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Type: Virtual Room 8D clear filter
Friday, April 10
 

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
 

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