Authors - Md. Mijanur Rahman, Mst. Tasnia Fahmida, Shithi Bhowmick, Md Tanzid, Zubaed Hossain, Zaid Bin Sajid Abstract - Halal industry has become a major foundation of the Islamic global economy, where religious adherence, consumer trust, and supply chain transparency are becoming increasingly critical. In spite of the existing systems, halal poultry supply chains are persistently confronted by the problem of fragmented stores, dependence on centralized databases and a restricted real-time traceability system. These limitations present the greatest risks of mislabeling and non-compliance of regulations. The performance of existing blockchain-IoT traceability systems becomes increasingly doubtful as they grow more complex because of scalability issues and lack of integration with halal regulatory systems, as well as automatic compliance monitoring. This paper suggests a solution to these issues: a Blockchain-IoT Integrated Halal Poultry Traceability System (BIHPTS) implemented on Hyperledger Fabric. The Proposed system Integrates IoT telemetry for constant data gathering, off-chain storage using InterPlanetary File System (IPFS) to counteract the expansion of on-chain storage, and a dual-governance, rule-based structure based on smart contracts. This framework ensures that distributed, immutable, and secure records are accessible together with the supply chain. The system validates the use of halal feed, authorized slaughtering processes, transportation constraints, and environmentally acceptable threshold limits.
Authors - Indrajitsinh J. Jadeja, Nirav P. Maniar Abstract - Agriculture plays a vital role in ensuring food security, yet traditional crop selection and yield estimation practices often fail to account for complex interactions among soil, climatic, and environmental factors. Recent advances in machine learning (ML) have shown significant potential in addressing these challenges by enabling data-driven decision support for farmers. This paper presents a comprehensive review of machine learning–based crop recommendation and yield prediction techniques, focusing on their effectiveness in improving agricultural productivity and sustainability. The study analyzes various supervised and ensemble learning models applied to soil quality parameters such as nitrogen, phosphorus, potassium, pH, moisture, and climatic variables. Emphasis is placed on multimodal data integration, highlighting how the fusion of soil, weather, and remote sensing data enhances prediction accuracy. The review also discusses current limitations, including data scarcity, model generalization, and real-time deployment challenges, particularly in resource-con-strained farming environments. Finally, the paper identifies key research gaps and future directions toward developing robust, scalable, and intelligent agricultural decision-support systems.
Authors - Thony Enechi, Tevin Moodley Abstract - The legal profession is in a transformative era, driven by technological advancement and global shifts in businesses. This study aims to explore key factors influencing legal sustainability performance in Indian Law firms, with A focus on Environmental, Social, and Governance (ESG) practices through an Artificial Intelligence (AI)-Enabled computational intelligence perspective. While ESG frameworks are widely adopted across industries, their application in the legal sector remains limited due to overreliance on qualitative assessment and the absence of a computational decision mechanism. Considering legal infrastructure as a complex socio-technical system, this research adopts digitization and AI to enhance ESG-based accountability and governance. The pro-posed framework applied the Fuzzy Delphi Method to aggregate 30 legal experts’ knowledge and the Fuzzy DEMATEL to computationally model interdependencies among ESG performance factors. This enables systematic identification of critical sustainability drivers and their causal relationships. The study contributes a computational intelligence-driven sustainability framework de-signed for the legal industry, offering both theoretical and practical insights for technology-enabled ESG implementation. The proposed intelligent system sup-ports informed decision-making and strengthens environmental law enforcement and accountability within Indian law firms. Future research guidelines are also outlined
Authors - P.Srikanth, Immanuel Anupalli, P.Sudheer Abstract - Halal industry has become a major foundation of the Islamic global economy, where religious adherence, consumer trust, and supply chain transparency are becoming increasingly critical. In spite of the existing systems, halal poultry supply chains are persistently confronted by the problem of fragmented stores, dependence on centralized databases and a restricted real-time traceability system. These limitations present the greatest risks of mislabeling and non-compliance of regulations. The performance of existing blockchain-IoT traceability systems becomes increasingly doubtful as they grow more complex because of scalability issues and lack of integration with halal regulatory systems, as well as automatic compliance monitoring. This paper suggests a solution to these issues: a Blockchain-IoT Integrated Halal Poultry Traceability System (BIHPTS) implemented on Hyperledger Fabric. The Proposed system Integrates IoT telemetry for constant data gathering, off-chain storage using InterPlanetary File System (IPFS) to counteract the expansion of on-chain storage, and a dual-governance, rule-based structure based on smart contracts. This framework ensures that distributed, immutable, and secure records are accessible together with the supply chain. The system validates the use of halal feed, authorized slaughtering processes, transportation constraints, and environmentally acceptable threshold limits.
Authors - Om Sarvaiya, Maulik Shah Abstract - Brain-computer interface systems can help people who are unable to communicate due to paralysis or severe motor disabilities. In this work, we im plemented an EEG-based P300 speller that allows users to select characters by focusing on a visual stimulus.The system functions by means of the P300 signal that appears when the user identifies their target character. We developed a com plete pipeline that includes feature extraction, machine learning model classifi cation, and preprocessing of EEG data. The system was tested using the BNCI Horizon 2020 P300 dataset, and the results showed that character selection accu racy ranged from 82% to 86%.Random Forest performed better compared to other classifiers in our implementation. The system was designed in a modular way so that future improvements can be added easily. This implementation shows that EEG-based communication systems can be developed using accessible tools and can support basic communication for people with severe motor impairments.
Authors - Anita Anand, Shivangi Surati Abstract - Artificial intelligence has transformed the predictive analysis of electoral processes by enabling a deeper understanding of candidates' preferences and behaviors through digital data. This study aimed to develop and compare deep learning models for sentiment analysis based on aspects of Ecuadorian electoral opinions. The Cross-Industry Standard Process for Machine Learning methodology was adopted. A dataset of Spanish-language comments collected from YouTube and Twitter, associated with presidential candidates, was constructed. Three classification architectures were implemented: BETO, BETO with Long Short-Term Memory (LSTM), and BETO with Bidirectional LSTM (BiLSTM). The results show that the hybrid architecture BETO with BiLSTM achieves the best performance, with an F1-score of 84.51% and precision of 85.09%, surpassing the other architectures and reaching levels comparable to international studies that employ BERT and hybrid models in political analysis. This model was integrated into an interactive dashboard that allows users to visualize the distribution of positive, neutral, and negative sentiment by candidate, making it a valuable tool for analyzing digital public opinion trends in Ecuador. Future work includes incorporating data balancing techniques, expanding the volume and time frame of comments, integrating demographic and geographic variables, and exploring more advanced models based on transformers and Large Language Models.
Authors - Valeria Alexandra Yunga Manzanillas, Pablo Andres Figueroa Juca, Nelson Oswaldo Piedra Pullaguari Abstract - In the digital era, the global emergence of COVID-19 has necessitated the development of transformative technology to redefine how we interact with and manage public health crises. To effectively slow mortality rates, this work emphasizes the critical requirement for accurate and rapid diagnostic methods that enable early-stage disease detection. Drawing on the necessity for more efficient systems, this paper proposes a high-fidelity diagnostic framework utilizing Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Transfer Learning algorithms. Implemented through a TensorFlow-based 3-class classification strategy, the system was evaluated using a dataset of 817 chest X-ray images (comprising COVID-19, pneumonia-affected, and normal images). The experimental results yielded accuracies of 93.29% for the CNN, 92.68% for the DNN, and a superior 97.56% for the Transfer Learning approach, which outperforms the state of the art methods. These results demonstrate that such high-fidelity computational models provide the conceptual clarity and robustness needed to revolutionize traditional diagnostic methods. By providing instant feedback and a meaningful interpretation of complex medical imagery, the proposed system allows clinical practitioners to achieve precise detections in significantly less time.
Authors - Shahin Makubhai, Ganesh R Pathak, Pankaj R Chandre, Raju Gurav Abstract - Artificial intelligence (AI)–driven personalization is increasingly embedded in digital customer journeys to enhance relevance and efficiency. However, such systems simultaneously raise concerns related to surveillance, autonomy, and trust, particularly in data-intensive service environments. This study investigates how AI personalization intensity and recommendation quality influence perceived surveillance, perceived autonomy, trust, customer experience, and loyalty within AI-enabled hotel journeys. Using a quantitative approach, survey data were collected from 200 hotel guests who interacted with AI-based personalization features. The proposed model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that AI personalization in-tensity and recommendation quality significantly increase perceived surveillance and perceived autonomy, while perceived surveillance plays a central role in trust formation. In contrast, customer experience and loyalty are weakly explained by AI personalization alone. The study contributes to ICT research by demonstrating that AI-driven systems primarily shape cognitive and perceptual mechanisms rather than directly driving behavioral outcomes, highlighting the importance of human-centered and ethically designed AI personalization in digital service con-texts.
Authors - My-Phuong Ngo, Hoang-Thanh Ngo, Loan T.T. Nguyen Abstract - Automated classification of enterprise support tickets is a foundational natural language processing (NLP) task for intelligent service management systems. While trans-former-based models have achieved strong performance on benchmark datasets, their behavior under real-world enterprise constraints—such as class imbalance, do-main shift, calibration reliability, and retraining cost—remains insufficiently under-stood. This paper presents a comprehensive and reproducible NLP framework for enterprise ticket classification, systematically evaluating classical machine learning baselines, full fine-tuning of transformer encoders, and parameter-efficient fine-tuning using Low-Rank Adaptation (LoRA). Extensive experiments are conducted on a large enterprise-style ticket corpus using time-based splits, out-of-domain testing, imbalance stress, calibration analysis, inference latency, and ablation studies. Results show that transformer-based models substantially outperform classical baselines, while LoRA achieves comparable performance to full fine-tuning with significantly reduced training overhead. The proposed evaluation protocol and findings provide practical guidance for deploying robust and efficient NLP systems in enterprise environments.
Authors - Hiren Darji, Devarsh Chandiwade, Tushar Panchal, Meenakshi Chandra, Swapnil Gharat Abstract - Strategic decision making in time dependent systems often involves complex trade-offs between short-term performance gains and long-term degradation effects. Designing effective strategies in such environments requires accurate modelling of performance evolution and careful evaluation of discrete intervention decisions. This paper presents an intelligent strategy simulation framework that integrates data-driven modelling and predictive analytics to evaluate decision strategies under progressive performance degradation. Using high-frequency Formula 1 telemetry data as a representative case study, the proposed framework models lap-time evolution as a function of degradation age and operational context. Both regression-based models and neural network predictors are employed to estimate performance trends, enabling comparison between linear baselines and nonlinear learning approaches. A simulation engine is then used to evaluate multiple strategic scenarios by incorporating degradation dynamics and discrete intervention penalties, allowing quantitative assessment of alternative decision policies. The framework enables direct comparison of strategy outcomes through cumulative performance metrics and visual race progress analysis, providing interpretable decision support. Experimental results demonstrate that both degradation rate and decision timing have a signiicant impact on overall system performance. Furthermore, neural network models consistently outperform linear regression in capturing non-linear degradation behaviour, particularly during extended operational phases. Although demonstrated using motorsport telemetry data, the proposed approach is generalizable to a wide range of real world optimization and decision-support problems involving degradation, uncertainty, and staged decision points.
