PDT

The Role of ICT in Empowering Women-Led MSMEs: Analysing WhatsApp Commerce as a Catalyst for SDG 5 & SDG 8 in Tier-3 India
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E
Authors - Agna S. Nath, Arun Kumar V N, Aswathi K.B
Abstract - This study investigates the role of WhatsApp Commerce—the use of WhatsApp for business transactions—in empowering women-led Micro, Small, and Medium Enterprises (MSMEs) in Tier-3 cities of India, while advancing Sustainable Development Goals (SDGs) 5 (Gender Equality) and 8 (Economic Growth). Despite India’s digital revolution, gender disparities persist, with women leading only 20% of MSMEs and facing barriers like digital exclusion and sociocultural constraints. Grounded in Kabeer’s Empowerment Theory and Davis’s Technology Acceptance Model, the research employs a mixed-methods approach, combining surveys of 400 women entrepreneurs and 20 case studies across four Tier-3 cities (Bareilly, Jalgaon, Dibrugarh, Erode). Key findings reveal that WhatsApp usage intensity correlates with a 27% revenue increase (β=0.27, *p*
Paper Presenter
avatar for Arun Kumar V N
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 7E, Virtual Room E

9:30am PDT

Uncovering Consumer Patterns: The RN-Algorithm for Market Basket Analysis
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E
Authors - Kingkar Prosad Ghosh, Ankan Roy, Anupam Singha, Tabassum Tajin Ratry, Nafisha Hossain
Abstract - Market Basket Analysis (MBA) identifies co-occurring products for pricing, cross-selling, and placement. This paper reviews Apriori and FP-Growth and introduces the RN-Algorithm, a method that focuses on rules with high lift and confidence by measuring pairwise item co-occurrence. In retail-style transactions, RN provides competitive or superior performance in identifying the top rules while remaining simple to distribute. This study examines RN’s complexity, how its runtime scales with the number of unique items and basket width, and offers a practitioner-oriented comparison of algorithms. We also address RN’s limitations (e.g., pair growth in wide baskets) and propose mitigations, including minimum item support, top-K selection, and distributed counting. The paper concludes with actionable guidance on when to prefer RN over FP-Growth or Apriori in modern e-commerce applications.
Paper Presenter
avatar for Tabassum Tajin Ratry
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 7E, Virtual Room E

9:30am PDT

A Framework to Safeguard Electronic Health Records and Ensure Privacy in Cloud Storage
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - T. Sruthi, Martha Sheshikala
Abstract - Cloud computing has brought significant benefits to healthcare systems, especially when it comes to improving data access and storage efficiency. However, it also rais-es major concerns around privacy and security, particularly for Electronic Health Records (EHRs). In this paper, we propose a new framework designed to protect EHRs in cloud environments. The framework uses a combination of symmetric and asymmetric encryption techniques to ensure that data stays private during both storage and transmission. It also includes advanced access control measures that regulate who can access the data based on established policies. Our tests show that the framework successfully reduces the risk of unauthorized access while keeping system performance intact. These results highlight the importance of using strong privacy protections to safeguard sensitive health data in cloud-based healthcare applications. This research builds on Dutta et al.'s (2023) work, which explored hybrid encryption techniques to improve the security of health data in the cloud.
Paper Presenter
avatar for T. Sruthi
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 7F, Virtual Room F

9:30am PDT

Artificial Intelligent: An Effective Tool for Knowledge Management in Higher Education
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Sanasam Bimol, Mutum Indrakumar Meetei
Abstract - In higher education, knowledge management (KM) is important for encouraging innovation, helping students learn better, and making university management more effective. However, traditional ways of handling knowledge face problems like too much information, poor systems for finding content, and not enough personalized learning experiences. Using artificial intelligence (AI) can help by automating data handling, making information easier to retrieve and more accurate and offering customized learning paths. This paper looks at how AI can be used in KM within higher education, focusing on its ability to improve the creation, sharing, organization, and decision-making processes of knowledge. It also examines issues like the AI infrastructure needed for KM, compares different approaches, discusses strategies for development, and addresses the challenges and ethical questions that come with using AI in educational settings.
Paper Presenter
avatar for Sanasam Bimol
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 7F, Virtual Room F

9:30am PDT

Integrating AI and Behavioral Analytics for Advanced Insider Threat Detection: A Cross-Disciplinary Approach Combining Cybersecurity and Cognitive Science
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Praveen Savarapu, Shankar Lingam. M
Abstract - Insider threats pose significant risks to organizational cybersecurity, often arising from complex human behaviors that traditional detection systems struggle to identify (Smith & Johnson, 2023). This study proposes a novel, cross-disciplinary approach in-tegrating artificial intelligence (AI) and behavioral analytics to enhance insider threat detection. By combining machine learning techniques with cognitive science principles, the framework captures nuanced behavioral patterns and psychological indicators that precede malicious insider activities (Lee et al., 2022). This work contributes to advancing proactive risk mitigation strategies by bridging technical cybersecurity defenses with human behavioral insights, for both researchers and practitioners.
Paper Presenter
avatar for Praveen Savarapu
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 7F, Virtual Room F

9:30am PDT

Linking System Effectiveness to Workforce Productivity: A Study on the Use of Electronic Accounting Systems
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Mark Anthony A. Gavino, Rafaela Medilane A. Gonzales, Queenie Marie E. Manilag, Angeline G. Reyes, Rey Mark C. Sadoy, Lester P. Acoba
Abstract - With the rapid advancement of technology, traditional manual accounting methods are increasingly being replaced by Electronic Accounting Systems (EAS) to improve organizational efficiency and decision-making. This study aimed to examine the perceived effectiveness of EAS and its impact on the productivity of accounting personnel in Makati City. Utilizing a quantitative-descriptive research design, data were collected from 152 finance officers and accountants with at least six months of experience using EAS through a structured Likert-scale questionnaire. The study assessed EAS effectiveness across five dimensions—speed, accuracy, security, reliability of information, and decision-making—and evaluated productivity based on motivation, satisfaction, performance, absenteeism, and turnover. Using partial least squares-structural equation modeling (PLS-SEM), results revealed a strong and statistically significant relationship between perceived effectiveness and productivity. Reliability and validity analyses confirmed the robustness of the measurement model. The findings indicate that effective EAS use significantly enhances both system performance and employee productivity. The study concludes that investing in well-implemented EAS can lead to improved efficiency and workforce outcomes. Practical recommendations include providing continuous user training, choosing secure and adaptable systems, integrating EAS into business strategies, and regularly evaluating system impact.
Paper Presenter
avatar for Lester P. Acoba

Lester P. Acoba

Philippines
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 7F, Virtual Room F

9:30am PDT

Modelling the Barriers to Gen Z Adoption of Privacy-Preserving AI-Enabled Health Apps
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Payel Das, Uditaa K, Hariprasad
Abstract - Artificial Intelligence (AI) is redefining healthcare through enhanced diagnostics, personalized interventions, and preventive monitoring. Privacy-preserving AI-enabled health applications—leveraging technologies such as federated learning and differential privacy—hold the potential to protect sensitive health data while delivering actionable insights. Yet, adoption among Generation Z (Gen Z) remains inconsistent, hindered by complex and interlinked barriers. This study identifies and models ten critical barriers—Awareness Deficit, Data Misuse Anxiety, AI Credibility Doubt, Surveillance Concern, UX–UI Friction, Digital Health Knowledge Gap, Regulatory Ambiguity, Human Displacement Fear, Perceived Health Irrelevance, and Eco–Ethical Concerns—through a survey of 142 Gen Z respondents in India. Using Interpretive Structural Modeling (ISM) and MICMAC analysis, the research reveals a hierarchical structure were foundational drivers cascade into immediate deterrents, demonstrating that isolated interventions may be insufficient. By integrating Protection Motivation Theory (PMT) and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the study offers a dual lens capturing both resistance (security–privacy dimension) and adoption (utility–experience dimension) dynamics. Findings provide actionable guidance for developers, policy-makers, educators, and healthcare providers to address root causes, bridge knowledge gaps, enhance trust, and align solutions with Gen Z’s values, fostering informed and sustained adoption.
Paper Presenter
avatar for Uditaa K
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 7F, Virtual Room F

9:30am PDT

Prompt Engineering Intervention for Enhancing Proficiency and AI Self-Efficacy in Undergraduate Management Education
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Vijay Makwana
Abstract - The rapid integration of artificial intelligence (AI) into business necessitates a curricular shift in management education toward practical AI literacy. This study addresses the gap between the availability of AI tools and students' ability to leverage them effectively. It evaluates a structured prompt engineering intervention designed to improve undergraduate business students' AI self-efficacy, foundational AI knowledge, and practical prompting skills. Using a mixed-methods, pre-test/post-test design, the study included 78 undergraduate business administration students at a university in Gujarat, India. Participants engaged in a workshop that introduced advanced prompting strategies for business applications. Data were collected via a validated AI Self-Efficacy Scale, an AI Knowledge Assessment, and a Prompt Engineering Skill Rubric. The findings show statistically significant improvements in AI knowledge and self-efficacy, corroborated by qualitative evidence of enhanced prompt sophistication. The results underscore the intervention's efficacy in cultivating essential AI competencies. This research offers a replicable pedagogical framework for integrating prompt engineering into business curricula, providing key insights for educators seeking to prepare future professionals for an AI-driven land-scape.
Paper Presenter
avatar for Vijay Makwana
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 7F, Virtual Room F

9:30am PDT

Robust Face Recognition Under Occlusion Using Attention-Enhanced Angular Margin Loss
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Chau Huy Thuan, Nguyen Dac Thanh Phuoc, Bui Chau Sao, Le Phu Thanh, Tong Van Dinh, Hung Nguyen-Huu, Vinh Dinh Nguyen
Abstract - The widespread use of facial masks poses a major challenge for face recognition systems, often leading to drastic performance degradation. This paper presents a systematic, multi-stage framework to build a highly robust face recognition model against such occlusions. We perform extensive ablation experiments by gradually introducing three main enhancements over a strong Triplet Loss baseline: (1) Mask-Aware Sampling to explicitly learn cross-mask invariances; (2) a Spatial Attention module to adaptively focus on un-occluded facial regions; and (3) the state-of-the-art ArcFace loss to maximize the embedding's discrimination power. Extensive experiments demonstrate that our final model not only achieves an outstanding F1-score exceeding 97% across all verification scenarios (unmasked-unmasked, masked-masked, and cross-mask) but, more critically, exhibits exceptional robustness, reducing the performance variance between the easiest and hardest scenarios from 5.9% in the baseline to a mere 2.0%. Furthermore, we evaluate our models also on the Labeled Faces in the Wild (LFW) which is a standard face benchmark and our final model achieved an efficient improvement of +8.9% accuracy improvement over the baseline in the general case of LFW, demonstrating that our model acts effectively in general when it may not be necessarily occlusive situation as well.
Paper Presenter
avatar for Chau Huy Thuan
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 7F, Virtual Room F

9:30am PDT

Role of Digital marketing in the era of Artificial Intelligence
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Sarvani Kocherlakota, Lavanya Goura, Poojitha Panchakarla, Thupakula Kushwanth, Jangala Venkata Shanmukha
Abstract - The impact of artificial intelligence in digital marketing has emerged as a prominent research topic in recent decades, with numerous findings published in leading international journals. To investigate its current research landscape and emerging trends, we conduct a bibliometric analysis by using R software 4.5.0, and VOSviewer to identify the research trend, emerging topics, collaborations among countries, authors, documents and the contributing academic journals during the study period. Lecture Notes in Networks and Systems journal is having the highest publications and Journal of Operations Management is having the most cited document. Sharma A is the most contributing author to the literature. India is the most contributing country with around 12.5% of total publications. By research gap analysis, the future direction in the domain of artificial intelligence in digital marketing can be green marketing, ethical AI, personalization, automation and prediction.
Paper Presenter
avatar for Thupakula Kushwanth
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 7F, Virtual Room F

9:30am PDT

Understanding the Drivers of Financial Literacy and Digital Investment Behaviour among Generation Z
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Nandgopan R, Pavan Shankar R, Santanu Mandal
Abstract - This research investigates the interconnected factors of financial literacy and digitised investment behaviour of Generation Z inhabiting the financial ecosystem in India that is increasingly becoming digitised. The study examines nine significant drivers through the lens of a cross-sectional survey involving 115 respondents aged 18–27 years, including financial knowledge, trust in digital finance platforms, digital literacy, social media and peer effect, perceived financial risk, consumer experience and design of the application, financial socialization by parents, awareness of regulations, and the perception of uncertainty in the economy. ISM is used for the identification of hierarchal relationships between all the variables, whereas MICMAC analysis is used for classifying the variables on the basis of driving power and dependence power. Our results indicate that regulatory awareness, perception of economic uncertainty, and financial socialization by parents are the exogenous high-driving determinants of the intermediate enablers of financial literacy and digital literacy, and the intermediate determinants of the dependent factors like trust, perceived risk, and user experience. This hierarchy demonstrates that policy and practice must build on higher-ordered enablers before approaching determinants of behaviours at lower order levels. The study contributes theoretically by integrating the Theory of Planned Behaviour and Technology Acceptance Model, and offers practical recommendations for policymakers, educators, and fintech designers to foster sustainable financial literacy and digital investment participation among Gen Z.
Paper Presenter
avatar for Nandgopan R
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 7F, Virtual Room F

9:30am PDT

Website Defacement Detection using Machine Learning Technique
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Liladhar P. Dhake, Jayashree Katti, Sapana Kolambe
Abstract - Website defacement attacks have been a significant threat to both private and public organizations’ websites and web portals. Such attacks can have severe repercussions for website owners, disrupting website operations and tarnishing their reputation, potentially resulting in substantial financial losses. With our approach, we examined SVM, which is a type of machine learning for detecting website defacement. Our approach applied machine learning methods to develop classifiers that distinguish web pages into normal and attacked classes. Moreover, we collected a large number of features from the website's content and metadata to train and test the algorithms. This method is applicable to both static and dynamic websites; through training, it can learn to adjust to a wide range of page types. The use of an algorithm from machine learning to obtain results has shown that our approach achieves very high detection accuracy with a very low rate of false positives. Additionally, it should be noted that our approach does not require it to depend on massive computational capabilities.
Paper Presenter
avatar for Liladhar P. Dhake
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 7F, Virtual Room F

9:30am PDT

AARA: An AI Registration Assistant
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G
Authors - John Jenq
Abstract - An Artificial Intelligence (AI) agent is an expert that can handle tasks efficiently and accurately, and can execute specific tasks automatically. In this paper, we develop an AI class scheduling assistant system. This system consists of a large language model (LLM) which serves as an interface to the user, and an agent. This agent contains several sub-agents to perform the scheduling task. The data used by the agent was acquired from the University website. It was pre-processed and stored on an excel file that is accessible to the agent. One of the sub-agents will load the file and convert it into Python Pandas data frame. Another sub-agent has the function of finding all the available class sections based on the user’s input from the data frame. This available section information can be used later, along with the user’s data by other sub-agents. OpenAI GPT-4 model is used for the LLM. We implemented the system using Python programming language. According to our experimental results, the agent workflow runs smoothly and quickly, and the system performs the scheduling task accurately.
Paper Presenter
avatar for John Jenq

John Jenq

United States of America
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 7G, Virtual Room G

9:30am PDT

Adapting Large Language Models for Customer Service: A Comparative Study of RAG, RAFT and DoRA
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G
Authors - Anh Truong, An Nguyen, Huy Le, Thang Pham
Abstract - Large language models (LLMs) are being increasingly deployed in customer-service chatbot applications. While traditional fine-tuning approaches often suffer from hallucinations or require computationally expensive retraining, emerging retrieval-based and parameter-efficient methods are increasingly regarded as promising alternatives. However, comprehensive evaluation of these paradigms in a customer-service context remains limited. To address this gap, we conduct a comprehensive comparison of three fine-tuning paradigms - Retrieval-Augmented Generation (RAG), Retrieval-Augmented Fine-Tuning (RAFT), and Weight-Decomposed Low-Rank Adaptation (DoRA) - for customer-service chatbot applications. All methods share a common backbone and are evaluated on the Bitext customer-support dataset. RAG method achieves strong factual consistency at the cost of higher inference latency. RAFT delivers the best overall balance of intent-classification accuracy, coverage, and low hallucination with moderate latency overhead. DoRA extends LoRA (Low Rank Adaptation) by decomposing weight updates into magnitude and directional components for precise low-rank adaptation. On Bitext, DoRA’s end-to-end fine-tuning not only achieves medium accuracy and coverage but also suffers a higher hallucination rate due to limited training data. In larger-scale settings (e.g., SQuAD + 400K-row FAQ), DoRA demonstrates surprising low hallucination and high BLEU/ROUGE, proving that dataset size significantly influences its performance. The results highlight trade-offs between latency, accuracy, and factual reliability, while emphasizing the importance of data scale and retrieval grounding in deploying scalable, trustworthy LLM-based customer-service systems.
Paper Presenter
avatar for Anh Truong

Anh Truong

Vietnam
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 7G, Virtual Room G

9:30am PDT

An IoT-Enabled Real-Time Monitoring, Alerting, and Cloud Data Management System for Optimized Water Quality Control in Recirculating Aquaculture Systems (RAS)
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G
Authors - Tahfizul Hasan Zihan, Hana Sultan Chowdhury, Shirazim Munir Deap, Rubayed Mehedi, Farhad Alam, A. M. Shahabuddin, Mahady Hasan
Abstract - Recirculating Aquaculture Systems (RAS) have increasingly gained attention in Bangladesh due to their efficient water use. However, maintaining real-time water quality is challenging, as delayed anomaly detection often causes economic loss and compromises fish health. To address this, we developed a low-cost IoT-based monitoring and alert system that integrates industry-standard sensors to track key water parameters (pH, turbidity, dissolved oxygen, temperature, oxidation-reduction potential, total dissolved solids, and electrical conductivity) using Arduino microcontrollers and ESP8266 Wi-Fi modules. The total hardware cost was under USD 700, an order of magnitude cheaper than commercial systems. Sensor readings were published every 15 seconds via MQTT to the Thing Speak cloud platform, enabling real-time visualization and WhatsApp-based alerts for immediate intervention. Experimental deployment demonstrated the system’s capability to maintain optimal conditions consistently, identifying critical events such as abnormal water quality drops efficiently. Clear daily and monthly trends were observed, enabling predictive adjustments and informed automated decision-making. This IoT approach significantly improves operational efficiency and reliability in resource-limited aquaculture settings typical of Bangladesh, ensuring sustained fish productivity and welfare.
Paper Presenter
avatar for Tahfizul Hasan Zihan
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 7G, Virtual Room G

9:30am PDT

Citizens’ Perceptions of AI Government Chatbot Adoption
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G
Authors - Ayman Alarabiat, Yousef Alarabiat, Mahmoud AlZuabi, Mamoun Shakatreh
Abstract - Governments are increasingly leveraging artificial intelligence (AI) Chatbots to enhance e-service accessibility. However, Chatbots adoption among citizens’ remains low, limiting their intended benefits. This study explores citizens’ perspectives on government Chatbots adoption. A quantitative correlational research approach was employed, collecting 358 responses from Jordani-an citizens who had used government Chatbots in the last 6 to 12 months. An online survey measured ten key constructs: low complexity, relative advantage, compatibility, trialability, observability, trust, responsiveness, perceived intelligence, anthropomorphism, and Chatbot adoption. Data analysis using SPSS 24 revealed low willingness to adopt Chatbots, primarily due to concerns about Chatbot intelligence, responsiveness, and trust. Additionally, low observability and limited perceived relative advantage further hinder adoption. These findings provide insights for policymakers, government agencies, and Chatbot developers to enhance Chatbot functionality and user experience. Key recommendations include improving Chatbot intelligence and responsiveness, increasing public awareness, and fostering greater trust in the Chatbot. Addressing these factors can drive greater adoption, maximizing the efficiency and impact of AI-driven public services.
Paper Presenter
YA
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 7G, Virtual Room G

9:30am PDT

Collegial Supervision as a Catalyst for Digital Transformation: Insights from a Rural Indonesian Secondary Schools
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G
Authors - Maisyaroh, Agus Timan, Mustiningsih, Maulana Amirul Adha, Indra Lesmana, Rudy Ansar, Novia Putri Arianti
Abstract - Digital learning transformation in rural schools faces various challenges, including limited infrastructure and low digital literacy among teachers. This study aims to explore the contribution of collegial supervision in supporting this transformation. Using a descriptive qualitative approach, data were collected through in-depth interviews, observations, and documentation at a high-performing private secondary school in a rural area. The findings reveal that collegial supervision is grounded in values of trust, equality, shared reflection, and communal cooperation. This approach provides a collaborative space for teachers to exchange best practices, enhance their technological competencies, and develop effective digital teaching strategies. Despite challenges related to time, resources, and varying levels of understanding, collegial supervision has proven effective in fostering an innovative school culture that supports digital-era teaching and learning. This study contributes to the literature on educational supervision by offering context-specific insights and practical strategies for teacher development in resource-constrained schools.
Paper Presenter
avatar for Indra Lesmana

Indra Lesmana

Indonesia
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 7G, Virtual Room G

9:30am PDT

Deep Learning in Oncology: A Multi-Modality Survey of Diagnostic and Prognostic Models
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G
Authors - Nishat Shaikh, Parth Shah, Bimal Patel
Abstract - Deep learning has revolutionized oncology by enabling unprecedent-ed integration of multimodal data for cancer diagnosis and prognosis. This comprehensive survey presents the first systematic analysis of deep learning architectures across four major cancer types (lung, breast, skin, and brain) through three critical data modalities: medical imaging, histopathology, and genomics. Our unique contribution lies in providing a structured taxonomy of multimodal fusion strategies and identifying critical architectural innovations that have emerged in the 2021-2025 period. We systematically analyze 60+ recent studies, revealing that attention-based mechanisms and Transformer architectures demonstrate superior performance in handling heterogeneous cancer data compared to traditional CNN approach-es. Our analysis uncovers three key research gaps: (1) limited interpretability frameworks for clinical deployment, (2) insufficient standardization across institutions, and (3) scalability challenges for real-world implementation. This survey uniquely bridges the gap between theoretical deep learning advances and practical oncological applications by proposing a unified framework for multimodal cancer analysis. We provide actionable insights for researchers and clinicians, establishing clear directions for future development in AI-driven cancer care that addresses both technical innovation and clinical translation requirements.
Paper Presenter
avatar for Nishat Shaikh
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 7G, Virtual Room G

9:30am PDT

Fostering Resilience strategies through Smart Manufacturing, Innovation and Circularity in Agri-food supply chain
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G
Authors - Esthefano Palomino, Rodrigo Condor, Edgar Ramos, Ludwig Tocto, Victor Pimentel
Abstract - Agri-food supply chains are increasingly exposed to climate variability, resource constraints, and disruptive shocks, requiring strategies that enhance resilience and sustainability. This study applies the Best–Worst Method (BWM) to assess and prioritize four strategic dimensions: resilience, smart manufacturing, innovation, and circularity. The results indicate that resilience is the most critical enabler, while smart manufacturing and innovation provide significant complementary support, with circularity ranking lowest. These findings emphasize the central role of resilience in safeguarding agri-food systems and demonstrate how technology and innovation can strengthen long-term sustainability. The study contributes by offering a structured decision-making framework that helps managers and policymakers focus on the most impactful dimensions when designing more adaptive and sustainable agri-food supply chains.
Paper Presenter
avatar for Esthefano Palomino
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 7G, Virtual Room G

9:30am PDT

GENERATIVE ADVERSARIAL NETWORKS FOR MEDICAL IMAGE AUGMENTATION: A COMPREHENSIVE STUDY
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G
Authors - Sarika Pabalkar(Wagh), Kirti Jain
Abstract - Generative adversarial networks, or GANs, are strong tools for improving medical pictures because they can make high-quality virtual images that can be used to solve problems like limited datasets, image unpredictability, and poor diagnosis accuracy. This research looks at all the ways that GAN can be used in medical imaging and highlights the most important improvements in the process of segmentation reconstruction, disease identification, and cross-modal synthesis. A structured methodology based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was employed to identify, screen, and analyze 42 peer-reviewed articles published between 2020 and 2025 across databases such as PubMed, Scopus, IEEE, and ScienceDirect. The review compares widely used GAN architectures—including CycleGAN, Pix2Pix, DCGAN, and ProGAN— evaluating their strengths, limitations, and suitability for different imaging modalities such as MRI, CT, X-ray, ultrasound, and mammography. There are significant problems, including mode collapse, instability in training, no consistent evaluation standard, and few opportunities for clinical validation. The results show the increasing importance of GANs in generating clinically useful data for rare disease information and for cross-modality tasks. The discussion also indicated future interventions that would focus on model stability, ethical use, clinical incorporation, and generalization across populations. The aim of the review is to help fellow researchers and practitioners through assessing state-of-the-art GAN approaches and defining gaps that will need to be addressed prior to more adaptation of GANs in medical imaging.
Paper Presenter
avatar for Sarika Pabalkar(Wagh)
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 7G, Virtual Room G

9:30am PDT

Understanding and Mitigating Bias in Artificial Intelligence Systems: An Ontological Perspective
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G
Authors - Om Roy, Dhruv Shingala, Priyanka Patel
Abstract - Bias within Artificial Intelligence (AI) systems constitutes a profound challenge with substantial implications for fairness, accountability, and societal equity. This paper presents an exhaustive examination of the ontology of bias in AI, delving deeply into its conceptual underpinnings and exploring the intricate algorithmic consequences arising from biased data and models. By establishing a comprehensive and nuanced framework that categorizes diverse manifestations of bias and elucidates their origins, this study aims to foster a profound understanding of how bias permeates AI systems. Integrating interdisciplinary perspectives drawn from philosophy, sociology, and computer science, the ontology of bias is meticulously dissected to reveal its multifaceted nature. Furthermore, the paper investigates the profound impacts of these biases on critical decision-making processes and proposes multifaceted strategies for mitigating bias through ethical design, advanced algorithmic techniques, and stringent regulatory frameworks. Through detailed case studies and empirical analysis, this research highlights the inherent complexities in addressing bias and underscores the imperative for collaborative endeavors to cultivate equitable AI technologies.
Paper Presenter
avatar for Om Roy

Om Roy

India
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 7G, Virtual Room G

9:30am PDT

Using ChatGPT for Sustainable Development Education: Potentials, Risks, and Governance Needs
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G
Authors - Payel Das, Tejaswini Seelam, Rajeswari Annam
Abstract - This study investigates the intersection between generative AI, sustainable development and education by conducting a systematic review on the utilization of ChatGPT and other Large Language Models (LLMs) in relation to Sustainable Development Goals (SDGs), focusing on SDG 4 and SDG 12. The article explores how technology-enhanced learning and LLMs are challenging pedagogical traditions, practices, even ethical dilemmas, and calls for responsible governance. There were 59 peer-reviewed articles included between 2023-2025 which provided data synthesis. Three core clusters were discerned using the VOS viewer keyword co-occurrence map: (1) educational impact and learning outcomes, (2) stakeholder ethics and governance perspectives, and (3) integrity Challenges of AI-assisted instruction. The study also raises alarms about threats that could emerge from misinformation, academic cheating, algorithmic bias and unequal ac-cess. It also underscores the lesser-known environmental footprint of AI tools.
Paper Presenter
avatar for Tejaswini Seelam
Monday October 19, 2026 9:30am - 11:30am PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 7G, Virtual Room G

11:30am PDT

Session Chair Concluding Remarks
Monday October 19, 2026 11:30am - 11:32am PDT
Virtual Room A
Monday October 19, 2026 11:30am - 11:32am PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 7A, Virtual Room A

11:30am PDT

Session Chair Concluding Remarks
Monday October 19, 2026 11:30am - 11:32am PDT
Virtual Room B
Monday October 19, 2026 11:30am - 11:32am PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 7B, Virtual Room B

11:30am PDT

Session Chair Concluding Remarks
Monday October 19, 2026 11:30am - 11:32am PDT
Virtual Room C
Monday October 19, 2026 11:30am - 11:32am PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 7C, Virtual Room C

11:30am PDT

Session Chair Concluding Remarks
Monday October 19, 2026 11:30am - 11:32am PDT
Virtual Room D
Monday October 19, 2026 11:30am - 11:32am PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

11:30am PDT

Session Chair Concluding Remarks
Monday October 19, 2026 11:30am - 11:32am PDT
Virtual Room E
Monday October 19, 2026 11:30am - 11:32am PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 7E, Virtual Room E

11:30am PDT

Session Chair Concluding Remarks
Monday October 19, 2026 11:30am - 11:32am PDT
Virtual Room F
Monday October 19, 2026 11:30am - 11:32am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 7F, Virtual Room F

11:30am PDT

Session Chair Concluding Remarks
Monday October 19, 2026 11:30am - 11:32am PDT
Virtual Room G
Monday October 19, 2026 11:30am - 11:32am PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 7G, Virtual Room G

11:32am PDT

Session Closing and Information To Authors
Monday October 19, 2026 11:32am - 11:35am PDT
Virtual Room A
Exhibitors
avatar for Ashutosh Goyal
Monday October 19, 2026 11:32am - 11:35am PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 7A, Virtual Room A

11:32am PDT

Session Closing and Information To Authors
Monday October 19, 2026 11:32am - 11:35am PDT
Virtual Room B
Exhibitors
RJ
Monday October 19, 2026 11:32am - 11:35am PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 7B, Virtual Room B

11:32am PDT

Session Closing and Information To Authors
Monday October 19, 2026 11:32am - 11:35am PDT
Virtual Room C
Exhibitors
avatar for Anshika Jain
Monday October 19, 2026 11:32am - 11:35am PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 7C, Virtual Room C

11:32am PDT

Session Closing and Information To Authors
Monday October 19, 2026 11:32am - 11:35am PDT
Virtual Room D
Exhibitors
avatar for Richa Sharma
Monday October 19, 2026 11:32am - 11:35am PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

11:32am PDT

Session Closing and Information To Authors
Monday October 19, 2026 11:32am - 11:35am PDT
Virtual Room E
Exhibitors
avatar for Siddhesh Mogal
Monday October 19, 2026 11:32am - 11:35am PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 7E, Virtual Room E

11:32am PDT

Session Closing and Information To Authors
Monday October 19, 2026 11:32am - 11:35am PDT
Virtual Room F
Exhibitors
avatar for ANISHA MISHRA
Monday October 19, 2026 11:32am - 11:35am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 7F, Virtual Room F

11:32am PDT

Session Closing and Information To Authors
Monday October 19, 2026 11:32am - 11:35am PDT
Virtual Room G
Exhibitors
avatar for DHARMIK MODI
Monday October 19, 2026 11:32am - 11:35am PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 7G, Virtual Room G

12:13pm PDT

Opening Remarks
Monday October 19, 2026 12:13pm - 12:15pm PDT
Virtual Room A
Monday October 19, 2026 12:13pm - 12:15pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 8A, Virtual Room A

12:13pm PDT

Opening Remarks
Monday October 19, 2026 12:13pm - 12:15pm PDT
Virtual Room B
Monday October 19, 2026 12:13pm - 12:15pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 8B, Virtual Room B

12:13pm PDT

Opening Remarks
Monday October 19, 2026 12:13pm - 12:15pm PDT
Virtual Room C
Monday October 19, 2026 12:13pm - 12:15pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 8C, Virtual Room C

12:13pm PDT

Opening Remarks
Monday October 19, 2026 12:13pm - 12:15pm PDT
Virtual Room D
Monday October 19, 2026 12:13pm - 12:15pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:13pm PDT

Opening Remarks
Monday October 19, 2026 12:13pm - 12:15pm PDT
Virtual Room E
Monday October 19, 2026 12:13pm - 12:15pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 8E, Virtual Room E

12:13pm PDT

Opening Remarks
Monday October 19, 2026 12:13pm - 12:15pm PDT
Virtual Room F
Monday October 19, 2026 12:13pm - 12:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 8F, Virtual Room F

12:13pm PDT

Opening Remarks
Monday October 19, 2026 12:13pm - 12:15pm PDT
Virtual Room G
Monday October 19, 2026 12:13pm - 12:15pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 8G, Virtual Room G

12:15pm PDT

Deep Learning Architectures for Voice Recognition: A Comparative Analysis and Future Directions
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A
Authors - Seema Tripathi, Nikita Bhardwaj, Shivani Bansal
Abstract - Voice recognition technology has evolved into a cornerstone in contemporary applications, from virtual assistants and intelligent homes to biometric security and healthcare. Its potential to facilitate unobstructed human-computer interaction has hastened the need for more precise and more robust systems. GMM and HMM traditionally paved the way for voice processing. However, deep learning architectures—namely Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based models—have revolutionized the field by achieving unprecedented performance and flexibility. This research paper provides a comparative study of major deep learning models for voice recognition, critically evaluating their design rationales, merits, and shortcomings Some major challenges like scarcity of data, noise tolerance, computational efficiency, and interpretability of models are discussed. The paper also offers the perspectives of the future trends in work like the inclusion of self-supervised learning, light-weight models for edge devices, and multimodal systems that will influence the next voice recognition generation. The goal of this work is to offer an end-to-end perspective of what exists currently and stimulate future work in this very dynamic field.
Paper Presenter
ST
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 8A, Virtual Room A

12:15pm PDT

Emerging Research Themes in AI-Based Supply Chains, Transportation and Logistics: A Bibliometric Analysis
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A
Authors - Vipul Lanjewar, Sourish Joshi, Sujata Joshi, Menachem Domb
Abstract - AI is changing supply chain, transport, and logistics using neural net-works to improve decisions. It helps with real-time analysis, route planning, and demand prediction. A bibliometric analysis with Scopus data was performed, using VOSviewer and Biblioshiny to track trends, citations, and new themes in AI for these fields. The focus was on areas like warehouse automation, self-driving logistics, and smart transportation, while also tackling data security and ethical issues. By finding key research and gaps, this paper shows how AI can shape the future of these industries.
Paper Presenter
avatar for Vipul Lanjewar
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 8A, Virtual Room A

12:15pm PDT

Internal Conflicts: Examining the influence of arms and armed forces
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A
Authors - Anshika Srivastava, Rojalin Patri
Abstract - This study explores whether arms and armed forces increase the probability of armed conflict within a country. Though several studies in literature have highlighted the cases where arms and armed forces have influenced armed conflict within a country, none of them have provided empirical evidence to support this phenomenon. This study attempts to bridge the gap by providing a quantitative analysis across 45 countries over 28 years and further substantiates the arguments laid in qualitative studies. Random effect panel logistic estimates and year-wise logistic estimates have been used to capture the overall as well as year-wise significance of arms and armed forces on armed conflict within the country, respectively. Similarly, when the number of armed forces increase by one million, the odds for armed conflict becomes 3.97 times more and when military expenditure increase by 1%, the odds for armed conflict becomes 9.06 times more than before. These insights point towards the fact that arms, armed forces and militarism don’t necessarily help reduce violence.
Paper Presenter
avatar for Anshika Srivastava
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 8A, Virtual Room A

12:15pm PDT

Managing Professional and Personal Life A Study on Work-Life Balance Among Working Professionals
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A
Authors - Kalyani Yede, Deepak Sharma
Abstract - Work-life balance is of utmost importance to career workers managing the challenges of contemporary work environments and family duties. This research employs secondary data to investigate the determinants of work-life balance, its impact on worker satisfaction, productivity, and health, and organizational support as a facilitator of balance. The study finds that flexible work arrangements, supportive management, and organizational policies all contribute strongly to helping employees balance work and personal life. Excessive workload and a lack of support, on the other hand, lead to job dissatisfaction and stress. The study emphasizes a need for cooperative efforts between employers and employees to create a good work-life balance. This study offers useful insights to organizations that want to enhance organizational performance and employee well-being by implementing focused work-life balance programs.
Paper Presenter
avatar for Kalyani Yede
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 8A, Virtual Room A

12:15pm PDT

OPTIMIZING VIDEO SUMMARIZATION USING DEEP REINFORCEMENT LEARNING WITH DIVERSITY REPRESENTATIVE REWARD
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A
Authors - Namrata Patil, Pravin Desai, Ashvini Patil, Pragati Patil
Abstract - In the context of multimedia content of huge scale, effective and efficient video summarization has become more and more important for improving web browsing experiences and information overload. In this paper, we introduce a new video summarization method by modeling it as a sequential decision-making problem. A Deep Summarization Network (DSN) is proposed for predicting the probability of considering each video frame in terms of its possible contribution to the final summary. For training the DSN, we suggest an end-to-end deep reinforcement learning system that works in a fully unsupervised way, without requiring annotated labels or human intervention. Our approach has its foundation in a well-crafted reward function that assesses generated summaries along two primary dimensions: diversity and representativeness. These elements are designed to ensure that the chosen frames not only convey the main content of the video but also encompass a good number of visual and contextual variations. The DSN is trained to maximize these rewards, resulting in brief, informative, and non-redundant summaries. Extensive experiments are performed on two standard datasets to confirm the efficacy of the new method. The experimental results show that our method is capable of rendering competitive performance compared with state-of-the-art methods, especially under unsupervised scenarios, thus revealing excellent potential to be applied in real-world systems for large-scale video analysis and multimedia retrieval.
Paper Presenter
avatar for Pravin Desai
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 8A, Virtual Room A

12:15pm PDT

Predicting Esophageal Cancer with Machine Learning: An Automated Approach
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A
Authors - Janwale Asaram Pandurang, Minal Dutta, Savita Mohurle
Abstract - The machine learning algorithms used for the automated predction of esophageal cancer, a highly destructive disease that frequently exhibits at an advanced stage because it lacks early symptoms, is examined in this work. A huge dataset including clinical, demographic, and diagnostic information is used in the study to relate the classification algorithms of logistic regression and decision trees. With a incredible accuracy of 99.75% compared to 64.87%, the Decision Tree model clearly beaten Logistic Regression. Its capability to recognise complex, non-linear relationships in the data is responsible for Decision Tree's better performance, which makes it a useful tool for early cancer detection. The study identifies the need for validation on a variety of datasets and techniques to reduce overfitting, even as the results illustrate how machine learning may improve diagnostic accuracy. Improved clinical processes and better patient outcomes are made possible by us research's successful development of scalable, interpretable, and effective diagnostic tools.
Paper Presenter
avatar for Janwale Asaram Pandurang
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 8A, Virtual Room A

12:15pm PDT

Recommendation of Research Papers using KNN and Universal Sentence Encoder
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A
Authors - Jatin Kumar Sangewar, Chandan Kumar Sangewar, Gaurav Kumawat, Chirag Joshi
Abstract - In this research paper, we introduce a novel approach to improving research paper recommendation systems using the Universal Sentence Encoder (USE) to analyze and understand the textual content of academic papers, such as their title, abstract, and publication year. Our ultimate objective in the proposed methodology is to greatly enhance the accuracy and personalization of paper recommendations in favor of scholars, researchers, and students. Our research provides a thorough system architecture review, extensive experimentation with regard to state-of-the-art benchmarks, as well as an assessment of its probable impact on the world of academia. In addition to the technicalities of our method, we explore the larger research paper recommendation system context. This involves a close look at the issues that plague these systems, including data sparsity and the cold start problem, and the potential directions they offer for enhancing academic discovery. We also look at possible futures for the discipline, including the incorporation of user profiling, collaborative filtering methods, and ethical considerations into the building of these systems. The goal of this study is to push the field of research paper recommendations into a new generation based on the use of state-of-the-art Natural Language Processing and Machine Learning methods while ensuring a commitment to enhancing the academic experience. The new use of the USE model in this system offers intriguing possibilities for making more accurate and user-specific recommendations, which will eventually enable a more productive and efficient academic experience for users in general.
Paper Presenter
avatar for Chandan Kumar Sangewar
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 8A, Virtual Room A

12:15pm PDT

Smart Water Temperature Modulation Bottle
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A
Authors - Naina Kokate, Aoudumber D. Londhe, Priyanka Swapnil Raikar, Nikeeta Pande, Sanjana Sanjiv Nikam, Nethra Nadar, Indranil Nayak, Rhia Nevgi, Neil Oswal
Abstract - This project demonstrates about working of Peltier module with an new approach, we have tried making a smart way of using it by making a smart water temperature modulation bottle. The targets we wanted to achieve through this project was to make it energy efficient along with cost effective. the dual purpose of heating and cooling to be done as per our wishes. the main components which are used in this project are TEC1-12706 Peltier module, W1209 digital thermostat, two heatsinks and two fans. the project can be used for personal as well as professional fulfilling of needs such as getting water to a certain temperature or maintaining a medicine at a given temperature. the project can work on both battery and electricity.
Paper Presenter
avatar for Nikeeta Pande
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 8A, Virtual Room A

12:15pm PDT

Statistical Analysis of the Israeli-Palestinian Conflict: Patterns and Dynamics
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A
Authors - Ketaki Paranjape, Rajasi Barapatre, Sandhya Arora
Abstract - This study conducts a statistical analysis of the Israeli-Palestinian conflict to uncover patterns and relationships that characterize the dynamics of hostilities. The research examines critical questions about the association between event location, age, citizenship, and types of ammunition, employing statistical methods such as Z-scores, Chi-square tests, and F-tests on a comprehensive dataset. Key findings include significant regional variations in participation in hostilities, with Gaza showing the highest levels of engagement, and the predominance of the 21–30 age group in active involvement. Additionally, asymmetrical impacts were observed, with Palestinian civilians primarily affected by Israeli security forces, and Israeli civilians targeted by Palestinian actors. Analysis of ammunition usage reveals targeted engagement against younger age groups and highlights the varied means of violence in the conflict. These findings provide valuable insights into the conflict's demographic and regional dimensions, offering a foundation for data-driven conflict resolution strategies and humanitarian interventions.
Paper Presenter
avatar for Ketaki Paranjape
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 8A, Virtual Room A

12:15pm PDT

Work-Life Balance among Employee in IT Parks in Pune: A Research Study
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A
Authors - Kalyani Yede, Deepak Sharma
Abstract - This study looks into the effect of work-life balance on IT professionals in Pune, India, through analysis of determinants that affect this balance and measures to enhance it. A thorough review of literature demarcates important areas for study, such as the organizational policy, socio-cultural determinants, and perceptions. The research will fill the gap between research and practice by presenting culturally responsive suggestions to organizations looking to improve employees' job satisfaction and productivity. The research will, with a mixed-methods design, offer insights for policy-making and enhancing the quality of life of IT professionals.
Paper Presenter
avatar for Kalyani Yede
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 8A, Virtual Room A

12:15pm PDT

A Lightweight Framework for Implementing ISO/IEC 26550 in Small and Medium-sized Software Enterprises in Bangladesh
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B
Authors - Sayeda Rahnuma Akthar, Fatima Ibrahim, Muhammad Rezaul Islam, Marzan Binte Hasan, Md. Ataur Rahman, Zinia Hafiz, Farzana Sadia, Mahady Hasan, M. Rokonuzzaman
Abstract - This study explores the challenges faced by Bangladeshi software Small and Medium-sized Enterprises (SMEs) in adopting ISO/IEC 26550 for Software Process Improvement (SPI). Data collected from 12 SMEs revealed uneven implementation across key process areas, with high familiarity in domain requirements (up to 95\%) but limited engagement in testing and validation practices. To address these gaps, the study proposes a lightweight PDCA (Plan–Do–Check–Act) framework tailored to the unique resource constraints of SMEs. Un-like traditional models such as CMMI or ISO/IEC 12207, the proposed approach emphasizes simplicity, cost-effectiveness, and role-based adaptability, making it more practical for SMEs in emerging economies. The framework supports scalable and gradual process improvement, enhances quality assurance, and facilitates stronger alignment with ISO/IEC 26550 in resource-limited environments.
Paper Presenter
avatar for Fatima Ibrahim

Fatima Ibrahim

Bangladesh
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 8B, Virtual Room B

12:15pm PDT

Air quality prediction using Computational Intelligence
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B
Authors - Sushmitha, Vijayalaxmi
Abstract - This paper provides prediction of air quality using computational intelligence techniques. The study employs various Computational Intelligence techniques to evaluate the data associated with environmental pollution and hence to predict the air quality focusing on PM2.5 and PM10 concentrations. The performance of each model is assessed with standard evaluation metrics. Through a series of simulations, it is observed that the results suggest that computational intelligence techniques play an essential role in improving the accuracy of air quality prediction models.
Paper Presenter
avatar for Sushmitha
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 8B, Virtual Room B

12:15pm PDT

An Extensive Examination of the Development of Financial Technology in the Banking Sector: The Role of Trust as an Entry Barrier
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B
Authors - Jaride Chama, Hajraoui Kenza, Zaidane Kaoutar, Benabdelkrim El Filali Yasmine
Abstract - With technological developments and the digitalization of business processes in the banking sector, physical and virtual environments are complementing each other. Electronic channels are no longer considered as additional or less costly way of interacting with customers. Today, they represent a considerable and constantly growing part of the activities of bank institutions world-wide. The aim of this work is to assess the impact of trust in the development of fintech banking through a transnational analysis. The results of this work can be used to map the countries that studied this topic. The findings of this study reveal that, despite the fact that research in this field has been carried out in a wide variety of economic and cultural contexts (45 countries were surveyed), the previous studies agreed on the strong presence of trust as an entry barrier for the development of financial technology in the banking sector. In addition to trust, other adoption factors appear to be essential for consumers. The vulnerability resulting from digitalization in the banking sector has enabled us to formulate some suggestions for further investigations.
Paper Presenter
avatar for Jaride Chama
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 8B, Virtual Room B

12:15pm PDT

An In-depth Analysis of the Impact of Feature Transformation Learning on Classification Matrix in Driver Identification
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B
Authors - Md. Abbas Ali Khan, Md. Musfique Anwar, Mohammad Hanif Ali, Touhid Bhuiyan, Md. Ataur Rahman
Abstract - Personalized driving assistance, security, and usage-based insurance models all depend on driver identification in intelligent transportation systems. Conventional techniques for identifying drivers primarily rely on biometric or manually engineered feature data, which may not adequately capture the nuances of unique driving actions. Using time-series driving data, including vehicle speed, steering angle, and acceleration, we present a unique contrastive learning-based method for driver identification in this study. The suggested model uses contrastive learning to compare driving sessions from the same driver to those of other drivers to learn how to distinguish between them. Rather than requiring manual feature extraction, our method automatically extracts strong, high-dimensional representations of driving behavior. Concerning driving sequences, the contrastive learning framework aims to minimize the similarity between sequences from different drivers and maximize the similarity between sequences from the same driver. When applied to situations with various driving behaviors and a small amount of labeled data, our strategy outperforms more conventional classification techniques in accuracy. The suggested model offers a scalable and effective solution for real-world applications, as demonstrated by experimental findings on driving publicly available datasets. This model also greatly increases driver identification performance. We applied seven machine learning classification models. The Random Forest Classifier obtained the highest accuracy of 93.57%.
Paper Presenter
avatar for Md. Ataur Rahman
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 8B, Virtual Room B

12:15pm PDT

Content Marketing and Corporate Brand Awareness in the Philippines: A Structural Equation Model (SEM)
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B
Authors - Ronald C. Barriga, Dan Jeward Rubis, Arlene A. San Pablo, Helinor Y. Medina
Abstract - Content video marketing has become vital in the world of marketing for the past few years, in which companies are into it using content creators to expand brand awareness. The use of different social media platforms makes all this possible technological advancement like 5G connectivity that makes the connection faster, the continuous and fast innovation in the electronic industry has made everything easy to access. These make content marketing more vital in establishing corporate brand awareness. Brand recall, recognition and dominance through content video marketing has a significant impact to brand awareness, where brand recall essentially a dominant contributor to Brand Awareness. Furthermore, the model Structural Equation Modeling (SEM) shows that for content video marketing to be effective, the quality of each content must be given importance with consideration also to Knowledge Opinion Leader (KOL) and the Electronic Word of Mouth (eWOM) to strengthen brand awareness. Lastly, SEM positively indicates how significant the three factors mentioned are to brand awareness. The SEM model has a very good fit for the data, explaining a large portion of the variance in brand awareness. The results suggest that to improve brand awareness, strategies that enhance brand recognition, recall, and dominance should be prioritized.
Paper Presenter
avatar for Dan Jeward Rubis

Dan Jeward Rubis

Philippines
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 8B, Virtual Room B

12:15pm PDT

CyberShield AI: A Cyber-Bullying Detection Tool
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B
Authors - Dev Parekh, Jay Patel, Keshvi Patel, Dhaval Patel, Priteshkumar Prajapati
Abstract - This paper presents CyberShieldAI, a novel framework that leverages BERT (Bidirectional Encoder Representations from Transformers) models to detect cyberbullying in online text communications. Our approach harnesses BERT’s contextual understanding capabilities to identify subtle linguistic patterns, emotional undertones, and semantic structures commonly associated with cyberbullying content. By fine-tuning BERT on carefully curated datasets of online interactions, CyberShieldAI can effectively distinguish between harmless communications and those containing bullying, harassment, or other harmful content. The framework analyzes text bidirectionally, capturing crucial contextual relationships that traditional text classification methods might overlook. Experimental evaluations demonstrate that our BERT-based system achieves significant improvements in cyberbullying detection accuracy, sensitivity, and specificity compared to conventional machine learning approaches. This paper details the system architecture, implementation methodology, dataset preparation challenges, and performance metrics that validate the effectiveness of our strategy. CyberShieldAI represents a significant advancement in automated content moderation systems, which can help create safer online environments, particularly for vulnerable user populations, such as children and adolescents.
Paper Presenter
avatar for Dhaval Patel
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 8B, Virtual Room B

12:15pm PDT

DESIGN AND DEVELOPMENT OF A SYSTEM FOR UNVEILING INAUTHENTICITY IN AUDIO CONTENT
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B
Authors - Swati Kale, Jyoti Tipale, Shilpa Sonawane, Kshitij Mulay, Gaurav Patil, Nikita Khumkar
Abstract - This paper presents a novel multi-modal speech analysis system that integrates deep learning architectures and “Natural Language Processing (NLP)” techniques to address the limitations of traditional speech evaluation approaches. The proposed system combines a custom “Long Short-Term Memory (LSTM)” based neural network for temporal pattern analysis, TF-IDF vectorization for keyword extraction, real-time spectrogram analysis for acoustic feature extraction, and a dynamic content fetching system from Wikipedia datasets. The system demonstrates enhanced accuracy in content relevance detection, real-time processing with minimal latency, and a scalable architecture suitable for diverse ap-plications, including educational, professional, and research contexts. The study highlights the system's ability to bridge the difference in the theoretical abilities of speech analysis and their practical implementation. The proposed methodology contributes to the advancement of speech analysis by addressing the challenges of insufficient integration between acoustic and semantic analysis, static reference materials, and limited scalability.
Paper Presenter
avatar for Kshitij Mulay
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 8B, Virtual Room B

12:15pm PDT

Impact of Complex Engineering Problem Segment under Outcome-Based Education (OBE) in the Context of Engineering Education
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B
Authors - Md. Ataur Rahman, Sabrina Alam, Saadia Binte Alam, Mahady Hasan
Abstract - Outcomes-based education (OBE) is reshaping engineering education, with an emphasis on complex engineering problem solving, with clear outcomes directly related to industry needs. This study assessed the impact of including complex engineering problem-solving in the engineering curriculum within OBE on student skills. Through qualitative and quantitative research methods, including course curriculum analysis, student feedback, questionnaires, and student achievement. We collect 3 years of data. Data collected over a three-year research period (2022, 2023, and 2024) at Independent University, Bangladesh (IUB), this study assessed the improvement in skills such as critical thinking, application of advanced tools, sustainability awareness, professional ethics, teamwork, communication, project management, lifelong learning, and entrepreneurship. There are barriers to implementation, such as resource allocation, training of faculty members, and curriculum restructuring, with evidence of positive improvements in these categories. This study strongly recommends greater application of advanced engineering problem-solving techniques in OBE to match educational outcomes to professional engineering requirements better, ultimately producing graduates more adept at solving problems in the real world.
Paper Presenter
avatar for Md. Ataur Rahman
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 8B, Virtual Room B

12:15pm PDT

Leveraging Large Language Models for Enhanced URL Phishing Detection
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B
Authors - Rohit Yadav, Rohini Bhosale, Rahul Rathod, Prashant Dhotre, Yashraj Salunkhe Patil
Abstract - Phishing continues to be a prevalent threat in cybersecurity, using deceptive URLs to undermine user information and system integrity. Conventional machine learning methods, though effective when handcrafted features are used, tend to perform poorly in generalizing against more obfuscated patterns of attacks. This paper presents an effective phishing URL detection system that takes advantage of the contextual strengths of quantized Large Language Models (LLMs)—namely Mistral-7B and Mixtral-8x7B—combined with interpretable gradient boosting models like CatBoost. The LLMs are employed with prompt-based inference for semantic URL evaluation, whereas the CatBoost model employs lexical and statistical features for structured classification. Tested on a balanced dataset of more than 2.28 mil-lion URLs, the system has high accuracy and low false positive rates. SHAP-based visualizations are employed to offer transparent explanations for model decisions. The findings prove the real-world feasibility of integrating effective LLM reasoning with conventional ML in real-time phishing defense systems, particularly in resource-limited settings.
Paper Presenter
avatar for Rohit Yadav
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 8B, Virtual Room B

12:15pm PDT

When Reality Blurs: The Impact of Deepfakes on Psychology and Society
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B
Authors - Aryan G. Kale, Kajal P. Salampuriya, Nitin Prakash
Abstract - The ascent of artificial intelligence technologies, particularly the generative application of GANs, has changed the landscape of media production into a milieu that easily allows - creation of hyper-realistic yet deceptive content. While deepfakes have indeed been misused from entertaining, educative, and access provisions, their abuse causes dreadful threats to mental well-being and social harmony. This study explores the psychological effects of deepfakes, such as anxiety owing to identity theft, impersonation, trust erosion in digital media, cognitive dissonance because of the inability to tell the difference between real and fake content, and social isolation due to cyberbullying and non-consensual pornography.On the social front, deepfakes add to the pool of misinformation spreading, fake news politically entering the narratives, and a myriad of ethical dilemmas, adding to general confusion, anxiety, and distrust among the public at large in institutions. The economic designs revolve around hiring specialists to thwart deepfake manipulation and attending to its legal and psychological aftermath.To ameliorating this, the study points out a projective need for strategies of intervention. These would include developing an AI detecting tool that identifies and flags deepfakes, launching public awareness campaigns to increase digital literacy and critical thinking, instituting solid legal scaffolding with added ethical principles, and other support systems including counseling services supporting the victims of deepfake assaults.The study is primarily inclined toward discussing how integrated management approaches shall serve to address the issues faced concerning deepfakes, while the positives remain intact. Future research is necessary to explore the cultural and demographic nuances of deepfake impacts, understand the influence of the COVID- 19 pandemic on mental health in the context of deepfake exposure, and evaluate the effectiveness of emerging prevention and mitigation strategies.
Paper Presenter
avatar for Aryan G. Kale
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 8B, Virtual Room B

12:15pm PDT

Advanced Supply Chain Planning through the Human- Center to Foster Resilience and Sustainable Agrifood Supply Chain 5.0
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C
Authors - Nathalia Zacarias, Denys Huacanca, Edgar Ramos, Ines Tarazona, Donavan Fuqua
Abstract - Supply Chain Planning in agri-food supply chains faces increasing challenges due to global disruptions and resource constraints. Supply Chain 5.0, driven by human-centered approaches, enhances resilience and sustainability by aligning dynamic demand strategies with technological advancements in uncertain environments. To address these complexities, this study applies the Best- Worst Method (BWM) to prioritize key strategic criteria across four dimensions, enabling consistent and structured decision-making. The results emphasize the relevance of advanced supply chain planning and human-centered innovation in fostering adaptive resilience and shaping a more sustainable supply chain. These findings support the transition toward resilient, sustainable, and human-driven agri-food supply chains aligned with the Supply Chain 5.0 framework.
Paper Presenter
avatar for Nathalia Zacarias
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 8C, Virtual Room C

12:15pm PDT

Advancing Audit Effectiveness through AI-Driven Digital Audits and Auditor Competency: The Moderating Role of Technological Infrastructure
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C
Authors - Windy Permata Suyono, Marsellisa Nindito, Dwi Handarini, Hera Khairunnisa, Eka Septariana Puspa, Surya Anugrah, Rochma Sudiati, Rio Firnanda, Sabo Hermawan, Septi Nurmalita
Abstract - The transformation of auditing through Artificial Intelligence (AI) has redefined the way auditors perform their tasks in the digital era. This study aims to examine the role of digital audit and auditor competence in enhancing audit effectiveness, with technological infrastructure as a moderating variable. Using a systematic literature review approach, findings show that both digital audit and auditor competence have a significant positive impact on audit effectiveness. However, technological infrastructure exhibits a dual moderating role. It negatively moderates the relationship between digital audit and audit effectiveness, suggesting that inadequate infrastructure may hinder the benefits of digitalization. Conversely, it positively moderates the relationship between auditor competence and audit effectiveness, indicating that skilled auditors perform better when supported by robust infrastructure. These findings emphasize the need for a holistic strategy that combines advanced technology, auditor upskilling, and infrastructure readiness. This study contributes to the body of knowledge on audit digitalization and supports Sustainable Development Goal 9 (SDG 9) by promoting industry innovation and resilient infrastructure.
Paper Presenter
avatar for Windy Permata Suyono
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 8C, Virtual Room C

12:15pm PDT

ARTIFICIAL INTELLIGENCE POWERED CREDIT SCORING MODELS: ADVANCING FINANCIAL INCLUSION AMIDST LEGAL AND ETHICAL CHALLENGES IN INDIA
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C
Authors - Amita Verma, Simran Sankhyayan, Kritika Sheoran, Rukhsaar Dhaliwal
Abstract - Artificial Intelligence has evolved the operation and functioning of every sector and banking is no exception to it. The inclusion of AI has revolutionized banking operations to a significant extent, following the introduction of Automated Teller Machines and Online Banking, transforming traditional banking practices in India. This contribution intends to unravel the complex yet pressing intersections between technology and Banking. This paper examines how AI-powered credit scoring systems, compared to conventional methods, are utilized by banks and financial institutions to accurately assess credit scores and predict credit risks. Credit scoring has been completely transformed by artificial intelligence, which offers sophisticated models and predictive analytics that promise increased efficiency and accuracy. This paper discusses the benefit of leveraging AI technology in credit scoring in terms of financial inclusion of underserved populations of the country paving the way for the establishment of an equitable society. Nevertheless, there are drawbacks and restrictions of using Algorithm technology in credit scoring. This paper highlights the main legal issues and challenges, with particular attention to issues with interpretability, data privacy, data breaches, etc. The principles of fairness, accountability, and transparency are invoked, and the possibility of unintentional biases is called into question by this lack of explainability in AI credit scoring tools. All such issues are then weighed in the context of the existing legal framework in India which eventually highlights the grey areas in the legal realm that the concerned authorities like the legislature, RBI, and financial institutions should ponder upon to ensure the balance between innovation and justice.
Paper Presenter
avatar for Simran Sankhyayan
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 8C, Virtual Room C

12:15pm PDT

Chicken Disease Image Classification Using modern CNNs and Vision Transformers
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C
Authors - Howard Prioleau, Saurav K. Aryal, Jeremy Blackstone
Abstract - This study contributes to ongoing efforts in agricultural technology by advancing the application of artificial intelligence in poultry disease classification, crucial for sustaining agriculture amidst global demographic changes. Focusing on the Chicken Disease Image Classification task, the research evaluates and finetunes state-of-the-art Convolutional Neural Networks (CNNs) like EfficientNet and ConvNeXT, alongside Vision Transformers (ViTs) such as DeiT and Swin Transformer. These models, pretrained on ImageNet, are adapted to classify various poultry diseases from chicken fecal matter images, representing conditions like Coccidiosis, Healthy, New Castle Disease, and Salmonella. The study juxtaposes these advanced models with traditional machine learning approaches to assess their relative efficacy in handling class imbalances and complexities inherent in the dataset. Through a comprehensive evaluation using Precision-Recall curves and Confusion Matrices, the research sheds light on the nuances of model performance in disease identification. The findings underscore the challenges posed by imbalanced datasets and emphasize the need for balanced data in training machine learning models for disease classification. This work showcases the capabilities of current state of the art AI in agricultural settings, aiming to enhance early disease detection tools for poultry health management, especially beneficial in resource-limited environments.
Paper Presenter
avatar for Howard Prioleau

Howard Prioleau

United States of America
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 8C, Virtual Room C

12:15pm PDT

E-Commerce in Sustainable Supply Chain Management: A Bibliometric Analysis Approach
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C
Authors - Sarvani Kocherlakota, Poojitha Panchakarla, Padmanabhuni Venkata Sai Rushikesh Vardhan, Vecha Guhana Naga Gayathri
Abstract - E-commerce and supply chain management has emerged as a prominent research topic in recent decades, with numerous findings published in leading international journals. To investigate its current research landscape and emerging trends, we conduct a Bibliometric analysis by using R software 4.5.0, and VOSviewer to identify the research trend, emerging topics, and collaborations among countries, authors, documents and the contributing academic journals during the study period. Sustainability (Switzerland) journal is having the highest publications and Journal of Operations Management is having the most cited document. Zhang X is the most contributing author and Sun Y is the most cited author. China is the most contributing country with around 45% of total publications and also has high international collaborations. By research gap analysis, the future direction in the domain of supply chain management can be integration of live-streaming, emerging technologies, disruption risks, and consumer behavioral preferences.
Paper Presenter
avatar for Vecha Guhana Naga Gayathri
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 8C, Virtual Room C

12:15pm PDT

ECONOMIC BENEFITS OF NMT IMPLEMENTATION ON HEALTH AND ENVIRONMENT
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C
Authors - Jaskaran Singh Saini, Dinesh Kumar Saini, Bhawna Chahar
Abstract - Smart non-motorized transportation is crucial for the sustainable development of urban areas. An astronomical rise in the number of motorized vehicles, particularly in metropolitan cities, has compromised air quality and traffic conditions. NMT, as an effective mode of transport, is often neglected due to a lack of understanding of the long-term negative consequences of internal combustion engines (ICE) by policymakers and transport planners. The rising fuel costs and demand have inflated India's import bills and are expected to surge further until corrective measures are taken. NMT is a low-hanging fruit that can be implemented in urban hotspots at a relatively lower capital expenditure. It can help reduce the demand for fossil fuels and save the state enormous sums that would otherwise have been spent on capital-intensive carbon sequestration technologies. This study aims to highlight the significant economic benefits of NMT, based on data collected from 599 respondents in New Delhi and Chandigarh. The analysis reveals that, in addition to establishing the complex infrastructure of NMT, transport planners and policymakers must foster attitudinal change among citizens, i.e., the soft infrastructure.
Paper Presenter
avatar for Jaskaran Singh Saini
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 8C, Virtual Room C

12:15pm PDT

Financial Inclusion Through Health Insurance Literacy of Middle-Aged Class
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C
Authors - Adithi Varma PC, Venugopal J S, Mayadevi Thampatty PC
Abstract - This paper explores the intricate relationship between financial literacy and health insurance as key determinants in fostering financial inclusion among middle-aged class. By examining the mutual connections and co-influence between these concepts, the study aims to uncover how enhancing the health literacy to facilitate greater financial inclusion. Financial Literacy empowers individuals to make sound decisions regarding health insurance, risk management, healthcare cost, and insurance coverage. Conversely, Adequate health insurance which suits your health plan act as financial safety net, reducing huge health-related expenditures and promote financial stability. This study is conducted using a descriptive and analytical design, surveyed 101 respondents in Kozhikode District via convenient sampling through interview schedule. Data analysis involved mean scores, Chi-square, and t-tests. This research proposes that by integrating financial education with health literacy programs we can a create a synergistic effect that leading to an improved health outcome and enhanced financial well-being. This study will facilitate the development of comprehensive strategies that address the financial and health challenges faced by individuals and thereby promoting financial inclusion.
Paper Presenter
avatar for Adithi Varma PC
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 8C, Virtual Room C

12:15pm PDT

Improving Lattice-Based Cryptography for Post Quantum Security in IoT and Block Chain Communications
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C
Authors - Selvamani K, Kanimozhi S, Muthusundar SK, Saranraj G, Jagadeesh K
Abstract - With the advancement of quantum computing, conventional cryptographic approaches are losing strength against possible attacks, showing the necessity of secure alternatives such as lattice-based cryptography, especially for block chain and IoT implementations. Although lattice-based cryptography provides strong quantum resistance, its practical application is hindered by high computational overhead, intricate key management, and power consumption in resource-constrained IoT devices. Additionally, using it in block chain demands secure signature schemes and authentication protocols without compromising performance. Existing solutions compromise between security and efficiency at the expense of power consumption and higher latency. In an effort to solve these challenges, we present a more robust latticebased cryptographic scheme that enhances aggregate signatures for low communication overhead without compromising security. Our solution also provides a light-weight key exchange mechanism specific to IoT devices, enhancing scalability and energy consumption. Adaptive security elements are also included to counter potential post-quantum threats. Performance tests indicate that our framework substantially minimizes computational complexity while improving security compared to currently available alternatives. The findings attest to its capability to secure block chain-based IoT networks while providing efficient cryptographic operations. By overcoming the major shortcomings of lattice-based cryptography, our research provides a scalable, quantum-resistant solution for IoT and block chain application security in the post-quantum age.
Paper Presenter
avatar for Selvamani K
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 8C, Virtual Room C

12:15pm PDT

Understanding and Mitigating Emerging Social Engineering Cyber Attacks: A Vulnerability Analysis Approach
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C
Authors - Selvamani K, Kanimozhi S, Muthusundar SK, Saranraj S, Raju S
Abstract - In the contemporary digital environment, organizations across all industries are increasingly dependent on online services to enhance convenience, streamline operations, and boost overall efficiency. While this digital transformation offers undeniable benefits, it simultaneously exposes users and institutions to significant cyber security threats. Malicious actors constantly seek out vulnerabilities in these systems, employing sophisticated methods to gain unauthorized access to sensitive data. One of the most alarming and effective tactics used by cybercriminals is social engineering. Rather than targeting technological flaws, social engineering exploits human behavior, manipulating individuals through deception, coercion, or the illusion of trust to bypass even the most advanced technical security controls. As traditional cyber security defenses such as multi-layered encryption, firewalls, and vulnerability patches grow more robust, attackers are shifting their focus to the human element, which is often the weakest link in the security chain. Unlike technical attacks that require in-depth knowledge of systems and code, social engineering preys on emotions like fear, urgency, curiosity, or helpfulness. These psychological manipulations make it easier for attackers to extract confidential data or financial information without triggering alarms. This paper explores the most common forms of Social Engineering (SE) attacks, including phishing, pretexting, baiting, and tailgating. It also delves into the psychological principles behind these tactics and outlines essential preventative strategies to mitigate the growing threat of social engineering in today’s cyber landscape.
Paper Presenter
avatar for Selvamani K
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 8C, Virtual Room C

12:15pm PDT

Vulnerable Brain Region Identification in Early Stage of Alzheimer’s Disease (AD) using Magnetic Resonance Images (MRI): A Novel Unsupervised Feature Selection Algorithm
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C
Authors - Iroshan Aberathne, Don Kulasiri, Sandhya Samarasinghe
Abstract - Manual techniques are not yet capable of tracking such tiny abnormalities in the early stages of AD due to the inherent complex nature of the structural changes in the brain. Therefore, we propose a novel model agnostic unsupervised feature selection algorithm called Prominent Feature Selection (PFS) to identify significant or vulnerable brain regions via volumetric measurements extracted from MRI images. Features selected by PFS are evaluated in two aspects. First, we compare the results with several other feature selection methods to assess the interpretability of the selected features against age and gender. The experimental results show that the PFS results better describe the structural changes in brain regions against age and gender. Secondly, binary class classification is performed to observe the discriminative power of the selected features against Cognitive Normal (CN) vs. Early Mild Cognitive Impairment (EMCI) subjects. Binary class classification performance measures demonstrate the significance of PFS. It has higher F1 score values despite a lower number of features compared to other feature selection methods. Since, PFS can identify and rank the most significant brain regions at early the stage of AD, diagnosis may become more specific. PFS can also be used as an effective feature selection technique not only to enhance the model performance but also to extend the model’s interpretability which would improve personalized evaluation in areas like eXplainable Artificial Intelligence (XAI).
Paper Presenter
avatar for Iroshan Aberathne
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 8C, Virtual Room C

12:15pm PDT

A Decade of AI in Personal Financial Management: A Bibliometric Analysis of Trends, Intellectual Structures, and Research Opportunities
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D
Authors - Ayatulloh Michael Musyaffi, Rida Prihartini, Muhammad Ikhwan, Rochma Sudiati, Diah Armeliza, Maulana Amirul Adha, Maudy Shinta Maychella
Abstract - Over the past decade, the integration of Artificial Intelligence (AI) into personal financial management has emerged as a strategic trend, transforming how individuals approach budgeting, investing, retirement planning, and debt management. Although academic interest in this area has grown, systematic mapping of publication trends, intellectual structures, and research gaps remains limited. This study conducts a bibliometric analysis of 624 Scopus-indexed publications from 2015 to 2024 using the Biblioshiny platform. The findings indicate a marked increase in publications following the COVID-19 pandemic, with most contributions originating from institutions in Asia, while Western countries produced the most highly cited works. Author keyword analysis and Multiple Correspondence Analysis (MCA) mapping identified three major thematic dimensions: algorithmic decision support, behavioral finance applications, and experimental AI methodologies. Additionally, several underexplored yet promising areas were identified, including demographic personalization, the integration of ethical considerations, and behavioral-based AI design. This study provides a comprehensive knowledge map and offers actionable insights for advancing research in AI-driven personal finance.
Paper Presenter
avatar for Ayatulloh Michael Musyaffi
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm PDT

A Structural Equation Modeling Approach to Assess Project-Based Learning and Student Achievement in Higher Education
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D
Authors - S Anupama Kumar, Vijayalakshmi M N, Divya T L, Narasimha Swamy S
Abstract - Recent advancements in the Higher Education have led to the adoption of diverse learning approaches across the globe. The current educational frame-work incorporates multiple techniques, such as collaborative learning, active learning, cognitive learning, and more to improve the performance of the student. Experiential learning is one of the promising approaches, enabling students to experiment with concepts and learn through practical application. This paper explores the effectiveness of Project-Based Learning (PBL), an experiential learning approach, in a higher education course to improve student learning outcome. This research focuses on understanding the relationship between PBL, Collaborative Learning (CL), Iterative Learning (IL), Authentic Learning (AL), Disciplinary Course Learning . The research contributes towards the effectiveness of PBL in improving the student learning outcome. Structural equation model is used to attain the findings. A positive relation is found between the PBL, collaborative learning, disciplinary course learning, iterative learning, and authentic learning. This relation in large has improved the learning outcome of the student to a greater extent. Hence PBL is found to be an effective technique to be adopted in higher education and enable the students to acquire new knowledge using various methods. This in turn will improve the learning outcome of the students.
Paper Presenter
avatar for S Anupama Kumar
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm PDT

An Intelligent Framework for Non-Parametric Test Prediction and Execution Using Machine Learning
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D
Authors - Dhvani Shah, Disha Chandaria, Harshil Shah, Janhavi Patel, Abhijit Joshi
Abstract - Non-parametric tests are important in statistical analysis where data fails to meet assumptions like normality or homogeneity of variance. Nevertheless, choosing and implementing the right test is frequently statistical in nature, which is a limitation for most users. Therefore, we suggest an intelligent framework that automates the selection and running of non-parametric tests through machine learning. A well-curated dataset of statistical problems is fed into a Decision Tree Classifier, which is labeled as the best model with 93.4 percent accuracy to identify the most appropriate non-parametric test, such as the Runs Test, Wilcoxon Signed-Rank Test, and Mann-Whitney U Test. After prediction, the identified test is run automatically, and outputs are produced in a well-structured, interpretable manner. This method increases accessibility, enhances statistical analysis efficiency, and opens the way to further application of non-parametric testing in data-driven inquiry.
Paper Presenter
avatar for Harshil Shah
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm PDT

Attaining Sustainability in Circular Economy: A Bibliometric Analysis
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D
Authors - Sarvani Kocherlakota, Poojitha Panchakarla, Vellore Vandana, Buchi Harika
Abstract - Circular economy is gaining momentum in the field of production and consumption to achieve sustainable goals in this digital era. Many strategies have been evolved in functioning the digital technologies to enhance the circular economy. But out of these technologies, we are not aware which technology could be used to emerge circular economy to a great extent. Entrepreneurship represents a key factor in capturing new circular business opportunities. To investigate its current research landscape and emerging trends, we conducted a Bibliometric analysis by using R software 4.5.0, and VOSviewer to identify the research trend, emerging topics, collaborations among countries, authors, documents and the contributing academic journals during the study period. Journal of Cleaner Production is having the highest publications and Resources, Conservation and Recycling is having the most cited document. Kumar A is the most contributing author in publications and Kumar V is the most cited author. Netherlands is the most contributing affiliation for publications and United Kingdom high international references.
Paper Presenter
avatar for Vellore Vandana
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm PDT

Enabling SME Participation in Cybersecurity Information Sharing: A Human-centric, Socio-technical Model
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D
Authors - Matipa Ricky Ngandu, Ntima Mabanza, Gardner Mwansa
Abstract - Cybersecurity Information Sharing (CIS) among Small and Medium-sized Enterprises (SMEs) is increasingly recognized as essential for collective threat mitigation, yet participation remains low due to complex human, social, and organizational barriers. Underpinned by Social Cognitive Theory (SCT) this study explores how human factors such as trust, leadership engagement, awareness levels, and communication practices influence CIS participation within SME ecosystems. Drawing on academic literature and analysis of primary quantitative and qualitative data, we present a socio-technical model that conceptualizes CIS as a form of digital crowdsourcing, embedded within informal and semi-formal social networks. Our findings indicate that SME participation in CIS is significantly enhanced when trust-building practices, cultural alignment, and role-specific awareness are actively supported through peer in-fluence and community-based engagement mechanisms. The proposed model identifies the conditions under which SMEs are most likely to share security intelligence, including personalization of tools, clear governance structures, and inter-organizational trust scaffolding. This work emphasizes the role of social structures and crowdsourcing dynamics in driving effective cybersecurity engagement, offering practical implications for designing inclusive, scalable, and human-aware CIS platforms tailored for resource-constrained SMEs. This work contributes to broader efforts in boosting cyber resilience through socially grounded, technology-supported approaches.
Paper Presenter
avatar for Matipa Ricky Ngandu
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm PDT

Energy Sector in India: Studying Environmental Sustainability through BRSR Reporting
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D
Authors - Bhupinder Kaur, Shivani Gupta, Kritika Sheoran
Abstract - The corporate world is increasingly becoming aware of the dynamic nature of its laws and policies, and has actively started functioning towards it. The observance of ESG goals by corporations is a de-novo approach towards the achievement of SDGs, and is an improvement over the already existing concept of CSR. Even credit rating agencies are considering ESG factors while assessing the creditworthiness of corporates, because under the ESG regime, corporations are responsible for their Environmental, Social and Governance initiatives, towards a better and sustainable tomorrow. This paper studies the environmental sustainability of various industries of the energy sector that have been filing their BRSR reports while following their environmental policy. Studying energy sector is important, because ultimately, one of the prominent parameters of every BRSR report is the amount of energy consumed in the industrial process. Herein, a comparative study shall be made taking regard to amount of energy generated and the various other parameters reported in a BRSR report. The study reveals that BRSR reporting is an effective and reliable method to access the actual compliance of environmental policy regulations by corporations. Further, the paper also enlists various shortcomings in the BRSR reporting system.
Paper Presenter
avatar for Shivani Gupta
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm PDT

Harmonization of Financial Reporting and Accounting Standards in the Merger of Indonesian State-Owned Enterprises
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D
Authors - Eka Septariana Puspa, Laode Arahman Nasir, Dwi Handarini, Gentiga Muhammad Zairin, Surya Anugrah, Windy Permata Suyono, Septi Nurmalita, Rochma Sudiati, Irima Rahmadani, Nazwa Febriyani
Abstract - This study explores the harmonization of financial reporting and accounting standards in the merger of two Indonesian state-owned enterprises (SOEs), Perum X and Perum Y. Employing a qualitative case study approach, the research investigates the systemic differences in accounting policies, financial information systems, and reporting structures that pose significant challenges in the post-merger integration process. Data were collected through in-depth interviews, document analysis, and observation of the integration process. The findings reveal disparities in Chart of Accounts (COA) structure, PSAK implementation—particularly PSAK 16 and PSAK 71—and the use of financial systems, with X operating an ERP-based system while Y remains reliant on Excel-based processes. Regulatory interventions, such as asset restatement and capital participation adjustments, are also discussed as critical factors influencing harmonization efforts. A conceptual framework is proposed to guide future SOE mergers in aligning accounting systems and policies. The harmonization process enhances financial transparency, managerial decision-making, and auditability, but also highlights the need for national guidelines and institutional coordination. This study contributes to the literature on public sector accounting by providing practical insights and policy recommendations for improving the consistency and integrity of financial reporting in SOE mergers. It also opens avenues for further research on long-term outcomes and cross-sector comparisons of accounting harmonization in public enterprises.
Paper Presenter
avatar for Eka Septariana Puspa
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm PDT

Post-ERP Performance Shift in HRM: Evidence from FMCG Firms in Bangladesh
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D
Authors - Mohima Rahman, Mehzabul Hoque Nahid, Khandakar Tahurul Islam, Erfan Ovee Nomaan, Kazi Tareq Ullah
Abstract - This study investigates the critical factors influencing the post-ERP performance shift of Huamn Resource Management (HRM) in the Fast-Moving Consumer Goods (FMCG) organizations in Bangladesh, utilizing the Technology–Organization–Environment (TOE) framework. Drawing on the empirical evidence, the research evaluates the impact of five determinants, Competitive Environment, Perceived Compatibility, Perceived Efficiency, Top Management Support, and Training, on post-ERP performance shift in HRM. The findings re-veal that Competitive Environment, Perceived Compatibility, Perceived Efficiency, and Top Management Support significantly enhance post-ERP performance, whereas Training does not show a statistically significant effect. These results underscore the importance of aligning ERP systems with organizational processes, supported by effective leadership, system fit, and external responsiveness. The study also emphasizes the need for fostering an innovation-driven culture, strengthening vendor partnerships, and ensuring system scalability and compliance in the post-ERP phase. By addressing these strategic and operational factors, HR departments can improve efficiency, agility, and data integrity, ultimately enhancing organizational competitiveness. The research contributes to the growing discourse on digital transformation in HRM and offers actionable insights for practitioners, policymakers, and enterprise system stake-holders in emerging market contexts.
Paper Presenter
avatar for Khandakar Tahurul Islam
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm PDT

The Impact of Digital Tax Education and Financial App Use on Tax Literacy and Compliance Intention among E-Commerce MSMEs in Indonesia
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D
Authors - Dwi Handarini, Eka Septariana Puspa, Nuramalia Hasanah, Ratna Anggraini, Surya Anugrah, Windy Permata Suyono, Septi Nurmalita, Rochma Sudiati , Sabo Hermawan, Stefany Indiraguna Joka
Abstract - The rapid growth of Indonesia’s digital economy has brought millions of Micro, Small, and Medium Enterprises (MSMEs) into online marketplaces such as Shopee, Tokopedia, and TikTok Shop. However, despite their economic significance, MSME tax compliance remains critically low due to limited tax literacy and weak financial administration. This study examines the impact of digital tax education and financial application usage on tax literacy and tax compliance intention among e-commerce MSMEs. Drawing on the Theory of Planned Behavior (TPB), a quantitative explanatory approach was employed using data from 100 MSME respondents in Indonesia. Structural Equation Modeling (PLS-SEM) was applied to test direct and mediating effects. The results show that both digital tax education and financial app usage significantly improve tax literacy, which in turn strongly predicts tax compliance intention. Tax literacy also mediates the relationship between digital interventions and compliance behavior. These findings offer theoretical contributions by integrating digital behavioral factors into tax compliance models, and practical implications for policymakers to develop integrated strategies that combine tax education with financial technology empowerment. Strengthening digital literacy and promoting fintech adoption can increase voluntary compliance, expand the tax base
Paper Presenter
avatar for Eka Septariana Puspa
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm PDT

Voice-Enabled Chess: A Speech-Driven Approach to Interactive Gameplay
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D
Authors - Harsh Sobhashana, Jiya Bera, Margin Ankola, Nishat Shaikh
Abstract - Voice-Enabled Chess is an innovative Python-based application that enhances traditional chess gameplay with modern voice recognition technology. Designed for accessibility and engagement, the system allows players to exe-cute moves using voice commands, providing an intuitive and hands-free gaming experience. The application integrates speech-to-text conversion via the Speech Recognition library, while AI- driven decision-making ensures a challenging opponent. A graphical user interface (GUI) enhances usability, and Docker deployment simplifies distribution across platforms. This project targets a diverse audience, including chess enthusiasts, individuals with disabilities, and technology enthusiasts interested in voice-controlled applications. By leveraging Python’s extensive ecosystem, including NumPy, Pandas, and the Chess library, the system efficiently manages game logic and interactions. Through the fusion of traditional chess mechanics with modern artificial intelligence and speech recognition, Voice-Enabled Chess pro- motes inclusivity and innovation in digital game development.
Paper Presenter
avatar for Harsh Sobhashana
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm PDT

An Expository Deep Learning Approach for Network Intrusion Detection in Software Defined Networking
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E
Authors - E. C. Nwokorie, D. O. Njoku, M. E. Nwanga, S. A. Okolie, C. D. Anyiam, J. E. Jibiri, C. G. Onukwugha, I. H. Ajunwa, C. O. Amadi, F. O. Nwokoma, A. I. Otuonye, U. C. Onyemauche
Abstract - This research investigates the application of TabNet, a deep learning model optimized for tabular data, for binary classification of Software Defined Networking (SDN) traffic. Using a combination of normal and Open vSwitch (OVS) traffic datasets, the model was trained to differentiate between benign and potentially anomalous traffic patterns. The preprocessing pipeline included feature selection, normalization, and encoding, followed by model training using PyTorch TabNet. The classifier achieved a high validation accuracy of 99.51%, with strong precision and recall, highlighting its capability for real-time anomaly detection in SDN environments. Visualizations such as ROC curves, precision-recall plots, confusion matrices, and PCA projections further validated the model’s effectiveness and interpretability.
Paper Presenter
avatar for D. O. Njoku
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 8E, Virtual Room E

12:15pm PDT

Blockchain-Enabled Trust Platforms for Home-to-Home Shared Accommodation Services: A Quantitative Study in Tourism Management
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E
Authors - Tiurida Lily Anita, Arif Zulkarnain, Abdul Rauf Ridzuan
Abstract - The integration of blockchain technology into digital tourism platforms has sparked interest in how such innovations influence user trust and behavioral intentions. This study investigates the impact of digital value, smart contracts, and security-transparency on users’ intention to use blockchain-based services, with perceived trust as a mediating variable. A quantitative survey involving 200 Traveloka users was conducted using structured questionnaires. The analysis employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to validate the measurement and structural models. Results reveal that digital value and perceived trust significantly influence users’ intention to use Traveloka's blockchain-enabled services. Moreover, digital value and smart contracts positively affect perceived trust. However, the direct impact of security-transparency on perceived trust was found to be statistically insignificant. The mediation effect of perceived trust is confirmed only in the relationship between digital value and intention to use, while its mediating roles between smart contract and intention, and between security-transparency and intention, are not supported. These findings suggest that enhancing digital value and reinforcing trustworthy smart contract features are key to fostering trust and increasing user adoption. This study offers practical insights for digital tourism platforms aiming to deploy blockchain technology and contributes to the theoretical understanding of trust-based technology acceptance in the tourism domain.
Paper Presenter
avatar for Tiurida Lily Anita
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 8E, Virtual Room E

12:15pm PDT

Business Intelligence Impacts in Small and Medium Enterprises Performance: A Literature Review
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E
Authors - Zainab Shalehah, Santy
Abstract - Small and Medium Enterprises (SMEs) face increasing pressure to remain competitive in rapidly evolving markets. Business Intelligence (BI) has emerged as a strategic tool that supports data-driven decision-making and improves organizational performance. This study aims to explore the impacts, benefits, and challenges of BI implementation in SMEs through a systematic literature review (SLR) of relevant academic sources. Literature was collected from databases such as Scopus and Google Scholar and analyzed using thematic categorization based on BI usage and its performance outcomes in SMEs. The findings highlight that BI positively contributes to decision-making quality, operational efficiency, and financial performance. However, SMEs often face challenges such as high implementation costs, lack of skilled personnel, and integration difficulties. The success of BI adoption is highly influenced by contextual factors, including data quality, financial capacity, and strategic planning. This review concludes that while BI offers significant advantages for SMEs, a careful, well-planned approach is necessary to overcome adoption barriers. Further research is recommended to explore industry-specific BI solutions that address the unique needs of SMEs.
Paper Presenter
avatar for Zainab Shalehah
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 8E, Virtual Room E

12:15pm PDT

Comparative evaluation of open-source Automatic speech recognition tools for mathematical speech recognition
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E
Authors - Sadhana Mishra, Poonam Dang
Abstract - Automatic speech recognition (ASR) is increasingly being applied in various fields such as healthcare, Education and accessibility. However, ASR of mathematical expressions presents a unique challenge due to the symbolic, structured and hierarchical nature of math language. Accurate transcription of such expressions is essential for digital learning, documentation and computational processing. This paper presents a comparative study of four open-source ASR tools— Whisper, DeepSpeech, Vosk and PocketSphinx — focusing on their ability to recognize spoken mathematical expressions accurately. The study uses a dataset of several mathematical categories, such as algebra, trigonometry, calculus, and vectors, recorded by six speakers, to assess each tool's performance based on Word error rate (WER) and processing time. According to the results, Vosk processes information the quickest, whereas Whisper performs better in terms of accuracy. The findings provide insights into tool selection for educational or assistive technology applications involving math speech transcriptions.
Paper Presenter
avatar for Sadhana Mishra
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 8E, Virtual Room E

12:15pm PDT

Decentralized Public Donation Pools Covering Fees for Free Blockchain Protocols Usage
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E
Authors - Serhii Kozlov, Nazar Matsko, Yaroslava Chopa, Volodymyr Dubinin, Dmytro Nishchemenko
Abstract - This article proposes a solution to the problem of free decentralized protocol usage for a group of users who want to participate in the protocol without paying transaction fees and/or do not have a wallet with a key pair (Externally Owned Account). The proposed solution is a public donation pool that enables users to interact with any decentralized protocol during a specific event without owning a wallet and/or incurring blockchain gas fees.
Paper Presenter
avatar for Dmytro Nishchemenko
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 8E, Virtual Room E

12:15pm PDT

Determinants of User Satisfaction in Digital Payment System Transactions: A Philippine Perspective
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E
Authors - Andrea Dannielle Mayuga, KC A. Berdin, Mary Faith F. Lagmay, Love Joyce A. Mortel, Lovely M. Penafiel, Ashley D. Sacayanan, Manuel J. Logatoc, Steven C. Jocson
Abstract - This study evaluates the level of satisfaction with digital payment systems among residents in the Philippines. In an era where rapid technological advancement reshapes business transactions, digital payment platforms have emerged as critical drivers of efficiency and convenience. Employing data from survey questionnaires and pertinent digital‐literacy studies, the research applies Davis’s Technological Acceptance Model (TAM), with perceived usefulness (PU) and perceived ease of use (PEOU) as central predictors of users’ behavioral intentions toward mobile payments. The analysis explores three dimensions: users’ overall satisfaction, the most frequently used digital payment platforms, and variations across socio‐demo-graphic profiles. Findings indicate that respondents maintain a uniformly positive attitude toward digital payment systems, deeming them useful for everyday trans-actions and expressing strong willingness to recommend these platforms over traditional methods. Ease of use emerged as a key factor driving continued adoption intentions. Among socio‐demographic variables, gender and monthly in-come show significant correlations with satisfaction levels; in contrast, age, civil status, and highest educational attainment exhibit no notable influence. These results suggest that while digital payment systems enjoy broad acceptance across diverse user groups, targeted strategies that address gender, and income‐based preferences may further enhance user satisfaction. Policymakers and service providers can leverage these insights to refine platform design, optimize user education initiatives, and foster more inclusive adoption of digital payment technologies.
Paper Presenter
avatar for Steven C. Jocson

Steven C. Jocson

Philippines
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 8E, Virtual Room E

12:15pm PDT

Identifying the Role of ESG in the Global Capital Market
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E
Authors - Sarvani Kocherlakota, Poojitha Panchakarla, Ajay Amjuri, Sannakalla Sai Sree Charan
Abstract - Economic, Social and Governance is playing a predominant role in the capital markets to increase the awareness Global investments and to acquire knowledge on the investment avenues available across the world. To investigate its current research landscape and emerging trends, we conduct a bibliometric analysis by using R software 4.5.0, and VOSviewer to identify the research trend, emerging topics, and collaborations among countries, authors, documents and the contributing academic journals during the study period. Finance research letters is having the highest publications and Review of Accounting Studies is having the most cited document. Li Y is the most contributing author and Christensen HB, Hail L and Leuz C are the most cited author. China is the most cited country. Further research may happen in this area from the perspective of various countries like BRICS, various quantative techniques and scope of the study may be limited to individual country and so on.
Paper Presenter
avatar for Ajay Amjuri
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 8E, Virtual Room E

12:15pm PDT

Parametric Evaluation of State of The Art For Pesticide Recommendation and Diseases Detection System in Cotton Plant
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E
Authors - Rajkumar Patil, Vivek S. Deshpande, Nilesh P. Sable
Abstract - Pests pose a significant threat to cotton yields, leading to economic losses for farmers. This paper conducted a comprehensive literature review, examining the various types of pests affecting cotton crops both globally and specifically in India. We analyzed the actual yield losses ascribed to these pests as well. Furthermore, we reviewed a variety of literature on pesticide recommendations and cotton disease detection. In this paper we have chosen four Deep Learning (DL) models—VGG19, ResNet50, ResNet152V2, and Incep-tionV3 selection of this model was based on literature review. These models were trained and tested on a dataset of 2,293 images comprising affected and non-affected cotton plants and leaves. The output of our evaluation demonstrates how useful these models are in detecting diseases in cotton plants. Ultimately, the AI-based automated recommendation system allows farmers to use this information to make informed decisions about using pesticides resulting in better crop health and higher yield of cotton.
Paper Presenter
avatar for Rajkumar Patil
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 8E, Virtual Room E

12:15pm PDT

Positive Work-Life Balance in Women: A Strategic Driver of Mental Health and Empowerment in Academic Organizations
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E
Authors - Ronil Manohar, Melita Simoes, Arogyaswamy Karadi
Abstract - Today’s competitive jobs demand a sturdy balance between personal and professional life. Women are an integral part of the current workforce including academics wherein achieving a sustainable work-life integration is a critical challenge in the ever evolving dynamic workplaces despite institutional reforms, growing attention to gender equity and women empowerment. This study highlights the role of work-life balance, mental well-being, and organizational support structures among 103 women professionals that include teaching and non teaching fraternity in educational institutions across Hubli-Dharwad. According to reports, a substantial proportion of respondents, mostly Millennials and Generation Z, struggle to maintain balance, resulting in medical illnesses, stress, burnout, and disturbances in family life. These issues frequently result in lower productivity, job satisfaction and can hamper career progression and leadership aspirations, emphasizing the critical need for effective measures to help women achieve a sustainable work-life balance. This study employs a quantitative approach that made use of a structured data collection through a well drafted questionnaire which aims to explore the perception of women on work-life balance, mental health, and personal empowerment along with understanding the role of equitable policies, anti-harassment frameworks, as well as the implementation of flexible and inclusive work environments that promote a gender-balanced leadership wherein the needs and voice of women employees are addressed.
Paper Presenter
avatar for Arogyaswamy Karadi
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 8E, Virtual Room E

12:15pm PDT

Socioeconomic Indicator-Based Crime Prediction in Pakistan Using Machine Learning Techniques
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E
Authors - Usman Ali, Tariq Hussain, Muhammad Mujeeb Akbar, Shahzad Ali, Sajid
Abstract - In Pakistan, crime is always a significant economic and social issue affecting governance and public safety. Religious issues, poverty, and political instability have a very high contribution to increasing crime in the country. Despite highly effective efforts of authorities and prevention techniques, crimes are often inconsistent and lack effectiveness in determining crimes across the country. This study aims to analyze national crimes in Pakistan using machine learning and structured datasets to support more effective decision-making and understanding of crime rates across the country. Through the proposed framework, linear regression, the Decision Tree, and the Random Forest are applied to historical data obtained from the Kaggle repository. With different evaluation matrices and cost functions, the proposed model is interpreted and evaluated. The Evaluation metric includes R², and the cost functions include MSE, MAE, and RMSE. Linear Regression works effectively in comparison to others, providing the highest R², i.e., 99.9%. By using this framework, crimes from 2030 to 2050 are predicted with a difference of 5 years. These algorithms help to identify different types of crimes across the country based on different factors. Unlike traditional methods and black-box models, this research underscores the comprehensibility, making insights understandable for both law and policy makers. The study explains how interpretable frameworks can help authorities to find high-risk area predictions. In the future, the aim is to use deep learning techniques with explainable AI (XAI) for enhancement in this study, which can support the development of more public safety systems across the country.
Paper Presenter
avatar for Usman Ali

Usman Ali

Pakistan
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 8E, Virtual Room E

12:15pm PDT

A Case Study on an Inexperienced Team Using Waterfall for Game Development
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Sajidur Rahman, Md. Ataur Rahman, Saymadeen Tabassum, Farhad Alam, Mahady Hasan, Towsif Zahin Khan
Abstract - Our work explores the application of the Waterfall model in game development through a case study involving an inexperienced team developing a Space Invaders clone. While Agile methodologies are widely favored in modern game development for their flexibility, the structured, phase-based nature of Waterfall presents certain advantages for teams with limited experience in iterative processes. We investigate how clearly defined stages- requirement analysis, design, implementation, testing, and maintenance-provide direction, minimize scope creep, and can support systematic progress and project success despite the team’s inexperience. Key findings suggest that Waterfall was effective in maintaining project control, with clear documentation and milestone tracking helping reduce ambiguity. However, the model also posed challenges in managing late-stage design changes and balancing creativity within a rigid structure. The paper compares these findings with insights from the literature on Agile and hybrid methodologies, especially in the context of novice teams. The case study concludes that although Waterfall’s predictability can benefit inexperienced developers, its success relies heavily on disciplined planning, well-defined roles, and the ability to adapt within a structured framework.
Paper Presenter
avatar for Sajidur Rahman

Sajidur Rahman

Bangladesh
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 8F, Virtual Room F

12:15pm PDT

Developing LINE Application to prevent COVID-19 in Pregnant Women
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Krit Chaiwong, Wirote Jongchanachavawat, Ittipat Roopkom, Boonchart Kativat
Abstract - This research and development aimed to create a LINE application to prevent COVID-19 among pregnant women and to evaluate its effectiveness and user satisfaction. The study was conducted in four stages: (1) assessing the situation, needs, related factors, and feasibility of the LINE application; (2) developing the LINE application for COVID-19 prevention; (3) implementing a quasi-experimental study; and (4) refining and evaluating the application’s effectiveness. The research instruments included: (1) in-depth interview questions with content validity index (CVI) values ranging from 0.88 to 0.90; (2) field notes; (3) a questionnaire on COVID-19 prevention among pregnant women, comprising a knowledge assessment (KR-20 = 0.72) and a behavior assessment (Cronbach’s alpha = 0.93); and (4) a satisfaction survey for the LINE application (Cronbach’s alpha = 0.89). Data were analyzed using content analysis, frequency, percentage, mean, standard deviation, and paired t-tests. Results indicated that after using the LINE application, participants demonstrated significantly higher knowledge and improved preventive behaviors compared to pre-intervention levels (p < 0.001). Participants also reported a high level of satisfaction with the application. These findings suggest that the LINE application is an effective and well-received tool for promoting COVID-19 prevention among pregnant women.
Paper Presenter
KC
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 8F, Virtual Room F

12:15pm PDT

Disease Prediction in Sericulture and Automation
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Seema V Kedar, Shabana Pirjade, Avinash Chhabu Shelar, Dheeraj Sharma, Harshita Daftari, Rushikesh Manoj Gholap
Abstract - Around 9.76 million peoples in the rural and semi-urban area are depend on the sericulture for their livelihood. Monitoring the health and environment of silk worm is a crucial factor of sericulture industry. Which is done manually by farmers or care taker by monitoring them time to time and keeping track of their environmental conditions which are favourable temperature and humidity. By automating this process lot of time, work and labour cost of farmers can be saved and utilized for other work. For doing so Arduino will be used which will be connected to temperature sensor, and humidity sensor which will be used for taking input signal from the system and define the current environment conditions of the system. The current environmental conditions will be compared by threshold values which will be which will be predefined. If the value of temperature is greater than threshold the fan which is another component will be switched on to cool down the temperature. If the temperature is lower than the threshold the bulb will be switched on to rise the temperature. All the status report of the system will be given send to famers phone by using the GSM module. Throughout the process of monitoring the health of silk worm always had a front foot. Cause one diseased worm can make another worm ill. Hence, to avoid this a disease detection system which will detect diseases silk worm will help farmers. For doing so CNN and Image processing algorithms are used for disease detection on time This model will classify the diseased silkworm in different diseases based on which it is suffering from. CNN is trained with its auto generated features. An average accuracy of 85%, 75% and 59% for classification of healthy and diseased silkworms.
Paper Presenter
avatar for Rushikesh Manoj Gholap
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 8F, Virtual Room F

12:15pm PDT

Empowering Road Safety Analysis: State-wise Accident Data Visualization with Power BI
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Parth Modhvadiya, Harshil Kothiya, Nishat Shaikh, Jalpesh Vasa
Abstract - The main goal of this paper is to study and examine accident data in India on a national, state, and major city level between 2017 and 2021. It focuses on identifying patterns, key factors, and differences across regions. Road accidents have been a serious issue in India for a long time. Research indicates that injuries resulting from road accidents remain a significant challenge for public health, affecting both the economy and society. These accidents are particularly common in states like Tamil Nadu, Maharashtra, and Uttar Pradesh. The paper uses data from various sources, including government reports, to better under-stand the scale of the problem, regional variations, key risk factors, and possible ways to prevent accidents. One of the main reasons for road accidents in India is careless driving. Furthermore, the rapid increase in vehicle ownership has played a key role in the rising number of accidents. Data from the Ministry of Road Transport and Highways indicates that registered motor vehicles in India grew from 24.8 crore in 2017 to 28.9 crore in 2020, marking an approximate 16.5% rise. This surge in vehicles has caused more traffic congestion, making it harder for drivers to travel safely.
Paper Presenter
avatar for Parth Modhvadiya
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 8F, Virtual Room F

12:15pm PDT

Exploring Large Language Models to Assist Finite Element Analysis
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Hector Rafael Morano Okuno
Abstract - Nowadays, Large Language Models (LLMs) are utilized in various fields of knowledge to assist users in performing tasks after being prompted with a question. However, further exploration is needed to determine their scope, as they are constantly being trained to improve and enhance their capabilities. This article investigates an LLM to decide whether or not it can assist users in interpreting results from finite element analyses and whether it can suggest modifications to the analyzed parts to improve the results of said analyses. This work was conducted during the Cyber-Physical Systems course, held in the August-December 2024 semester, with students of mechatronics, robotics, and computational systems engineering. Among the results, it was found that LLMs are capable of recognizing images with beam diagrams featuring different types of support and loads, as well as correctly interpreting finite element analysis results generated by software tool applications, with the potential to provide recommendations for improving these results.
Paper Presenter
avatar for Hector Rafael Morano Okuno
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 8F, Virtual Room F

12:15pm PDT

Forecasting inflation and stock prices in India’s energy sector: a comparative analysis of classical and deep learning models
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Hetansh Shah, Hitarth Bhatt, Jay Topiwala, Hitanshu Shah, Pradnya Saval, Shruti Mathur
Abstract - This paper compares classical and deep models for forecasting stock prices of India’s top energy stocks (ONGC, NTPC, RELI) under inflation stress measured by the Energy Price Index (EPI). Although classical models like Prophet and Holt-Winters accurately forecast the inflation series, an LSTM network with an Attention mechanism forecasts much better for stock price forecasting, especially for volatile stocks. The paper further demonstrates that pure-energy stocks (ONGC, NTPC) are more inflation-sensitive than diversified RELI. The findings have pragmatic implications for investors and policymakers on risk management in the energy market.
Paper Presenter
avatar for Hitarth Bhatt
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 8F, Virtual Room F

12:15pm PDT

From Perception to Adoption: A Model of Trust and Intention to Use for Digital Payment Solutions
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Stanny Dewanty Rehatta, Meta Bara Berutu, Inkreswari Retno Hardini, Gita Safitri
Abstract - The rapid growth of Buy Now, Pay Later (BNPL) services in Indonesia, especially Shopee PayLater, has created new patterns in consumer behavior and technology adoption in digital finance. While these services offer practical benefits and financial flexibility, they also raise concerns related to data privacy, potential misuse of personal information, and system security. This study investigates the factors influencing users’ behavioral intention to use Shopee PayLater by extending the Technology Acceptance Model (TAM). The proposed model includes six key variables: Perceived Ease of Use, Perceived Usefulness, Perceived Risk, Perceived Security, User Trust, and Intention to Use. A quantitative method was applied by distributing a structured online questionnaire to Shopee users in Indonesia. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that Perceived Ease of Use positively influences both User Trust and Intention to Use, although its effect on intention is not statistically significant. Perceived Risk negatively affects both Trust and Intention, with a significant impact on Trust. Perceived Security unexpectedly has a negative but marginally significant effect on Intention to Use. User Trust is found to be a strong and significant predictor of Intention to Use, indicating its central role in mediating other variables in the model. These findings suggest that, beyond ease and usefulness, users' trust and risk perceptions significantly affect adoption. Strengthening security assurance and reducing perceived risk are essential strategies to improve trust and drive continued use of BNPL services such as Shopee PayLater in the Indonesian digital finance environment.
Paper Presenter
avatar for Gita Safitri

Gita Safitri

Indonesia
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 8F, Virtual Room F

12:15pm PDT

Machine Learning-Based Clustering for Tuberculosis Patient Segmentation by Risk Factors and Symptom Profiles: A Case Study of Ngao District, Lampang
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Budsaba Inkiew, Wongpanya S. Nuankaew, Thapanapong Sararat, Pratya Nuankaew
Abstract - Tuberculosis (TB) remains a global health threat, especially in low- and middle-income countries like Thailand, where rural areas have limited healthcare resources. This study uses a machine learning–based clustering framework to stratify TB patients and screening data in Ngao District, Lampang Province. It combines data sources—including a registry of 899 TB cases and 15,318 chest X-rays—and addresses challenges like data heterogeneity, missing values, and inconsistent formats through cleaning, imputation, and feature engineering. Using the k-means algorithm, evaluation metrics such as Silhouette Score, Davies–Bouldin Index, and Calinski–Harabasz Index confirmed three optimal clusters. Each cluster shows distinct demographic, clinical, and epidemiological features, highlighting TB risk diversity. The findings demonstrate machine learning's potential to support targeted public health interventions, optimize resources, and improve TB prevention and control. This scalable framework offers insights for other regions facing similar infectious disease challenges, integrating AI into public health systems.
Paper Presenter
avatar for Budsaba Inkiew
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 8F, Virtual Room F

12:15pm PDT

Mapping Resistance to AI in Education: A Structural Analysis of Gen Z Adoption Barriers
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Aishwini VR, Aravindan B, Santanu Mandal
Abstract - The integration of Artificial Intelligence (AI) in education offers unprecedented opportunities for personalization, efficiency, and innovation, yet resistance to adoption persists among Generation Z learners. This study investigates the structural interplay of ten key barriers to AI adoption in higher education, integrating the Technology Acceptance Model, Resistance to Innovation Theory, and Cognitive Load Theory. Using Interpretive Structural modelling (ISM) and MICMAC analysis on survey data from 118 Gen Z respondents, the research identifies lack of trust in AI, data privacy concerns, and algorithmic bias as high-driving factors, influencing dependent barriers such as low perceived usefulness and poor user experience. Digital fatigue and cognitive overload emerge as central linkage variables, mediating relationships between upstream drivers and downstream disengagement. Findings highlight the pivotal role of faculty support and pedagogical integration in shaping adoption attitudes. The study reframes resistance not as reluctance, but as a signal of systemic misalignment between learner expectations, institutional practices, and AI design. Practical implications call for transparent, inclusive, and cognitively considerate AI tools, robust governance frameworks, and AI literacy initiatives to foster equitable and sustainable adoption.
Paper Presenter
avatar for Aishwini VR
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 8F, Virtual Room F

12:15pm PDT

Unveiling Barriers to Sustainable Fashion Consumption Among Gen Z
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Prabhuram S P, B Sathish Kumar, Santanu Mandal
Abstract - The global fashion industry’s environmental footprint has heightened the urgency for sustainable fashion adoption, yet Generation Z (Gen Z)—despite high sustainability awareness—shows limited engagement. This study investigates the systemic barriers impeding sustainable fashion consumption among urban, digitally active Indian Gen Z consumers. Drawing on the Theory of Planned Behaviour and Resistance to Innovation Theory, the research identifies ten interlinked barriers, including lack of awareness, limited availability, price sensitivity, trend obsession, peer influence, greenwashing confusion, low perceived impact, convenience bias, brand loyalty, and inconsistent messaging. A mixed qualitative–quantitative approach employing Interpretive Structural Modelling (ISM) and MICMAC analysis was applied to 137 valid responses collected in June–July 2025. Findings reveal that lack of awareness and limited availability are foundational drivers shaping intermediate constraints—such as price sensitivity and low perceived impact—which ultimately influence entrenched behaviours like brand loyalty and trend fixation. The ISM hierarchy underscores the multi-level nature of resistance, while MICMAC classification highlights critical driving factors for targeted interventions. The study contributes to sustainable consumption theory by mapping structural interdependencies and offers actionable insights for policymakers, educators, and brands seeking to align Gen Z fashion choices with Sustainable Development Goals 12 and 13.
Paper Presenter
avatar for B Sathish Kumar
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 8F, Virtual Room F

12:15pm PDT

Adaptability of 5G, IoT, and AI in Philippine Digital Infrastructure: Comparative Insights from ASEAN and Global Leaders
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G
Authors - Mary Anne Q. Zheng, Gerry Paul C. Genove
Abstract - This paper presents a global benchmarking analysis to evaluate the Philippines’ infrastructure adaptability to 5G, IoT, and AI. Based on a systematic literature review of 35 peer-reviewed studies, industry analyses, and government reports from 2018 to 2025, it evaluates the current state of deployment, identifies key barriers, and proposes actionable strategies for alignment with international best practices. Results indicate that 5G coverage is expanding in metropolitan areas, IoT applications are emerging in sectors such as utilities and agriculture, and AI integration is concentrated in enterprise and pilot projects. However, socioeconomic inequality, an archipelagic geography, bureaucratic inefficiencies, and fragmented governance emerge as primary constraints on broader adaptability and inclusivity. Comparative analysis against global leaders and selected ASEAN peers, including Japan, South Korea, the United States, Germany, Malaysia, Vietnam, Thailand, and Singapore, highlights the importance of regulatory coherence, coordinated infrastructure investment, and integration with socioeconomic strategies. Policy lessons from peer nations demonstrate how centralized spectrum management, regulatory sandboxes, rural inclusion programs, and digital upskilling initiatives can accelerate adaptation. The findings highlight the need for coherent policies, shared infrastructure models, and capacity-building frameworks to align the Philippines with international best practices.
Paper Presenter
avatar for Mary Anne Q. Zheng
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 8G, Virtual Room G

12:15pm PDT

Beyond Engineering to Order: Embracing Leagile in Smart Manufacturing Planning for Boost Sustainability
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G
Authors - Monica Pilco, Lisset Ugaz, Edgar Ramos, Donovan Fuqua
Abstract - The volatility and complexity of the marketplace require planning approaches that balance efficiency and adaptability in engineer-to-order (ETO) manufacturing environments. This study brings together lean, agile (Leagile) principles and smart manufacturing planning to improve responsiveness without compromising operational performance. Using the Fuzzy Analytic Hierarchy Process (F-AHP) methodology, this study combines a comprehensive literature review with the evaluation of strategies and drivers in a manufacturing case study. The results highlight the importance of Digital Integration, Digital Value Stream, and Integrated Planning to achieve leagile and sustainable operations. Conclusively, this study offers practical suggestions for leveraging digital drivers to strengthen resilience and long-term sustainable smart manufacturing.
Paper Presenter
avatar for Monica Pilco
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 8G, Virtual Room G

12:15pm PDT

Conceptualizing Islamic Social Enterprise and Islamic Social Entrepreneurship: Insights from a Systematic Literature Review
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G
Authors - Wahyu Wastuti
Abstract - This study conducts a Systematic Literature Review (SLR) of 30 Scopus-indexed articles published between 2016 and 2025 to examine the conceptualization and development of Islamic Social Enterprise (ISE) and Islamic Social Entrepreneurship (ISEn). The review reveals that ISE is predominantly framed as an organizational form integrating Islamic principles into governance, accountability, and social welfare structures, while ISEn emphasizes individual-driven innovation and entrepreneurial behavior aligned with sharī‘ah values. Despite their differences, both models converge in their pursuit of financial sustainability, social justice, and community empowerment. Thematic analysis identifies four recurring domains in the literature: governance, innovation, sustainability, and maqāṣid al-sharī‘ah. Governance studies highlight ac-countability, transparency, and stakeholder trust, while research on ISEn emphasizes digitalization, creative financing, and entrepreneurial intention. Sustainability is viewed not only in terms of economic viability but also as encompassing ethical and social commitments, with maqāṣid al-sharī‘ah providing a normative framework that guides decision-making and ensures alignment with faith-based objectives. The findings reveal that the conceptual boundaries be-tween ISE and ISEn remain blurred but complementary, suggesting their integration offers both institutional stability and entrepreneurial dynamism. This study contributes to the academic discourse by clarifying definitions, synthesizing thematic trends, and identifying research gaps. Practically, the results offer implications for policymakers in designing supportive ecosystems, for practitioners in building hybrid models, and for academics in advancing theoretical clarity.
Paper Presenter
avatar for Wahyu Wastuti

Wahyu Wastuti

Indonesia
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 8G, Virtual Room G

12:15pm PDT

Enhancing Intrusion Detection with Hybrid Deep Learning(CNN-LSTM) and Tree-Based Models with XGBoost, and LightGBM
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G
Authors - Jaimin Dave, Chintan Shah, Premal Patel
Abstract - Intrusion Detection Systems (IDS) have been instrumental in preventing emerging threats from compromising a network. One of the key challenges in the field is to design an IDS with high detection coverage and low false positives. This study investigates the potential of hybrid deep learning systems to address this challenge. We developed a CNN+LSTM model that leverages the spatial feature extraction power of CNN and the temporal sequence learning capacity of LSTM. Experimental results demonstrate that the proposed CNN-LSTM model consistently outperforms LightGBM and XGBoost, achieving the highest accuracy (92.7%), precision (89.5%), recall (81.8%), and F1-score (84.3%), thereby confirming its robustness and effectiveness for intrusion detection. Additionally, comprehensive assessments indicate that for more complex and hidden attack patterns, the CNN+LSTM model is more advanced in terms of detection availability and effectiveness. The relevance of this work emphasises the need to modernise IDS systems by using hybrid deep learning techniques. Furthermore, the combination of convolutional neural networks with LSTM improves detection capacity and supports feature-level detail for new hazards.
Paper Presenter
avatar for Jaimin Dave
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 8G, Virtual Room G

12:15pm PDT

Implementing Zero Trust Architecture in SMEs: Readiness, Barriers, and Tailored Support Strategies – Evidence from the Czech Republic
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G
Authors - Lukas Herout, Arvind Panwar
Abstract - This paper explores the adoption of Zero Trust Architecture (ZTA) in small and medium-sized enterprises (SMEs) in the Czech Republic, aiming to identify key barriers, enabling factors, and readiness levels for its implementation. Drawing on survey data from 230 SME respondents, we investigate aware-ness of ZTA principles, current security practices, and the perceived value of specific support measures. The study is guided by clearly defined research questions and hypotheses. We construct a composite readiness index based on technical and organizational measures and analyze its relationship with firm size, security maturity, and training frequency. Key findings indicate that while ZTA awareness is relatively low (only 43.4% had heard of the term), many companies have already implemented foundational elements aligned with ZTA. Security readiness is significantly higher among firms with formal security policies and regular employee training. Medium-sized enterprises show notably greater implementation levels than micro and small firms. The most frequently requested forms of support include practical implementation guides, financial aid, and consultations with experts. These preferences vary by firm size, with smaller firms emphasizing financial support and larger ones favoring professional services. The paper concludes with a roadmap for SME-oriented ZTA adoption, emphasizing the need for scalable, low-barrier solutions and targeted support. Our findings provide empirical insights to inform policy design, training programs, and vendor strategies for secure digital transformation in the SME sector.
Paper Presenter
avatar for Lukas Herout

Lukas Herout

Czech Republic
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 8G, Virtual Room G

12:15pm PDT

On-Device Tree-Based IAQ Forecasting on ESP32: A Comparative Evaluation
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G
Authors - Hasin Mahir, Tahfizul Hasan Zihan, Md. Shirazim Munir, M. M. Kamal, Mahady Hasan, Md. Tarek Habib
Abstract - Accurate Indoor Air Quality (IAQ) forecasting can support affordable and privacy-conscious interventions in classrooms and workplaces. However, most existing systems depend on cloud-based inference, which introduces additional cost, network dependency, and reliability issues. In this work, we present a comparative evaluation of three ensemble tree models—Random Forest, XGBoost, and LightGBM—implemented directly on an ESP32-S3 microcontroller for edge-based IAQ prediction of CO2 and PM2.5. A unified feature extraction strategy was employed, combining temporal encodings, lagged variables, and short rolling aggregates. Models were trained offline, converted into C, and embedded into the device firmware. Their performance was assessed with respect to (i) forecasting accuracy, using MAPE and RMSPE, and (ii) resource efficiency, including flash storage, RAM usage, and inference latency. Experimental results indicate that boosted methods (XGBoost and LightGBM) provide higher accuracy than Random Forest while staying within the strict memory and timing limits of the ESP32. This demonstrates the feasibility of fully self-contained IAQ forecasting on ultra-low-cost hardware, without reliance on cloud resources. Furthermore, we outline trade-offs between model complexity and efficiency, and provide a reproducible toolchain for exporting tree ensembles into deployable ESP32 firmware, highlighting their practicality for resource-constrained IAQ monitoring.
Paper Presenter
avatar for Hasin Mahir

Hasin Mahir

Bangladesh
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 8G, Virtual Room G

12:15pm PDT

Quantum-Enhanced Hybrid Intelligence: Fuzzy Logic Meets Neural Networks in Data-Driven Systems
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G
Authors - Amogh Kotha Nagaraj, Rajesh Eswarawaka, Aadi B Kumar, Sujay D
Abstract - This paper will examine the synthesis of fuzzy logic, neural network applications and hybrid computational techniques with quantum computing for data-driven system improvement. The intersection of these innovations will provide exciting directions for intelligent decision-making, adaptive learning, and effective data processing. Fuzzy logic offers a mechanism for dealing with uncertainty, whereas these hybrid techniques blend the powers of multiple paradigms of computation. Neural networks provide the capability of deep learning, and quantum computing provides new means for optimization and parallelism. In this work, the theoretical background, real-world applications, and prospects of quantum-assisted hybrid intelligence systems are presented. The results imply that combined approaches can greatly enhance performance in high-complexity data environments, opens up opportunities for future intelligent systems.
Paper Presenter
avatar for Amogh Kotha Nagaraj
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 8G, Virtual Room G

12:15pm PDT

Securing Systems Through Log Anomaly Detection: From Traditional Learning to LLMs
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G
Authors - Pimal Khanpara, Prasham Shah, Sharada Valiveti, Gaurang Raval
Abstract - This article examines recent advancements in log anomaly detection, a key factor in ensuring system reliability and security. Research is categorized into four main approaches like traditional machine learning, deep learning, transformer-based models, and other emerging methods leveraging large language models (LLMs). Evaluations across benchmark datasets such as HDFS, BGL, Thunderbird, and Spirit reveal a steady progression in detection capabilities, with LLM-based approaches generally outperforming earlier methods. Techniques like LogRAG, LLMeLog, and EagerLog achieve F1-scores above 99% on certain datasets, marking substantial improvements over traditional baselines. Current research trends include multimodal analysis, few-shot and zeroshot learning, explainable AI, lightweight models, and deeper semantic understanding of logs. These directions are increasingly important as modern systems generate massive, diverse log volumes that demand more scalable and intelligent anomaly detection solutions. However, persistent challenges like log parsing accuracy, effective feature extraction, and model adaptability to evolving log formats continue to limit performance in real-world applications. This review provides a structured overview of current methods, highlights the strengths of LLM-based techniques, and outlines promising areas for future research to advance the field of log anomaly detection.
Paper Presenter
avatar for Sharada Valiveti
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 8G, Virtual Room G

12:15pm PDT

Simulation and Analysis of DDoS Attacks using ML on MAVLink protocol in UAV Communication
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G
Authors - Sudarshan Sharma, Amita Chauhan, Sakshi Kaushal
Abstract - Unmanned Aerial Vehicles (UAVs) have revolutionized industries such as defense, agriculture, and logistics due to their adaptability and ease of deployment. However, their growing dependence on wireless communication protocols, including MAVLink, UAVCAN, and UranusLink, introduces significant security challenges. This research offers a comprehensive security evaluation of the MAVLink protocol through experimental simulations in Software-In- The-Loop (SITL) environments. The study simulates Distributed Denial-of-Service (DDoS) attacks at both the Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) layers to identify protocol vulnerabilities. Captured network traffic is compiled into a structured dataset and analyzed using machine learning algorithms, specifically Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) algorithms, to differentiate between normal and malicious packets. The findings highlight critical security weaknesses in MAVLink communications and introduce a robust simulation and classification pipeline designed to enhance the effective detection of attacks, thereby improving UAV security.
Paper Presenter
avatar for Sudarshan Sharma
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 8G, Virtual Room G

12:15pm PDT

Transforming Capital Markets through Blockchain Technology: Research Trends, Challenges, and Regulatory Insights
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G
Authors - Suparna Ray, Sushruthan Babu, Niranjan Babu
Abstract - Our study explores the transformative role of Blockchain Technology (BCT) in capital markets (CM) employing a mixed-method research design. We synthesize extant research and analyse global regulatory case studies, particularly focusing on the Indian context. Utilising both bibliometric analysis and qualitative case study research, our paper highlights the evolution of academic dis-course, publication trends, and practical applications of Distributed Ledger Technology (DLT) across equities, debt, and derivatives markets. Our findings indicate that while BCT promises efficiency, transparency, decentralization, and cost reduction, its large-scale adoption remains constrained by regulatory uncertainty, scalability concerns, and institutional resistance. This paper emphasizes the emerging regulatory frameworks in the US, EU, and Asia-Pacific, acknowledging diverse approaches ranging from innovation-friendly sandboxes to cautious restrictions. We find limited empirical evidence on systemic risk, tokenization, liquidity, and ESG-linked instruments; hence, we propose future research avenues that integrate finance, law, computer science, and public policy. Through coherent mapping of the global regulatory landscape and reviewing scholarly contributions, our paper contributes to understanding how BCT is reshaping CM infrastructure and provides pathways for policymakers, regulators, and market participants toward responsible and sustainable digital transformation.
Paper Presenter
avatar for Sushruthan Babu
Monday October 19, 2026 12:15pm - 2:15pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 8G, Virtual Room G

2:15pm PDT

Session Chair Concluding Remarks
Monday October 19, 2026 2:15pm - 2:17pm PDT
Virtual Room A
Monday October 19, 2026 2:15pm - 2:17pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 8A, Virtual Room A

2:15pm PDT

Session Chair Concluding Remarks
Monday October 19, 2026 2:15pm - 2:17pm PDT
Virtual Room B
Monday October 19, 2026 2:15pm - 2:17pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 8B, Virtual Room B

2:15pm PDT

Session Chair Concluding Remarks
Monday October 19, 2026 2:15pm - 2:17pm PDT
Virtual Room C
Monday October 19, 2026 2:15pm - 2:17pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 8C, Virtual Room C

2:15pm PDT

Session Chair Concluding Remarks
Monday October 19, 2026 2:15pm - 2:17pm PDT
Virtual Room D
Monday October 19, 2026 2:15pm - 2:17pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

2:15pm PDT

Session Chair Concluding Remarks
Monday October 19, 2026 2:15pm - 2:17pm PDT
Virtual Room E
Monday October 19, 2026 2:15pm - 2:17pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 8E, Virtual Room E

2:15pm PDT

Session Chair Concluding Remarks
Monday October 19, 2026 2:15pm - 2:17pm PDT
Virtual Room F
Monday October 19, 2026 2:15pm - 2:17pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 8F, Virtual Room F

2:15pm PDT

Session Chair Concluding Remarks
Monday October 19, 2026 2:15pm - 2:17pm PDT
Virtual Room G
Monday October 19, 2026 2:15pm - 2:17pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 8G, Virtual Room G

2:17pm PDT

Session Closing and Information To Authors
Monday October 19, 2026 2:17pm - 2:20pm PDT
Virtual Room A
Exhibitors
avatar for Ashutosh Goyal
Monday October 19, 2026 2:17pm - 2:20pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 8A, Virtual Room A

2:17pm PDT

Session Closing and Information To Authors
Monday October 19, 2026 2:17pm - 2:20pm PDT
Virtual Room B
Exhibitors
RJ
Monday October 19, 2026 2:17pm - 2:20pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 8B, Virtual Room B

2:17pm PDT

Session Closing and Information To Authors
Monday October 19, 2026 2:17pm - 2:20pm PDT
Virtual Room C
Exhibitors
avatar for Anshika Jain
Monday October 19, 2026 2:17pm - 2:20pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 8C, Virtual Room C

2:17pm PDT

Session Closing and Information To Authors
Monday October 19, 2026 2:17pm - 2:20pm PDT
Virtual Room D
Exhibitors
avatar for Richa Sharma
Monday October 19, 2026 2:17pm - 2:20pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

2:17pm PDT

Session Closing and Information To Authors
Monday October 19, 2026 2:17pm - 2:20pm PDT
Virtual Room E
Exhibitors
avatar for Siddhesh Mogal
Monday October 19, 2026 2:17pm - 2:20pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 8E, Virtual Room E

2:17pm PDT

Session Closing and Information To Authors
Monday October 19, 2026 2:17pm - 2:20pm PDT
Virtual Room F
Exhibitors
avatar for ANISHA MISHRA
Monday October 19, 2026 2:17pm - 2:20pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 8F, Virtual Room F

2:17pm PDT

Session Closing and Information To Authors
Monday October 19, 2026 2:17pm - 2:20pm PDT
Virtual Room G
Exhibitors
avatar for DHARMIK MODI
Monday October 19, 2026 2:17pm - 2:20pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 8G, Virtual Room G

2:58pm PDT

Opening Remarks
Monday October 19, 2026 2:58pm - 3:00pm PDT
Virtual Room A
Monday October 19, 2026 2:58pm - 3:00pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 9A, Virtual Room A

2:58pm PDT

Opening Remarks
Monday October 19, 2026 2:58pm - 3:00pm PDT
Virtual Room B
Monday October 19, 2026 2:58pm - 3:00pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 9B, Virtual Room B

2:58pm PDT

Opening Remarks
Monday October 19, 2026 2:58pm - 3:00pm PDT
Virtual Room C
Monday October 19, 2026 2:58pm - 3:00pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 9C, Virtual Room C

2:58pm PDT

Opening Remarks
Monday October 19, 2026 2:58pm - 3:00pm PDT
Virtual Room D
Monday October 19, 2026 2:58pm - 3:00pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

2:58pm PDT

Opening Remarks
Monday October 19, 2026 2:58pm - 3:00pm PDT
Virtual Room E
Monday October 19, 2026 2:58pm - 3:00pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 9E, Virtual Room E

2:58pm PDT

Opening Remarks
Monday October 19, 2026 2:58pm - 3:00pm PDT
Virtual Room F
Monday October 19, 2026 2:58pm - 3:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 9F, Virtual Room F

2:58pm PDT

Opening Remarks
Monday October 19, 2026 2:58pm - 3:00pm PDT
Virtual Room G
Monday October 19, 2026 2:58pm - 3:00pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 9G, Virtual Room G

3:00pm PDT

Agriculture with IoT-Enabled Smart Sensors: A new era of Agriculture Sustainability
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A
Authors - Shamama Kamal, Shailesh Saxena
Abstract - This paper examines the transformative impact of Internet of Things (IoT) technologies on agriculture, highlighting their role in enhancing efficiency, productivity, and sustainability. By integrating sensor devices, data analytics, and real-time connectivity, IoT enables precision farming practices and interconnected farming ecosystems. Key components include soil monitoring, crop health analysis, and smart irrigation systems, optimizing resource management and enhancing crop yields. Despite challenges like data security and interoperability, the potential benefits of IoT in agriculture, such as resource optimization and sustainable practices, underscore its significance. In conclusion, IoT emerges as a vital enabler for sustainable and efficient farming practices, promising a more resilient agricultural sector and contributing to global food security.
Paper Presenter
avatar for Shamama Kamal
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 9A, Virtual Room A

3:00pm PDT

Detecting Malicious Android Apps through Static Features using Machine Learning
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A
Authors - Priteshkumar Prajapati, Jugal Mehta, Prince Tadhani, Parth Shah, Amit Thakkar, Dipak Dabhi
Abstract - In the current era of mobile technology, Android’s widespread use and customisation by developers have also made it simpler for hackers to identify and exploit security flaws. Android malware continues to evolve, and one of the biggest concerns is how it quietly exploits app permissions and runs in the background to carry out harmful tasks without users even being aware of it. To address this emerging issue, the research proposes a static analysis-based solution for Android malware detection, utilising features derived from application permissions and API call behaviour. The study investigated a range of machine learning algorithms, including traditional methods such as Logistic Regression, SVM, and KNN, as well as more advanced approaches like Neural Networks and ensemble techniques like Random Forest, XGBoost, and CatBoost. We trained and tested the models using a well-organised set of Android app datasets and then measured their performance by analysing the results with a confusion matrix, focusing on key metrics such as accuracy, precision, recall, and F1-score. The confusion matrix clearly shows where our approach performs well, especially in real-world situations where there’s an uneven number of malicious and safe apps. Our results highlight the effectiveness of ensemble learning methods in enhancing mobile security while also providing a clear comparison of how different algorithms approach the task of detecting Android malware.
Paper Presenter
avatar for Priteshkumar Prajapati
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 9A, Virtual Room A

3:00pm PDT

Enhancing Athletic Performance Through an AI-Based Sports Application
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A
Authors - Nayan Suresh Patil, Shabana Pirjade, Yash Dattatray Londhe, Yogeshwar Anil Patil, Tej Tushar Jadhav
Abstract - This study presents an AI-powered mobile application designed to enhance athletic performance and promote active lifestyles. By evaluating existing sports applications, it explores their roles in boosting user engagement, motivation, and achievement of fitness goals. Challenges such as user consistency and data privacy are also considered. The findings suggest that well-designed sports apps, supported by responsive feedback systems, can play a significant role in personal training. To address the difficulties faced by aspiring athletes, especially in urban settings, this project introduces a feature-rich platform that connects users to nearby sports academies and offers structured guidance across various disciplines like cycling, athletics, and running. A key innovation is the integration of IoT-based monitoring devices, which provide real-time feedback through sensor-based data analysis. This facilitates accurate performance tracking and injury prevention, empowering users with personalized insights for continuous improvement.
Paper Presenter
avatar for Nayan Suresh Patil
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 9A, Virtual Room A

3:00pm PDT

Founder Verifier: An AI-Driven System for Automated Startup Founder Reputation Analysis and Verification
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A
Authors - Radhika V. Kulkarni, Gaurav Sulsule, Sanika Kolawale, Urmila Kakarwal, Prathmesh Deshmukh, Prakash Sharma
Abstract - The burgeoning startup ecosystem necessitates robust and efficient methods for evaluating the credibility and historical performance of founders and their ventures. Traditional due diligence processes are often manual, time-consuming, and susceptible to information overload and inherent biases. This paper presents "Founder Verifier," an Artificial Intelligence (AI) driven web application designed to automate and enhance the initial stages of founder reputation analysis. The system integrates web scraping capabilities with Natural Language Processing (NLP) techniques to provide a multifaceted assessment. Key functionalities include sentiment analysis of web-derived text using the NLTK VADER lexicon, Named Entity Recognition (NER) for geographical context ex-traction via spaCy, keyword-based potential controversy detection, and assimilation of failure/industry insights. These analyses culminate in a consolidated web report featuring a heuristically derived reputation score, categorized sentiment snippets, links to relevant external profiles, and downloadable PDF summaries. An operational dashboard further provides analytics on system usage and observed trends. This paper details the system's architecture, the methodologies employed for data acquisition and multi-modal analysis, the formulation of the reputation score, and discusses the structure and utility of its outputs. The Founder Verifier aims to provide a rapid, data-driven preliminary assessment tool for stakeholders in the startup ecosystem.
Paper Presenter
avatar for Gaurav Sulsule
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 9A, Virtual Room A

3:00pm PDT

Greening the Workforce: A Systematic and Bibliometric Review of Green Human Resource Management Literature (2000–2025)
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A
Authors - Ch Sahyaja, Gorripotu Eekshitha Bhagavathi, Karthick.M, Ch Shankar
Abstract - This study presents a comprehensive, systematic, and bibliometric review of Green Human Resource Management (GHRM) literature from 2000 to 2025, highlighting key trends, influential authors, research clusters, and emerging themes. Using Scopus as the primary data source, a total of 137 articles were analyzed with tools including VOSviewer, R (Biblioshiny), and Excel. The findings reveal a significant growth in GHRM publications, especially after 2020, with South and Southeast Asian countries leading in research output and collaboration. Co-authorship and co-citation analyses revealed some well-known contributors, including Ramayah T., Yusliza M.Y., and Jabbour C.J.C., and important publication outlets such as the Journal of Cleaner Production and IJHRM. From the keyword co-occurrence mapping, we observe dominant research themes surrounding sustainability, environmental performance, green behavior, and innovation. Besides, this study provides a roadmap for future studies by outlining areas of theoretical void, emergent domains of study and a call to an interdisciplinary and empirical approach.
Paper Presenter
avatar for Gorripotu Eekshitha Bhagavathi
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 9A, Virtual Room A

3:00pm PDT

Multi-Class Crop Prediction Based on Soil and Climate Parameters Using Random Forest, XGBoost, and MLP
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A
Authors - Aishwarya V. Kadu, KTV Reddy
Abstract - This research investigates the effectiveness of machine learning and deep learning models in multi-class crop classification using an agricultural dataset comprising 3,450 records from the Wardha district of Maharashtra, India. The dataset includes essential soil and climatic parameters such as pH, nitrogen (N), phosphorus (P), potassium (K), Mn, Fe, Cu, Zn, S i.e Nutrients and micronutrients temperature, humidity, wind speed, Cloud and rainfall, covering 11 crop types. Data preprocessing involved label encoding for su-pervised learning tasks. The study developed and evaluated three predictive models: Random Forest, XGBoost, and Multi-Layer Perceptron (MLP). Model performance was assessed using classification metrics like accuracy and confusion matrix, as well as regression metrics such as MAE, MSE, RMSE, and R-squared. Feature importance analysis was performed to deter-mine the influence of input variables on model predictions. XGBoost showed the highest classification accuracy, while MLP demonstrated strong learning capability. The findings emphasize the value of combining predictive performance and interpretability, highlighting the applicability of AI-driven methods in enhancing crop recommendation systems and advancing precision agriculture across varied agro climatic zones.
Paper Presenter
avatar for Aishwarya V. Kadu
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 9A, Virtual Room A

3:00pm PDT

Predicting Diabetic Retinopathy using Convolutional Neural Networks
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A
Authors - Purva Agarwal, Somya R. Goyal
Abstract - Diabetic Retinopathy (DR) is a widespread stage of diabetes that influences the retina and, if untreated, can lead to blindness. DR is one of the primary reasons for blindness, especially among working-age populations in developing countries. Treatment can solely assist in preserving vision due to the irreversible nature of the disease. Timely identification and intervention substantially diminish the likelihood of blindness. Manual diabetic retinopathy diagnosis with retinal images is costly, time-consuming, and labor-intensive. The automatic detection of diabetic retinopathy disease can be possible using deep leaning methods specially CNN. In this paper, several diabetic retinopathy detection methods based on deep learning are evaluated. The accuracy of the suggested method is 94.04%.
Paper Presenter
avatar for Purva Agarwal
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 9A, Virtual Room A

3:00pm PDT

QUANTUM COMPUTING AND WORKFORCE MANAGEMENT: REVOLUTIONIZING HR ANALYTICS
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A
Authors - Damini Wankhede, Shubham Kishor Kadam, Utkarsha Wanjari
Abstract - Quantum computing is a ground-breaking new technology, and its disruptive potential can affect workforce management and HR analytics. This paper talks about its prominent role in addressing new challenges, including workforce honing, talent management, employee retention, cobalt matrix personalization, and diversity management. Organizations can leverage quantum algorithms to optimize their allocation of resources, recruitment and finding patterns in multidimensional datasets for better decision-making. These innovations are not only improving operational efficiencies — they are also facilitating more personalized and equitable approaches to the workforce. However, the addition of quantum computing to HR systems poses its own set of problems. Technical roadblocks, like high infrastructure costs and a shortage of skilled workers, prevent wider adoption. HR schedules powered by automation can lead to biased results when data privacy and fairness of algorithms-related ethical concerns are addressed. Implementing such technology is beset by further complications like organizational readiness and change resistance needing to be effectively planned for and managed. The paper recommends actionable recommendations for HR leaders, including phased integration, investment in quantum literacy, and the establishment of pilot projects. Further studies should employ realistic architectures that prioritize scalability, cost-efficiency, and the reduction of algorithmic biases that make quantum solutions more widely accessible and equitable. Because of that, quantum computing can change the way HR analytics are performed, and new opportunities for diverse industries emerge.
Paper Presenter
avatar for Shubham Kishor Kadam
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 9A, Virtual Room A

3:00pm PDT

The Role of Hospital Administrators in Enhancing Healthcare Quality through Leadership and Quality Improvement Initiatives
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A
Authors - Rohini Chavhan, Michael Savariapitchai, Priyanka S. Dhore
Abstract - Hospital administrators contribute substantially to healthcare quality advancement through their dual performance as operational managers and strategic leaders who drive changes. This study examines the multifaceted responsibilities of hospital administrators who enhance patient care quality by implementing leadership roles and executing policies and managing resources and developing their workforce. This study surveyed healthcare workers across the Sawangi region through 104 structured questionnaires which measured training delivery and resource distribution along with communication channels and leadership activities. The analysis of administrative behaviors against quality improvement outcomes utilized Spearman's Rank Correlation with Independent Samples t-tests. The research demonstrates that standardized staff workshops together with adequate financial resources and well-defined communication goals lead to improved staff involvement as well as superior patient security alongside superior clinical outcomes. Administrators who underwent formal training displayed superior abilities to lead continuous quality improvement efforts. The study demonstrates how hospital administrators drive patient-centered and safe healthcare delivery systems through strategic leadership execution which connects organization objectives to evidence-backed procedures. Strong leadership development along with policy support stands essential for maintaining quality care at high levels within transforming healthcare systems.
Paper Presenter
avatar for Rohini Chavhan
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 9A, Virtual Room A

3:00pm PDT

UPI Usage Among Migrant Workers in Tamil Nadu: An Analysis of Awareness and Challenges
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A
Authors - Pranesh P, Adwaitha K Raj, Durga Lashmi C.V
Abstract - Many individuals are interested in the Unified Payments Interface (UPI) technology since it is secure, user-friendly, and widely used in India. UPI (Unified Payments Interface) is incredibly popular in India, with millions of users and billions of transactions processed daily. This study explains about the UPI usage and the awareness about the migrant workers in Tamil Nadu. This study consists of 100 responses from the migrant workers, and their opinion towards the UPI usage. Most of the people think that the UPI usage is convenient for them to use in their smartphone. This study also examines the UPI preference of the respondents and their purposes. It also has the dominant results that all the respondents are aware of the UPI systems and pursuing it. This study has also consisted an insight of the digital literacy of the migrant workers. It also brings us the challenges that are faced by the people at the time of using UPI. UPI, the Unified Payments Interface, has been experiencing intermittent technical issues leading to partial transaction declines, according to the National Payments Corporation of India (NPCI). These issues can cause problems with fund transfers, payments, and app functionality.
Paper Presenter
avatar for Pranesh P
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 9A, Virtual Room A

3:00pm PDT

AI-GHRM Integration Model (AGIM): A Multi layered Framework for Sustainable and Secure Workforce Analytics in Hospitality
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B
Authors - Anubrata Dhar Chaudhuri, Raj Chakraborty
Abstract - The hospitality sector faces increasing pressure to integrate sustainability into operations while maintaining high productivity. Green Human Re-source Management (GHRM) practices have proven to enhance employee engagement and promote eco-friendly organizational citizenship behavior. How-ever, traditional GHRM frameworks lack the digital, real-time analytics needed to respond dynamically to workforce changes and sustainability goals. This research work proposes the AI-GHRM Integration Model (AGIM), a novel conceptual framework that extends the classical Stimulus-Organism-Response (SOR) paradigm into a dynamic feedback loop powered by artificial intelligence (AI). AGIM consists of a multi-layered architecture incorporating AI-based predictive analytics for attrition and engagement forecasting, NLP-driven sentiment analysis of employee well-being, IoT-enabled automation of sustainability compliance, and privacy-preserving security mechanisms. This paper outlines the AGIM conceptual model, compares its dimensions with traditional GHRM, and presents a hypothesis matrix linking AI-powered interventions to employee and sustainability outcomes. A mixed-method research methodology is discussed, blending qualitative case studies with quantitative analytics. Use case scenarios illustrate AGIM’s implementation phases in a hotel setting. The discussion highlights theoretical contributions (extending SOR with AI feed-back) and practical implications for business information systems in “smart secure systems”. Future work includes empirical validation and expansion to other service industries.
Paper Presenter
avatar for Anubrata Dhar Chaudhuri
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 9B, Virtual Room B

3:00pm PDT

Confidence intervals for Difference between the Percentiles of the Delta-Rayleigh Distributions
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B
Authors - Nerisa Thornsr, Sa-Aat Niwitpong, Suparat Niwitpong
Abstract - The delta-Rayleigh distribution comprises a mixture of zero and non-negative values following the Rayleigh distribution. Percentiles are practical tools for describing skewed data and are widely used in several fields. The purpose of this study is to construct confidence intervals for different percentiles of the delta-Rayleigh distributions using generalized confidence interval (GCI), normal approximation (NA), method of variance estimates recovery (MOVER), percentile bootstrap confidence interval (PBCI), and Bayesian methods. Monte Carlo simulations were conducted to evaluate the performance of these methods using coverage probabilities and average widths. The results indicate that the GCI method outperformed others. For a real-world data application, we utilized a dataset on CO concentration to assess the performance of the presented methods.
Paper Presenter
avatar for Nerisa Thornsri
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 9B, Virtual Room B

3:00pm PDT

Evaluating the Effectiveness of Indonesia’s Core Tax Administration System: Challenges, Early Impacts, and Lessons for Digital Tax Reform
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B
Authors - Surya Anugrah, Sabo Hermawan, Christian Wiradendi Wolor, Eka Septariana Puspa, Windy Permata Suyono, Dwi Handarini
Abstract - Indonesia’s bold leap toward digital tax reform culminated in the 2025 launch of its Core Tax Administration System (CTAS) a centralized platform aimed at streamlining taxpayer services, boosting compliance, and modernizing revenue administration. This study critically evaluates the early- stage implementation of CTAS using a dual-theoretical lens: the Technology Acceptance Model (TAM) and Institutional Theory. Drawing on government documents, user surveys, and media analysis, findings reveal a paradox while users widely acknowledge CTAS’s potential to simplify tax filing, technical glitches, complex interfaces, and poor system responsiveness undermine its usability and acceptance. Moreover, institutional misalignments such as inadequate training, low digital literacy, and resistance to change pose structural barriers to reform success. By integrating behavioral insights with systemic analysis, this research highlights the multilevel challenges of digital transformation in public finance. The paper concludes with actionable lessons for emerging economies navigating the tension between technological innovation and institutional readiness in the digital era.
Paper Presenter
avatar for Surya Anugrah

Surya Anugrah

Indonesia
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 9B, Virtual Room B

3:00pm PDT

Intelligent Resume Evaluation Using LLMs: Skill Gap Identification and Job Compatibility Scoring
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B
Authors - Krishna Saurabh Soni, Pranshi Talati, Jash Mandani, Dweepna Garg, Parth Goel
Abstract - Recruitment has become a lot more complicated with a surge in the number of applicants for each job. Traditional resume screening methods are time-consuming and subjective—and that leads to inefficiencies and biases in candidate selection. What we need is a more efficient, fair and accurate way to evaluate resumes. That’s where a new resume screening system comes in. By using Retrieval-Augmented Generation (RAG) with the Gemini API, we can improve the accuracy, efficiency and fairness of resume evaluation. That system pulls out the most relevant information from resumes, compares a candidate’s skills to the job description and gives you a selection probability score-complete with reasoning. We combined Natural Language Processing (NLP), machine learning and a resume dataset from Kaggle to refine the evaluation process. The experiments show the model can identify missing skills, match candidate qualifications to job requirements and give job seekers valuable insights. Integrating RAG into the hiring process can make a real difference to recruitment outcomes-and reduce bias and manual workload.
Paper Presenter
avatar for Krishna Saurabh Soni
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 9B, Virtual Room B

3:00pm PDT

Kinecharge: A smart kinetic energy to electric eco energy generation from cycling motion for smart device charging
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B
Authors - Naina Kokate, Supriya Gorde, Rashmi Kale, Sayali Mulik, Kashmira Naik, Kartik Mundwadkar, Atharva Navale, Piyush Nakade
Abstract - The rapid increase in portable electronic device usage has intensified the demand for sustainable and self-sufficient energy solutions. KineCharge addresses this need by innovatively harvesting kinetic energy generated during cycling and converting it into usable electrical power. The system uses a DC motor directly connected to a bicycle wheel, which produces electricity as the wheel rotates. This generated electrical energy is regulated through a diode to ensure proper current flow and is managed via a battery charging module. The harvested energy can be stored in a rechargeable battery for later use or accessed immediately through a USB output to charge smartphones and other portable devices. This design offers an environmentally friendly and costeffective alternative to traditional power sources, particularly benefiting cyclists, outdoor enthusiasts, and individuals in remote or off-grid locations. KineCharge promotes clean energy utilization while providing a practical means to keep devices powered during travel. By leveraging mechanical energy from cycling, it reduces reliance on conventional electrical grids and supports the adoption of sustainable energy practices. Additionally, the project serves as an educational tool that demonstrates principles of energy conversion and renewable power generation. Designed for easy installation on most bicycles, KineCharge offers a portable and green energy solution adaptable to a variety of users. It exemplifies innovative thinking in renewable energy and contributes to fostering energy independence and environmental sustainability in everyday life.
Paper Presenter
avatar for Sayali Mulik
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 9B, Virtual Room B

3:00pm PDT

Optimizing Tax Revenue to Support the Achievement of SDG 13: Mitigating Climate Change through Carbon Tax Policy
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B
Authors - Surya Anugrah, Rochma Sudiati, Eka Septariana Puspa, Windy Permata Suyono, Sabo Hermawan, Septi Nurmalita
Abstract - Carbon tax policies have been widely adopted as instruments for climate change mitigation; however, their implementation in developing countries continues to face significant structural and political challenges. This study aims to analyze the influence of per capita carbon emissions and tax revenue on carbon tax rates in ten developing countries during the period 2021-2023. Utilizing a panel data regression approach with fixed effects models, this research examines the causal relationship between environmental and fiscal factors and the determination of carbon tax rates. The estimation results reveal that per capita carbon emissions have a negative and statistically significant effect on carbon tax rates, with a coefficient of -6.09 (p0.05), suggesting that fiscal considerations are not yet a primary determinant in the design of carbon tax policies in developing economies. The policy implications of this study highlight the need for more progressive and integrated carbon tax reforms aligned with sustainable development agendas, particularly in support of Sustainable Development Goal (SDG) 13. This study contributes novel empirical evidence to the green fiscal literature in developing countries and offers strategic recommendations for policymakers to optimize the role of carbon taxation in global emissions reduction efforts.
Paper Presenter
avatar for Surya Anugrah

Surya Anugrah

Indonesia
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 9B, Virtual Room B

3:00pm PDT

Promoting Sustainable Futures through Quality Education
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B
Authors - Navya H, Ananthavalli Thangam
Abstract - Education is an important tool in empowering people with the required knowledge and influencing their perspective and attitude for transforming the society. This assumes more importance in the context of the goal of lifelong learning. Achieving goals require behaviour change at multiple levels. This study is an attempt to assess the impact of education on the perspectives of people in promoting eco-friendly behaviour. Quality education is invariably connected with certain goals such as reducing poverty and promoting gender equality. As we drive towards the targets of SDGs, a closer look at the intricate relation of education with the goals of zero hunger, gender equality and poverty reduction points towards the urgency in using education not merely as a goal in itself but a catalyst for attaining the desired goals. Equipped with the necessary skills, education provides opportunities for employment. Coupled with the awareness of sustainable development, the goals of economic advancement can be attained with less damage to the environment. Inclusive education with provision of equal opportunities for women leads to gender equality and participation of women in progress. A cross-sectional survey was taken to assess the perspective of the role of education as a catalyst to promote the SDGs. The results strongly indicated the need to provide quality education with the right strategy and policy interventions. There are economic and social challenges to overcome. The role of institutions is to be emphasized. Concerted efforts by academicians, corporates and the government can strengthen the pillars of economy, environment and equity.
Paper Presenter
avatar for Navya H

Navya H

India
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 9B, Virtual Room B

3:00pm PDT

Research and analyze stock trends in Vietnam's banking industry based on real data
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B
Authors - Tran Phuong Duy, Nguyen Gia Tuan Anh
Abstract - This study aims to evaluate the trend of stock price fluctuations in the Vietnamese banking industry in the period of 2019-2023 by integrating real-world data with quantitative models and modern machine learning techniques. Data is collected from reputable sources such as HOSE, Vietstock and CafeF, including financial indicators (EPS, ROE, P/E, ROA), macroeconomic factors (operating interest rates, exchange rates, foreign cash flows) and market sentiment index (Vietnam VIX). The applied analytical models include linear regression, logistic regression, GARCH, VAR, VaR and Random Forest. The research results show that the VIX index has a clear impact on bank stock prices, and the proposed system is capable of supporting investors in determining the appropriate trading time, classifying stocks according to risk and expected return. The article not only provides a quantitative perspective but also provides an effective visualization tool, contributing to improving the quality of investment decisions in the Vietnamese stock market.
Paper Presenter
avatar for Tran Phuong Duy
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 9B, Virtual Room B

3:00pm PDT

Role-Specific Gender Differences in Employee Attrition: Evidence from a Multi-Method Workforce Study
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B
Authors - Usha Sadhani, Melita Simoes, A M Kadakol
Abstract - In today’s dynamic work environment, employee attrition is a crucial challenge for organizations that are impacted by demographic and job-related factors. This study examines the role of gender in attrition across different job roles and encompasses a multi-method approach taking into consideration 1,470 employees. The analysis found that there is no significant influence of gender in the attrition rates. Job satisfaction and tenure of an employee within an organization emanated as stronger predictors of attrition than gender, aligning with the established theories that highlighted the preference of job-related factors over demographic characteristics that helps in the retention of an employee. The logistic regression model showed a statistically significant result but was not very effective at predicting who leaves, highlighting the complexity of attrition and the role of factors not included in the study. Overall, the findings suggest that gender alone is insufficient to explain attrition patterns, highlighting the importance of contextual factors such as job role and employee satisfaction. This research contributes to the ongoing dialogue on diversity and retention by demonstrating that effective talent management should consider the nuanced interactions between job characteristics and individual experiences rather than relying on broad demographic generalizations.
Paper Presenter
avatar for Usha Sadhani
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 9B, Virtual Room B

3:00pm PDT

The role of leadership styles in preventing burnout and promoting a sustainable work environment in healthcare organizations
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B
Authors - Priyanka S. Dhore, Michael Savariapitchai, Rohini Chavhan
Abstract - Repeatedly, being burnt out, constitutes a severe health issue, affecting employee welfare, and negatively impacting the long-term sustainability of health organizations. It revolves around different types of management styles that don't burn an employee out, but instead create an environment conducive to work, given health institutions. Exploring the work of four different dimensions of leadership: transformational, ethical, health-promoting, and resilient; would be towards reducing burning out and improving employee well-being, as well as creating a positive organizational culture. The study adopted a quantitative approach wherein close-ended survey questionnaires were used to collect data from a sample of 101 healthcare employees. Results revealed that empathic, supportive, and team-focused behavior of leaders has considerable impact on the burnout and job satisfaction levels of workers. In addition, interventions such transformational and health-promoting leadership have been found to have a positive influence on organizational health, supportive policies, and retention of employees, among others. Workplace leadership praise the productivity and balance. Leadership helps to focus Tasks. It encourages the distribution of workloads. It sets up mental health projects and it backs employee welfare. Healthcare leadership requires listening to concerns raised by employees, creating a work-life balance, and enforcing fair policies to prevent burnout. There is evidence in favor of having an enlightened discussion for healthcare leaders that improves the environment balancing conditions between burnout-prevention and better performance of the organization.
Paper Presenter
avatar for Priyanka S. Dhore
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 9B, Virtual Room B

3:00pm PDT

A Federated Learning Framework for Secure Crop Disease Detection in Decentralized Agricultural Environments
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C
Authors - Mohammad Nasar, Mohammad Abu Kausar, Md. Abu Nayyer
Abstract - Smart farming technologies, particularly image-based crop disease detection systems, are significantly transforming agriculture by enabling farmers to respond promptly to emerging threats. However, deploying centralized AI models in remote or rural farming communities remains challenging due to limited internet connectivity, heterogeneous data sources, and privacy concerns. This paper presents a federated learning (FL) framework designed to support secure and privacy-preserving crop disease detection in decentralized agricultural environments. In the proposed approach, lightweight convolutional neural networks (CNNs) are trained locally on crop images captured by drones or field sensors, ensuring that sensitive data remains at the source. Instead of transmit-ting raw data, only model updates are shared with a central server, which aggregates them using the Federated Averaging (FedAvg) algorithm. To enhance security and model integrity, a cosine similarity-based filter is incorporated to detect and discard malicious or corrupted updates. Additionally, an attention-based heatmap module is introduced to visually highlight disease-affected regions in the crop images, improving system transparency and building user trust. Experimental evaluations on the PlantVillage dataset and real-world drone imagery of wheat and tomato crops demonstrate that the framework achieves an average 4.8% improvement in F1-score under non-IID data conditions com-pared to centralized models, alongside a 9.3% reduction in communication overhead at 50 nodes. This solution is well-suited for resource-constrained rural settings and contributes to scalable, intelligent, and sustainable agricultural digitization.
Paper Presenter
avatar for Mohammad Nasar
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 9C, Virtual Room C

3:00pm PDT

Active Directory – Attacks and Defense
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C
Authors - Makrand Kadam, Prashant Dhotre
Abstract - Active Directory (AD), created by Microsoft, plays a central role in how most organizations manage user identities, access, and system permissions. Because of its importance in controlling access across enterprise networks, it’s a prime target for cyber attackers. This paper takes a closer look at how AD is structured, highlighting key vulnerabilities and common methods used by attackers—like stealing credentials, escalating privileges, using Pass-the-Hash, or creating Golden Tickets. It also examines real-life security breaches to show just how critical AD security has become. Beyond identifying threats, the paper outlines practical defenses such as improved auditing, network segmentation, hardening of domain controllers, and adopting zero trust models. By bringing together current research and best practices from the field, this study offers cybersecurity professionals and system administrators a clear, actionable guide to better protect their AD setups against today’s increasingly sophisticated threats.
Paper Presenter
avatar for Makrand Kadam
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 9C, Virtual Room C

3:00pm PDT

AI and Inner Peace: AI-Powered Mental Wellness through the Teachings of the Bhagavad-Gita
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C
Authors - Rajeshwari S B, Sajini G, Jagadish S Kallimani
Abstract - In today's fast-paced world, people frequently experience high levels of emotional stress and struggle to access personalized mental health support. Traditional mental health services often face challenges such as limited accessibility and a generalized approach, which can overlook individual emotional nuances. This one-size-fits-all method may fail to address the specific needs of individuals. The need for more responsive and individualized mental health resources has never been greater, highlighting the importance of innovative solutions that offer contextually relevant and personalized care. The Srimad Bhagavad Gita, a timeless spiritual text, offers profound teachings on managing emotions, finding purpose and maintaining balance in life. This project aims to develop a Mental Health Conversational Assistant that provides users with motivational responses rooted in the wisdom of the Bhagavad Gita. This chatbot lever-ages NLP and NLU to detect both the emotional state and the intent behind user queries. It uses advanced techniques like emotion detection and semantic understanding to analyze user input and respond with relevant verses from the Bhagavad Gita. By integrating Hugging Face's emotion detection models and sentence-transformer embeddings, chatbot is capable of providing tailored, contextually relevant verses from Bhagavad Gita that match both the user’s emotions and the meaning of their queries.
Paper Presenter
avatar for Rajeshwari S B
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 9C, Virtual Room C

3:00pm PDT

AI-Driven Disease Surveillance and Outbreak Response: A Transparent Informatics Framework for Resource-Limited Healthcare Systems
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C
Authors - Reena S. Satpute, Nura Muhammad Sani
Abstract - Effective disease surveillance and outbreak response are high public health priorities, particularly in resource-limited healthcare systems facing significant challenges with data collection, predictive modeling, and coordinated intervention strategies. It hence proposes an integrated AI-driven framework responding to these challenges and strengthening the capacity of public health authorities to better manage infectious disease outbreaks. This framework will integrate real-time data from different sources such as electronic health records, syndromic surveillance, environmental indicators, and social media in a harmonious way into one centralized repository for the enablement of advanced predictive modeling techniques. It mainly comprises a disease forecasting model, an outbreak detection algorithm, and an intervention optimization tool integrated to provide early warning, accurate prediction, and data-driven decision support. An extensive performance evaluation of the framework was conducted using accuracy, sensitivity, specificity, and area under the ROC curve as some of the metrics. The results are very satisfactory, while the overall accuracy is 94.8% (95% CI: 93.2%-96.4%) with an AUC-ROC of 0.967 (95% CI: 0.951 - 0.983). Comparative performance analysis using state-of-the-art deep learning architectures further establishes the superiority of the proposed hybrid model. Transparency and explainability in the framework were guaranteed up to an extent that allows understanding the models and decision-making process by the public health authorities.
Paper Presenter
avatar for Nura Muhammad Sani
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 9C, Virtual Room C

3:00pm PDT

Forecasting Cryptocurrency Trends: Applying ARIMA and Machine Learning to Predict XRP and Bitcoin Prices
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C
Authors - Adulfo R. Arevalo, Ronald L. Pancho
Abstract - This study examines the forecasting performance of three prominent models— Autoregressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), and Random Forest (RF)—in predicting the short-term price movements of Bitcoin (BTC) and XRP using real daily closing price data from January to June 2024. The research aims to determine which model delivers the most accurate forecasts by applying each technique to actual market data and evaluating their predictive outputs using three standard accuracy metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). Results show that ARIMA consistently outperformed both SVR and Random Forest in forecasting accuracy, recording the lowest average errors across all metrics for BTC and XRP. While SVR performed poorly in capturing XRP's volatility, Random Forest showed moderate results but lagged behind ARIMA. These findings highlight ARIMA's robustness and practicality as a forecasting tool for short-term cryptocurrency price movements. The study concludes with model-specific recommendations, encouraging the use of ARIMA for near-term price prediction and suggesting further exploration into hybrid and deep learning models for future research.
Paper Presenter
avatar for Adulfo R. Arevalo
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 9C, Virtual Room C

3:00pm PDT

Gesture and Speech Enabled Virtual Mouse for Assistive Human-Computer Interaction
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C
Authors - Rajeshwari S B, Jagadish S Kallimani
Abstract - In the realm of human-computer interaction, the development of input devices accommodating diverse user needs is paramount. Traditional peripherals like mice and keyboards, though effective in typical settings, often falter in specialized conditions such as low-light environments. Addressing these constraints, this study introduces a novel gesture and voice-controlled mouse system. By leveraging real-time video and audio inputs, the system interprets user gestures and voice commands, eliminating reliance on conventional input tools. Additionally, it tackles unresolved interface design issues, striving to optimize user experience and broaden computing resource accessibility. Key to its functionality is the integration of MediaPipe for precise gesture recognition and natural language processing for voice command interpretation. Utilizing standard webcam and microphone hardware enables hands-free cursor control and command execution, particularly aiding users with mobility limitations. Methodologically, the system blends foundational theoretical principles with innovative design concepts for practical realization. Rigorous testing demonstrates a 90% accuracy rate in gesture recognition and a notable 60% accuracy in low-light conditions. Voice command interpretation achieves an impressive 95% accuracy across various accents and speech patterns, with a significant reduction in task completion time com-pared to conventional setups. This research underscores the potential of an affordable and adaptable technology, promising enhanced digital accessibility through ubiquitous webcam and microphone integration.
Paper Presenter
avatar for Rajeshwari S B
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 9C, Virtual Room C

3:00pm PDT

Habit and Perceived Safety on E–Wallet Adoption in Vietnam
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C
Authors - Thanh D. Nguyen, Anh T. N. Nguyen
Abstract - This study examines how habitual behavior and users’ perceptions of security influence their intention to adopt and use e-wallet services. Building upon the original Technology Acceptance Model (TAM), the research integrates elements from both the Unified Theory of Acceptance and Use of Technology (UTAUT) and its extended version, UTAUT2. Data were collected from 265 valid respondents via an online survey and analyzed using reliability testing, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). The findings reveal that perceived security, general privacy concerns, and habit all positively affect the intention to adopt e-wallets. Further-more, this intention significantly influences actual usage behavior.
Paper Presenter
avatar for Thanh D. Nguyen
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 9C, Virtual Room C

3:00pm PDT

MACHINE LEARNING ALGORITHMS FOR DETECTING FAKE NEWS ON SOCIAL MEDIA
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C
Authors - Nureni Ayofe Azeez, Solomon Akinboro, Isiekwene Chinyere Chioma, Charles Van der Vyver, Awoseemo Babajide
Abstract - The propagation of fake news in the digital age has raised momentous concerns about the integrity of information dissemination. This research aims to carry out a comparative assessment of machine learning algorithms for identifying fake news in social media. To achieve this, an attempt was made to collect a wide-ranging dataset of news articles, encompassing both real and fake sources. In the initial phase of the research, we preprocess the news data, extracting relevant features such as text content, metadata, and source credibility. Leveraging a wealth of labelled data, these models learn to differentiate between genuine and fake news articles, while also providing insights into the characteristics that contribute to their classification. In a world where information authenticity is paramount, the implementation of this system provides a valuable tool for discerning consumers of news and enables the swift identification of misinformation sources. The models were trained using four different datasets. Several machine learning algorithms were used for carrying out comparative assessment in terms of their efficiency and accuracy to detect fake news. Selected metrics were chosen for a comparative assessment. At the end of the experiments, for Dataset 1, the Passive Aggressive Algorithm had the highest accuracy of 0.8333, while for Dataset 2, the Passive Aggressive Algorithm had the highest accuracy of 0.914285. However, for Dataset 3, the AdaBoostClassifier Algorithm had the highest accuracy of 0.9142857. Furthermore, in Dataset 4, several algorithms which including Logistic Regression, Passive Aggressive, Ridge Classifier, SGD Classifier, AdaBoost Classifier, Bagging Classifier, Extra Tree Classifier, Gradient Classifier, Random Forest, NUSVC, Linear SVC, Decision Tree, BernoulliNB, and GaussianNB, all had the highest accuracy of 1.0000000. A bar chart was used to represent the results graphically, showing the minimum, maximum, and quartile range.
Paper Presenter
avatar for Nureni Ayofe Azeez
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 9C, Virtual Room C

3:00pm PDT

Real-Time Deepfake Audio Detection Using Lightweight CNNs
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C
Authors - Akash Karale, Pratvina Talele
Abstract - With the rapid advancement of deepfake audio technology, producing artificial voices that sound remarkably realistic is now simpler than ever. As this becomes available In addition to opening up new creative possibilities, it also poses grave security, disinformation, and fraud concerns. Although many of the deepfake audio detection tools available today are accurate, their application in real-time situations is limited by their high processing overhead. In this work, we introduce a Convolutional Neural Network (CNN)-based lightweight deepfake audio detection model. Our model can detect AI-generated speech accurately and with minimal processing demands by utilizing Mel-Spectrogram features and an effective CNN architecture. The model works well with high accuracy and low latency, according to tests on standard datasets, which makes it appropriate for real-world uses like media verification and voice authentication. Looking ahead, we aim to improve the model's ability to withstand adversarial attacks and adapt to new types of deepfake generation techniques. In the future, we would like to improve the model's robustness against hostile attacks and make it more flexible to accommodate new deepfake generation techniques
Paper Presenter
avatar for Akash Karale
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 9C, Virtual Room C

3:00pm PDT

Recognizing Manipulated Images Using Deep Learning in Flask
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C
Authors - Ram Joshi, Rutuja Khedkar, Prema Sahane, Shantanu Totewad, Nikhil Tarate, Prajodeep Patil, Kedarnath Zunjare
Abstract - Deepfakes are manipulated or AI-generated images and videos that appear authentic but are not. A decade ago, detecting deepfakes was relatively straightforward due to the limitations of the technology. However, recent advancements in AI, machine learning, and deep learning have made deepfakes nearly undetectable. Although this technology is often used for entertainment, it is increasingly being used for malicious purposes. The widespread availability of AI tools has further facilitated the creation of convincing deepfakes by fraudsters. Given that people tend to trust what they see and hear, they are particularly susceptible to the influence of realistic deepfakes.
Paper Presenter
avatar for Prajodeep Patil
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 9C, Virtual Room C

3:00pm PDT

A Novel System to Capture and Create DIACOM and PACS Database
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D
Authors - Kantharaju, Srinidhi G A
Abstract - Healthcare institutions worldwide are increasingly adopting digital im-aging solutions to replace traditional film-based radiological systems. This re-search presents the development of an advanced Picture Archiving and Communication System (PACS) designed to address current limitations in medical image management. Our proposed system focuses on seamless integration of legacy im-aging equipment through innovative format conversion capabilities, specifically transforming proprietary image formats into standardized DICOM files. The implementation demonstrates significant improvements in storage efficiency, trans-mission speed, and overall workflow optimization within hospital environments. Through comprehensive testing and validation, we established that our solution successfully bridges the gap between older imaging modalities and modern digital infrastructure requirements. The system architecture incorporates robust security measures, user-friendly interfaces, and scalable database management to support growing institutional needs.
Paper Presenter
avatar for Kantharaju
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm PDT

Advancing E-Governance in the Philippines: Strategies for Optimizing Components and Enhancing Digital Public Service Performance
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D
Authors - Ronald L. Pancho, Maria Teresa N. Pancho, Roland M. Simbre
Abstract - Using digital technologies to enhance government operations and e-service delivery, e-governance encompasses diverse components crucial for improving digital initiatives and government e-service performance. This study examines how various elements of e-governance influence the effectiveness and efficiency of e-government e-services. The Philippines has actively pursued e-governance initiatives to encourage citizen participation, provide access to information and service delivery, enhance transparency and accuracy, and promote communication and interaction. In this context, the government's e-service performance becomes pivotal in assessing the effectiveness and efficiency of digital initiatives undertaken by the government. There were six hundred eighty-three (683) government e-services users to whom the respondents replied through Google Forms. The study used a self-made survey questionnaire and employed Cronbach's Alpha to test reliability and internal consistency. The experts conducted content validation, and non-probability purposive sampling techniques were used in which respondents were directly involved. The finding reveals that e-governance components positively correlate with government e-services' performance in effectiveness and efficiency. The study relates how the use of e-governance components affects e-services performance. The idea suggests that increasing e-governance practices enhances government e-services performance. Therefore, optimizing the e-governance components and enhancing their performance are essential in improving digital public service initiatives.
Paper Presenter
avatar for Ronald L. Pancho

Ronald L. Pancho

Philippines
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm PDT

Evaluating AI-Optimized Mobile Design: A Mixed-Method Study on Bootstrap Performance and ChatGPT Text Analysis for MSME Websites
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D
Authors - Sabo Hermawan, Ryna Parlyna, Surya Anugrah, Inkreswari Retno Hardini, Ferry Setyadi Atmadja, Fajri Hamdani, Alifah Nur Rahmawati, Cornellius Seno Adriano
Abstract - This study examines the effectiveness of combining AI-optimized mobile design with Bootstrap performance improvements and ChatGPT-driven text analysis for Micro, Small, and Medium Enterprise (MSME) websites. A mixed-methods approach was utilized to assess the effects of these interventions. Performance measures, particularly Largest Contentful Paint (LCP), exhibited a significant 40% enhancement following modification. Simultaneously, text analysis performed with ChatGPT-4 attained an accuracy of 86.7% in detecting significant content concerns. This research fills a notable vacuum in the literature by offering a comprehensive analysis of technical and AI-based solutions for enhancing the online presence of MSMEs. The results highlight the capability of AI to improve the technical performance and content quality of mobile-optimized websites, providing practical insights for MSME digital strategies. Subsequent research will investigate wider geographical contexts. These findings are further contextualized within the Technology Acceptance Model (TAM), emphasizing the perceived usefulness and ease of use of AI-enhanced mobile websites.
Paper Presenter
avatar for Sabo Hermawan

Sabo Hermawan

Indonesia
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm PDT

Exploring Satellite Imaging: From Technology to Landsat Image Creation with Raw Data
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D
Authors - Veena N D, Anithadevi M D
Abstract - This investigation examines contemporary satellite imaging method-ologies and their implementation in Earth observation applications. We analyze the operational principles of various sensing platforms, data acquisition techniques, and processing algorithms used in modern remote sensing applications. Our research focuses on spectral band optimization and image reconstruction methodologies using actual satellite datasets. A comprehensive case study utilizing Kilauea Volcano imagery from Hawaii demonstrates practical applications of multi-band data processing techniques. Through systematic analysis of seven spectral channels from Landsat-7 Enhanced Thematic Mapper Plus sensor, we evaluate the effectiveness of different wavelength combinations for terrain characterization. Our experimental results indicate optimal spectral configurations for various surface feature identification tasks. The study concludes with an assessment of current technological capabilities and operational constraints affecting satellite-based Earth observation systems.
Paper Presenter
avatar for Veena N D
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm PDT

Opioid Disease Mortality Rate Analysis in United States using AI
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D
Authors - Sanjay Yadav, Sanjay Kumar, Sakshi, Aaryan
Abstract - Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostics, optimizing treatment plans, and enabling datadriven public health strategies. This paper focuses on the application of AI to combat the opioid overdose crisis in the United States, utilizing machine learning techniques—particularly reinforcement learning—to inform policy interventions. We explore the use of deep Q-networks (DQN) trained on CDC opioid mortality data to simulate optimal intervention strategies across states, demonstrating a potential reduction of 15% in projected deaths. Additionally, we apply clustering and trend analysis on multi-year overdose data to uncover regional dynamics and emerging hotspots. The study also examines key ethical considerations, data privacy solutions, and integration challenges within AI-driven healthcare systems. By combining algorithmic insights with epidemiological realities, this work offers a scalable and explainable AI framework to support equitable healthcare policy design.
Paper Presenter
avatar for Sanjay Kumar
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm PDT

Redefining Audit Practices in the Digital Era: A Systematic Review on Blockchain Integration
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D
Authors - Septi Nurmalita, Surya Anugrah, Gentiga Muhammad Zairin, Rochma Sudiati, Eka Septariana Puspa, Windy Permata Suyono, Hera Khairunnia
Abstract - In the era of accelerated digital transformation, blockchain has emerged as a disruptive force in the auditing profession. This study presents a Systematic Literature Review (SLR) of 20 peer-reviewed articles from Scopusindexed journals (2020–2025), critically examining the opportunities and challenges of blockchain adoption in auditing. The review finds that blockchain enhances audit quality through real-time data access, immutable transaction records, and automation via smart contracts—leading to improved transparency, fraud detection, and operational efficiency. However, adoption remains limited due to regulatory uncertainty, high implementation costs, technical interoperability issues, and organizational resistance within traditional audit firms. Emerging concerns such as private key vulnerabilities, ethical ambiguities, and role conflicts in interdisciplinary audit teams further complicate adoption. Despite these barriers, recent literature highlights new opportunities, including collaborative audit ecosystems and the rise of Audit 4.0, where blockchain is integrated with AI and IoT for predictive, real-time assurance. The study calls for global regulatory harmonization, cross-platform audit standards, and empirical validation of blockchain-based audit systems. It also proposes a future research agenda centered on the convergence of blockchain and artificial intelligence to enhance risk detection, audit automation, and digital accountability. The findings offer actionable insights for regulators, auditors, and scholars navigating the evolving nexus of technology and assurance.
Paper Presenter
avatar for Septi Nurmalita
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm PDT

Smart Campus Experience: Integrating Web-Based ERP, Big Data Analytics, and AI-Powered Dashboards to Enhance Student Engagement in International Programs
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D
Authors - Sabo Hermawan, Surya Anugrah, Ryna Parlyna, Windy Permata Suyono, Eka Septariana Puspa
Abstract - This study investigates the impact of integrating web-based Enterprise Resource Planning (ERP) systems, Big Data Analytics (BDA), and AI-powered dashboards to create a Smart Campus environment that enhance student engagement in international programs. Employing a mixed-methods approach over one academic semester, the research incorporates system log analysis, surveys, inter-views, and focus groups. The findings reveal a significant improvement in aca-demic engagement (mean score = 7.8, σ = 1.1), student satisfaction (68% positive feedback), and retention rates among international students. AI dashboards enabled early interventions for at-risk students, while ERP and BDA supported administrative efficiency and personalized services. These outcomes contribute to Sustainable Development Goal (SDG) 4: Quality Education, by promoting inclusivity, efficiency, and digital transformation in higher education.
Paper Presenter
avatar for Sabo Hermawan

Sabo Hermawan

Indonesia
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm PDT

Smart Grip on a Budget: Designing a Cost-Effective EMG- Driven Prosthetic Arm
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D
Authors - Ketaki Patil, Om Nalagune, Shravani Patil, Sangita Bavkar
Abstract - This paper presents the design and construction of a low-cost, Arduino Nano-based myoelectric prosthetic arm to increase access to assistive technology to underprivileged populations. The system leverages surface electromyography (sEMG) signals recorded from non- invasive Ag/AgCl electrodes to signal voluntary muscle contraction. The signals are interpreted by a simple threshold-based control algorithm, eschewing the use of advanced machine learning in favor of reliable and responsive performance. Upon exceeding a calibrated threshold, servo motors built into a 3D-printed PLA arm are actuated to mimic simple hand movements like opening and closing. The use of open-source hardware, low-cost materials, and additive manufacturing processes like FDM 3D printing guarantees a low-cost manufacturing process, permitting simple assembly, repair, and modification. The prosthesis is energy efficient, modular, and lightweight, ideal for educational use, rural hospitals, and prototyping. Experimental testing confirms consistent performance and real-time responsiveness to muscle activity. In summary, this paper demonstrates a low-cost, accessible, and scalable approach to functional prosthetic limb design, closing the gap between advanced biomedical solutions and those communities in need of them.
Paper Presenter
avatar for Ketaki Patil
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm PDT

Sustainable Business Model for BUMDes Wukirsari: A Study on Circular Economy Approach
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D
Authors - Unggul Purwohedi, Sabo Hermawan, Ria Rahma Nida, Yeti Lastuti
Abstract - This study proposes a sustainable business model for BUMDes (Village-Owned Enterprises) Wukirsari, specifically examining a circular economy approach integrated with information systems to support Sustainable Development Goal 12 (Responsible Consumption and Production). The research ad-dresses the gap of limited circular economy adoption and digital integration in rural enterprises. Utilizing a mixed-methods approach, the study involved 18 respondents and employed tools such as Business Model Canvas, SWOT analysis, and Circular Mapping. Google Sheets, Forms, and Drive were integrated as the core information system for data tracking and management, demonstrating a practical application of information systems for business management in a rural context. Validation was achieved through triangulation of data sources and expert review. The results identify key circular units, including composting, recycled crafts, and eco-tourism, integrated into a comprehensive circular model with digital tracking mechanisms. The proposed model demonstrates improved efficiency, enhanced transparency, and local feasibility, offering a replicable frame-work for other rural enterprises. This research contributes to the novelty of tech-enabled circularity in rural contexts, providing a practical pathway for BUMDes to enhance sustainability and economic resilience through a robust business model. While the study's limitations include a small sample size and single-site focus, it lays a foundational understanding for future broader implementations of sustainable circular business models.
Paper Presenter
avatar for Sabo Hermawan

Sabo Hermawan

Indonesia
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm PDT

Words-to-Fly: A Function-Based Approach to Voice-Controlled UAVs
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D
Authors - Rama Satish K V, Hemavathi J, Shrihari Kulkarni, V.V Sathya Sai Likhith, Balasubramani A, Shashaank Raj CS
Abstract - The integration of AI-driven voice-controlled systems is transforming human-drone interaction, enabling intuitive and efficient UAV operation. This paper presents a novel approach for voice-commanded drone navigation using a fine-tuned T5 model, ensuring structured execution through predefined flight functions. The system incorporates Whisper for speech-to-text conversion and AirSim for high-fidelity simulation via its Python API. By leveraging NLP and real-time processing, the model accurately maps user commands to drone actions while maintaining execution safety. Experimental results demonstrate 97% accuracy in function mapping, highlighting the system’s reliability and responsive-ness. This work advances AI-powered UAV control, enabling applications in search-and-rescue, autonomous inspections, and hands-free drone operations.
Paper Presenter
avatar for Rama Satish K V
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm PDT

Advancements in Sentiment Analysis: A Comprehensive Survey of Techniques, Models, and Real-World Applications
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E
Authors - Unnati Parmar, Jatin Modh
Abstract - Sentiment Analysis, or opinion mining, is the computational study of opinions, sentiments, emotions, and attitudes expressed in texts. Due to the rapid expansion of social media, e-commerce platforms, and digital communication, huge amount of opinion rich information is generated daily. This tremendous surge has also substantially increased the importance of sentiment analysis for understanding public opinion, monitoring brand image, forecasting market trend and enhancing customer relations. This paper also offers a detailed survey of SA methods, from classic lexicon-based methods to recent deep learning models and transformers such as BERT and RoBERTa. We test their effectiveness on bench-mark datasets with empirical experiments and discuss their pros and cons in various real-world scenarios. Beyond these popular approaches, we present a range of domain-specialized techniques, multilingual processing, and hybrid architectures synthesizing symbolic and statistical methods. We empirically show that the transformer models outperform LR, SVM, LSTM and BERT on the IMDB dataset in achieving better understanding of context-specific nuances. We next provide a number of problematic applications of sentiment analysis including sarcastic expression detection, domain adaptation, data unbalance and sentiment ambiguity. Various application scenarios in marketing, healthcare, finance and politics are also included in the paper. Finally, we describe future research avenues focusing on explainable sentiment models, real-time analysis systems, and multimodal fusion approaches that take into account text, audio and visual features.The goal of this study is to provide researchers and practitioners with a fundamental framework for creating sentiment analysis systems that are intelligent, scalable, and reliable.
Paper Presenter
avatar for Unnati Parmar
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 9E, Virtual Room E

3:00pm PDT

AI-Driven Sustainable Marketing in the Metaverse and Consumer Purchase Intentions: A TPB Analysis
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E
Authors - Rhytheema Dulloo, Aditi Singh, Vinay Kiran
Abstract - The convergence of artificial intelligence, sustainability conscious-ness, and immersive metaverse technologies has created unprecedented opportunities for transformative marketing communications that influence consumer behavior in virtual environments. Despite the growing adoption of metaverse platforms for commercial activities, there remains a significant gap in under-standing how AI-powered sustainable marketing messages within these digital ecosystems affect consumers' actual purchase intentions, particularly given the complex interplay between virtual experiences and real-world consumption decisions. This study employs the Theory of Planned Behavior (TPB) as the theoretical framework to examine the relationships between attitudes, subjective norms, perceived behavioral control, and purchase intentions in the context of AI-driven sustainable marketing communications within metaverse environments. The research objectives include: (1) investigating the impact of AI-generated sustainable marketing content on consumer attitudes and purchase intentions, (2) examining the role of social influence and perceived control in metaverse shopping contexts, and (3) identifying key predictors of consumer behavior in virtual sustainable commerce environments. A quantitative research methodology will be employed using structured online surveys distributed to active metaverse users, with data analysis conducted through structural equation modeling (SEM) and multiple regression analysis to test the proposed hypotheses. Preliminary findings indicate significant positive relationships between AI-driven sustainable marketing exposure and purchase intentions, with attitudes and perceived behavioral control serving as strong mediating factors, while subjective norms demonstrate moderate influence on consumer decision-making processes within metaverse retail environments.
Paper Presenter
avatar for Rhytheema Dulloo
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 9E, Virtual Room E

3:00pm PDT

AI-Driven Underwriting in Life Insurance: Transforming Risk Assessment in e-Business
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E
Authors - Sonaly Maharaj, Rhulani Maluleka, Abelwe Maluleka
Abstract - Life insurance is not immune to the transformative impact of artificial intelligence (AI). AI offers unprecedented levels of precision, speed, and customization in a field historically reliant on laborious manual procedures and statistical assumptions. This paper investigates how AI-driven underwriting can be incorporated into life insurers’ digital business strategy, using Momentum’s in-app screening tool as a primary case study. The study proposes a theoretical AI underwriting architecture that maximizes risk assessment and policy issuance by leveraging machine learning algorithms, biometric information, and behavioral insights. The advantages, disadvantages, opportunities, and threats of adopting AI underwriting are analyzed through a SWOT analysis, highlighting ethical and legal considerations around algorithmic bias, regulatory compliance, and explainability. The findings suggest that while AI underwriting can provide insurers a competitive edge in a crowded market, its implementation requires robust frameworks, ethical design, and regulatory alignment.
Paper Presenter
avatar for Sonaly Maharaj

Sonaly Maharaj

South Africa
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 9E, Virtual Room E

3:00pm PDT

Decoding Career Hesitations in Online Discourse: Topic Modeling Analysis of Social Media Posts on Managerial Roles
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E
Authors - Suresh Panampilly, Murale Venugopalan
Abstract - Despite the prestige associated with leadership roles, many employees are reluctant to become managers. This poses challenges for organizations aiming to strengthen their managerial pipeline to meet long-term strategic goals. In this study, managerial reluctance is studied by analyzing candid posts on Reddit. LDA topic modelling using 15 posts revealed 6 themes. The themes uncover psychological, organizational and ethical factors. Some results align with established theories (e.g. Expectancy, equity and role theories). Others highlight unexplored areas such as ethical dilemmas and identity conflicts. This study confirms that topic modelling on social media data can give valuable insights into managerial reluctance. By using data from online discourses to draw theoretical insights, this study offers conceptual contributions and practical in-sights which can be used to develop inclusive managerial talent in organizations.
Paper Presenter
avatar for Suresh Panampilly
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 9E, Virtual Room E

3:00pm PDT

Dialogic Knowledge in AI Systems
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E
Authors - Otmane Azeroual
Abstract - This paper introduces Dialogic Artificial Intelligence (DAI)—a conceptual and computational framework for modeling how AI systems engage with human epistemic preferences in processes of knowledge retrieval and semantic representation. Grounded in Wittgenstein’s theory of language games, DAI facilitates context-sensitive interpretation of user intent through dialogical interaction. Using multiverse hypergraphs and a mechanism called Dispute Learning (DL), the framework maps diverse queries onto epistemic subgraphs that reflect over-lapping but distinct knowledge communities. Unlike existing preference-matching models, DAI emphasizes pluralism, transparency, and contestability in meaning construction. Real-world applications include academic search, semantic interoperability, and policy analysis—pointing toward a more dialogical paradigm in knowledge-driven AI systems.
Paper Presenter
avatar for Otmane Azeroual
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 9E, Virtual Room E

3:00pm PDT

Enhancing Tax Compliance among E-Commerce MSMEs: The Impact of Digital Financial Technology and Tax Knowledge with Digital Trust as a Moderating Vari-able
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E
Authors - Windy Permata Suyono, Dwi Handarini, Nuramalia Hasanah, Ratna Anggraini, Rio Firnanda, Surya Anugrah, Eka Septariana Puspa, Septi Nurmalita
Abstract - The digital transformation of Micro, Small, and Medium Enterprises (MSMEs) in the e-commerce sector has created significant opportunities to enhance tax compliance through the adoption of digital financial technology. De-spite these advancements, tax compliance remains relatively low in many developing countries. This study investigates the impact of digital financial technology adoption and tax knowledge on the tax compliance behavior of e-commerce-based MSMEs, with digital trust examined as a moderating variable. A quantitative approach was employed, utilizing survey data from 120 MSME actors operating on major e-commerce platforms such as Tokopedia, Shopee, Lazada, Blibli, and Bukalapak. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that both digital financial technology and tax knowledge significantly influence tax compliance. Moreover, digital trust positively moderates these relationships, amplifying the effects of digital financial technology and tax knowledge on compliance behavior. These results highlight the critical role of a trustworthy digital ecosystem and comprehensive tax literacy programs in promoting voluntary tax compliance among MSMEs. The study provides meaningful insights for policymakers, tax authorities, and FinTech developers, contributing to the growing body of literature on digitalization and tax governance in emerging economies.
Paper Presenter
avatar for Windy Permata Suyono
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 9E, Virtual Room E

3:00pm PDT

Hybrid Kernel SVM and Boosting Approaches for Accurate Autism Spectrum Disorder Screening
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E
Authors - Usman Ali, Shaiza Shahid, Misbah, Shahzad Ali, Sheraz Aslam, Kainat Mustafa
Abstract - Autism Spectrum Disorder (ASD) is a neurocognitive condition that affects social connection, expressive abilities, and behavioral patterns. This study aims to diagnose ASD using machine learning (ML) techniques based on behavioral and demographic data. The dataset sourced from Kaggle includes features such as age, gender, ethnicity, social interaction behaviors, and responses to standardized screening questionnaires. To improve accuracy, various ML models are implemented, including NB, AdaBoost, SVM, LightGBM, KNN, and XGBoost. Additionally, advanced Ensemble Methods are applied, such as Hybrid Kernel SVM, a boosting classifier, and a stacking classifier. Among all methods, the stacking classifier delivered the best performance, achieving 99.74% accuracy with the minimum MSE of 0.2. This result supports the development of an intelligent system to aid in early autism diagnosis and intervention planning. Future research could enhance this approach by integrating neuroimaging biomarkers, generative AI, or more advanced ML frameworks, aiming for even greater precision and robustness in a real-world clinical setting.
Paper Presenter
avatar for Shahzad Ali

Shahzad Ali

Pakistan
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 9E, Virtual Room E

3:00pm PDT

ICT as a Teaching Tool for Understanding the Complexity of Strategic Management
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E
Authors - Martina Beranek
Abstract - Industry and services are undergoing fundamental changes in connection with new technologies, the advent of AI, global competition and at the same time the contradictory process of partial deglobalization, changing markets, material shortages, local regulations and changing trade barriers, especially in the form of tariffs. The already difficult situation is complicated by major military conflicts in the Middle East, Ukraine and some regions of Africa. These aspects place high demands on the work of managers at all levels of management. It is therefore essential for university students to prepare for the above facts and to understand the complexity of strategic management as best as possible. ICT in the form of business simulations is a very suitable tool to teach students system thinking and explain the principles and principles of a strategic approach in business. Empirical research shows that students prefer project-based learning using ICT tools to classic frontal teaching and that this form of teaching based on the use of not only ICT but also AI promotes adaptability of learning, contributes to digital literacy and leads to a much higher level of readiness for future professions. Business simulation not only allows the development of the entrepreneurial spirit but also leads to the improvement of soft skills. Finally, it has been proven that thanks to the feedback provided by the simulation, students see the impact of their decisions more or less immediately and can thus understand the complexity and complexity of the decision-making process at the strategic level of business management. The document recommends expanding the use of strategic simulations in higher education to meet the demands of a rapidly evolving and changing business environment.
Paper Presenter
avatar for Martina Beranek

Martina Beranek

Czech Republic
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 9E, Virtual Room E

3:00pm PDT

Orchestrating Organizational Sustainability: The Impact of Green Recruitment, Training, Performance Management, Involvement & Reward Systems through Green Leadership
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E
Authors - Ch Sahyaja, Ch Shankar, Madhuri. K, Jaswanthi Guda
Abstract - The paper evaluates how the integrated Green Human Resource Management (GHRM) practices, such as the green recruitment and selection of employees, green sustainability training, green performance management, employee engagement and green compensation and rewards, play a role in green leadership leading to sustainability capacity of organizations. The research was based on Ability Motivation Opportunity (AMO) and resource-based view (RBV) conceptualizations, and the analysis was done using partial least squares SEM on 205 HR and the line managers of the manufacturing and service-based organization, upon using the questionnaires issued to the mentioned managers in ADANCO 2.2.1. Findings reveal that green recruitment and selection (E = 0.303), employee involvement (E = 0.350) and green compensation (E = 0.250) do play a significant role in influencing the green leadership with a combinational effect of 60.3 per cent (R2 = .603). Green leadership plays an important role in exerting a favourable impact on sustainability (BR2 = .543) and helps to mediate among all three GHRM practices (Sustainability-mediated effects). Green leadership mediates between the GHRM behaviours of sustainability (.189), recruiting (.218) and promoting (.155) in direct effect (p < .005). Although organizational green culture positively affects sustainability (156), it does not have such an effect on the connection between leadership and sustainability. This brings out the point that green leadership holds the key to sustainability.
Paper Presenter
avatar for Jaswanthi Guda
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 9E, Virtual Room E

3:00pm PDT

Social Media in Promoting Sustainable Marketing: A Systematic Literature Review
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E
Authors - Md Faiz Ahmad, Md Asadul Haque, Ananya Chowdary Muppana, Aasritha Malineedi, Dharani Gudivaka, Sri Krishna Surya Putta
Abstract - The present study offers a systematic literature review (SLR) providing insights on the role of social media in promoting sustainable marketing. The SLR is carried out to gain an understanding of the phenomenon. Scopus database was used to identify, extract, select and review related articles using search keywords. A total of 27 articles were deemed fit for this study. This SLR recognizes current research avenues related to social media in sustainable marketing using TCCM framework. The study used co-citation and keyword occurrence analyses to explore the nuances of the field under consideration. The study yields valuable insights for academia, government, green marketers to practically implement strategies and suggesting future opportunities for research.
Paper Presenter
avatar for Ananya Chowdary Muppana
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 9E, Virtual Room E

3:00pm PDT

Efficient Car Logo Detection via YOLOv8 and Attention Mechanism Fusion
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Nhat Minh Nguyen, Tu Anh Nguyen, Huy Thai Hinh, Quang Nhat Nguyen, Thong Minh Phuc Nguyen, Vinh Dinh Nguyen
Abstract - Car logo detection is crucial for intelligent transportation systems. This paper compares attention mechanisms integrated into YOLOv8m for car logo detection, evaluating Efficient Channel Attention (ECA), SimAM, and their combination against a baseline YOLOv8m (95.2% [email protected], 48.6% [email protected]:0.95, 79.3 GFLOPs). On a dataset of 5372 images, ECA achieves 94.5% [email protected] and 50.2% [email protected]:0.95 with 79.1 GFLOPs, SimAM reaches 96.1% [email protected] and 50.0% [email protected]:0.95 with 79.1 GFLOPs, and the combined approach attains 95.3% [email protected] and 50.1% [email protected]:0.95 with 79.3 GFLOPs. All variants maintain real-time performance with inference speeds of 29.29 FPS (ECA), 28.69 FPS (SimAM), and 27.95 FPS (combined), compared to the baseline’s 28.32 FPS on an NVIDIA RTX 3050 laptop GPU. These results demonstrate that integrating lightweight attention mechanisms can substantially enhance detection performance in data-constrained scenarios while preserving real-time efficiency.
Paper Presenter
avatar for Nhat Minh Nguyen
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 9F, Virtual Room F

3:00pm PDT

Empowering Auditor Performance Through Digital Access and IT Infrastructure: The Mediating Role of Auditor Competence
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Windy Permata Suyono, Dwi Handarini, Eka Septariana Puspa, Surya Anugrah, Wida Aristanti, Rio Firnanda
Abstract - This study investigates the influence of digital access and IT infra-structure on auditor performance, with auditor competence as a mediating variable. Grounded in the Technology–Organization–Environment (TOE) and Task–Technology Fit (TTF) frameworks, data were collected from 110 auditors in Jakarta and West Java using a structured online survey. Structural Equation Modelling with Partial Least Squares (SEM-PLS) was employed to analyse the relationships. The results reveal that while digital access does not have a direct effect on auditor performance, it significantly influences auditor competence, which in turn enhances performance. IT infrastructure shows both direct and indirect effects through auditor competence. These findings underscore the critical role of auditor competence in leveraging technological resources to achieve optimal performance. The study provides practical insights for audit institutions aiming to enhance audit effectiveness through digital capability development and infra-structure investment. It also contributes to the growing literature on digital trans-formation in the auditing profession, especially within the context of emerging economies.
Paper Presenter
avatar for Windy Permata Suyono
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 9F, Virtual Room F

3:00pm PDT

Fine-Tuning BART for Multi-Label ICD-9 Prediction from Clinical Discharge Summaries
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Ch Geethika Gayatri, P Lavanya, S Manvitha Reddy, Nikitha K
Abstract - Accurate and automated assignment of ICD-9 codes from clinical narratives is essential for healthcare analytics, and clinical decision support. However, the task remains complex due to the high dimensionality of the label space, variability in clinical language, and the multi-label nature of diagnostic documentation. This paper presents a novel deep learning framework for multi-label ICD-9 code prediction from discharge summaries using a fine-tuned Bidirectional and Auto-Regressive Transformers(BART). Targeting real-world clinical documentation scenarios, the proposed approach models unstructured medical narratives using a sequence-to-sequence architecture that captures both global context and fine-grained semantic cues. Each discharge summary is tokenized and encoded using BART’s bidirectional encoder and autoregressive decoder, enabling robust multi-label classification across the top 20 most frequent ICD-9 codes. Evaluation on the MIMIC-III dataset demonstrates strong performance, achieving a F1 score of 71%, while also showing balanced precision-recall behavior across high and low-frequency codes. ROC-AUC confirms the model’s discriminative capability across imbalanced labels. These results validate the model’s capability to handle complex, multi-label classification scenarios common in clinical documentation.
Paper Presenter
avatar for S Manvitha Reddy
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 9F, Virtual Room F

3:00pm PDT

Formative Assessment of Spoken English Using Large Language Models in a Controlled Intervention Study
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Mahimaben Panjabi, Vijay Makwana
Abstract - This study investigated the efficacy of Large Language Models (LLMs) for providing formative assessment of spoken English within a mixed-methods, one-group pre-test/post-test design. The research involved 30 intermediate English as a Second Language (ESL) learners and five expert faculty members. The design integrated quantitative pre-test/post-test data with qualitative analysis of AI-generated feedback, student reflections, and faculty interviews. During the intervention, all students used LLM tools (ChatGPT and Google Gemini) for practice and feedback. To ensure a robust, triangulated assessment, a coder-based framework was implemented where human experts evaluated spoken tasks against linguistic criteria. A paired-samples t-test revealed a statistically significant improvement in speaking proficiency from pre-test to post-test. Thematic analysis of qualitative data indicated that LLM-generated feedback was perceived as useful, accessible, and effective in reducing learner anxiety. This multi-faceted approach affirms that LLMs can be effective supplementary tools in language education and offers a holistic model for their integration into formative assessment practices.
Paper Presenter
avatar for Mahimaben Panjabi
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 9F, Virtual Room F

3:00pm PDT

Nonlinear Classification on MiniRocket Architecture for Fish-Freshness Prognostics
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Raghavan Vaidhyaraman, Aryavardhan Modi, Jayakumar Kaliappan
Abstract - This paper aims to evaluate the performance of multiple non-linear classification techniques. Time-series classification has become central to many sensor-driven applications, where capturing temporal patterns efficiently and accurately is critical. In this paper, we evaluate and extend the proposed MiniRocket framework for classifying multivariate sensor data, focusing on a practical “Fish Freshness” monitoring use case. Precise quantification of post-harvest fish freshness underpins foodsafety compliance and public-health protection while curbing spoilageinduced economic losses across the cold-chain. In operational practice, high-fidelity freshness classification enables dynamic inventory routing, just-in-time processing, and trustworthy quality labelling, thereby reducing food waste and reinforcing consumer confidence throughout seafood supply networks. MiniRocket is a highly efficient convolution-based feature extractor that transforms each raw time series into a fixed-length vector of pattern-frequency features (Proportion of Positive Values). We demonstrate that, when paired with a simple linear classifier such as Ridge Classifier, MiniRocket achieves state-of-the-art accuracy while requiring orders of magnitude less computation than deep-learning alternatives. We have demonstrated that combining MiniRocket with Random Forest Classifier yields greater than or equal to 90% classification accuracy, particularly improving performance on ambiguous classes like ”Semi-Fresh.” The model’s simplicity, scalability, and efficiency make it suitable for real-time applications. Our findings validate MiniRocket as a practical solution for robust, low-latency time-series classification in resource-constrained environments.
Paper Presenter
avatar for Raghavan Vaidhyaraman
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 9F, Virtual Room F

3:00pm PDT

Optimizing Feature Selection for Medical Diagnosis Systems Using Differential Evolution
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - GGS Pradeep, Thrilok. Kolla, N Vijayalakshmi, Rajesh Sharma R
Abstract - Machine learning has aided the improvement of medical diagnostic systems that help to detect diseases accurately in the present era. The work gave a mathematical and algorithmic handbook for characteristic selection with a binary Genetic Algorithm (GA) of particular use to jobs involving medical diagnosis. Clinical datasets usually have many dimensions and redundancy, which most time affects model performance and increases computational complexity. In this study is presented a mathematical and algorithmic guide for selecting features through a binary Genetic Algorithm (GA), especially suitable for medical diagnostic tasks. The proposed method seeks to obtain the most informative subset of features by optimizing a fitness function that balances between classification accuracy and dimensionality reduction. The work develops a thorough mathematical model that incorporates data preprocessing, binary encoding of feature subsets, and repetitive evolutionary optimization. The metric used in testing the classification model is standard performance metrics-accuracy, sensitivity, specificity, and F1 score, summarized using a confusion matrix. Understanding feature selection stability across generations is also examined and visualized in a multidimensional performance space. The results would indicate a direct convergence of the model into high-performing feature configurations while avoiding risk for overfitting. An interpretative paradigm, speed-up in computations, as well as high reliability in diagnosis, therefore pushing this method as a tool of great value in clinical decision support systems.
Paper Presenter
avatar for GGS Pradeep
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 9F, Virtual Room F

3:00pm PDT

Optimizing Multi-Agent and Functional Architectures for Enhanced Financial Trading Performance: VinTradeAgent Case Study
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - An Dinh Van, Anh Nguyen Thi Linh, Phuong Pham Nguyen Hien, Hung Nguyen Gia, Trinh Tran Thi Kieu, Hung Nguyen Quang
Abstract - This study proposes and evaluates a hybrid framework that integrates multi-agent systems and functional architecture for automated financial trading in Vietnam. The framework assigns specialized roles - fundamental, technical, news, market, trader and risk agents - operating over multimodal stock market data (OHLCV, technical indicators, firm fundamentals, and textual news). Agents interact via structured horizontal debates and vertical risk gating to produce auditable trading proposals. Experimental results demonstrate improved risk-adjusted returns and enhanced explainability, while modular design supports deployment for back-office automation (e.g., report generation and opportunity identification). The study contributes a methodology for combining agentic large language model (LLMs) with disciplined functional pipelines and provides practical guidance for deploying such systems in emerging markets characterized by high volatility and limited liquidity. Implications for FinTech adoption and regulatory alignment in Vietnam are discussed. Vin-Trade-Agent is available at https://github.com/thanhENC/Vin-Trade-Agent.
Paper Presenter
avatar for An Dinh Van
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 9F, Virtual Room F

3:00pm PDT

Predictive Modeling of Customer Churn and Personalized Subscription Recommendations for OTT Platforms
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Kavitha Dhanushkodi, Uma Sankary A
Abstract - With the explosive growth of Over-the-Top (OTT) plat- forms, reducing subscriber churn has emerged as a central problem for service providers. This paper introduces a single framework for churn pre- diction and customized subscription plan recommendation, capitalizing on the advantages of Graph Neural Networks (GNN) and Reinforcement Learning (RL). Churn prediction was carried out on four models: Random Forest, XGBoost, Long Short-Term Memory (LSTM), and GNN, with the GNN model showing better ability to detect potential churners by being able to model rich relationships in user data. For personalized recommendations, both Cosine Similarity-based and Deep Q-Learning (DQN)-based approaches were utilized, with DQN providing dynamic, user-specific plan recommendations. The model was trained and tested on a balanced dataset with 147,269 training samples and 25,000 test samples, and performance was gauged using accuracy, recall, F1-score, and AUC-ROC. Analysis shows that the use of GNN for churn prediction and DQN for recommendation enhances the efficacy of retention mechanisms and increases user satisfaction. The technique empowers OTT providers with an effective means of proactive user interaction and churn prevention.
Paper Presenter
avatar for Uma Sankary A
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 9F, Virtual Room F

3:00pm PDT

Strategic Media Insights on Video-on-Demand (VoD) Platforms through Graph Analytics
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Ghanem Ghanem Binhamooda AlDhaheri, Khalifa Mohamed Abdulla Alansi, Hazza Meqbel Ali Alameri, Gurdal Ertek, Ananth Chiravuri
Abstract - In today’s digital entertainment landscape, video-on-demand (VoD) streaming platforms are essential for accessing a broad range of content. This study examines the content of extensive media catalogs available on some of the major streaming platforms, namely Amazon, Apple, Disney, HBO, Netflix, and Paramount. We used a structured dataset and custom-developed a graph analytics methodology that provided a thorough analysis of the data. The sample collection of algorithmically generated graph visualizations help draw basic and in-depth insights, targeted to support informed decision-making for content creators, platform managers, and viewers alike.
Paper Presenter
avatar for Ananth Chiravuri

Ananth Chiravuri

United Arab Emirates
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 9F, Virtual Room F

3:00pm PDT

The 'Grammarly Effect': A Mixed-Methods Study on the Impact of Instantaneous AI Feedback on Student Writing Anxiety and Revision Processes
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Kinjal Bhatia
Abstract - The integration of AI-powered writing assistants like Grammarly into academic life has fundamentally altered the writing landscape. While these tools provide immediate feedback, their influence on the psychological and procedural aspects of student writing is not well understood. This paper introduces the "Grammarly Effect," examining it through a sequential explanatory mixed-methods study. The research investigated how real-time AI suggestions affect the writing anxiety and revision habits of undergraduate students. In the quantitative phase (N=124), a quasi-experimental de-sign using pre- and post-task surveys found a significant decrease in anxiety associated with surface-level correctness (e.g., grammar, spelling). However, the qualitative phase, involving think-aloud protocols and interviews with a subset of students (n=15), revealed a more nuanced situation. While anxiety over superficial errors di-minished, a reliance on the tool fostered a different anxiety related to authorial originality and self-trust. Analysis of revision behaviors showed a consistent pattern of uncritical acceptance of AI suggestions, prioritizing surface-level fixes over deep, rhetorical engagement. The study concludes that the "Grammarly Effect" is a paradoxical phenomenon, highlighting a critical need for pedagogical approaches that foster AI literacy, empowering students to use these tools as aids rather than oracles.
Paper Presenter
avatar for Kinjal Bhatia
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 9F, Virtual Room F

3:00pm PDT

A Survey on Image Forgery Detection Techniques Using Error Level Analysis and Convolutional Neural Networks
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G
Authors - D. S. Vangao, Pallavi Khatri
Abstract - Image forgeries have become much more common due to the widespread use of sophisticated digital image manipulation tools, which makes it difficult to independently confirm authenticity in domains like social media, digital forensics, and journalism. This survey study paper offers a thorough analysis of the most recent methods for identifying image modifications including copy-move and splicing forgeries, with an emphasis on combining Convolutional Neural Networks (CNNs) with Error Level Analysis (ELA). We evaluate important approaches from recent literature based on their architectures, datasets, and performance metrics. We do so via methodically analyzing both contemporary deep learning techniques and traditional methods that rely on handcrafted features. Our investigation shows that hybrid ELA-CNN techniques routinely beat alternative techniques, attaining accuracy rates exceeding 90% on common datasets such as CASIA. More reliable, real-time solutions are required, nevertheless, as problems like identifying subtle tampering and increasing computational effectiveness continue to arise. We wrap up by discussing the present state of the field's limits and potential avenues for future research in digital image forensics.
Paper Presenter
avatar for D. S. Vangao
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 9G, Virtual Room G

3:00pm PDT

Analysing Current Cyber Threats using the Advanced Statistical Techniques
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G
Authors - Chethana R.M., S.P. Manikandan
Abstract - Organisations around the world face never-before-seen difficulties as a result of the quickly changing cybersecurity landscape, which calls for advanced analytical techniques to recognise and anticipate threat trends. In order to analyse current cyberthreats and their organisational impact, this study uses sophisticated statistical techniques such as ANOVA, regression analysis, machine learning classification, and clustering algorithms. We examine threat distribution patterns, financial impact correlations, and predictive modeling capabilities for ten key threat categories malware, phishing, social engineering, ransomware, insider threats, supply chain attacks, DoS/DDoS attacks, zero-day exploits, credential theft, and AI-powered attacks by thoroughly analysing 360 cyber security incidents that occurred across several Indian cities between 2019 and 2024. The most significant predictor of financial impact, according to our statistical analysis, is incident type (F=4.58, p
Paper Presenter
avatar for Chethana R.M.
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 9G, Virtual Room G

3:00pm PDT

Enhancing Query Understanding Using Deep Contextual Embeddings in Information Retrieval Systems
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G
Authors - GGS Pradeep, Thrilok. Kolla, Rajesh Sharma R, Akey Sungheetha, U Ananthanagu, Pellakuri Vidyullatha
Abstract - This paper outlines a holistic framework for improving query comprehension in information retrieval (IR) systems through deep contextual embeddings. Using Sentence-BERT, we transform the queries and documents into dense semantic representations and then calculate cosine similarity for the relevance measure. We especially emphasize interpretability by adding various statistical and visualization techniques, like heatmaps, KDE plots, UMAP projection, dendrograms, and box plots, to identify and analyze latent semantic patterns and keep close tabs on the inner workings of how different documents relate to such varied query formulations. We also follow an avenue of the interquartile dispersion and distributional behavior of similarity scores to understand the embedding consistency and discriminative power. This methodology, therefore, encompasses both retrieval accuracy and explainability as it provides visible insights into improving query reformulation, semantic search, and content recommendation. Experimental visualizations have further demonstrated their effectiveness towards deep semantic alignment, especially during the processing of complex multi-topic corpora. The abstract is technically solid and easily comprehensible because it is rationally structured and clearly states the research’s methodology, instruments, and objectives.
Paper Presenter
avatar for Thrilok. Kolla
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 9G, Virtual Room G

3:00pm PDT

Evolutionary Multi-Objective Optimization of Neuro-Fuzzy Systems in Non-Stationary Time Series Forecasting
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G
Authors - GGS Pradeep, Thrilok. Kolla, U Ananthanagu, Akey Sungheetha, Rajesh Sharma R
Abstract - Predicting non-stationary time series is still a tough nut to crack: the statistical properties may vary, and so will the temporal dynamics. In this context, a robust and interpretable approach involving the integration of a Takagi–Sugeno neuro-fuzzy inference system and evolutionary multi-objective optimization is proposed. The NSGA-II optimization procedure minimizes prediction accuracy alongside model complexity, evolving the fuzzy rule base and membership parameters and the linear consequents. Contrary to the conventional opaque models, a neuro-fuzzy architecture affords some degree of transparency, letting one see how the functional temporal patterns have been learned. Results of the experiments conducted demonstrate the ability of the system to adapt to structural changes in the data, with a corresponding better capability of reducing residual errors and increasing model compactness using a synthetic non-stationary dataset. Further, residual oscillations and fuzzy rule surfaces, alongside the convergence behavior and the Pareto optimality, present powerful evidence in support of the proposed model’s effectiveness and interpretability. The proposed framework represents a potent alternative for real-time adaptive forecasting in dynamic environments where precision and explainability are both crucial. Simulated non-stationary time series will be good enough to justify the approach of lag-embedding. Thoughtful integration of interpretable fuzzy logic and adaptive learning via evolutionary optimization would strengthen the contribution.
Paper Presenter
avatar for Thrilok. Kolla
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 9G, Virtual Room G

3:00pm PDT

Exploring the potential of 5G: A new era in wireless communication
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G
Authors - Ritu Raut, Sudhir Agarmore
Abstract - The kick to stride the new era in wireless communication begins once 5G technologies holding the promises of speeds previously unheard of and extremely low latency with the capacity to connect a large number of devices at once [4]. This article reviews the potential of 5G in transforming such industries as healthcare, driverless cars, smart cities, and the Internet of Things. It discusses enabling technologies such as millimeter waves, massive MIMO (Multiple Input Multiple Output), and network slicing [1], among others, and also its infrastructure, security, and the possibility of availability of the frequency spectrum. For this purpose, this research aimed to provide an inclusive overview of how this new generation of wireless technology may impinge upon the future of worldwide communication and connectivity by assessing both the benefits and challenges that emanate from 5G.
Paper Presenter
avatar for Ritu Raut
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 9G, Virtual Room G

3:00pm PDT

Exposing Digital Deception: Evaluating the Power of Image Forensics Tools
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G
Authors - D. S. Vangao, Pallavi Khatri, Diya Khatri
Abstract - In the digital age, the proliferation of image manipulation has raised significant concerns regarding the authenticity and integrity of visual content. This study, explores the critical role of image forensic tools in detecting digital forgeries and ensuring the credibility of photographic evidence. A comprehensive evaluation of a range of widely used image forensic tools—including but not limited to FotoForensics, JPEG snoop, Izitru, Forensically, and Amped Authenticate—assessing their performance in identifying various types of tampering, such as splicing, cloning, resampling, and compression artifacts is done in this work. Each tool is evaluated on the basis of criteria such as detection accuracy, supported forgery type, user interface, automation capabilities, and analysis depth. The comparative analysis reveals strengths and limitations unique to each tool, offering insights into their suitability for different investigative scenarios. Our findings highlight that no single tool excels universally; rather, a combination of tools often yields more reliable results. This study underscores the necessity for cybersecurity professionals, digital investigators, and media analysts to adopt a multitool strategy for robust image authentication.
Paper Presenter
avatar for D. S. Vangao
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 9G, Virtual Room G

3:00pm PDT

Fuzzy-Neural Hybrid Models for Early Detection of Neurodegenerative Disorders Using Multimodal Medical Data and Temporal Pattern Analysis
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G
Authors - GGS Pradeep, Thrilok. Kolla, Rajesh Sharma R, Akey Sungheetha, N Vijayalakshmi, Pellakuri Vidyullatha
Abstract - The early identification of neurodegenerative diseases, like Alzheimer’s and Parkinson’s disease, is crucial for their management, although difficult due to subtle, heterogeneous, and evolutionarily conditioned clinical patterns. In this paper, we present a fuzzy-neural hybrid that incorporates the beneficial properties of fuzzy inference systems and temporal deep learning toward improved early diagnosis. Our model analyzes longitudinal multimodal medical data (neuroimaging and clinical scores), combining fuzzy rule-based feature encoding with Long Short- Term Memory (LSTM) networks for temporal pattern extraction. Interpretability is provided by assigning Gaussian membership functions, while the LSTM component encodes the dynamics of disease progression. The proposed system is validated on synthetic and benchmark datasets, showing robust classification performance and excellent temporal tracking of patient trajectories. Interpretations of internal representations are provided through visualizations such as confusion matrices, t-SNE embeddings, and 3D PCA trajectories. The results indicate that the hybrid approach provides both powerful predictions and transparent operation, making it an excellent option for clinical decision support in neurodegenerative diagnostics. The authors propose a hybrid diagnostic system that uses Gaussian fuzzy membership functions for interpretable feature encoding and LSTM networks for temporal sequence modeling of multimodal medical data. The authors validated their method with synthetic data as well as benchmark datasets, achieving perfect binary classification in their experimental results.
Paper Presenter
avatar for Thrilok. Kolla
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 9G, Virtual Room G

3:00pm PDT

Green Digital School Management as Technology-Driven Strategies for Achieving Sustainable and Eco-Friendly School Governance
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G
Authors - Agus Timan, Maisyaroh, Maulana Amirul Adha, Indra Lesmana, Anabelie Villa Valdez, Arum Sri Banowati
Abstract - The urgency of climate change, environmental degradation, and the need for sustainable development has placed education at the forefront of global transformation. As schools evolve into eco-conscious institutions, there is growing interest in integrating digital technologies to enhance sustainability-driven governance. This has led to the emergence of Green Digital School Management (GDSM), a model that synergizes environmental responsibility with technology-based school leadership. This study aims to systematically explore the core com-ponents, implementation challenges, and effective strategies of GDSM by con-ducting a Systematic Literature Review (SLR) based on PRISMA guidelines. An initial pool of 892 articles was screened, 87 high-quality studies were selected for thematic analysis. The findings indicate that GDSM encompasses digital environmental policies, IoT-based monitoring, and participatory platforms that engage school communities in green practices. Despite significant barriers, including infrastructure gaps, limited digital-environmental literacy, and policy frag-mentation, several innovative strategies have emerged, such as digital teacher training, cross-sector partnerships, and gamified environmental education tools. This review contributes to the conceptual development of GDSM and provides actionable recommendations for policymakers, educators, and technology providers seeking to build smart, green, and sustainable school ecosystems.
Paper Presenter
avatar for Arum Sri Banowati
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 9G, Virtual Room G

3:00pm PDT

New Method to Construct a Database and Design a Business Process from Intuitional Document Using Generative AI
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G
Authors - Tsunenori Inakura, Shotaro Imai, Kunihiko Takamatsu, Sayaka Matsumoto, Masao Mori
Abstract - Institutional Research (IR) in universities needs data from many different systems, but it is often hard to know what data is stored and where. To solve this, it is important to design both databases and business processes together. In this study, categorical databases, ontology logs (ologs), and event driven process chains (EPCs) are used to connect institutional documents with data and process design. Ologs describe concepts and their functional relations in a formal but also readable way, while EPCs show how events and functions go step by step in the real processes. Generative AI was used to support both tasks. The AI helps to read documents, to extract concepts and functions, and to check consistency. By comparing ologs and EPCs, both sides can be improved and give a unified view of data and processes. This makes it possible to design information systems for IR with more consistency and less effort.
Paper Presenter
avatar for Tsunenori Inakura
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 9G, Virtual Room G

3:00pm PDT

Tomato Ripeness Evaluation through Cutting-Edge Deep Learning Models
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G
Authors - Vijay Siva, Vijayakumar Ponnusamy
Abstract - According to the United Nations, global population will rise from 7 billion currently to 9 billion in 2050. The world will need far more food, and agriculture will face remarkable pressure to meet demand. Emerging application of deep learning in agriculture includes ripeness detection of tomato which harvest tomato in appropriate time and prevents rotten of tomato. This saves the framer from loss. Tomato was classified as ripe, half-ripe, and unripe, based on maturity stage of fruit. Various deep learning methodologies were analyzed for maturity detection of tomato. Multimodal approach for maturity detection is suggested for enhanced accuracy in ripeness detection.
Paper Presenter
avatar for Vijay Siva
Monday October 19, 2026 3:00pm - 5:00pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 9G, Virtual Room G

5:00pm PDT

Session Chair Concluding Remarks
Monday October 19, 2026 5:00pm - 5:02pm PDT
Virtual Room A
Monday October 19, 2026 5:00pm - 5:02pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 9A, Virtual Room A

5:00pm PDT

Session Chair Concluding Remarks
Monday October 19, 2026 5:00pm - 5:02pm PDT
Virtual Room B
Monday October 19, 2026 5:00pm - 5:02pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 9B, Virtual Room B

5:00pm PDT

Session Chair Concluding Remarks
Monday October 19, 2026 5:00pm - 5:02pm PDT
Virtual Room C
Monday October 19, 2026 5:00pm - 5:02pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 9C, Virtual Room C

5:00pm PDT

Session Chair Concluding Remarks
Monday October 19, 2026 5:00pm - 5:02pm PDT
Virtual Room D
Monday October 19, 2026 5:00pm - 5:02pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

5:00pm PDT

Session Chair Concluding Remarks
Monday October 19, 2026 5:00pm - 5:02pm PDT
Virtual Room E
Monday October 19, 2026 5:00pm - 5:02pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 9E, Virtual Room E

5:00pm PDT

Session Chair Concluding Remarks
Monday October 19, 2026 5:00pm - 5:02pm PDT
Virtual Room F
Monday October 19, 2026 5:00pm - 5:02pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 9F, Virtual Room F

5:00pm PDT

Session Chair Concluding Remarks
Monday October 19, 2026 5:00pm - 5:02pm PDT
Virtual Room G
Monday October 19, 2026 5:00pm - 5:02pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 9G, Virtual Room G

5:02pm PDT

Session Closing and Information To Authors
Monday October 19, 2026 5:02pm - 5:05pm PDT
Virtual Room A
Exhibitors
avatar for Ashutosh Goyal
Monday October 19, 2026 5:02pm - 5:05pm PDT
Virtual Room A Bangkok, Thailand
  Virtual Room 9A, Virtual Room A

5:02pm PDT

Session Closing and Information To Authors
Monday October 19, 2026 5:02pm - 5:05pm PDT
Virtual Room B
Exhibitors
RJ
Monday October 19, 2026 5:02pm - 5:05pm PDT
Virtual Room B Bangkok, Thailand
  Virtual Room 9B, Virtual Room B

5:02pm PDT

Session Closing and Information To Authors
Monday October 19, 2026 5:02pm - 5:05pm PDT
Virtual Room C
Exhibitors
avatar for Anshika Jain
Monday October 19, 2026 5:02pm - 5:05pm PDT
Virtual Room C Bangkok, Thailand
  Virtual Room 9C, Virtual Room C

5:02pm PDT

Session Closing and Information To Authors
Monday October 19, 2026 5:02pm - 5:05pm PDT
Virtual Room D
Exhibitors
avatar for Richa Sharma
Monday October 19, 2026 5:02pm - 5:05pm PDT
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

5:02pm PDT

Session Closing and Information To Authors
Monday October 19, 2026 5:02pm - 5:05pm PDT
Virtual Room E
Exhibitors
avatar for Siddhesh Mogal
Monday October 19, 2026 5:02pm - 5:05pm PDT
Virtual Room E Bangkok, Thailand
  Virtual Room 9E, Virtual Room E

5:02pm PDT

Session Closing and Information To Authors
Monday October 19, 2026 5:02pm - 5:05pm PDT
Virtual Room F
Exhibitors
avatar for ANISHA MISHRA
Monday October 19, 2026 5:02pm - 5:05pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 9F, Virtual Room F

5:02pm PDT

Session Closing and Information To Authors
Monday October 19, 2026 5:02pm - 5:05pm PDT
Virtual Room G
Exhibitors
avatar for DHARMIK MODI
Monday October 19, 2026 5:02pm - 5:05pm PDT
Virtual Room G Bangkok, Thailand
  Virtual Room 9G, Virtual Room G
 

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Saturday, October 17
Sunday, October 18
Monday, October 19
Benchasiri 1
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Benchasiri 3
Benchasiri 4
Benchasri 1
Virtual Room A
Virtual Room B
Virtual Room C
Virtual Room D
Virtual Room E
Virtual Room F
Virtual Room G
  • Inaugural Session
  • Physical Technical Session 1A
  • Physical Technical Session 1B
  • Physical Technical Session 1C
  • Physical Technical Session 1D
  • Physical Technical Session 2A
  • Physical Technical Session 2B
  • Physical Technical Session 2C
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  • Virtual Room 5A
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