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5th World Conference on Information Systems for Business...
Venue: Virtual Room F clear filter
Sunday, October 18
 

9:28am PDT

Opening Remarks
Sunday October 18, 2026 9:28am - 9:30am PDT
Virtual Room F
Sunday October 18, 2026 9:28am - 9:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 4F, Virtual Room F

9:30am PDT

Assessing Strategic Alignment for Green Finance Adoption in Manufacturing MSMEs for Green Growth
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Sivakami B U, M. Suresh
Abstract - In a world racing toward net-zero emissions, how can MSMEs strategically align their business operations to adopt green finance? Despite MSMEs’ growing recognition of sustainable development, they continue to face persistent challenges in aligning their strategies with green finance adoption due to financial, regulatory, and knowledge-related barriers. The extant literature provides limited insights into the strategic alignment for adopting green finance. To ad-dress this gap, the study develops a Fuzzy Logic-based assessment framework to evaluate alignment levels and identify weaker attributes. The framework specifies five criteria and nineteen attributes. Findings reveal a state of “moderately aligned” with four among nineteen attributes requiring immediate attention. The framework offers MSMEs a structured pathway to assess their alignment and strategically position themselves for green finance adoption and sustainable growth.
Paper Presenter
avatar for Sivakami B U
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 4F, Virtual Room F

9:30am PDT

ECGGoDevice: A Low-Cost Multifunctional Wearable for Integrated Real-Time Cardiac and Mobility Monitoring
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Premchand, Jayant Kalra, Ajoy Kumar, Sahil Sankhyan, Varun Dutt
Abstract - Cardiovascular diseases (CVDs) continue to be the globe's leading cause of death, and therefore, there is a need for low-cost, long-term cardiac monitoring technologies especially in low-resource environments. Current wearable ECG technologies are limited by cost, proprietary design, and single functionality. We introduce ECG Go Device, a low-cost, portable, multifunctional, wearable device with real-time ECG monitoring, heart rate variability (HRV) analysis, fall detection, and step counting in one platform. The system utilizes a high-fidelity analog front-end and inertial sensing to provide mobility aware cardiac monitoring. A 44-subject validation trial compared ECG Go Device with a clinical-grade Shimmer3 ECG system and the Fitbit Sense 2 on 12 standard time-domain HRV measures and 10-day step count. The ECG Go Device demonstrated good agreement (all p > .05; r > .93) with Shimmer3, and high correlation in step count measure (r = .94). With clinically robust signal quality, real-time wireless transmission, and safety alert capability, ECG Go Device provides a scalable solution for longitudinal cardiac and mobility monitoring especially for eldercare and underserved communities.
Paper Presenter
avatar for Premchand
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 4F, Virtual Room F

9:30am PDT

ERP–SCM Integration in the Digital Era: A Bibliometric–Systematic Review and Framework for Technical, Organisational, and Strategic Alignment
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Sarath M, M Suresh
Abstract - Enterprise Resource Planning systems and Supply Chain Management systems shape how organizations plan, coordinate, and deliver goods and services. When they function together, they improve efficiency, visibility, and decision making. Research on this integration, however, is scattered across industries and viewpoints, making it hard to see the full picture. This study clarifies that picture through a Bibliometric Systematic Literature Review (B-SLR). A PRISMA-based review identified and screened relevant studies with clear criteria to ensure transparency. Ninety-nine publications from the Scopus database, covering 2020 to 2025, were retrieved. From these, twenty studies were selected for qualitative synthesis, while the full set underwent bibliometric analysis to map the field through performance metrics, collaboration networks, keyword co-occurrence, and thematic clusters. The results show a rising focus on ERP and SCM integration, driven by digital transformation and the demand for resilient supply chains. The analysis high-lights five recurring dimensions: scope, enabling technologies, outcomes, success factors, and barriers. These insights form a Five-Dimensional ERP and SCM Integration Framework that links theory with practice and provides a foundation for future research and implementation.
Paper Presenter
avatar for Sarath M
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 4F, Virtual Room F

9:30am PDT

Extended Lifelong Sustainable Inquiry-based Community Learning (LSiCL) for Human-LLM Learning Based on Eduinformatics
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Kunihiko Takamatsu, Sayaka Matsumoto, Katsuhiko Murakami, Hidehiko Kamei, Kazuya Tsuruta, Naoya Honda, Yumi Ishige, Shimpei Matsumoto, Hiroya Kawasaki, Ikuhiro Noda, Kenya Bannaka, Tetsuhiro Gozu, Tomoyuki Sakai, Ryosuke Kozaki, Aoi Kishida, Hibiki Ito, Koichi Akashi, Sayaka Hama, Gerald Prescott, Masahiro Uchida, Akira Nakamura, Yasuhiro Kozaki, Tamotsu Mori, Shintaro Tajiri, Naruhiko Shiratori, Shotaro Imai, Kenichiro Mitsunari, Yasuo Nakata, Sachio Hirokawa,Masao Mori
Abstract - This paper extends the Lifelong Sustainable Inquiry-based Community Learning (LSiCL) framework, based on Edu in formatics, to address challenges and opportunities presented by the rise of Large Language Models (LLMs) in education. LLMs like ChatGPT are fundamentally transforming education, with human-LLM and inter-LLM learning relationships opening new frontiers. This research explores whether the traditional human-to-human LSiCL can be meaningfully extended to encompass human-LLM and LLM-LLM interactions while retaining its core principle of collaborative inquiry. Our analysis demonstrates that LSiCL principles are applicable across all these configurations, paving the way for sustainable and scalable Human-LLM Learning ecosystems. This frame-work is crucial for improving resource efficiency, promoting educational equity, and enhancing global educational sustainability.
Paper Presenter
avatar for Kunihiko Takamatsu
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 4F, Virtual Room F

9:30am PDT

Preparing and Making Digital Video Lectures for e-Learning – A Case Study
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Nakhoon Baek
Abstract - Since the computers are introduced, there have been many meaningful attempts to use computers as education tools. Additionally, the wide use of web servers on the internet makes video-on-demand services available especially in the entertainment area. They also developed digital video lectures, which record video lectures as digital contents, and provide them also as parts of video-on-demand services. During the COVID-19 pandemic in the year of 2020, almost all schools around the world provided digital video lectures. Since the digital video lectures can provide high-quality lectures to lots of people at relatively-low costs, it is now expected to be much actively used in near future. In this paper, we will present more details of what preparations and choices are possible, what to do, and what to avoid, in the process of providing digital video lectures, especially in the field of computer science. At this time, these kinds of literature surveys are seriously lacking, and thus, our case studies will be used to further improve the overall quality of digital video lectures and expand the e-learning markets. This paper will be a good starting point for providing check points and reference items to lecturers who prepare and provide digital video lectures.
Paper Presenter
avatar for Nakhoon Baek
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 4F, Virtual Room F

9:30am PDT

reKYC for Fintech Security: A Privacy-Preserving Approach to Digital Wallet Verification
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Ikram Ahamed Mohamed, Hafiz Abdulla, Mohaideen Mohamed Mohabilasha, Fiyaz Ahmed, Pankaj Chandre, Parikshit Mahalle
Abstract - With the surge in digital wallet usage across fintech platforms, secure and privacy preserving Know Your Customer (KYC) mechanisms have become increasingly critical. This survey reviews the current state of digital identity verification frameworks, emphasizing the challenges posed by centralized KYC models, including data breaches, lack of user consent, and regulatory fragmentation. We systematically analyze more than 50 recent studies and industry practices addressing privacy-preserving identity protocols, decentralized identity (DID) systems, cryptographic techniques (such as zero-knowledge proofs), and access control architectures. The paper proposes a reference architecture reKYC based on common patterns found in the literature, integrating layered components for identity verification, access governance, trust evaluation, and regulatory compliance. We classify existing approaches by their trust model, cryptographic basis, and consent mechanisms. Key gaps identified include limited regulatory interoperability, weak user-centric control models, and lack of scalable trust frameworks. This survey concludes with open research directions and a future outlook for secure, privacy-aligned digital wallet verification.
Paper Presenter
avatar for Ikram Ahamed Mohamed

Ikram Ahamed Mohamed

United States of America
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 4F, Virtual Room F

9:30am PDT

Reviving Indian knowledge systems: implementation, execution, and impact on modern education
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Ujwala Kshirsagar, Asmita Dani
Abstract - The National Education Policy 2020 (NEP 2020) encourages the inclusion of an Indian Knowledge System (IKS) in the Higher education curriculum. In traditional Indian philosophy, the search for knowledge wisdom and truth was always seen as the highest goal of human life. The scholars made significant contributions in many fields likes mathematics, medicine, astronomy, engineering, surgery, architecture, navigation, shipbuilding, arts, yoga, and even chess. These valuable traditions and contributions should be preserved, studied, developed, and used in new ways through our education system to benefit future generations This paper focused on strategies for implementing IKS Indian Knowledge System into curriculum of higher education institutes, to roadmap efficient execution of IKS and impact of addition of IKS on modern syllabus.
Paper Presenter
avatar for Ujwala Kshirsagar
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 4F, Virtual Room F

9:30am PDT

Scenario-based Crowdfunding Framework to Accelerate Corporate Social Responsibility and Empower Employees in Society 5.0
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Vikas Shah, Aarav Shah, Aarush Shah
Abstract - The crowdfunding landscape, positioned at the intersection of entrepreneurship and technological innovation, compels organizations to address rising competitiveness while embedding corporate social responsibility (CSR) and advancing employee empowerment. Traditional crowdfunding approaches often struggle to meet the primary objectives due to inconsistent mechanisms to ac-count for regulatory changes, cultural diversity, and the demand for measurable social impact. A persistent limitation of existing practices is the absence of standardized frameworks and measurable indicators capable of capturing the organizational and societal value of crowdfunding. The paper introduces a Scenario-based Crowdfunding Framework (SCRF) that categorizes scenarios and defines actionable objectives to enhance alignment between CSR and employee empowerment. SCRF is a lightweight and adaptable methodology incorporating elementary mechanisms to evaluate, refine, and strengthen the social and organizational impact of crowdfunding. An empirical case study of crowdfunding for Artificial Intelligence (AI) initiatives and programs is performed, providing evidence of how scenario-based initiatives can serve as a strategic lever for CSR and employee empowerment in the context of Society 5.0. The research identifies key determinants influencing crowdfunding effectiveness and examines their implications for CSR integration and organizational engagement with scenario-driven analysis, governance cycle, and impact evaluations. The study ex-amines and contributes to accelerating CSR initiatives, positioning employees as empowered change agents, and advancing the broader societal vision of So-ciety 5.0.
Paper Presenter
avatar for Vikas Shah

Vikas Shah

United States of America
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 4F, Virtual Room F

9:30am PDT

The Repercussions of the Chinese Business Culture on the Motivations of Generation-Y to Work in Chinese-Owned Garment Companies
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Nurianna Thoha, Stephen Wijaya
Abstract - The purpose of this research is to explore the motivational factors of Generation Y (Gen Y) to work in Chinese-owned garment companies and to analyze how these factors are linked to Herzberg’s theory of motivation and the Chinese business culture since based on the previous research many companies do not properly motivate Gen Y employees. A qualitative method by utilizing exploratory study was applied for this research. In-depth semi-structured inter-views with 25 Gen Y employees were conducted in two Chinese-owned garment companies in the Greater Jakarta area of Indonesia, to collect the primary data. The interviews were recorded, and the data was used to develop the transcripts, which were then analyzed using thematic data analysis. The research results showed that Herzberg’s theory of motivation and Guanxi are not always aligned with the motivations of Gen Y employees to work in Chinese-owned garment companies. For example, quality of relationship and status are aligned with Guanxi but not aligned with Herzberg’s theory of motivation. However, several Guanxi factors are aligned with Herzberg’s theory of motivation, such as trust and responsibility, and recognition. In conclusion, there is a connection between motivations, Chinese business culture, and generation cohort which is GenY employees who are currently working in both garment companies in the Greater Jakarta area of Indonesia.
Paper Presenter
avatar for Nurianna Thoha
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 4F, Virtual Room F

9:30am PDT

Transforming Plant Disease Classification through AI and Methodological Integration
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F
Authors - Akash Patel, Hardik kumar Jayswal, Rishi Patel, Prem Trivedi
Abstract - This paper provides an in-depth analysis of various methods and concluding things for plant disease detection and classification, utilizing combinations of machine learning and deep learning approaches. The literature review highlights three primary methods: computer vision-based ML models, DL architectures, and spectroscopy-based techniques. While computer vision methods, including segmentation and manual feature extraction, have been extensively used, they are time-consuming and less effective for datasets with minimal visible symptoms. Deep learning, with its capability for automatic feature extraction, demonstrates significant potential, although challenges such as high computational costs and overfitting persist. Spectroscopy methods present alternative strategies, yet they are not universally applicable across all types of plant diseases. Our analysis suggests that optimized methodologies, capable of handling diverse datasets and overcoming the limitations of visual-based techniques, are essential for the timely and accurate classification of plant diseases. Future research must ad-dress these challenges to develop more robust, scalable, and efficient disease detection systems.
Paper Presenter
avatar for Akash Patel
Sunday October 18, 2026 9:30am - 11:30am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 4F, Virtual Room F

11:30am PDT

Session Chair Concluding Remarks
Sunday October 18, 2026 11:30am - 11:32am PDT
Virtual Room F
Sunday October 18, 2026 11:30am - 11:32am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 4F, Virtual Room F

11:32am PDT

Session Closing and Information To Authors
Sunday October 18, 2026 11:32am - 11:35am PDT
Virtual Room F
Exhibitors
avatar for ANISHA MISHRA
Sunday October 18, 2026 11:32am - 11:35am PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 4F, Virtual Room F

12:13pm PDT

Opening Remarks
Sunday October 18, 2026 12:13pm - 12:15pm PDT
Virtual Room F
Sunday October 18, 2026 12:13pm - 12:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 5F, Virtual Room F

12:15pm PDT

A Holistic Review of Deep Learning Methods in Speech Emotion Recognition
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Shital Khaparde, Rakesh Verma
Abstract - In the field of Speech Emotion Recognition (SER) the research is gaining more attention, with the ability to augment human-computer interaction by allowing systems to recognize emotional states from speech. The research area has evolved significantly, with most of the advancements being due to deep models of learning which are particularly good at extracting subtle patterns from raw audio data directly. The review discussed the current research, with focus on the use of prevalent deep learning architectures—e.g., Long Short-Term Memory networks (LSTM), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), , and hybrid models like CNN-BiLSTM—for SER tasks. The area of research has come a long way, some of the major challenges still persist. The design of dependable SER systems is still beset by issues such as imbalanced datasets, the ongoing difficulty of effective feature selection, noise sensitivity, and computational efficiency problems. Although deep learning has advanced remarkably, the review emphasizes that significant obstacles must still be overcome before the area can provide consistently trustworthy performance in actual situations. This paper proposes several future research including broadening and diversifying datasets, increasing the cross-linguistic strength of the model, and better real-time system performance.
Paper Presenter
avatar for Shital Khaparde
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 5F, Virtual Room F

12:15pm PDT

AI-Enabled Resource Synergy Model (AI-RSM): A Novel Framework for Resource Resilience for Smart Cities
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Sharon Elizuba Koshy, Shyam A.V, Padmadas Sundaram, Sofia Rani Shaik
Abstract - The growing complexity of urban ecosystems, fueled by rapid population expansion, is intensifying the challenges cities face in achieving sustainability, circularity, and resilience. This evolving landscape demands innovative management approaches that integrate technological advancements and resource optimization strategies to support resilient urban development. This research proposes a management-oriented framework for Artificial Intelligence enabled Industrial Symbiosis networks within smart cities, focusing on enhancing resource recovery and establishing closed-loop systems. By leveraging AI technologies of predictive analytics, dynamic matchmaking, and optimization algorithms, the framework aims to identify, coordinate, and scale symbiotic partnerships among industries, utilities, and municipal stakeholders. Rather than emphasizing the technical development of AI algorithms, the study addresses strategic governance models, stakeholder engagement mechanisms, and policy interventions that facilitate the effective integration of AI in managing industrial symbiosis networks. Drawing on principles of the circular economy and smart infrastructure governance, this research highlights how AI can drive adaptive and resilient resource flows within urban environments. By bridging technological innovation with strategic management practices, the study contributes to the evolving discourse on sustainable smart city development and offers actionable insights for policymakers, city planners, and infrastructure managers.
Paper Presenter
avatar for Padmadas Sundaram
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 5F, Virtual Room F

12:15pm PDT

Breaking the TAM Barrier: How Personal Innovativeness Enhances ICT Acceptance Among University Students
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Mohar Banerjee Biswas, Srikant Das
Abstract - Individuals display different levels of innovativeness towards technology and this innovativeness is a crucial personality trait to understand adoption behavior. Personal Innovativeness in Information Technology (PIIT) has emerged as a strong personality trait that influences the adoption behavior towards Information and Communication Technologies (ICT) in higher education. This research analyses the influence of PIIT on the perception of utility and simplicity of ICT by higher education students. The study extends the Technology Acceptance Model (TAM) by adding PIIT and analyses how it affects the perceived usefulness (PU), perceived ease of use (PEOU) and subsequent behavior intention (BI) towards ICT adoption. For the data collection, 493 sample data were obtained from university students using an online structured questionnaire and was processed using Structural Equation Modeling (SEM). The findings of the study reveal that PIIT significantly impacts the PU and PEOU, indicating that students who are more innovative will view ICT as beneficial to their academic task and will find it easy to use. PIIT also has an indirect influence on BI via the construct of PU and PEOU, emphasizing its mediating role. The findings propose that developing PIIT amongst learners can improve students' preparedness to adopt ICT in academics. The research has important implications for stakeholders in developing specialized interventions, training, and curriculum designs that nurture creative mindsets, thus enabling stronger integration of technology in learning.
Paper Presenter
avatar for Mohar Banerjee Biswas
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 5F, Virtual Room F

12:15pm PDT

Deconstructing Barriers to Digital Well-Being Among Gen Z
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Santanu Mandal, Srinija M, Sukruthi M
Abstract - This study investigates the structural barriers hindering digital well-being among Generation Z (Gen Z) through an integrated lens of Self-Determination Theory (SDT) and Cognitive Load Theory (CLT). Using a cross-sectional survey conducted between June and July 2025, 126 valid responses were collected from Indian Gen Z participants, comprising students and early-career employees. Ten barriers—including lack of awareness, absence of digital literacy, mixed media messages, platform design addiction, and peer pressure—were examined for their interrelationships using Interpretive Structural Modelling (ISM) and MICMAC analysis. Results reveal that foundational barriers such as lack of awareness, absence of digital literacy, and mixed media messages act as high-driving factors, creating cascading effects on dependent barriers like inconsistent sleep patterns, low perceived control, and productivity guilt. From the SDT perspective, these drivers undermine autonomy and competence, while CLT explains how cognitive overload from addictive design features exacerbates maladaptive digital behaviours. The findings highlight the need for multi-stakeholder interventions—spanning policy, education, workplace culture, and ethical platform design—to address root causes and promote sustainable digital wellness. This research advances theoretical integration between SDT and CLT, offering a diagnostic framework for targeted, systemic interventions aligned with SDG 3 and SDG 12.
Paper Presenter
avatar for Sukruthi M
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 5F, Virtual Room F

12:15pm PDT

Dependency of Sentiment on its aspect using Large Language Model
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Rupsa Sarkar, Jaydev Mishra
Abstract - Natural language processing (NLP) has several key applications, including sentiment analysis (SA). SA can bed escribed as a procedure that identifies the polarity of a sentence as well as its goal through analysis. SA is now the most active NLP research area. Aspect-based sentiment analysis, which is a subset of SA, is the process of examining a sentence's structure and determining its polarity. This field has been grown because now people feel free to share their thoughts, opinions, expressions. Internet, social media are now the massive resource of opinion assuming. In this paper, we have used Sem Eval 2014 task 4 dataset to focus on Aspect-based Sentiment Analysis (ABSA) problem. We have focused on previous work Based on Aspect-based Sentiment Analysis and Some work of Sentiment Analysis based on conversational data. We have used GPT2 for this task.
Paper Presenter
avatar for Rupsa Sarkar
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 5F, Virtual Room F

12:15pm PDT

EVALUATING THE APPROPRIATENESS OF STATISTICAL TECHNIQUES IN GRADUATE RESEARCH: A DIAGNOSTIC APPROACH
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Mark Anthony C. Ochoa, Aloysius J. Aurelio, Stephan Kupsch
Abstract - This sub-study evaluates the application of descriptive and inferential statistics in graduate research at DMMMSU-CGS. Findings show that while nominal and interval/ratio scales are often treated with appropriate measures (frequency counts and means), ordinal data is frequently misrepresented using the mean instead of median or mode. Such lapses compromise validity and highlight gaps in statistical literacy. This misuse underscores the need for statistical diagnostics prior to test selection and the inclusion of assumption checks in research guidelines. Moreover, analysis of appropriateness and accuracy revealed that while proper methods generally produce accurate results, some accuracy still arises from misapplied tools due to compensating factors. These findings stress the importance of enhanced statistical training and methodological rigor. . . .
Paper Presenter
avatar for Mark Anthony C. Ochoa
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 5F, Virtual Room F

12:15pm PDT

From Prompts to Persuasion: Parameter-Efficient Adaptation of Open LLMs for Privacy-Aware Email Marketing
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Jordan Cardenas, Fabian Cardenas, Marcos Levano, Billy Peralta
Abstract - In today’s digital economy, where personalization has become a cornerstone of effective marketing strategies, companies face the dual challenge of increasing advertising impact while safeguarding sensitive customer information. Despite the rapid progress of large language models (LLMs), existing commercial solutions often neglect the integration of synthetic data to reduce privacy risks and enhance adaptability, leaving organizations dependent on external providers. To address this gap, our work fine-tunes open-source LLMs (LlaMa2, Mistral, and Zephyr) with synthetic datasets generated via GPT, aiming to produce customized marketing emails tailored to demographic and behavioral features. This thesis demonstrates not only the feasibility but also the competitiveness of such models by evaluating outputs with standard metrics (BLEU, ROUGE) and human-like scoring through GPT-4, showing that open-source models can approximate the performance of proprietary alternatives at significantly lower cost. The results confirm that fine-tuned LLMs with synthetic data represent a viable solution for enterprises seeking efficiency, personalization, and internal control of data.
Paper Presenter
avatar for Billy Peralta
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 5F, Virtual Room F

12:15pm PDT

Liberty to Equality: Hotel Management Application Level of Diversity Management in Selected Department of Tourism Accredited Hotels in Pangasinan
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Rricky Tim Solomon Sison, Romary Reyes Lincod
Abstract - Diversity management has become a strategic priority in the hospitality industry, particularly in regions marked by increasing demographic variation. This study investigates the perceived effects of diversity management on the work environment in Department of Tourism (DOT) accredited hotels in Western Pangasinan, Philippines. It also examines the relationship between administrative diversity management application and employees’ experiential perceptions. This quantitative-descriptive research employed an adapted and expert validated instrument, refined through pilot testing. Using purposive and stratified sampling, data were collected from 53 participants (10 supervisors/managers and 43 employees) across five DOT-accredited hotels. Four sub-variables were examined: employee engagement, creativity and innovation, stereotyping and bias, and employee retention. Analytical tools included mean, average weighted mean (AWM), one-way ANOVA, and Pearson correlation. The study revealed that diversity management was perceived to be highly effective, particularly in fostering stronger employee engagement and enhancing creativity in the work-place. Overall, its impact on the organizational environment was regarded as very positive. Nevertheless, the findings also indicated that administrative strategies showed a weak and statistically non-significant relationship with employee perceptions, suggesting that structural approaches alone may not be sufficient to shape employees’ views. Among the demographic factors considered, marital status emerged as the only variable with a meaningful influence, highlighting its relevance in understanding how diversity initiatives are experienced within the hospitality context. The study suggests a disconnect between diversity policies and their perceived impact, highlighting the need for inclusive implementation. Limitations include the small, localized sample. The findings emphasize the im-portance of participatory diversity planning, inclusive leadership training, and institutional mechanisms to ensure meaningful and sustainable diversity out-comes in hotel organizations.
Paper Presenter
avatar for Rricky Tim Solomon Sison
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 5F, Virtual Room F

12:15pm PDT

Terra INVicta: A browser-based adaptive game for cognitive training
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Sebastian Carrasco, Pablo Schwarzenberg, Marcos Levano, Carla Taramasco, Billy Peralta
Abstract - In an era where rapid, accurate keyboarding underpins virtually every academic, professional and everyday digital interaction, conventional drill-based tutors still struggle to sustain user engagement and to adapt difficulty in real time. Addressing this gap, we present Terra INVicta, a browser-native serious game that defends Earth from procedurally generated “cosmic” threats only when the player types their associated words correctly, thereby combining cognitive processing and motor skills practice in an engaging and accesible way. This paper details the game’s design workflow and a first-round evaluation of its gameplay mechanics and adaptive-difficulty engine, which modulates challenge through a mixed time–score progression factor. Initial tests indicate that Terra INVicta delivers fluid animation, near-instant keystroke-to-action responsiveness, and sustains a “flow” state across diverse player abilities, confirming its viability as a cognitive training game. Beyond validating its educational value, we highlight lessons learned and propose future work—including their application as a tool for cognitive assessment and improvement of cognitive and motor-skills in older adults. An interactive prototype is available at https://inverosimilitudes.github.io/ TerraINVicta/.
Paper Presenter
avatar for Pablo Schwarzenberg
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 5F, Virtual Room F

12:15pm PDT

Unmanned Aerial Vehicle Navigation Using Map-Based Localization
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F
Authors - Sanjeeb Prasad Panday, Ravi Gautam, Basanta Joshi, Aman Shakya, Anunaya Pandey
Abstract - The proposed approach focuses on autonomous UAV (Unmanned Aerial Vehicle) navigation and obstacle detection using only visual sensors specifically, the on-board front camera. Unlike traditional methods that rely on multiple sensors (e.g., LiDAR, radar, or GPS), this vision-based system aims to reduce hardware complexity and cost while maintaining robust performance in diverse environments. By leveraging computer vision and deep learning techniques, the UAV processes real-time camera feed data to detect obstacles, map surroundings, and plan collision-free paths. Key challenges include handling dynamic environments, varying lighting conditions, and real-time processing constraints. The system employs feature extraction, depth estimation, and semantic segmentation to interpret visual data, enabling the UAV to navigate autonomously without external aids. Advantages of this approach include reduced sensor dependency, lower power consumption, and improved adaptability in GPS denied or cluttered spaces (e.g., indoor settings or dense urban areas). However, limitations may arise in low-visibility conditions (e.g., fog or darkness) or with texture-less surfaces that complicate depth perception. The method aligns with advancements in lightweight AI models optimized for edge computing, ensuring efficient onboard processing. Future enhancements could integrate multi-camera setups or hybrid sensor fusion for increased reliability. Overall, this vision only navigation strategy offers a scalable and cost-effective solution for UAV autonomy, particularly in applications like surveillance, inspection, and disaster response where simplicity and agility are critical.
Paper Presenter
avatar for Ravi Gautam
Sunday October 18, 2026 12:15pm - 2:15pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 5F, Virtual Room F

2:15pm PDT

Session Chair Concluding Remarks
Sunday October 18, 2026 2:15pm - 2:17pm PDT
Virtual Room F
Sunday October 18, 2026 2:15pm - 2:17pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 5F, Virtual Room F

2:17pm PDT

Session Closing and Information To Authors
Sunday October 18, 2026 2:17pm - 2:20pm PDT
Virtual Room F
Exhibitors
avatar for ANISHA MISHRA
Sunday October 18, 2026 2:17pm - 2:20pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 5F, Virtual Room F

2:58pm PDT

Opening Remarks
Sunday October 18, 2026 2:58pm - 3:00pm PDT
Virtual Room F
Sunday October 18, 2026 2:58pm - 3:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 6F, Virtual Room F

3:00pm PDT

A COMPREHENSIVE BENCHMARK FOR EVALUATION INFERENCE STRATEGIES ON LARGE SCALE TABULAR DATASETS
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Tushaar Yenduri, S S R Subramanya Hemant Konduri, Kalyan Netti
Abstract - This study examines three inference strategies—serial, multi-core parallel (Joblib), and distributed memory (MPI)—to address scalability challenges in applying machine learning models to large tabular datasets. Using the HIGGS dataset as a representative benchmark of high-volume scientific data, a Random Forest classifier was trained and validated with performance metrics of 73.3% accuracy and 81.25% ROC-AUC. The results offer concrete guidance on selecting appropriate inference backends based on system architecture and dataset size, particularly for practitioners in high-performance computing and scientific machine learning domains.
Paper Presenter
avatar for Tushaar Yenduri
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 6F, Virtual Room F

3:00pm PDT

Advanced Planning for Lean, Circular and Sustainable Decision-Making in Textile Manufacturing company: A TOPSIS Approach
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Kevin De la Cruz, Alberto Taza, Edgar Ramos, Donovan Fuqua
Abstract - The textile industry is facing growing pressure to transition toward production systems that are sustainable, efficient, and aligned with circular economy principles. This study develops a structured multi-criteria decision-making framework designed to integrate lean practices, circularity, and sustainability into strategic planning for textile manufacturing. The methodology applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach to evaluate and rank key operational practices considering their environmental, operational, and economic impacts. Results indicate that prioritizing lean initiatives with embedded circularity practices provides a dual advantage: improving short-term efficiency while simultaneously supporting broader sustainability goals. The proposed model offers decision-makers in the textile sector a practical and evidence-based tool to address challenges of competitiveness, environmental regulation, and sustainable value creation. Ultimately, this research contributes to advancing integrated planning approaches that strengthen the ongoing transition toward sustainable manufacturing systems.
Paper Presenter
avatar for Kevin De la Cruz
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 6F, Virtual Room F

3:00pm PDT

AI-Driven Stakeholder-Centered Curriculum Design for Global Media Innovation: A Case Study from Thailand
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Matana Charoenwongsa, Sangkama Sarawat, Laknara Chandraramya, Waralak Vongdoiwang Siricharoen, Thirachit Saenphon
Abstract - The global media landscape is experiencing a profound shift, shaped by the rise of artificial intelligence (AI), growing cross-cultural content demands, and evolving patterns of work. This study investigates how active stakeholder participation can guide the development of an AI-integrated curriculum within global media education. Emphasis is placed on nurturing technological fluency, interdisciplinary perspectives, and ethically grounded innovation. Drawing on a curriculum design initiative from Thailand’s Global Media Industry and Innovation Master’s Programme, the research explores how AI-driven tools, media production technologies, and entrepreneurial approaches are brought together in educational practice. Insights were collected from over 50 stakeholders—including media entrepreneurs, policymakers, educators, and students—and examined through thematic content analysis. The research highlights three core priorities that shaped the curriculum design: strengthening technological fluency, deepening global media literacy, and encouraging social innovation through entrepreneurship. It proposes a collaborative model that addresses technological change while centring human agency, with practical implications for curriculum reform and the future of sustainable media ventures in an AI-influenced era.
Paper Presenter
avatar for Sangkama Sarawat
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 6F, Virtual Room F

3:00pm PDT

An Innovative Hybrid Approach to Image Steganography:Combining Non-Linear Cost Functions with Structured MatrixOperations for Enhanced Security
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Chethana N S, Anithadevi M D
Abstract - We introduce a groundbreaking steganographic technique that merges non-linear cost evaluationmethods with binary triangular matrix structures to achieve superior data concealment in color images.Our approach tackles the inherent weaknesses of conventional linear cost models by implementing amultiplicative framework that more effectively maintains the statistical characteristics of images whileoptimizing embedding performance. The integration of structured binary matrices enables intelligentposition selection through organized randomization, preserving both visual fidelity and cryptographicsecurity. Our methodology incorporates sophisticated texture evaluation, self-adjusting parameter tuning, andmulti-channel analysis to deliver outstanding results across various image categories. Through rigoroustesting, we demonstrate remarkable improvements over current leading methods, achieving averagePSNR values of 52.34 dB and SSIM scores of 0.9847 across standardized test collections from multipleimage databases. The system exhibits substantially enhanced resilience against contemporarysteganalysis tools while supporting high embedding rates up to 0.8 bits per pixel with strengthenedsecurity assurances.
Paper Presenter
avatar for Chethana N S
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 6F, Virtual Room F

3:00pm PDT

BIG DATA-DRIVEN CORRECTION COEFFICIENTS IN THE 360-DEGREE FEEDBACK METHOD
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - YURY MIKHEEV, URSULA PODOSENIN, SERGEY V. SYCHOV
Abstract - This study investigates the reliability and validity of the 360-degree feedback method by focusing on three primary sources of bias: respondent roles, interpersonal relationships, and individual evaluative styles. Drawing on data from over 15,000 respondents across multiple organizations, it applies a Python-based statistical algorithm to quantify the impact of these biases on competency ratings. Findings reveal that such factors can account for up to half of the variance in assessments. Mitigation approaches include evaluator anonymity, structured training, and correction coefficients. With these measures in place, 360-degree feedback continues to serve as an effective tool for fair and accurate performance appraisal, supporting personnel decisions and sustainable talent management.
Paper Presenter
avatar for SERGEY V. SYCHOV
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 6F, Virtual Room F

3:00pm PDT

Comprehensive Assessment of Deep and Traditional ML Approaches for Multiclass Skin Disease Recognition
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Zarif Wasif Bhuiyan, Zaed Bin Monir Atib, Mahady Hasan, Farhad Alam, Md. Tarek Habib
Abstract - Early and accurate identification of common skin conditions is essential for appropriate therapy and better clinical outcomes. However, diagnostic expertise is often limited in resource-constrained healthcare or remote settings. We present a robust deep learning approach based on a custom convolutional neural network (CNN) to automatically classify five prevalent skin diseases (acne, contact dermatitis, nail fungus, scabies, and urticaria) from clinical images. A curated dataset of 8,900 clinical images (1,780 per class) was compiled, with standardized resizing, normalization, and extensive augmentation applied to simulate real-world variability and enhance generalization. The CNN architecture consists of five sequential convolutional blocks with progressively increasing filters to extract high-level features, accompanied by dropout layers to mitigate overfitting, and includes a fully connected layer prior to the SoftMax output. On evaluation, the model achieved an overall classification accuracy of 99%, demonstrating robust performance and high diagnostic precision across all categories. It notably outperformed conventional machine learning classifiers as well as state-of-the-art pretrained deep networks on the same task. This exceptional performance, combined with strong generalization capabilities, underscores the model’s reliability and suitability for deployment in real-world clinical practice and tele dermatology settings. In such scenarios, rapid and accurate screening for multiple skin conditions can significantly improve patient care and enable earlier interventions.
Paper Presenter
avatar for Zaed Bin Monir Atib
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 6F, Virtual Room F

3:00pm PDT

Enhancing Open-Source Software Security Through Intelligent Monitoring of Community-Sourced Code Snippets
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Amruta Khanaj, Sourabh Khanaj, Smita S. Sangewar
Abstract - This research investigates the security implications of reusing code snippets of Stack Overflow in open-source software projects. We present a novel framework that proactively identifies and mitigates security risks associated with outdated Stack Overflow code snippets. Our methodology combines static code analysis techniques, continuous monitoring of Stack Overflow post changes, and an automated developer notification system. We analyzed 1.5 million code snippets from Stack Overflow approximately across 5000 popular open-source GitHub projects, focusing on programming codes. Our observations reveal that there are a significant number of reused snippets becoming outdated over time, with developers often missing critical security updates made to the original Stack Overflow posts. We showcase the effectiveness of our framework in detecting potential vulnerabilities and alerting developers to security relevant updates in reused code. The research emphasizes the need for continuous monitoring of community-sourced code and suggests solutions to ensure its secure integration in software development practices. Our work contributes to the broader understanding of software supply chain security and the evolving nature of code reuse in the open-source ecosystem.
Paper Presenter
avatar for Amruta Khanaj
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 6F, Virtual Room F

3:00pm PDT

Grading, Packaging, and Real-Time Transport Management System of Agriculture
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Mst.Sadia Siddika, Sherin Sultana, Md. Tarek Habib, Sabrina Alam, Md. Mahbub Alam, Mahady Hasan
Abstract - Agricultural products are graded based on their quality and adherence to standards. Managing the grading, packaging, and transportation of products, especially perishable items like food, is crucial for maintaining freshness and safety. Packaged goods are transferred to storage facilities and prepared for transportation. To maintain product freshness, a well-organized and traceable supply chain is essential. This study demonstrates how technologies such as the Internet of Things (IoT) can facilitate an agricultural supply chain system that uses maps, computer vision, smart packaging, and smart barcodes. These technologies help track products in real-time, ensure data security via databases, and streamline supply chain operations through web-based platforms. Using an app-based system, the solution highlights traceability, automation, and user-friendly interfaces to optimize efficiency from farm to market. The app also provides automated alerts and real-time inventory updates for both customers and sellers. Additionally, the system should integrate IoT-based temperature and humidity monitoring to maintain the quality of perishable goods. Overall, this study suggests that combining these advanced technologies into a unified system can address challenges in traditional methods, such as delays and product quality loss.
Paper Presenter
avatar for Mst.Sadia Siddika
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 6F, Virtual Room F

3:00pm PDT

Intelligent information system for proactive management of a printing enterprise
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Kostaryev D.B., Tevyashev A.D., Sizova N.D., Tkachenko V.P.
Abstract - An intelligent information system for proactive management of a printing enterprise based on artificial intelligence is proposed. Approaches to proactive enterprise management grounded in Industry 4.0/5.0 are substantiated, including the use of relevant scientific methods and mathematical models. The architecture of the information system, based on mathematical models and scientific principles, has been developed. The results of evaluating the effective-ness of implementing and using this intelligent information system at a printing enterprise are presented.
Paper Presenter
avatar for Kostaryev D.B.
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 6F, Virtual Room F

3:00pm PDT

Rural Bangladesh Before COVID: Baseline Trends and District Forecasts from the Life in the Field Program
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F
Authors - Zarif Wasif Bhuiyan, MD. Humayun Kabir, Paramita Saha, Md Mahbub Alam, Mahady Hasan
Abstract - Urban–rural evidence in Bangladesh is often fragmented or short-lived, limiting its value for tracking change. This study leverages the Life in the Field Experience at Independent University, Bangladesh to build a pre-COVID baseline from eight annual waves of student-collected surveys spanning 2013–2020 across six districts: Jessore, Bogra, Mymensingh, Sylhet, Barishal, and Dinajpur. Heterogeneous spreadsheets are harmonized into standardized sector indices for education, medications and health service use, employment, marital history, acute morbidity, and chronic morbidity. A transparent pipeline handles header inconsistencies, variable shifts, and missingness, producing comparable annual series. ARIMA models are estimated for Jessore to generate five-year projections and are interpreted alongside cross-district benchmarking using z-scores. Results show steady gains in education that level off, an employment dip followed by recovery, rising medication use, low and stable acute morbidity, a gradual increase in chronic morbidity, and largely stable marital patterns. Forecasts are framed as extensions of pre-COVID dynamics with explicit uncertainty. The pipeline provides a reusable template for short annual series and supports policy diagnosis at district level. Future work will extend forecasting to all districts, incorporate structural-break tests and hierarchical models, and enrich indices with external covariates and administrative validation.
Paper Presenter
avatar for Zarif Wasif Bhuiyan
Sunday October 18, 2026 3:00pm - 5:00pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 6F, Virtual Room F

5:00pm PDT

Session Chair Concluding Remarks
Sunday October 18, 2026 5:00pm - 5:02pm PDT
Virtual Room F
Sunday October 18, 2026 5:00pm - 5:02pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 6F, Virtual Room F

5:02pm PDT

Session Closing and Information To Authors
Sunday October 18, 2026 5:02pm - 5:05pm PDT
Virtual Room F
Exhibitors
avatar for ANISHA MISHRA
Sunday October 18, 2026 5:02pm - 5:05pm PDT
Virtual Room F Bangkok, Thailand
  Virtual Room 6F, Virtual Room F
 
Monday, October 19
 

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

9:30am 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

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

11:32am 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

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

12:15pm 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

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

2:17pm 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

2:58pm 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

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

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

5:02pm 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
 

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