5th World Conference on Information Systems for Business Management: Full Schedule

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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

T. Sruthi

India

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

Sanasam Bimol

India

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

Praveen Savarapu

India

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

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

Uditaa K

India

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

Vijay Makwana

India

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

Chau Huy Thuan

Vietnam

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

Thupakula Kushwanth

India

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

Nandgopan R

India

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

Liladhar P. Dhake

India

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

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

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

Krit Chaiwong

Thailand

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

Rushikesh Manoj Gholap

India

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

Parth Modhvadiya

India

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

Hector Rafael Morano Okuno

Mexico

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

Hitarth Bhatt

India

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

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

Budsaba Inkiew

Thailand

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

Aishwini VR

India

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

B Sathish Kumar

India

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

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

Nhat Minh Nguyen

Vietnam

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

Windy Permata Suyono

Indonesia

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

S Manvitha Reddy

India

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

Mahimaben Panjabi

India

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

Raghavan Vaidhyaraman

India

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

GGS Pradeep

India

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

An Dinh Van

Vietnam

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

Uma Sankary A

India

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

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

Kinjal Bhatia

India

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

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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