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Friday, April 10
 

9:28am GMT+07

Opening Remarks
Friday April 10, 2026 9:28am - 9:30am GMT+07

Invited Guest & Session Chair
avatar for Prof. Vishnu Kumar

Prof. Vishnu Kumar

Assistant Professor, Morgan State University, United States
avatar for Dr. Dushyantsinh B. Rathod

Dr. Dushyantsinh B. Rathod

Professor & HOD, Gandhinagar Institute of Technology, India
Friday April 10, 2026 9:28am - 9:30am GMT+07
Virtual Room D Bangkok, Thailand

9:30am GMT+07

A Governed Forecasting and Anomaly Detection Framework for Live Birth Planning in Provincial Health Systems
Friday April 10, 2026 9:30am - 11:30am GMT+07
Authors - Britt Kristoff B. Montalvo, Vicente Pitogo
Abstract - This paper presents a data-driven forecasting and anomaly detection dashboard for live births in Surigao del Norte, utilizing the Family Health Service Information System (FHSIS) data from 2021 and onwards. The research methodology is based on the CRISP-DM framework, with business under-standing for the needs of maternal services planning in the provinces and municipalities, data preparation for municipalities by quarters, time aware modeling, evaluation, and deployment through the API and visualization layer. The research employs several machine learning techniques for forecasting, such as ARIMA/SARIMA, Exponential Smoothing (ETS and Holt-Winters), and the Prophet method, along with a naïve method. The performance of the models is evaluated through the symmetric Mean Absolute Percentage Error (sMAPE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE). A strict evaluation criterion for the deployment of the model is also implemented, such as the availability of sufficient data points in the past for the model to be deployed (i.e., 12 data points in the past), the accuracy of the model (sMAPE < 20%), and the performance of the model in comparison with the naïve method (MASE < 1). A low confidence filter is also implemented for the series with intermittent data to prevent incorrect results. The results show high reliability of the forecasting model for the entire province and better interpretability for strategic planning. However, the results also show that some of the municipalities with low population volumes and intermittent data points pose a challenge in the operation of the model.
Paper Presenter
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand

9:30am GMT+07

A Hybrid Wavelet CNN Vision Transformer Framework with Explainable AI for Medical Image Classification
Friday April 10, 2026 9:30am - 11:30am GMT+07
Authors - Shriram Dange, Namdeo. M. Sawant, Sumeet S Ingole, Somnath A. Zambare
Abstract - Medical image classification is of immense importance in the context of early-stage diagnosis of various neurological diseases, including Alzheimer’s disease and brain tumours. However, it remains infeasible for conventional deep learning architectures to efficiently encode frequency domain information and long-range spatial dependencies found in medical images. In this paper, a novel Hybrid Wavelet CNN Vision Trans-former, coupled with Explainable Artificial Intelligence, has been proposed for efficient and accurate medical image classification. In the proposed architecture, the application of discrete wavelet transform, convolutional neural networks, and Vision transformers for medical image classification has been presented. Additionally, explainability aspects have been addressed using the Grad-CAM technique. The proposed model was experimented with using two datasets: one for Alzheimer’s disease MRI and another for brain tumours. The experimental results reveal that the proposed deep learning architecture achieves an accuracy of 96.8%, precision of 0.96, and recall of 0.97, F1score of 0.97 for the brain tumours dataset, which beats conventional CNN, vision Transformer, and Wavelet CNN architectures. The integration of explainable AI further enhances model transparency and clinical reliability, making the proposed framework suitable for real-world medical diagnostic applications.
Paper Presenter
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand

9:30am GMT+07

AI-Powered Investment Assistant
Friday April 10, 2026 9:30am - 11:30am GMT+07
Authors - Sherly K.K., Merin Jose, Aleena Gerard Nidhiry, Amit Shibu Kadambamoodan, Alfahad Shahi
Abstract - This paper introduces an AI-based investment assistant that helps users to understand the fundamental principles of the financial markets. This work is mainly focused on stock market data to provide accurate insights and helps in various decision-making purposes. The rising volatility in the financial markets, massive data set, and the complexity of financial instruments, makes decision-making in financial sectors more difficult to individual investors.In order to cope with this problem, our model integrates time series forecasts, large language model intelligence with real-time financial information with interactive visualizations and personalized insights. The suggested system will interpret user queries in natural language with the help of a Large Language Model (Gemini 2.5 Flash) and extracts the corresponding stock tickers and financial objects and transforms them into structured inputs to be used in predictive analysis. Past and current stock market data are retrieved with the help of yfinance API and fed into an LSTM-based time-series predictive model that predicts future price fluctuations.The results predicted are presented in interactive charts created with Plotly, which users can analyze trends easily and compare several stocks. The system can also give personalized recommendations, textual summaries of stock movements (moving up or down), multi-turn chatbot conversations, portfolio, wishlist and real time price moves besides forecasting. The proposed investment assistant improves the gap between complicated financial information and practical results by incorporating natural language comprehension, deep learningbased prediction, and intuitive visualization etc. The system promotes user knowledge and helps them in effective decision making .
Paper Presenter
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand

9:30am GMT+07

Beyond Continuity: Modeling Discontinuous Risk in Altcoin Portfolios via Merton Jump-Diffusion and EWMA Covariance
Friday April 10, 2026 9:30am - 11:30am GMT+07
Authors - Ekleen Kaur
Abstract - Traditional risk frameworks, including the Geometric Brownian Motion (GBM) and stationary GARCH models, fail to account for the "volatility bursts" and "flash crashes" endemic to the altcoin market. This study the third in a series on cryptoeconomic risk introduces a multi-asset Merton Jump-Diffusion (MJD) model integrated with an Exponentially Weighted Moving Average (EWMA) covariance matrix to model portfolio risk in altcoin-only environments. By focusing exclusively on high-beta altcoins (XRP, SOL, ADA) and we address a critical gap by excluding market-anchor assets to isolate long-tail volatility dynamics neglected in existing literature. We implement a dual-model approach: a baseline MJD simulation and a "Capped Return" MJD model designed to mitigate unrealistic exponential price paths in long-horizon forecasts. Our results using Monte Carlo Value-at-Risk simulations demonstrate that incorporating a Poisson-driven jump component (j = 2.0) significantly improves λthe capture of tail risk compared to continuous models indicating pathological exponential growth without suppressing crash dynamics. Our work provides a technically rigorous framework for managing portfolios in decentralized, high-liquidity-shock environments. Backtesting via Kupiec’s Proportion of Failures test indicates that jump-based, non-stationary models achieve statistically consistent risk coverage. These findings suggest discontinuous modeling as a prerequisite for regulatory-grade risk estimation in high-beta crypto assets.
Paper Presenter
avatar for Ekleen Kaur

Ekleen Kaur

United States

Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand

9:30am GMT+07

Bridging Linguistic Diversity: Enhancing NER Performance through Large Language Models on Indian & Foreign Languages
Friday April 10, 2026 9:30am - 11:30am GMT+07
Authors - Makrand Dhanokar, Anirban Sarkar, Prajakta Dange Sant, Shivakarthik S, Krishnanjan Bhattacharjee, Swati Mehta
Abstract - Named Entity Recognition (NER) is an essential task for sequence labelling and information extraction that plays a fundamental role in subsequent Natural Language Processing (NLP) applications, such as information retrieval, question answering, knowledge graph development, and machine translation. Although significant advancements have been made in NER for high resource languages, achieving effective entity recognition in Indian languages continues to be an unresolved research challenge because of linguistic diversity, complex morphology, typological differences, flexible word order, script differences, and prevalent codemixing. The scarce presence of annotated datasets and the lack of standardized evaluation metrics further limit supervised and transfer learning methods in these low resource environments. This document introduces a multilingual NER framework rooted in Sentence embeddings derived from Large Language Models (LLMs) and inference guided by prompts. The suggested method employs contextual; language independent embeddings obtained from pretrained multilingual LLMs to encode semantic representations of Indian and foreign languages within a common embedding space. Rather than using traditional token level classification, entity recognition and classification are achieved via structured prompting, allowing for zero-shot and few-shot generalization without the need for task specific finetuning. The system guarantees that entity identification and retrieval take place in the same language as the input text, maintaining linguistic accuracy and reducing error propagation caused by translation. To tackle domain variability and informal writing, constraints/guardrails for prompts and simple rule-based normalization are utilized to manage orthographic differences, script inconsistencies, and codemixed phrases often found in user generated content and social media. Experimental assessment across various Indian languages shows reliable enhancements in precision, recall, and F1score compared to traditional neural and transformer-based benchmarks, especially in low resource conditions. The findings suggest that embeddings powered by LLMs along with prompt-based reasoning provide a scalable and data efficient option for multilingual NER. This project advances the development of resilient, inclusive, and language adaptive systems for extracting information in linguistically varied settings.
Paper Presenter
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand

9:30am GMT+07

Explainable Deep Learning Driven Transaction-level Customer Spending Behavior Analysis for Fraud Detection in a Big Data Framework
Friday April 10, 2026 9:30am - 11:30am GMT+07
Authors - Asmaul Hosna Sadika, M. M. Musharaf Hussain, Mohammad Shamsul Arefin
Abstract - Credit card transaction analysis is challenged by severe class imbalance with evolving spending behavior and large-scale financial data. Many existing fraud detection approaches rely on supervised learning and assume stable fraud labels, limiting robustness under changing fraud prevalence. This study presents a large-scale, multi-year credit card trans action dataset stored in partitioned Parquet format and conducts a systematic comparison of classical machine learning, supervised deep learning, and unsupervised deep learning models for customer spend ing behavior analysis. An exploratory behavioral analysis characterizes spending heterogeneity, temporal regularities, and channel and category variations. Supervised sequence models based on LSTM and CNN ar chitectures are evaluated alongside unsupervised sequence autoencoders and hybrid detection pipelines across fraud rates ranging from 2-12%. To ensure fair evaluation under extreme imbalance, models are assessed using ranking-based metrics under fixed alert budgets, including pre cision–recall area under the curve and recall-at-K. A hybrid of Autoen coder and LSTM architectures achieves the highest performance for large systems. An integrated XAI module is introduced to derive important features providing interpretable insights.
Paper Presenter
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand

9:30am GMT+07

Eye Movements and Their Influence on Cognitive Processing
Friday April 10, 2026 9:30am - 11:30am GMT+07
Authors - Christian Vera, Christian Torres-Moran
Abstract - This study examines how students distribute visual attention and coordinate gaze with response selection when solving image-supported multiple-choice questions in a Google Forms interface. Twenty-five students participated, selected through convenience sampling under explicit inclusion and exclusion criteria, while both fixations and click events were recorded. Oculomotor signals were processed using clustering algorithms to derive participant-specific gaze AOIs and click AOIs, complemented by a 3×3 grid-based spatial analysis to quantify global space utilization. Metrics were computed including time to first fixation, total fixation duration and fixation counts per area, transitions between areas, and the proportion of pre-response fixations within the region where the click was executed. Results show a systematic concentration of fixations in the central band of the interface, where the image and response options are located, with one or two dominant areas accounting for most fixation time. The optimal number of gaze clusters ranged from two to eight across participants, reflecting more focused versus more exploratory strategies. A high level of attention–action coupling was observed, with 80% to 95% of clicks occurring within the same area that concentrated most fixations. These findings support the use of eye track-ing as a tool for cognitive validation of item design and inform principles for more efficient and transparent digital assessments.
Paper Presenter
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand

9:30am GMT+07

Hybrid Deep Learning and Quantum Approach for Multimodal Deepfake Detection
Friday April 10, 2026 9:30am - 11:30am GMT+07
Authors - V. Abarna, R. Shyamala
Abstract - The rapid advancement of artificial intelligence has significantly enhanced deepfake generation techniques, posing serious challenges to digital media authenticity, cybersecurity, and misinformation control. Conventional detection approaches often rely on single-modality analysis, limiting their effective-ness against sophisticated synthetic media. This paper proposes a multimodal deepfake detection framework that integrates visual, audio, textual, and behavioral biometric information using a hybrid deep learning architecture combined with a variational quantum learning approach. Deep neural models are employed for feature extraction across modalities, including convolutional networks for visual artifacts, transformer-based models for speech and text analysis, and bio-metric behavioral assessment such as eye movement, lip synchronization, and motion consistency. A hierarchical fusion mechanism aggregates modality-specific representations, while a variational quantum classifier enhances classification robustness through hybrid quantum–classical learning. An explainability module provides insight into modality contributions and prediction confidence, supported by a web-based dashboard for real-time interaction. The proposed framework aims to improve detection reliability, interpretability, and practical deployment in applications such as digital forensics, social media verification, and cybersecurity. This work presents a conceptual architecture and implementation roadmap to support future research in multimodal deepfake detection.
Paper Presenter
avatar for V. Abarna
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand

9:30am GMT+07

Is Common Hardening Methods Really Sufficient? A Risk Analysis on Current ICS Vulnerabilities
Friday April 10, 2026 9:30am - 11:30am GMT+07
Authors - Emine YAZICI, Alper UGUR
Abstract - Critical infrastructures are of strategic importance to the security of societies, economic stability, and the continuity of public services. However, with digitalization, these infrastructures are facing progressively complex cyber threats such as supply chain exploitation, ransomware, and AI-assisted targeted attacks. Traditional hardening methods are becoming insufficient in the face of these developments. This study examines the types of attacks and threat trends that have emerged in the literature in recent years; and evaluates the effectiveness of hardening methods applied against them at the software, physical, and organ izational levels. The findings indicate that, due to the dynamic nature of threat vectors, utilized common risk analysis and hardening strategies are insufficient to deliver the expected security outcomes. However, the literature lacks a risk analysis score and hardening guide for decision-makers regarding current threat models and attack techniques. In this study, risk scores based on CVSS were cre ated for up-to-date threats in the ICS field, and hardening mechanisms were also proposed according to the mechanisms behind the related threats and their ef fects.  We aim to address existing shortcomings to some extent by calculating the risk scores of new attacks and to make ICS more secure through proposed hard ening mechanisms against these risks. The sustainability of security can be achieved through holistic security policies that include multi-layered approaches, continuous monitoring, adaptive response mechanisms and advanced approaches such as Zero Trust architecture, AI-based anomaly detection, and hybrid defense systems in the domain where traditional measures fall short.
Paper Presenter
avatar for Alper UGUR
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand

9:30am GMT+07

Time-Synchronized Industrial Data Analytics for Current Unbalance Mitigation in HVJ Electric Boilers: An FMEA-Guided Approach
Friday April 10, 2026 9:30am - 11:30am GMT+07
Authors - Nurkholis, Katherin Indriawati
Abstract -This paper presents a case study on a High Voltage Jet (HVJ) electric boiler, focusing on current unbalance (CU) risk identification and mitigation us ing a combined data-analytics and Failure Mode and Effects Analysis (FMEA) framework. Power-quality assessment follows IEC 61000-4-30 for voltage un balance (VU), while CU interpretation refers to NEMA MG-1 and IEEE recom mendations. The proposed workflow integrates (i) instrument classification (Class A for voltage), (ii) time synchronization across logger/PLC/power-quality analyzer to avoid timestamp drift, and (iii) historian-based data pre-processing (outlier cleaning, scaling, and missing-data handling) prior to statistical analysis. Results show an average CU of 6.85% with a standard deviation of 0.48% and a maximum of 15.92%, indicating operational periods exceeding common industry limits. FMEA highlights electrode aging/damage, loose/corroded cable connec tions, and supply power-quality issues as the dominant contributors. Recom mended actions include online phase-current monitoring, improved water-chem istry and blowdown management, and control optimization of the VFD-driven boiler circulation pump (BCP).
Paper Presenter
avatar for Nurkholis

Nurkholis

Indonesia

Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand

11:30am GMT+07

Session Chair Concluding Remarks
Friday April 10, 2026 11:30am - 11:32am GMT+07

Invited Guest & Session Chair
avatar for Prof. Vishnu Kumar

Prof. Vishnu Kumar

Assistant Professor, Morgan State University, United States
avatar for Dr. Dushyantsinh B. Rathod

Dr. Dushyantsinh B. Rathod

Professor & HOD, Gandhinagar Institute of Technology, India
Friday April 10, 2026 11:30am - 11:32am GMT+07
Virtual Room D Bangkok, Thailand

11:32am GMT+07

Session Closing and Information To Authors
Friday April 10, 2026 11:32am - 11:35am GMT+07

Moderator
Friday April 10, 2026 11:32am - 11:35am GMT+07
Virtual Room D Bangkok, Thailand

12:13pm GMT+07

Opening Remarks
Friday April 10, 2026 12:13pm - 12:15pm GMT+07

Invited Guest & Session Chair
avatar for Dr. Bimal Patel

Dr. Bimal Patel

Associate Professor, KDPIT, CSPIT, CHARUSAT University, Gujarat, India
Friday April 10, 2026 12:13pm - 12:15pm GMT+07
Virtual Room D Bangkok, Thailand

12:15pm GMT+07

AI-Driven Secure Intelligent Systems for Next-Generation Cyber-Physical Applications
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Authors - Gabriel Wilson, Krutthika Hirebasur Krishnappa, Aliaa Salim, Nigel Gwee, Sudhir Trivedi, Shizhong Yang, Tapan Sarkar, Mathieu Kokoly Kourouma
Abstract - This paper presents the design and generation of a novel high-fidelity intrusion detection dataset specifically targeting 5G core control-plane attacks. The dataset is constructed using an Open5GS based testbed integrated with my5G-RANTester, enabling realistic sim ulation of benign UE registration and advanced authentication-layer attacks, including MAC failure, SQN desynchronization, replay, brute force, NAS message manipulation, and denial-of-service scenarios. From raw packet captures, 25 protocol-aware features are engineered, com bining flow-level statistics with entropy-based and sequence-consistency indicators that reflect 5G-AKA signaling logic. To validate the dataset’s effectiveness, multiple machine learning models—ranging from Decision Trees to ensemble methods such as Random Forest and XGBoost—are evaluated using Accuracy, F1-score, and cross-validation metrics un der class imbalance conditions. Experimental results demonstrate that ensemble models achieve near-perfect classification performance with strong generalization capability, highlighting the discriminative power of semantic-aware features. The findings confirm that context-aware fea ture engineering is essential for reliable intrusion detection in virtualized 5G core infrastructures.
Paper Presenter
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand

12:15pm GMT+07

An Ensemble Learning Approach to Cardiac Catheterization
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Authors - Tahani Muftah Abdulsalam, Amina Abdo, Kaled milad, Nouri Bader Mahjoub, Suad Mohammed
Abstract - The Solana Blockchain has found a good change around the world by allowing decentralized applications (Dapps) to be built on its high transaction speeds and low fees. This will open up a whole new level of scalability for de velopers, giving them more ways to create and innovate in a wide range of mar kets, including the DeFi (Decentralized Finance) market, Non- fungible Tokens (NFTs), Gaming, Cryptocurrencies, Social Networks, and more. The Solana eco system is growing at an unprecedented rate. New users and developers are having trouble finding projects that interest them, and developers are having trouble get ting their projects in front of potential users. As a result, many potential projects with high potential have gone unnoticed because of the overwhelming amount of obsolete and conflicting information as well as only partial information being available. The end result has led to confusion, frustration and poor project man agement for many users and developers within the Solana ecosystem. To solve these issues for Solana developers we are creating a community of Solana devel opers through a web based platform which allows Solana developers to showcase their works that are associated with Solana Blockchain.
Paper Presenter
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand

12:15pm GMT+07

An EOQ replenishment policy with varying deterioration, stock-sensitive demand and money inflation
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Authors - Sai Jagnyaseni Rana, Trailokyanath Singh, Pallavi Joshi, Sudhansu Sekhar Routray
Abstract - This paper presents a data-driven closed-loop (CL) identification and controller reconstruction framework for interacting multivariable processes, validated on the benchmark Wood-Berry (WB) distillation column. CL reaction curve data are employed to identify process dynamics without interrupting operation. The measured step responses of diagonal and interaction channels are modeled using secondorder plus time-delay (SOPTD) structures, whose parameters are estimated through a hybrid particle swarm optimization (PSO) and nonlinear least-square fitting (NLSF) refinement scheme. The identified models are reduced to first-order plus time-delay (FOPTD) form using Skogestad’s approximation and further refined for improved accuracy. Based on the optimized FOPTD models and measured CL responses, decentralized PID controller are reconstructed using both PSO and reinforcement learning (RL) via a proximal policy optimization (PPO) agent. Simulation studies demonstrate that while PSO achieved reliable controller recovery, the RL-based approach provides superior transient matching and reduced tracking error. The results validate the effectiveness of the proposed framework for CL identification and data- driven controller reconstruction in interacting multivariable systems.
Paper Presenter
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand

12:15pm GMT+07

Analysis of Financial Market Dynamics using Neurocomputing for COVID 19 Regime Transitions
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Authors - N. Ahana Priyanka, R. Harishkanna, R. Sneka Nandhini
Abstract - Financial markets are widely modeled as rational systems. However, practical evidence suggests that collective decision-making is influenced by interacting emotionally, risk-based, and control mechanisms. To capture this intricacy, this study introduces the Financial Connectome, a neuroscience-inspired pipeline that models the market as a collective cognitive network. This work investigates the long-standing disconnect between neuroscience and finance by mutually analyzing value, risk, sentiment, and control processes at the market level. Building on neurobiological theories of decision-making, a Neuro-Decision Systems (NDS) framework is suggested to examine the market dynamics reorganization under systemic stress. The framework is applied to 1,516 trading days of the NIFTY Bank Index spanning 2017–2023, encompassing the COVID-19 crisis period. The results indicate a significant structural reconfiguration of market states. The Neuro-Decision Score (NDS) exhibits a statistically significant post-COVID shift toward risk dominance, with Kolmogorov–Smirnov, permutation, and Mann–Whitney U tests all rejecting the null hypothesis (p < 0.001). In addition, average state persistence increases by approximately 24%, indicating greater temporal rigidity in market dynamics. The ML-generalized NDS further strengthens the distributional separation, increasing the observed effect size from small to medium magnitude. Post-pandemic markets exhibit heightened sensitivity, reflected by higher activation frequencies across all cognitive systems. These findings suggest that market behavior undergoes measurable cognitive reorganization during periods of extreme uncertainty. The framework provides a structured approach for analyzing regime reconfiguration under sustained uncertainty.
Paper Presenter
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand

12:15pm GMT+07

Comparative Analysis of Machine Learning and Transformer-Based Models for News Topic Classification in Low-Resource Myanmar
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Authors - Ei Sandar Myint, Khin Mar Soe
Abstract - Hallucination occurs when large language models (LLMs) produce information that is incorrect or not supported by facts, posing a significant challenge to the safe and reliable use of these models. Recent research on hallucination detection and prevention is summarized, and important directions for future work are identified. The need for detailed detection methods that can pinpoint exactly where errors occur, as well as techniques for handling hallucinations in long and complex responses, is emphasized. Analysis of model internal states is highlighted as a key approach to understanding the causes of hallucinations. Emerging chal lenges in multi-modal models that process both text and images are dis cussed, along with the growing focus on preventing hallucinations rather than only detecting them after generation. Additionally, the importance of addressing hallucination issues in multilingual and low-resource lan guage settings is underscored. This review aims to support the develop ment of more trustworthy and inclusive language technologies.
Paper Presenter
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand

12:15pm GMT+07

Integrating Storytelling and Game-Based Learning in Primary English Education: A Teacher-Based Needs Analysis
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Authors - Min Wai Yan Oo, Jirarat Sitthiworachart
Abstract - Plant diseases pose a major threat to agricultural productivity, food security, and the preservation of medicinal plant species. Early and accurate disease identification is essential to minimize crop losses; however, traditional diagnostic methods rely on manual inspection and expert knowledge, which are often time-consuming, expensive, and not easily accessible to farmers in rural areas. To overcome these limitations, this paper proposes a Smart System for Identifying Leaf Disease Detection using Artificial Intelligence (AI) and Computer Vision techniques. The primary objective of the proposed system is to develop an automated, scalable, and web-based solution capable of identifying plant species and detecting leaf diseases through image analysis. The system utilizes Computer Vision algorithms to extract critical visual features such as color variations, texture patterns, and morphological characteristics from uploaded leaf images. A deep learning–based classification model processes these features to determine whether the leaf is healthy or diseased. The frontend interface is developed using React and TypeScript, ensuring an interactive and responsive user experience, while backend AI processing is integrated through secure API services. Experimental evaluation demonstrates high classification accuracy and reliable confidence scores under varying environmental conditions. The system also provides treatment recommendations to promote sustainable agricultural practices. By integrating AI driven analytics with modern web technologies, the proposed system enhances early disease detection, reduces dependency on expert consultation, and contributes to sustainable farming, improved crop management, and digital preservation of medicinal plant knowledge.
Paper Presenter
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand

12:15pm GMT+07

Predictive Modelling approaches for Detection of Skin Disease - Future Research Directives
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Authors - Maharajpet Sheela, Roy Ratnakirti, Thakur Manish Kumar
Abstract - The swift expansion of networked vehicles and city traffic has presented major challenges to the management of traffic in smart cities and therefore solutions that are intelligent and privacy-protecting are needed. In this paper, a Drift-Aware Edge-Federated Spatio-Temporal Intelligence (EF-STI) model that utilizes Long Short-Term Memory (LSTM) networks to predict traffic flowing predictively and accurately is offered. Instead of using a traditional centralized or cloud-based model, EF-STI allows individual vehicle or roadside edge units to locally-train a lightweight LSTM model, which is only encrypted model parameters are shared with an aggregator located globally. In order to deal with the non-static and dynamic traffic, a drift-aware federated optimization plan is implemented, which enables the system to adjust to the sudden change and different traffic patterns. The framework uses Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication to predict traffic density and flow with minimal latency enabling proactive interventions to traffic management problems including dynamic signal control, route recommendations, and congestion warnings. It is proved by experimental analysis that EF-STI has better prediction accuracy, lesser communication overhead, and better adaptability than traditional methodology. The article demonstrates a special intersection of edge computing, privacy-sensitive federated learning, spatio-temporal LSTM modeling, and vehicular networking, building intelligent transportation systems to be scalable, secure, and autonomous in traffic management.
Paper Presenter
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand

12:15pm GMT+07

Sentiment Analysis of Uzbek Social Media Posts: Methods and Research
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Authors - Botir Boltayevich Elov, Guzal Tursunpulatovna Malikova, Malika Suyunova Odil qizi, Feruzakhon Mukhiddinovna Bobokhonova, Shamsiddin Mukhiddinovich Primov
Abstract - The Internet of Things (IoT) has spread rapidly, significantly increasing several secu-rity vulnerabilities, as traditional detection systems are becoming insufficient to manage the vol-ume and diversity of traffic that characterizes modern networks. The review provides a compre-hensive analysis of recent advances in learning-based intrusion detection systems (IDS), focusing primarily on deep learning, traditional learning, machine learning, and hybrid frameworks. Through critically evaluating a diverse range of state-of-the-art studies, the review explores dif-ferent methodological solutions, data, and performance measurement in the field. The available empirical results show that, although deep learning models are better at identifying complex pat-terns in the data, traditional machine learning algorithms require less computational power. In addition, hybrid and ensemble models often outperform single-method options, but often with high computational cost. The review outlines a number of important challenges, including the issue of class imbalance and the fact that models are not very interpretable. It argues that light-weight and interpretable AI systems should be a priority in future studies, and the gap between theoretical academic frameworks and practical IoT applications would be minimized.
Paper Presenter
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand

12:15pm GMT+07

Smart Campus Surveillance and Guidance System
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Authors - Sriram V.A, Rajkumar P.N, Babu M
Abstract - Imagine smart glasses as the ultimate wearable sidekick— poised to change everything from daily navigation to factory work—but they’re stuck in neutral thanks to tech glitches, user frustrations, and market messiness. Picture powerhouse AI like YOLOv8 smashing object detection for the visually impaired at 92.7% [email protected], with 94% precision, 91% recall, and 0.93 F1-scores, or DeepLabv3+ delivering sharp segmentation at 89% accuracy, 93% precision, 0.82 IoU, and 0.18 RMSE; yet real-world hits like Meta Ray-Bans limp on 85-160 mAh batteries for just 30 minutes of action, eye-tracking wobbles at 1.2◦ RMSE (dreaming of sub-0.5◦), and custom CNNs nail 96% navigation accuracy with 0.12m MAE but guzzle 40% more power in slim designs. Folks love gestures that cut task times 35-40% over voice (gaze hitting 88% precision, just 12% error), but older users battle 25% extra mental strain dropping acceptance to 47%, 60% report gaze-control fatigue, and even Wang’s health- care apps with 95% diagnostic recall lose 30% usability sans standard interfaces—add 1.2 million Ray-Ban Metas sold by 2025 via Llama-3.1’s zippy 87% query accuracy under 2s latency, but 80% privacy jitters, 70% interoperability woes from 101 studies, and Yoo’s stellar 91% industrial boosts (15s/task MAE) all yell for beefier 400+ mAh batteries, ethical AI under 5% false positives, and shared standards to grab that $31.5B market.
Paper Presenter
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand

12:15pm GMT+07

Unmasking Dark Patterns: A Data-Driven Framework for Detection and User Awareness
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Authors - Mugdha Kulkarni, Diya Oswal, Rudra Kadam, Sachin Pande, Gargi Meshram
Abstract - The swift growth of digital interfaces has facilitated manipulative design practices called dark patterns, which take advantage of cognitive biases to manipulate users and subvert informed decision-making.
Though widespread across e-commerce, social media, and other areas, automated identification and empirical knowledge of user vulnerability are still in their infancy. This work introduces an end-to-end framework integrating a GenAI-augmented browser add-on for real-time detection of dark patterns with systematic estimation of user awareness and behavioural reactions. A new Pattern Vulnerability Index (PVI) measures the threat from individual patterns according to frequency, unawareness among users, and potential damage. Cross-platform analysis identified high-risk patterns like Discount Anchoring, Urgency, and cost-related manipulations to be frequently overlooked by users. Clustering identifies scenarios in which several deceptive patterns occur in co-presence, including checkout processes, promotional displays, and subscription pitfalls.
The results highlight the moral significance of manipulative interface design and establish the capability of machine-based tools to promote user safeguard, sensitize, and guide regulation and design efforts. This study provides a basis for consumer-oriented solutions and future research towards more transparent and ethical online encounters.
Paper Presenter
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand

2:15pm GMT+07

Session Chair Concluding Remarks
Friday April 10, 2026 2:15pm - 2:17pm GMT+07

Invited Guest & Session Chair
avatar for Dr. Bimal Patel

Dr. Bimal Patel

Associate Professor, KDPIT, CSPIT, CHARUSAT University, Gujarat, India
Friday April 10, 2026 2:15pm - 2:17pm GMT+07
Virtual Room D Bangkok, Thailand

2:17pm GMT+07

Session Closing and Information To Authors
Friday April 10, 2026 2:17pm - 2:20pm GMT+07

Moderator
Friday April 10, 2026 2:17pm - 2:20pm GMT+07
Virtual Room D Bangkok, Thailand

2:58pm GMT+07

Opening Remarks
Friday April 10, 2026 2:58pm - 3:00pm GMT+07

Invited Guest & Session Chair
avatar for Dr. Seamus Lyons

Dr. Seamus Lyons

Assistant Professor, International College of Digital Innovation, Chiang Mai University, Thailand

avatar for Dr. Archana S. Banait

Dr. Archana S. Banait

Assistant Professor, Department of Computer Engineering, MET's Institute of Engineering, India
Friday April 10, 2026 2:58pm - 3:00pm GMT+07
Virtual Room D Bangkok, Thailand

3:00pm GMT+07

A Hybrid RegNetX-Hierarchical Bidirectional Linear Cross Attention Framework for Medicinal Plant Leaf Identification
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Authors - Madhusmita Chakraborty, Vijay Kumar Nath, Deepika Hazarika
Abstract - Due to morphological similarities between species, environmental variability, and the requirement for specialized knowledge, accurate identification of medicinal plants is still difficult, despite their critical role in primary healthcare systems around the world. A Hybrid RegNetX-Hierarchical Bidirectional Linear Cross Attention framework referred to as HR-HBCA framework for identifying medicinal plants from leaf photos is presented in this work. Multi-scale features are extracted using a RegNetX backbone, and computationally efficient linear crossattention is used in Hierarchical Bidirectional Linear Cross-Attentive Fusion (HBLCAF) blocks to integrate shallow spatial and deep semantic representations. Balanced contextual exchange across scales is achieved by bidirectional cross-attentive fusion. The HR-HBCA framework shows strong performance under notable intra-class variability, with accuracies ranging from 93.79% to 99.73% when tested on five diverse public datasets.
Paper Presenter
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand

3:00pm GMT+07

Adult Learners’ Preferences for Pedagogical Interface Agents: An Analysis Based on Noticeable Features
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Authors - Ntima Mabanza
Abstract - Research that examines the use of Pedagogical interface agents (PIAs) in digital learning environments has demonstrated that PIAs can increase learner engagement, motivation, knowledge retention, and improve the learning outcomes. Despite that, there is limited empirical understanding of which PIA’s particular features are very noticeable and preferred by learners. A mixed-methods approach was used in this study, combining initial training, task completion, and feature rating questionnaires with 62 adult participants. This approach was used to examine adult learner preferences for PIAs’ noticeable features, such as appearance, voice, and movement. The study findings indicate that adult learners prioritize PIAs’ movement, followed by their appearance, and lastly their voice. The findings of this study provide very useful design guidelines for developing effective learner-centered PIA systems that maximize engagement and satisfaction.
Paper Presenter
avatar for Ntima Mabanza

Ntima Mabanza

South Africa

Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand

3:00pm GMT+07

Convolutional Neural Network Model Ablation for Accurate Single MRI Super-Resolution
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Authors - Imene Kichah, Amir Aieb, Antonio Liotta, Muhammad Azfar Yaqub
Abstract - The rapid growth of Information and Communication Technologies (ICT) has profoundly altered educational systems by redefining teaching practices, institutional processes, and professional expectations. Within the broader context of sustainable development and smart education, ICT has emerged as an important facilitator of efficiency, accessibility, and innovation. This paper presents a conceptual analysis of how ICT can contribute to sustainable development through its influence on teachers’ work–life balance and job satisfaction in ICT-enabled learning environments. While ICT adoption has the potential to enhance instructional flexibility, autonomy, and efficiency, excessive digital connectivity, intensified workload, and blurred work–life boundaries may adversely affect teachers’ well-being. The paper identifies work life balance as a key mediating factor linking ICT use to job satisfaction and long term professional sustainability. Furthermore, the study situates teachers’ well being within the broader framework of sustainable development, emphasizing its relevance to Sustainable Development Goals such as SDG 3 (Good Health and Well-Being), SDG 4 (Quality Education), and SDG 8 (Decent Work and Economic Growth). The analysis underscores the need for human-centred, policy-driven, and ethically oriented ICT integration strategies that prioritize teacher well-being alongside technological advancement. The paper contributes to the discourse on sustainable and intelligent education systems by highlighting that the long-term effectiveness of ICT-driven educational transformation depends on balanced digital practices that support teachers’ work–life balance and job satisfaction.
Paper Presenter
avatar for Imene Kichah
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand

3:00pm GMT+07

Drivers of Gen Z Impulsive Buying: Host, Emotion, and Quality in TikTok Live
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Authors - Aleah Prameswari Kalyana Merkadea Purnomo, Muhammad Aras
Abstract - TikTok Live Shopping has been rapidly growing and the way consumers and brands interact has changed, with emotional and communicative engagement leading the way to driving purchases. However, there is minimal literature to understand the impact of how host performance, emotional euphoria, and perceived quality value combine to affect impulse buying, specifically in reference to preloved fashion and the Generation Z cohort. This study aims to fill the gap in literature by examining the impact of these three components on impulse buying behavior from the perspective of Integrated Marketing Communication (IMC). In this study, a quantitative method was used by conducting an online survey with 136 respondents from Generation Z who have bought items through TikTok Live Shopping. The data was analyzed using Partial Least Squares–Structural Equation Modeling (SEM-PLS). Emotional euphoria is the only antecedent with a statistically significant positive relationship with impulsive buying behavior. Host performance and quality value have a positive relationship but are statistically insignificant. Moderately, the model explains 57% of the variance in impulsive buying (R² = 0.570) showing moderate predictive power. Emotional stimulation is the largest driver of im-pulsive buying, while cognitive evaluation centered around quality is merely justifying a post purchase rationale. This paper illustrates that in live commerce, emotional irrationality is more dominant than communicative rationality, offering a new dimension to the IMC paradigm in the context of real-time social commerce and underlining the criticality of emotional engagement in live sessions for improving customer conversion.
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand

3:00pm GMT+07

How Word of Mouth, Branding, and Exclusivity Shape Consumer Visit Intention
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Authors - Matthew Abrham Kristanto, La Mani, Cindy Magdalena, Maudi Aulia Saraswati, Annisa Atha Hanifah
Abstract - Digital Twins (DTs) are increasingly explored for integrating BIM and IoT in facility management, yet many implementations remain fragmented, weakly governed semantically, and difficult to scale. This paper presents a BIM-centric DT framework for the MaCA museum Living Lab in Turin, combining indoor–outdoor environmental sensing, automated BIM synchronization, IFC-based interoperability, and a prototype temporal analytics layer. The methodology links shared-parameter modeling, Dynamo–Python synchronization, and room-/zone-level identifier logic to validate end-to-end snapshot-to-BIM integration on a one-week monitoring dataset. Results confirm robust parameter mapping, successful serialization of custom space-level IFC property sets, and the feasibility of a dual-layer DT strategy in which BIM/IFC supports semantic-spatial contextualization while external temporal platforms support analytics and dashboard visualization. The core contribution lies in defining a scalable and standards-aligned workflow for cultural facilities based on identifier persistence, modular synchronization, interoperability, and data-quality-aware DT deployment.
Paper Presenter
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand

3:00pm GMT+07

Hybrid AI-Enabled IoT Imaging Framework for Early-Stage Multi-Label Tomato Leaf Disease Detection
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Authors - Md. Abdul Malek Sobuj, Md. Faruk Abdullah Al Sohan, Afroza Nahar, Saeeda Sharmeen Rahman
Abstract - Tomato leaf diseases lead to significant losses in yield and quality, especially in developing areas where timely diagnosis and expert help are scarce. Early and accurate disease detection is vital for sus tainable crop protection and better agricultural productivity. This pa per proposed a hybrid AI-IoT imaging framework for early-stage multi label tomato leaf disease detection in real-field agricultural settings. The proposed hybrid framework combines camera-based IoT sensing, edge and cloud computing, and a lightweight hybrid CNN, the Transformer model, to allow continuous monitoring, timely diagnosis, and decision support. The proposed hybrid framework merges local feature extrac tion with global context modeling to enable accurate multi-label clas sification while being suitable for deployment on devices with limited resources. A conceptual performance comparison and case study show the practical feasibility and benefits of this approach regarding diagnos tic reliability, scalability, and cost-effective deployment. The framework aims to improve early disease identification, reduce crop losses, and sup port precision agriculture practices. This study offers a practical and scalable solution for intelligent tomato disease management and aids the development of sustainable AI-IoT-based smart agriculture systems.
Paper Presenter
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand

3:00pm GMT+07

IoT-Based Smart Railway Crossing System Using Sensors for Real-Time Train Detection and Safety Enhancement
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Authors - Fahima Sultana Smrity, Md. Ibrahim Tanjim, Md. Faruk Abdullah Al Sohan, Afroza Nahar, Saeeda Sharmeen Rahman
Abstract - Solar-powered systems in railway crossing safety are an effi cient approach for ensuring continuous monitoring and accident preven tion in risky and less supervised areas. Solar energy ensures the reliability of the system, while the components connected to it are optimized for en ergy efficiency and long-range communication. In the transportation sec tor, IoT-enabled safety devices are gaining importance, and railway cross ings are a key example. This paper proposes a simplified solar-powered model, called Smart Railway Crossing Protection (SRCP), for railway au tomation using IoT-based sensing and communication. This model intro duces an energy-efficient design with LiFePO4 battery backup, MPPT based solar adaptation, and wireless communication of the LoRa model, focusing on reducing functional costs and dependence on manual su pervision compared to traditional railway safety systems. The proposed system aims to increase real-time responsiveness, ensure stability in re mote places, and improve the overall security of the passenger and vehi cle. Moreover, the SRCP model emphasizes scalability and adaptability, underlining its importance for various railway infrastructures.
Paper Presenter
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand

3:00pm GMT+07

Performance Analysis of UAV Assisted Free Space Optical Communication Link
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Authors - Subhrajyoti Sunani, Prasant Kumar Sahu, Debalina Ghosh
Abstract - Topic detection is an essential task in Natural Language Processing (NLP) that enables the automatic classification of text into predefined categories. However, research challenges in the Myanmar language remain limited due to the lack of annotated corpora and linguistic challenges. In this study, word-level segmentation is employed to capture more semantically meaningful units for topic detection, such as အနုပညာ (art), ဥပဒေ (law), အာားကစာား (sports), and နည ားပညာ (technology). The study trains and evaluates the system on a dataset of News articles categorized into 12 predefined topics: agriculture, art, crime, disaster, economy, education, foreign affairs, health, politics, religion, sports, and technology. A variety of models was examined, covering traditional machine-learning baselines, a deep learning sequence model, and transformer-based architectures. Logistic Regression and Naïve Bayes were tested and achieving accuracies of 0.73 and 0.63, respectively, with Logistic Regression outperforming Naïve Bayes as a stronger linear baseline. The LSTM model, which incorporates sequential dependencies, improves performance further with an accuracy of 0.85. Transformer based approaches deliver the best results: DistilBERT achieves 0.87 accuracy, while word level mBERT achieves 0.95 accuracy at its peak, demonstrating the effectiveness of word-level approaches for Myanmar topic detection. Overall, the findings demonstrate that while traditional models offer useful baselines, deep learning and especially transformer-based architectures provide substantial gains in accuracy and reliability for Myanmar topic detection. This research highlights the effectiveness of modern transformer-based methods for low resource language applications and sets a benchmark for future work in Myanmar NLP.
Paper Presenter
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand

3:00pm GMT+07

RadVision: Topological Data Analysis and Vision Transformers for Automated Radiology Report Generation
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Authors - Ayana Soman, Diya P. Varghese, Elizabeth Anna Liju, Ethel Jimmy, Liyan Grace Shaji, P R Neethu
Abstract - Radiology report generation is a vital and time-consuming part of medical imaging workflows. It is often shaped by heavy workloads and differences in opinions among observers. This paper presents RadVi sion, an AI-driven platform designed to automatically generate prelimi nary radiology reports from medical imaging data, with a specific focus on MRI scans. The framework uses Vision Transformers (ViT) for global feature extraction and Topological Data Analysis (TDA) to identify structural and shape-based abnormalities that traditional deep learning methods might miss. To improve understanding and clinical reliability, RadVision includes explainability tools like Grad-CAM heatmaps and persistence diagrams from TDA. A transformer-based language model creates structured, editable diagnostic reports with confidence scores, allowing for effective validation by humans. The system is accessible through a secure web dashboard, facilitating collaborative annotation, feedback-based model improvement, and smoother workflow integration. Experimental tests across various radiological cases show better diagnos tic support, greater transparency, and less reporting effort. These results position RadVision as a scalable and clear AI tool to assist radiologists and promote efficient and reliable medical reporting.
Paper Presenter
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand

3:00pm GMT+07

When Credibility Goes Viral: Influencer Impact on TikTok Purchase Behavior
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Authors - Aura Meivia Safira Arsya, Ricardo Indra, Shafa Salsabila Risfi Febrian, Benedicta Kalyca Kyatimanyari
Abstract - This study analyzes the extent to which credibility from influencers impacts consumers' buying behavior. The focus will be on how the intention to buy impacts this relationship as the problem is being analyzed in the context of social commerce on TikTok. The study is developed within the framework of Source Credibility Theory which suggests that consumers’ perception and consequent behavior are influenced by the perceived degree of the spokesperson’s Attractiveness, Trustworthiness, and Expertise. The study employs a quantitative explanatory methodology. A purposive sampling technique was used to collect data from a sample of 100 active TikTok users who follow the provided influencer. The analyzed relationships will be quantified using Structural Equation Modelling with Partial Least Squares (SEM-PLS). The research results concluded that influencer credibility increases the intention to buy, but does not increase the purchasing decision. The intention to buy completely mediates the relationship between influencer credibility and purchasing decision. This demonstrates that influencer credibility is a significant factor in the intention to buy behavior, but it is the intention that is essential in order to convert the persuasive influence into actual buying behavior. The study contributes to digital marketing communication research by extending Source Credibility Theory to the context of short-video social commerce platforms.
Paper Presenter
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand

5:00pm GMT+07

Session Chair Concluding Remarks
Friday April 10, 2026 5:00pm - 5:02pm GMT+07

Invited Guest & Session Chair
avatar for Dr. Seamus Lyons

Dr. Seamus Lyons

Assistant Professor, International College of Digital Innovation, Chiang Mai University, Thailand

avatar for Dr. Archana S. Banait

Dr. Archana S. Banait

Assistant Professor, Department of Computer Engineering, MET's Institute of Engineering, India
Friday April 10, 2026 5:00pm - 5:02pm GMT+07
Virtual Room D Bangkok, Thailand

5:02pm GMT+07

Session Closing and Information To Authors
Friday April 10, 2026 5:02pm - 5:05pm GMT+07

Moderator
Friday April 10, 2026 5:02pm - 5:05pm GMT+07
Virtual Room D Bangkok, Thailand
 

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