Authors - Rahul Basu Abstract - Spinodal decomposition in binary alloys produces complex, interconnected microstructures with fractal-like characteristicsduring early and intermediate stages of phase separation. This paper presents a computational framework for simulating three-dimensional (3D) spinodal decomposition using the Cahn–Hilliard phase-field model, with emphasis on fractal dimensionanalysis of the evolving microstructures. The model incorporates CALPHAD-consistent free-energy descriptions (via commontangent interpolation for miscibility gaps) for benchmark alloys such as Cu–Ni and Fe–Cr. Simulations in 3D revealinterconnected networks with fractal dimensions typically in the range 2.4–2.8 during coarsening (deviation <5\% RMSE fromFe–Cr APT data), consistent with experimental observations. Fractal analysis via box-counting ($\log(1/r)=0$–$1.2$) andcorrelation functions ($r=5$–$20$ dx) quantifies morphological complexity, providing insights into scaling behavior and self-similarity. The approach leverages efficient FFT-based solvers for large-scale 3D computations (up to 256$^3$), aligning withuseful descriptors for data-driven materials design, microstructure prediction, and alloy performance optimization. Resultshighlight the transition from early-stage fractal-like patterns to late-stage Ostwald ripening (with LS recovery on larger grids),offering quantitative metrics for alloy engineering.
Authors - S SRINIVASA REDDY,N SARASWATHI, K CHARITHA, L GOPAL KRISHNA Abstract - Accurate identification of paddy crop growth stages plays a crucial role in effective agricultural planning, crop management, and yield estimation. Paddy cultivation is highly sensitive to environmental conditions, disease progression, and growth variability, making continuous and automated monitoring essential. This paper presents an AI-driven framework for automated paddy growth stage identification and yield readiness estimation using deep convolutional neural networks. The proposed system employs the EfficientNetV2-S architecture trained on heterogeneous paddy plant image datasets collected from multiple public sources. To address inconsistencies in labeling across datasets, a semantic stage-mapping mechanism is introduced to map dataset-specific visual classes into standardized paddy growth stages. Furthermore, a confidence-weighted yield readiness index is formulated to provide an interpretable estimate of crop maturity and harvest readiness based on predicted growth stages. The trained model is deployed using a Flask-based web application that supports real-time inference, result visualization, and storage of historical predictions. Experimental results demonstrate stable convergence, high classification accuracy, and reliable generalization across different growth stages. The proposed framework effectively bridges visual growth stage classification and yield estimation, offering a practical and scalable solution for precision agriculture and decision support systems
Authors - Shilpa H. Gujar, Abhijeet B. Auti, Nisha A. Auti Abstract - It is possible to increase the acceptability of small wind turbines for wind regions with low wind velocities for rural as well as urban sectors by placing them inside diffusers. The research on development of various diffusers is a major re-search area nowadays. Curved flanged diffusers can deliver better performance by adding a cylindrical throat section between converging and diverging sections. This research paper presents a systematic study on short curved flanged diffusers with converging-diverging sections and extended uniform throat between them. Twenty-five diffuser models are studied using Computational Fluid Dynamics using ANSYS Fluent. These models are finalized using the design of experiments for six variables at five levels. The throat diameter for all diffuser models is fixed. The investigation is performed by considering radial average velocity and percentage velocity variation along the radial planes. The global velocities are observed as 1.18 to 1.47 times that of the radial average velocities. The diffuser dimensions are optimized to maximize radial average velocity and to minimize the velocity variation along the radial planes. The diffuser with optimized dimensions is manufactured and tested experimentally in a wind tunnel. Good matching is seen between the predicted results and experimental results. The optimized diffuser has the ability to produce more than two times the power that of the turbine without a diffuser.
Authors - Prathilothamai M., R. Rinitha, Priyanshu Raj, Jishnu Hari, Lucky Goyal Abstract - The rapid growth of industrialization and urbanization has intensified the release of emerging air and water pollutants, posing significant threats to environmental sustainability and public health. This paper presents an Internet of Things (IoT) driven monitoring and forecasting framework designed for the early detection of emerging contaminants in air and water systems. The proposed system integrates distributed sensor nodes for real-time measurement of key environmental parameters, including particulate matter, volatile organic compounds (VOCs), heavy metals, pH, turbidity, and dissolved oxygen. Data collected from heterogeneous IoT sensors are transmitted through secure communication proto-cols to a cloud-based analytics platform. Advanced data processing and machine learning models are employed to identify pollution patterns, predict contamination trends, and generate early warning alerts. The framework emphasizes scalability, low power consumption, and cost-effectiveness to support deployment in urban, industrial, and remote environments. Experimental evaluation demonstrates improved detection accuracy and forecasting reliability compared to conventional monitoring approaches. The proposed solution enables proactive environmental management, supports regulatory compliance, and contributes to sustainable development by facilitating timely intervention and mitigation strategies for emerging air and water pollutants.
Authors - Prerna Agarwal, Bharat Gupta, Pranav Shrivastava, Saquib Hussain, Kareena Tuli, Amaanur Rahman, Aishwarya Keshri Abstract - We propose a classification method for Ise-katagami stencil images based on SIFT keypoints and an optimal matching framework. Ise-katagami are traditional Japanese stencil papers originally developed for kimono dyeing, many of which have been preserved over long periods yet lack annotation. Because of copyright-related limitations, methods based on conventional deep learning or transfer learning―which typically depend on large labeled datasets―cannot be readily applied. To address this challenge, the proposed method formulates the classification task as an optimal matching problem over sets of SIFT keypoints, allowing robust comparison of local image structures without relying on pixellevel features. The method requires only a small number of copyrightfree training images to extract representative features for each class, thereby eliminating the need for large-scale training data and enabling fast classification. According to the experimental evaluation, our method computes a suitable decision threshold within seconds, whereas the PCAbased method demands more than 3,000 seconds for optimization, despite both achieving almost perfect classification accuracy.
Authors - V.Mohanraj, J.Senthilkumar, Y.Suresh, K.Selvaraj, B.Valaramathi, S.Sivanantham, B I Hemantt Kumar, Ishwarya P Abstract - The increаsing scаle аnd comрlexity of globаl migrаtion flows hаve creаted significаnt chаllenges for trаditionаl migrаtion mаnаgement systems, раr-ticulаrly in terms of efficiency, dаtа рrocessing, аnd timely decision-mаking. Re-cent аdvаnces in Аrtificiаl Intelligence (АI) offer new oррortunities to enhаnce migrаtion governаnce through intelligent dаtа аnаlysis, аutomаtion, аnd smаrt communicаtion systems. This рарer exаmines the role of АI in modern migrаtion mаnаgement, with а focus on border control, visа аnd аsylum рrocessing, migrаtion flow forecаsting, аnd migrаnt integrаtion services. The study emрloys а structured quаlitаtive аnd comраrаtive аnаlyticаl аррroаch, synthesizing recent аcаdemic literаture, internаtionаl рolicy documents, аnd аррlied digitаl migrаtion systems. АI аррlicаtions аre аnаlyzed within а smаrt governаnce frаmework, emрhаsizing their contribution to communicаtion efficiency, risk аssessment, аnd decision-suррort рrocesses. The findings indicаte thаt АI-bаsed biometric identificаtion, mаchine leаrning–driven risk аssessment, аnd рredictive аnаlytics significаntly imрrove the аccurаcy аnd sрeed of migrаtion-relаted рrocedures. Nаturаl lаnguаge рrocessing tools further enhаnce communicаtion between аuthorities аnd migrаnts by fаcilitаting multilinguаl informаtion аccess аnd 2 service delivery. However, the аnаlysis аlso reveаls criticаl chаllenges, including аlgorithmic biаs, dаtа рrivаcy risks, limited trаnsраrency, аnd the need for humаn oversight in high-stаkes migrаtion decisions. The рарer concludes thаt АI cаn serve аs а key enаbler of smаrt migrаtion governаnce when imрlemented аs а decision-suррort tool within ethicаl, trаnsраrent, аnd humаn-centered regulаtory frаmeworks. The study рrovides рrаcticаl insights for рolicymаkers аnd system designers seeking to integrаte АI into smаrt communicаtion аnd digitаl gov-ernаnce аrchitectures for sustаinаble migrаtion mаnаgement.
Authors - Ischyros Gangbo, Ghislain Vlavonou, Pelagie Houngue, Joel T. Hounsou, Fulvio Frati Abstract - One of the major phenomena in recent decade remains the massive proliferation of data, directly linked to the adoption and expansion of new technologies and the increasing automation of processes, affecting numerous fields such as the economy, education, and cybersecurity. This exponential increase in almost every area is accompanied by an intensification of threats. It is within this context that new approaches are being defined, as traditional security mechanisms are showing their limitations. To counter attacks, several tools, including intrusion prevention and detection systems (IDS), have been designed. IDS are devices intended to monitor an information system in order to react effectively in the event of an attack. To this end, IDS use mechanisms that allow them to listen to the system covertly in order to detect abnormal or suspicious activities and enable effective preventative action against the risks of intrusion. The objective of this article is to compare the performance of the following models: XGBoost, CNN, CNN-LSTM for multiclass classification with a hybrid model. The dataset was first transformed into a sequential format. CNN, CNN-LSTM, and XGBoost models were independently implemented as standalone classifiers to perform intrusion detection. Furthermore, a hybrid CNN-LSTM-XGBoost model was designed, where deep spatiotemporal features learned by the CNN-LSTM network were used as input to an XGBoost classifier for final decision-making. Comparative experimental results show that XGBoost and Hybrid models achieve effective detection performance, the hybrid architecture especially in detecting complex and minority attack categories.
Authors - Yavor Dankov, Boyan Bontchev, Valentina Terzieva, Elena Paunova-Hubenova, Aleksandar Dimov Abstract - The growing demand for lightweight, high-performance, and sustain-able machine structures has accelerated the adoption of intelligent digital design methodologies in modern manufacturing. Conventional CAD-based design approaches rely heavily on manual iterations, limiting efficient exploration of complex design spaces and multi-objective trade-offs. This paper presents a hybrid AI-assisted generative design and topology optimization framework for intelligent lightweight optimization of machine structural components, with ap-plication to column-type machine structures and complex non-prismatic industrial brackets. The proposed framework integrates parametric CAD modeling, finite-element-based structural analysis, CAD-embedded generative design, and an AI-inspired algorithmic decision layer for automated evaluation and ranking of design alternatives. Key performance indicators—including mass, stiffness, stress, deflection, fatigue index, and additive-manufacturing constraints—are digitally processed and combined into a composite performance score to sup-port objective design selection. In the first case study, a rectangular machine column is evaluated across multiple volume-fraction configurations, achieving approximately 20% mass reduction while retaining 96% structural stiffness with minimal increases in stress and deflection. The second case study applies generative design to a complex industrial support bracket under multiple load cases, generating twelve feasible solutions that are algorithmically ranked based on performance and manufacturability. The results confirm that AI-assisted evaluation enables efficient design space exploration and supports intelligent, sustain-ability-driven engineering decisions for advanced digital manufacturing systems.
Authors - Damla Karagozlu, Kian Jazayeri, Ahmet Adalier Abstract - The security of resource-constrained Internet of Things (IoT) devices is increasingly reliant on Zero-Trust Architecture (ZTA) models, as continuous authentication and behavioral-based trust are providing new models to help mitigate against more sophisticated threats. The proposed framework helps strengthen secure and reliable digital infrastructure for emerging smart technologies and connected environments. In developing a ZTA security framework specifically for limited re-sources (IoT), the study proposed a lightweight version that combines Elliptic Curve Cryptography (ECC)-based authentication, real- time determination of trust scores, and the use of machine learning to detect behaviorally-based attack pat-terns from a real attack dataset. In addition, the real-time analysis of device trust scores provides a means to understand which devices are performing in accordance with established expectations or displaying behavior consistent with an at-tack, and when these devices will reach those levels. Combining a lightweight ECC authentication with a (trust) behaviorally-driven approach to anomaly detection provides a means to enforce Zero-Trust by minimizing any adverse effects on computational performance ability in IoT environments. Therefore, the approach provides a practical and scalable foundation for Zero-Trust security in future IoT deployments where devices will have limited hardware resources.
Authors - Steven Saltos-Minaya, Tatiana Zambrano-Solorzano Abstract - The high rate of digital communication has heightened the possibility of fake government announcement getting into the institutions bringing about misinformation and interference in their operations. In an effort to overcome this issue, this paper will be a proposal of a blockchain verification framework that will guarantee the authenticity, integrity, and reliability of any digital notices issued by the government. The system stores cryptographic hashes of official documents in a blockchain Hyperledger, which produces an audit trail that is immutable and unalterable. The entire files of the notices are safely distributed on the InterPlanetary File System (IPFS) which is decentralized and provides scalable and permanent storage which cannot be censored. Smart contracts running on the Hyperledger platform automatically provide access control, authorization checks on authorized government publishers and a robust cryptographic assurance of authenticity and non- repudiation. The schools and institutions can check the notices in real time using an intuitive React-based frontend, with the application logic being dealt with by the Node.js/Express backend as well as communicating with the blockchain layer. Other characteristics like tracking of reputation of publishers, version management and database of instant notification are also added to advance trust and transparency. The suggested solution provides a secure, scaled-up, and highly visible channel of communication between government and educational organizations with the lowest level of system complexity and without the need of any machine-learning parts.
Authors - Deepali Newaskar, Saurabh Parhad, Anjali Yadav, Siddhi Shinde, Samika Karne, Atharva Nangare, Misbah Shaikh Abstract - This study investigates the effectiveness of a student-driven development (SDD) approach utilizing ChatGPT to create SQL-based inventory management systems for Micro, Small, and Medium Enterprises (MSMEs), with a focus on contributing to Sustainable Development Goal (SDG) 12 (Re-sponsible Consumption and Production). A mixed-method study involving 30 student-MSME collaborations was conducted to evaluate the resulting systems based on stock accuracy, reporting efficiency, and user satisfaction. The quantitative results demonstrate significant performance enhancements, with systems achieving average scores of 7.7 for stock accuracy, 7.63 for reporting efficiency, and 8.17 for user satisfaction (on a 10-point scale). Technical analysis showed ChatGPT's pivotal role in input validation (15 cases), SQL query construction (8 cases), and report optimization (7 cases). The most frequent SQL commands were SELECT (14 instances), UPDATE (11 instances) and INSERT (5 instances), highlighting robust data handling. The findings confirm that integrating AI tools like ChatGPT within an SDD framework can deliver practical, scalable, and sustainable digital solutions for MSMEs, advancing digital trans-formation while reinforcing the applied role of higher education in achieving global sustainability goals. These results highlight the potential of student-led AI-assisted development as a scalable model for MSME digital transformation aligned with SDG 12.
Authors - Lavanya K, Srinidhi G A Abstract - The pace with which artificial intelligence (AI) has been adopted in decision-critical applications has, in turn, elevated the need to have more than merely accurate AI systems that are also transparent and comprehendible. Although the complex machine learning models can be highly predictive, its black box strategy creates a question mark on the aspects of trust, accountability, and usability in real-world systems based on artificial intelligence. This paper examines the tradeoff between accuracy and transparency in interpretable machine intelligence and oranges by pointing to the trade-offs that exist between predictive accuracy and model explanation. There is a proposed structured framework which is used for comparing and investigating the black-box and interpretable models on the basis of quantitative performance measures and explainability measures. The article highlights the importance of explainable AI methods of post-hoc in improving the transparency of models without affecting the accuracy of the model significantly. Using a systematic assessment, the paper shows that interpretable machine intelligence may be used to help make reliable decisions and maintain competitive predictive performance. The results help in the creation of credible AI-based systems as it provides information about the creation of models that are effective in balancing the accuracy and interpretability when applied to different application settings.
Authors - Dev Kumar Prajapat, Chakshum Mittal, Abhishek Sharma, Jatin Yadav, Mohammad Shaad, Vishal Shrivastava, Ram Babu Buri, Akhil Pandey Abstract - This paper presents Printify, a real-time, location-based service platform revolutionizing document printing workflows via a dual-interface architecture: a Flutter-based mobile app for end-users and a React/TypeScript Progressive Web Application (PWA) for shopkeepers. Addressing inefficiencies like delays, security vulnerabilities, and service discovery limitations, Printify leverages Firebase for instantaneous cross-platform state synchronization. The PWA utilizes Service Workers for offline functionality and secure protocols enabling paymentconditional document release. Evaluations show a 73% reduction in processing latency, 95% improvement in service discovery, and Lighthouse scores exceeding 92. The platform achieves PCI-DSS compliance and end-to-end encryption, establishing a novel hybrid mobile-web paradigm for location-based services.
Authors - Suresh Reddy, Immanuel Anupalli, P.Sudheer Abstract - This paper presents a comparative framework for detecting knee and elbow form errors in overhead press videos using machine learning. Using more than 2,000 videos from the Fitness-AQA dataset, three models are evaluated: an Inception-based Long Short-Term Memory (LSTM) network with residual connections, a custom stacked LSTM network, and a feedforward neural network baseline. Human pose keypoints are extracted using MediaPipe, and frame-to-frame differences are computed to encode motion dynamics. The dataset includes temporally localized annotations with explicit start and end timestamps for knee and elbow errors, resulting in a class-imbalanced classification task. Model performance is evaluated using accuracy, precision, recall, F1- score, and confusion matrices. Experimental results demonstrate that the Inception-based LSTM consistently outperforms the alternative architectures, followed by the custom LSTM, while the feedforward baseline performs substantially worse. These findings highlight the importance of temporal modeling and multi-scale feature extraction for fine-grained Action Quality Assessment in weightlifting.
Authors - Tanvir Ahmed Fahim, Md. Sohel Rana, Shamsul Arefin Bipul, Tanvir Hasan, Niyaz Mahmud MD. Mujahid, Hridoy Datta Abstract - The rapid expansion of Information and Communication Technologies (ICT) has transformed financial inclusion from a policy objective centered on access into a data-driven process mediated by digital identity systems, algorithmic credit assessment, and fintech platforms. While ICT-enabled financial inclusion promises efficiency, scalability, and outreach to marginalized populations, it simultaneously raises profound concerns relating to personality rights, including identity, dignity, autonomy, privacy, and reputation. This paper advances a normative and conceptual analysis of Personality Rights–Based Financial Inclusion through ICT, arguing that contemporary financial systems increasingly construct a digital economic identity that determines an individual’s financial opportunities and exclusions. Such identities, often generated through opaque algorithms and data profiling, risk reducing individuals to abstract data points, thereby undermining human dignity and meaningful self-determination. The paper develops a conceptual framework that positions ICT as the mediating layer between individuals and financial inclusion outcomes, with personality rights functioning as essential normative safeguards. Central to this framework is the articulation of the Right to Economic Self-Representation, which recognizes the individual’s entitlement to access, understand, contest, and contextualize their digital financial profile. By reframing financial inclusion as a rights-dependent process rather than a purely technological or developmental intervention, the paper highlights the dangers of algorithmic exclusion, permanent economic stigmatization, and surveillance-based inclusion. The study contributes to interdisciplinary scholarship at the intersection of ICT law, financial regulation, and human rights by proposing a rights-compatible model of inclusive finance. It argues that embedding personality rights into the design and governance of financial technologies is crucial to ensuring that financial inclusion operates as a mechanism of empowerment rather than control. The paper concludes that sustainable and legitimate digital financial inclusion must balance technological innovation with the preservation of human dignity and agency.
Authors - Janina Odette S. Vidallon, Apolinar P. Datu, Dominic T. Urgelles, Aljen B. Cabrera, Erika Joy F. Lagos, Lady Anne R. Logdat, Shenclaire A. Galero, Ericka Jean M. Amparo 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 - Sejal Vaishnav, Sanskrati Jain, Suman Dikshit, Vashvi Srivastava, Shailendra Sharma, Vaishnav Preeti Prakash, Vishal Shrivastava, Ram Babu Buri, Mohit Mishra Abstract - Traditional object detection systems are limited in their ability to capture the complexity of urban scenes, often overlooking critical spatial, contextual, and functional relationships required. This paper introduces Urban Scene Intelligence, a Semantic Anchor-and-Expand (SAE) framework that integrates multi-modal perception, structured scene graph construction, and controlled narrative generation to produce grounded descriptions of urban environments. The proposed modular architecture incorporates OWL-ViT for open-vocabulary object detection, SegFormer for semantic segmentation, DepthAnything for spatial depth estimation, Qwen2-VL for attribute enrichment, and OCR for extracting textual context. Unlike end-to-end multimodal models, the threestage pipeline explicitly separates visual perception, symbolic reasoning, and language generation, thereby improving interpretability and factual grounding. By unifying heterogeneous visual cues into a symbolic representation and generating context-aware descriptions from this representation, the SAE framework establishes a transparent and extensible approach to urban scene understanding in complex real-world environments.
Authors - Sara OULED LAGHZAL, Abdelmajid El Ouadi Abstract - Musculoskeletal disorders (MSDs) are a significant occupational health problem in the automotive industry [1].Manual and semiautomated assembly work often exposes workers to repetitive movements and non-neutral wrist positions. Conventional ergonomic assessments are often ad hoc and subjective, limiting their ability to capture positional variations and cumulative strain over time. This article proposes a framework for continuous improvement using artificial intelligence that combines a convolutional neural network-based classification of wrist position (CNN) and a rapid upper limb assessment (RULA)[2] in real time. The convolutional neural network distinguishes between acceptable and unacceptable wrist postures during task execution, and the RULA layer translates the posture data into standardised biomechanical risk indicators. Empirical tests in an industrial context have shown that the CNNRULA hybrid system reliably detects even subtle deviations in wrist position that are difficult to detect by visual observation. This enables comfortable, data-driven proactive interventions in an Industry 4.0 environment.
Authors - Darshika Dudhat, Riya Jagani, Sarita Thummar Abstract - Plant diseases represent one of the major threats for the world's food security and agricultural productivity. In this paper, we present a novel deep CNN model which is improved by the Squeeze-and-Excitation (SE) modules and the Attention Gates (AGs), for multi-class plant disease classification based on five crops including apple, maize, grape, potato, tomato. With large number of image data set and a well-designed training strategy, the established model demonstrates good performance in all aspects including 99% accuracy, 0.99 F1-score and excellent specificity. Exploratory studies are performed through feature visualization and Grad-CAM interpretability. The intense robustness and interpretability of the model give it high potential for practical agricultural applications. The main research methodologies of this paper have: • The proposed Method of Attention-based Deep CNN Model combines (SE) blocks and Attention Gates (AGs), which further improve the channel-wise spatial feature leaning for plant disease classification. • Proposes the Grad-CAM visualizations to show disease-specific regions on leaves and achieves the state-of-the-art performance on five representative crop disease classification tasks. •Introducing attention mechanisms greatly improved the model's ability to focus on disease-related features, as evidenced by its strong generalization performances across a wide array of disease classes.
Authors - Ekanand Mungra, Roopesh Kevin Sungkur Abstract - Today's increasing energy demand, particularly in developing regions, supports both economic growth and the improvement of living conditions. However, these regions experience power outages frequently, due to the high energy consumption of commercial buildings. This research examines energy usage in smart commercial buildings by analyzing data from in-building sensors, collected at ten-minute intervals for more than four months. The aim is to forecast the consumption of energy of these buildings while utilizing AI generated scenarios to generate simulations resembling real-life energy usage situations, thereby improving our model’s predictions. In the era of smart buildings, accurate predicting energy usage does not only facilitate cost savings for businesses, but it also presents an opportunity for revenue generation, particularly through the surplus energy supplied back to the grid from renewable sources such as solar panels. Unlike conventional approaches, this research employs MLPRegressor, a sophisticated model, to analyze and predict intricate patterns of energy usage from the sensor data. This research is particularly significant for advancing energy management strategies in commercial sectors of developing countries, promoting energy independence and efficiency.
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.
