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.
Associate Professor and Head, Department of Artificial Intelligence and Data Science, Vidyavardhini's College of Engineering and Technology, Maharashtra, India
Authors - Aiswarya Rajan K K, K Nattar Kannan Abstract - This study presents a systematic literature review on the emergence, adoption, and challenges of AI-driven Human Resource Management (AI-HRM). Thematic synthesis and bibliometric insights were used to analyze eighteen Scopus-indexed studies published between 2019 and 2024 using the PRISMA framework. Using the Technology Acceptance Model (TAM/UTAUT), Socio-Technical Systems (STS) Theory, and Responsible AI principles, the review shows how AI improves HRM by automating repetitive tasks, facilitating data-driven decision-making, and allowing for individualized employee development. However, ethical risks like algorithmic bias, lack of transparency, privacy issues, and employee resistance continue to be major obstacles. The results imply that only when technological capabilities are in line with human judgment, organizational culture, and ethical governance can AI pro-vide long-term value in HRM.
Authors - Padma Lakshmi G, Swetha V, Monik Raj Murugan S, Srinivasa Perumal R, Lakshmi Priya G G Abstract - We have proposed ”Haze to vision: Pipeline for Underwater Image Restoration, Enhancement and Object detection”.The images captured underwater suffer from bluish tint,greenish tint,haze,color distortion. As light travels in water it will undergo scattering, refraction and absorption, the higher the wavelength will be observed first, and the lower wavelength will be absorbed later. This phenomenon affects the bluish/greenish color in the captured images . To study underwater species, underwater environments, we need good quality images and videos. The images captured underwater are poor quality. There have been several researches yet they have many drawacks.We have proposed pipeline.Our model consists of restoration,enhancement,object detection. Restoration process built from deep convolutional neural network called autoencoder .Which has been trained by 5000 synthetic images. The second model is the self-supervised enhancement model. The selfsupervised model is trained for 10,000 epochs of 5,000 datasets.We have used the customized gan model to obtain the best results.We have also used transfer learning and residual network for the improvement of the model.We have reached the PSNR value of 38.33 . CIQUE value 0.82 and UIQM 0.5.Our third model is object detection model. We have used the latest version of YOLOv5 for the betterment and the best object detection model.
Authors - Kishore S, Jeganathan L, Janaki Meena M, Ummity Srinivasa Rao, Jayaram Balabaskaran Abstract - Finding movies from an enormous number of movies that fit our interests and preferences becomes a challenging endeavor. Because recommendation systems address information overload by recommending the most appropriate products to users, they have become widely used in today’s world. The majority of recommendation systems disregard the constraints of the user such as not suggesting certain exceptional movies to them because they aren’t as popular as others. Furthermore, the lack of transparency about how these recommendation algorithms operate creates concerns regarding accountability. In this work, we propose an improved ALS-based recommendation framework that is implemented on Apache Spark and uses HDFS for processing and storing data. In order to address the long tail bias problem, we utilize the ALSbased framework that enhances exposure to low-frequency items through strong interaction filtering. This study employs SHAP to improve transparency and facilitate fairness analysis by explaining the elements generating recommendations to overcome this limitation. Root Mean Square Error (RMSE) and Top-K long-tail exposure metrics are used to assess the model’s performance on a large movie interaction dataset.
Authors - Pradnya Gotmare, Aryan Halkude, Manish Potey Abstract - The high pace of the data-driven applications growth in the distributed settings has enhanced the pressure to ensure that the data sharing infrastructure remains secure, efficient, and privacy-sensitive. The classic centralized data sharing architectures have the intrinsic limitations of being single-point-of-failure, untransparent, and unauthorized access to data, and prone to data corruption. To curb these hurdles, this paper proposes a decentralized approach of sharing secure data with the use of blockchain technology. The suggested system also uses the decentralized and unalterable features of blockchain to provide data integrity, transparency, and confidence among the involved parties without involving third-party intermediaries. Access control policies are the policies implemented using smart contracts to allow only trusted users to access the shared data. The solution is to keep sensitive information in off-chain repositories, where blockchain limitations of storage and scalability do not exist, yet cryptographic hash values and access control measures (ACMs) are stored in the blockchain registry. This design makes sure that the data transactions are confidential and data verifiability and auditability maintained.
Authors - Mutiara Ayu Mawaddah, Norhalina Senan, Mohd Norasri Ismail, Larisang, Muchlis Almubaraq Abstract - With the growing use of smart meters, massive amounts of electricity consumption data are being generated every day. Managing and analyzing this data efficiently is a big challenge. In this study, we generated a smart meter dataset of 10 million records, adding realistic anomalies such as missing values, noise, and unusual spikes to reflect real-world conditions. The data was stored in Hadoop Distributed File System (HDFS) on a single-node virtual machine running on Kali Linux for distributed processing . Using Apache PySpark, we cleaned the data, filled in missing values, identified outliers, and normalized features. For predicting electricity consumption, we trained a linear regression model which achieved a Root Mean Squared Error (RMSE) of 0.0141 and a R2 score of 0.9891, showing that the model predicts consumption very accurately. Overall, this study demonstrates a practical end-to-end approach that combines big data tools and machine learning for smart meter analytics. In the future, this workflow could be extended to multi-node clusters to improve fault tolerance and handle even larger datasets.
Authors - Shilpa Dhopte, Lalit Damahe Abstract - The food portion estimation is a critical component of automated dietary assessment systems, enabling better monitoring of nutritional intake and supporting healthcare, weight management, and public health applications. Traditional self-reporting methods are often inaccurate and time-consuming, motivating the need for computer vision–based approaches that can reliably estimate food portions from images captured in real-world conditions. This paper presents deep learning pipeline for food portion estimation that integrates image preprocessing, deep learning–based segmentation, and geometric volume computation. The data preprocessing with Mask R-CNN used for precise food seg-mentation, providing pixel-level masks and bounding boxes that isolate individual food items from complex backgrounds. The segmented mask is used to estimate the pixel area of the food region. Experimental evaluation demonstrates that the proposed method achieves high segmentation accuracy, with a segmentation IoU of 87.6%, precision of 90.3%, recall of 88.9%, and an F1-score of 89.6%. The pixel area estimation error is limited to 6.8%, resulting in an overall portion estimation accuracy of 89.1%, indicating reliable and consistent performance across different food images. The proposed framework highlights the effectiveness of combining deep instance segmentation with geometric volume estimation for accurate food portion assessment. Future work will focus on multi-view image integration and real-time deployment in mobile dietary monitoring systems to enhance robustness and scalability.
Authors - Md. Shahidul Islam, Md. Murad Hossain, Omar Faruck Ansari Abstract - Time series prediction plays a critical role in monitoring and control of electrical power systems, particularly for detecting frequency fluctuations caused by imbalances between generation and demand. This study proposes an early warning framework for frequency fluctuation events using a hybrid k-Nearest Neighbour (KNN) and Dynamic Time Warping (DTW) approach combined with a global confidence interval based decision mechanism. Electricity frequency data collected from the New Zealand power grid over a six-month period were segmented into training, validation, and testing sequences. Alignment distances between historical and incoming sequences were used to identify precursor patterns indicative of impending frequency disturbances. Experimental results show that the proposed method achieves high warning accuracy with a very low false negative rate, outperforming baseline models such as ARIMA and LSTM. The findings demonstrate that KNN–DTW provides an effective and practical solution for early warning of frequency fluctuations, supporting improved operational reliability in modern power systems.
Authors - Gina Gallegos-Garcıa, Nidia A. Cortez Duarte, Jose A. Arellano Munguıa, Humberto A. Ortega Alcocer Abstract - "Communication has been a topic as ancient as man and at the same time so important that, over time, various forms have been cre- ated to facilitate it, among which stand out: mail, telephony, telegrams, and fax, to name a few. Nowadays many people use instant messaging applications to communicate with each other by feeling that their con- versations are protected. However, that feeling could not be further from reality and should not be taken lightly, since there are always groups focused on taking advantage of the vulnerability of this kind of applica- tions, resulting in users’ privacy being compromised. In this paper, we present the development of an instant messaging application that inte- grates a novel key establishment protocol based on a quantum-resistant algorithm. Our application employs cutting-edge lattice-based crypto- graphic techniques, ensuring robust security against quantum attacks while maintaining operational efficiency. Obtained results show the ap- plication’s viability by offering a practical solution to safeguard mobile communication in the impending quantum era."
Authors - Soumen Halder, Subhamoy Bhaduri, Binayak Mukherjee Abstract - Paraphrasing is significant in applications that require controlled lexical variation to original text with semantic equivalence, especially in educational assessment systems where student answers should be scored on more than surface level matching. Recent transformer-based paraphrasing models do not exhibit regulated structural changes but instead generate uncontrolled changes, are costly in terms of computation, and are not feasible in low-resource or real-time implementations. These limitations are overcome by this work with a lightweight synonymreplacement paraphrasing framework on the basis of exclusive embedding clustering. The proposed EEC-SRP model groups semantically similar words into local embedding clouds and limits the search of synonyms to the tiny areas, which lowers the complexity of search considerably. An embedding augmentation algorithm involves perturbation to form embedding clusters and a neural network is trained to output contextually favorable synonym embeddings in those clusters. Strict semantic fidelity and controlled lexical substitution is ensured by the model by maintaining word count and sentence structure. Experimental analysis of standard paraphrasing tasks show that the suggested methodology attains high levels of semantic similarity, competitive levels of BLEU and ROUGE, and significantly quicker inference than conventional embedding-based and transformer-based models. The proposed model can be effectively implemented in automated assessment systems, controlled text rewriting and resource-constrained applications of natural language processing due to its low memory footprint and computational efficiency.
Authors - Pablo Ramon, Josue Piedra, Nelson Piedra Abstract - The increasing numbers of deepfakes and AI tools have made it difficult to trust digital images these days. Images can be altered and ownership can be established without revealing private information. Current systems have many limitations, and systems that either rely on easyto change metadata or on cryptographic methods that are too costly like ZKSNARKs. To overcome these limitations, an authentication verification model has been presented named ZKP-Guard based on a Dual- Lock architecture framework. The detection system verifies an image is a real image by using ECDSA signatures and a custom ownership in the Schnorr-based Zero-Knowledge Proof for the protocol. This framework was tested on a dataset with significant number of images and produced desired results.
Associate Professor and Head, Department of Artificial Intelligence and Data Science, Vidyavardhini's College of Engineering and Technology, Maharashtra, India
Authors - Pablo Figueroa, Valeria Yunga, Pablo Ramon, Nelson Piedra Abstract - Traditional airport meet-and-greet operations are often characterized by a sea of physical placards and manual, paper-based logging systems. This manual approach not only creates logistical clutter in arrival halls but also leads to significant information lag and frequent data entry errors during the administrative reconciliation process. This paper presents the design and implementation of a centralized digital platform developed to streamline the coordination be-tween airport authorities, hotel representatives, and arriving passengers. Utilizing a responsive web-based architecture, the system eliminates the requirement for native application installations, thereby ensuring immediate accessibility for international travelers and hotel staff through their mobile devices. The platform integrates a multi-tier interface that facilitates real-time booking, automated digital check-ins, and instantaneous data synchronization. By replacing error-prone manual key-in tasks with an automated data pipeline, the system provides airport management with real-time operational visibility and analytics. Preliminary results from the implementation demonstrate a substantial reduction in guest waiting times and a marked improvement in data accuracy. Ultimately, this digital transition enhances terminal space management and provides a more seamless, professional experience for international arrivals, establishing a scalable model for modern airport ground handling services.
Authors - Monali Deshmukh, Payal Shete, Tanvi Pakhale, Pranjal Alhat, Krutika Salve Abstract - Because of their expensive price, large size, and reliance on lab settings, conventional oscilloscopes are inconvenient tools for signal analysis. They have made it necessary to have small, inexpensive, portable devices that can see waveforms outside of typical lab settings. The creation of a portable digital oscilloscope utilizing a 2.8-inch TFT display and an ESP32 microprocessor is detailed in this paper. Because of its autonomous operation, the gadget can record data in real time and display analog signals. Because it runs on batteries, the oscilloscope is affordable, lightweight, and portable. The ESP32 samples analog signals and displays them with user-controlled time-base settings. This oscilloscope has features including a grid display, waveform zooming, and freeze for convenience and readability. Both AC and DC signals can be monitored with an oscilloscope. According to tests, the device accurately displays common waveforms including sine, square, and sawtooth signals, which makes it ideal for embedded system development, simple troubleshooting, and instructional purposes.
Authors - Luis Anthony Hidalgo Ponce, Maricela Pinargote-Ortega Abstract - Technical support management in university environments often faces a high manual operational load due to the constant increase in digital service requests. This paper presents a multi-agent system based on Large Language Models (LLMs) designed to automate the ticket lifecycle, including classification, urgency-based prioritization, and intelligent routing. The proposed solution is built upon a modular architecture coordinated by an orchestrator agent and integrated with Retrieval-Augmented Generation (RAG) techniques to resolve frequent queries without human intervention. The system’s performance was evaluated through a controlled dataset, achieving a classification accuracy of 85.7% and a 100% effectiveness rate in user intent detection. The results demonstrate a significant reduction in response times compared to manual processes, validating the efficacy of generative artificial intelligence to optimize efficiency and user experience within university technology service desks.
Authors - Madhuri Surwase, Trupti Bansode, Jyoti Pawar, Smita Katkar, Vaishali Kalsgonda, Prakash Bansode, Namdev Falake Abstract - Automatic Speech Recognition (ASR) systems have achieved remarkable progress through deep learning and Transformer-based architectures, demonstrating near-human accuracy on clean audio. However, their performance degrades significantly under challenging conditions and specialized domains. This comprehensive study evaluates leading commercial ASR APIs—Google Cloud Speech-to-Text, Microsoft Azure Speech Service, AssemblyAI, Deepgram, OpenAI Whisper, Speechmatics, and others—across multiple dimensions: general speech recognition, low-quality forensic-like audio, domain-specific mathematical notation, and personalized speaker adaptation. Results demonstrate 100% accuracy on clean audio for leading systems (Deepgram, Speechmatics, Webkit SpeechRecognition), but dramatic performance degradation to 10− 81% word error rates on forensic-like audio. Analysis of domain-specific challenges reveals that none of the tested commercial ASR systems natively support direct transcription of mathematical symbols and Greek letters into structured symbolic output (e.g., LaTeX). The study identifies critical limitations in robustness, modularity, and domain adaptation, while highlighting promising customization mechanisms including custom vocabularies, language models, and post-processing integration. Performance improvements through speaker personalization ranged from 3% for natural voices to 10% for synthetic voices. Despite notable advances in end-to-end and Transformer-based approaches, ASR systems remain unsuitable for forensic applications and specialized domains without substantial customization and post-processing. Future research must address low-resource performance, linguistic diversity, robustness in extreme noise, and the integration of Large Language Models for semantic understanding. This paper synthesizes recent advances and critical gaps, providing a roadmap for advancing ASR technology in specialized and challenging acoustic environments.
Authors - Nurul Istiq faroh, Nur Asitah, Amiruddin Hadi Wibowo, Ricky Setiawan, Abdur-Razaq Aliyy Abolaji, Hendratno Abstract - Detecting structural breaks and anticipating volatility regimes in foreign exchange markets remain challenging due to the non-stationary and nonlinear nature of exchange rate dynamics. This study proposes a non-parametric framework for identifying structural breaks in the NZD/ USD exchange rate by integrating sliding-window volatility estimation, concentration bound based change point detection, and wavelet-based time frequency analysis. Volatility is first quantified using a movingwindow approach and compared against a Hoeffding bound to detect extraordinary events. The resulting change points are used to segment the exchange rate series into statistically reliable sequences, which are subsequently analyzed using wavelet scalograms. Empirical results reveal a consistent three-regime structure in the wavelet domain, comprising post-event reaction, stable market behavior, and pre-event escalation phases. Non-parametric statistical tests confirm significant differences in volatility distributions across these regimes, with the pre-event regime exhibiting markedly higher variability and acting as a precursor to structural breaks. The findings demonstrate that wavelet coefficients contain informative signatures of impending market instability. Overall, the proposed framework provides an interpretable and robust approach for analyzing regime-dependent volatility dynamics and offers valuable insights for early warning and risk management in currency markets.
Authors - Syeda Zaina Rohana Sneha, Mohammad Shamsul Arefin, M. M. Musharaf Hussain Abstract - This study details the development and evaluation of a web-based digital health platform that uses Optical Character Recognition (OCR) and Artificial Intelligence (AI) to automate the reading of medication labels and manage appointments. Users photograph medication labels and appointment slips, and the system automatically extracts and organizes relevant data to generate medication schedules, appointment calendars, and reminders with minimal manual effort. Designed with a user-centered approach to lessen cognitive load, the platform was tested with 35 users. Three experts verified the content validity of the assessment tool via the Item Objective Congruence (IOC) index. User satisfaction analysis indicates high approval, particularly for reducing the memory burden associated with medication routines and appointments. The results indicate that integrating OCR and AI can support continuous care, enhance usability, and increase patient engagement in the sustainable management of chronic diseases.
Authors - Tirupathi Rao Dockara, Manisha Malhotra Abstract - The prediction of cardiovascular disease (CVD) risk by machine learning is frequently impeded by duplicated and associated clinical characteristics, leading to complex and less robust models. Feature selection is therefore essential to improve model compactness while maintaining predictive performance. This study presents a systematic evaluation of meta-heuristic-based feature selection for CVD risk modeling under a standardized experimental setting. Feature selection is formulated as a wrapper-based optimization problem and evaluated using representative population-based meta-heuristic algorithms from multiple families. All methods are assessed using the XGBoost Histogram classifier on a public cardiovascular dataset comprising approximately 70,000 records with 13 clinical features. Experimental results show that meta-heuristic feature selection consistently reduces the number of input features by more than 60% while achieving comparable predictive performance across different algorithmic families. In addition, SHAP analysis is employed to examine the contributions of the selected features and support model interpretability.
Authors - Md. Shahidul Islam, Ronobir Chandra Sarker Abstract - The widespread adoption of artificial intelligence (AI) and automation is emerging as a central driver of productivity growth in European firms. Yet identifying the causal impact of AI adoption on firm productivity is complicated by endogeneity, selection bias, and heterogeneous treatment effects. This paper analyzes the productivity effects of AI and automation adoption using a unified framework that combines traditional econometric techniques with causal machine learning methods. Using firm-level data from Orbis merged with industry-level productivity and ICT capital measures from EU KLEMS for the period 2010–2023, we estimate both average and heterogeneous treatment effects. Double Machine Learning yields a robust average productivity gain of approximately 4.5 percent, while Causal Forests reveal substantial heterogeneity across industries, firm size, human capital, and digital maturity. The results provide credible causal evidence that AI adoption enhances firm productivity and highlight the importance of complementary capabilities in realizing its economic benefits.
Authors - Sonia Kuwelkar, Veena Gauns, Rohit Sopan, Sonia Shetkar, Dinanath Usgaonkar Abstract - Prompt engineering has emerged as an essential paradigm in leveraging desired behaviors from large language models (LLMs) without altering their parameters. Although the majority of the current literature has revolved around the introduction of novel prompt engineering strategies, there has been comparatively less emphasis on the contribution of the evaluation and optimization of prompts in concrete systems. In this paper, we offer a specialized review of prompt engineering from an evaluation/optimization centric viewpoint with a larger nod to conceptual developments and illumination rather than detailing the comparisons of approaches. Furthermore, we attempt to establish the concrete importance of prompt engineering via a real-life application, which resulted in improved performances in tasks through the process of prompt refinement and informal evaluations without the need to change the architecture and weights of the models. The paper will also introduce the deficiencies in prompt engineering in the realms of re-producibility, robustness, and the unavailability of standardized approaches in the aspect of concrete evaluations.
Authors - Domenico Vito, Carol Maione, Gabriela Fernandez, Catia Algieri, Sudip Chakraborty Abstract - The demand for long-endurance, intelligent drone systems is growing across diverse domains including defense, sports analytics, and industrial inspection. This paper presents the design and implementation of a solar-powered drone platform equipped with an autonomous, image-based range scoring system. Leveraging high-efficiency monocrystalline photovoltaic panels and Silicon- Carbide (SiC)-based lithium-ion batteries, the drone achieves extended flight durations while maintaining energy reliability. A centralized Energy Management System (EMS), featuring Maximum Power Point Tracking (MPPT) control, optimizes real-time energy harvesting and distribution. The platform also integrates an AI-enhanced thermal imaging module for precise target impact detection and scoring, with results computed using a multi-parameter range scoring model. An interactive Ground Control Station (GCS) interface enables intuitive mission planning, telemetry visualization, and data export. Experimental evaluations demonstrate significant gains in energy efficiency and scoring precision, underscoring the system’s potential for sustainable, autonomous aerial operations in real-world conditions.
Authors - Felix Kabwe, Jackson Phiri Abstract - The growth of Open Educational Resources (OER) has created a paradox of abundance, causing “academic infoxication” where students struggle to find content aligned with their competency levels. Traditional recommender systems often fail to interpret pedagogical context effectively. This paper presents the implementation and empirical validation of OPMAS, a multi-agent architecture orchestrated with LangGraph that utilizes Large Language Models (LLMs) to automate the curation and adaptation of educational resources. Unlike linear chatbots, OPMAS employs a state-graph of specialized agents (Router, Query, Search, Adaptation) to map user queries to European competency frameworks like DigComp. The system, built using Gemini 2.5 Flash and a hybrid retrieval strategy, was validated through a Minimum Viable Product (MVP). Results demonstrate a functional success rate of 95% in complex reasoning flows and a semantic precision of 0.77. Although the deep reasoning process introduces an average latency of 96 seconds, the system successfully prioritizes pedagogical relevance and content adaptation over immediate retrieval, proving the technical viability of agentic architectures for personalized education.
Authors - Minal Deshmukh, Aakash Dabhade, Daksh Jethwa, Siddhi Jadhav, Ketki Khirsagar Abstract - In this paper, we outline the design and implementation of a novel electronic voting kiosk, dubbed BlockVote, which helps counter identity-related fraud and data tampering via biometric and blockchainbased approaches. The proposed system is a standalone embedded system running on an ESP32-S3 SoC-based microcontroller. The system includes a touchscreen display for user input and an optical fingerprint sensor for identity checking. This collected bio-data and voting selection are then integrated in such a manner that a secure transaction is created through cryptography. This is then sent through the Node.js gateway, which leads it to the secure Ethereum-based blockchain network. Such an application of physical verification technologies with blockchain technology ensures that the proposed voting system is more secure than the traditional e-voting machines or e-voting websites. Block-vote is a hybrid security system in which hardware-based verification techniques are combined with blockchain-based data management in a power-saving, compact format. The prototype has shown proof of its functional viability, its module-based construction, and its reliability, particularly in the field of embedded systems. The experimental results demonstrate the system’s high precision, low latency, and robustness against illegitimate use. The suggested framework demonstrates the practical feasibility of blockchain and biometric technology in the creation of trustworthy electronic voting systems that can be used in both urban and rural areas.
Authors - S.D.P. Abeysekara, J.A.D.N. Jayakody, K.A. Dilini T. Kulawansa Abstract - Breast cancer is the second most prevalent cancer globally and a leading cause of death among women. According to the World Health Organization, over 2.3 million new cases are diagnosed annu ally, emphasizing the need for early and accurate detection.In this work, Wavelet-Driven Intelligent Model for Multi-Class Breast Cancer Diagno sis is proposed. In this proposed work, three level wavelet decomposition is used on BreakHis data to extract wavelet based features. These fea tures were fed to Artificial Neural Network Classifiers such as Multi-Layer Perceptron (MLP), Radial Basis Function (RBF) and Machine Learning Classifier Random Forest (RF). Multi-class classification (binary , be nign sub-types, 4 malignant sub-types) of breast tumour has been done. The experimental results show that RF achieved high accuracy of 94% for benign and malignant, 97% for benign sub- type and 92% for malig nant subtype classification compared to RBF and MLP. Deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are more effective when trained on large-scale datasets but for small datasets and limited resource environments, the proposed framework ensures efficient and consistent diagnostic approach. In future, a prototype breast cancer alert system can be developed using raspberry pie for real time application.
Authors - Md. Shahidul Islam, Atiqur Rahman, Md. Murad Hossain Abstract - This study examines the influence of both demographic and natural factors on climate change risk perception in New Zealand. Using data from a nationally representative survey, the analysis applies exploratory factor analysis to construct a composite measure of risk perception, followed by correlation and regression modeling to evaluate the relative contribution of environmental exposure and human characteristics. The findings indicate that while natural factors such as temperature anomalies and extreme weather exposure significantly shape perceived risk, demographic variables including prior disaster experience, trust in scientific institutions, and media exposure exert a stronger overall influence. These results underscore the importance of incorporating social and behavioral dimensions into climate risk assessments and policy development to enhance public engagement and adaptive capacity.
Authors - Shreyas M S, Kumar P K, Venkateswara Rao Kolli Abstract - The Newborns mostly use infant crying as their main form of communication and it represents a great variety of physiological and emotional conditions. Despite the high potential of automated infant cry analysis in early diagnosis and support of caregivers, the application in real-life still has low usage rates because of environmental noise, imbalance of classes, low interpretability, and high computational cost. This paper is a compilation of an effective, interpretable, and real-time infant cry classification system using a two-step hierarchical methodology. The first stage involves a distinction of cry and non-cry sounds to reduce the rate of false alarms due to background noise. The second stage involves categorizing detected cries into a particular intent. An adaptive feature fusion strategy based on reinforcement learning, gives the cepstral and prosodic and qualitative acoustic features dynamic weights, and SHAP-based explainability offers explicit feature interpretations. Data augmentation, SMOTE-Audio, and model pruning are used to find solutions to the issues of class imbalance, noise robustness, and deployment constraints. Experimental evidence shows that the proposed approach outperforms single feature base-lines, it is also stable in noisy environments and also attains significant parameter reduction without significant loss in performance, making it possible to run in resource-constrained devices in real time. The system is tested on a publicly available infant cry dataset which contains 889 audio samples of cry and non-cry signals in five categories of cry intent and was recorded in realistic conditions.
Authors - Md. Shahidul Islam, Md. Raihan Habib Abstract - Detecting structural breaks and anticipating volatility regimes in foreign exchange markets remain challenging due to the non-stationary and nonlinear nature of exchange rate dynamics. This study proposes a non-parametric framework for identifying structural breaks in the NZD/ USD exchange rate by integrating sliding-window volatility estimation, concentration bound based change point detection, and wavelet-based time frequency analysis. Volatility is first quantified using a movingwindow approach and compared against a Hoeffding bound to detect extraordinary events. The resulting change points are used to segment the exchange rate series into statistically reliable sequences, which are subsequently analyzed using wavelet scalograms. Empirical results reveal a consistent three-regime structure in the wavelet domain, comprising post-event reaction, stable market behavior, and pre-event escalation phases. Non-parametric statistical tests confirm significant differences in volatility distributions across these regimes, with the pre-event regime exhibiting markedly higher variability and acting as a precursor to structural breaks. The findings demonstrate that wavelet coefficients contain informative signatures of impending market instability. Overall, the proposed framework provides an interpretable and robust approach for analyzing regime-dependent volatility dynamics and offers valuable insights for early warning and risk management in currency markets.
Authors - Diego Perez-Lopez, Rodolfo Bojorque, Jorge Duenas-Lerin, Raul Lara-Cabrera Abstract - Accurate early detection of liver cancer remains a significant clinical challenge, primarily due to scarce annotated imaging data, inconsistencies in radiological interpretation, and the inherent opacity of deep learning models. To address these limitations, this study proposes a clinically informed, explainable deep learning framework designed specifically for low-annotation settings. The framework combines transfer learning with advanced visualization techniques, enabling both high diagnostic accuracy and medically meaningful outputs that integrate seamlessly into clinical workflows. Three pre-trained CNN architectures — ResNet-50, DenseNet-121, and EfficientNet-B4 — were adapted to liver cancer imaging through domain-specific fine-tuning. Model generalizability was reinforced by combining geometric data transformations with StyleGAN2-derived synthetic lesion generation. Model transparency was facilitated through Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP), while clinical trustworthiness was evaluated via predictive uncertainty quantification, subgroup bias analysis, and resistance to adversarial perturbations. The proposed framework was evaluated on the LiTS and TCGA-LIHC datasets, demonstrating a 15–20% improvement in accuracy over baseline models that consisted of standard convolutional neural networks trained from scratch without transfer learning or data augmentation. EfficientNet-B4 achieved 94.2% accuracy, 0.96 specificity, and an AUC-ROC of 0.978. Grad-CAM accurately highlighted tumor regions in 89.4% of cases, and Bayesian dropout identified 7.3% of predictions as uncertain. These findings demonstrate the framework’s potential for clinical deployment by balancing performance, transparency, and reliability.
Authors - Jutika Borah, Debarun Chakraborty, Bhabesh Deka, Rosy Sarmah, Siddeswara Bargur Linganna, Diptadhi Mukherjee, Ram Bilas Pachori, Mohit Khamele Abstract - Electroencephalogram (EEG) signal modeling for downstream tasks, such as classifying neurological states and identifying biomarkers, is essential for designing effective brain-computer interfaces. Conventional methods often treat EEG channels independently, overlooking inter-channel dependencies, while existing graph-based approaches address this limitation either through fixed electrode geometry or entirely data-driven connectivity. In this paper, we propose a graph representation framework that combines coherence-based spectral connectivity with domain-informed priors, such as anatomical structure and regional proximity, based on graph signal processing (GSP). The resulting representation embeds multichannel EEG signals as attributed graphs through graph convolutional networks (GCNN) to learn discriminative embeddings. Experimental results demonstrate that the hybrid framework enhances classification performance, with the proposed GCNN-deep model achieving the highest area under the receiver operating characteristic curve (AUC) across all datasets and reaching 93% on Dataset 1. These EEG datasets correspond to three independent populations and include recordings from both healthy individuals and patients with neurological disorders such as major depressive disorder (MDD) and epilepsy.
Authors - Samiksha Chougule, Kirti Satpute, Krishnraj Patil, Om Kumbhardare, Sumedha Patil Abstract - Rural communities face significant challenges in accessing essential healthcare services due to language barriers, limited health literacy, and insufficient medical support. Difficulties in understanding medical information, communicating symptoms, and interpreting diagnostic reports further restrict effective healthcare delivery. Moreover, unreliable internet connectivity limits the reach of conventional digital health platforms. This paper presents a Multilingual AI Health Assistant designed to operate on low-cost edge devices, enabling offline functionality to ensure continuous access and data privacy in low-connectivity areas. The proposed system integrates AI, ML, NLP, OCR, and speech recognition to allow users to interact in their native languages through text or voice. It analyzes user-reported symptoms to predict probable health conditions, translates complex medical reports and prescriptions into simplified, localized explanations, and provides recommendations for nearby healthcare facilities. Unlike internet-dependent telemedicine systems, this edge-based solution processes data directly on the device, safeguarding sensitive health information while maintaining reliability. By bridging linguistic and literacy gaps, the proposed assistant empowers rural populations with accessible and actionable healthcare insights, ultimately improving health outcomes in underserved regions.
Authors - Noor, Soumya Mukherjee, Shivraj Singh Yadav Abstract - The increasing numbers of deepfakes and AI tools have made it difficult to trust digital images these days. Images can be altered and ownership can be established without revealing private information. Current systems have many limitations, and systems that either rely on easyto change metadata or on cryptographic methods that are too costly like ZKSNARKs. To overcome these limitations, an authentication verification model has been presented named ZKP-Guard based on a Dual- Lock architecture framework. The detection system verifies an image is a real image by using ECDSA signatures and a custom ownership in the Schnorr-based Zero-Knowledge Proof for the protocol. This framework was tested on a dataset with significant number of images and produced desired results.
Authors - Sanket Shah, Jenice Bhavsar, Bhumi Shah, Jishan Shaikh, Khevana Raval, Ekta Vyas Abstract - Dyslexia is a neurodevelopmental condition that impairs reading fluency and phonological processing across languages. Early identification in school settings remains difficult because the Dyslexia Assessment for Languages of India (DALI) assessment tool requires expert administration which makes it difficult to implement in practice. The latest developments in artificial intelligence allow researchers to evaluate reading patterns through inexpensive devices which people commonly use. The research presents a system framework that uses multiple methods to combine webcam-based eye-tracking with voice analysis and machine learning methods for early dyslexia detection. The system examines tabular gaze and speech features through gradient-boosted models while using convolutional neural networks to encode spatial gaze patterns which include a meta-learning layer for multimodal fusion. The proposed framework enables practical implementation through its web-based interface which connects to secure backend services, thus providing schools with a privacy-protected and scalable method to conduct dyslexia assessments and provide personalized learning assistance in their resource-limited classrooms.
Authors - Geethashree A, Surabhi M R, Varshitha H N, Vipul S, Vivek M R Abstract - The RISC-V Vector Extension (RVV) enables scalable data-parallel processing through a flexible vector length architecture, offers a standardized and scalable approach to vector computing. Derived from an analysis of existing RVV architectures, this paper presents a focused architectural study and implementation of a basic RVV-based vector extension. Unlike complex, high-performance designs, the proposed architecture prioritizes simplicity and clarity, implementing only essential vector arithmetic and memory instructions. The vector extension is integrated with a single-cycle scalar RISC-V core, and instruction decoding is implemented and verified at RTL level. Functional simulation confirms correctness of RVV instruction decoding. This work bridges the gap between theoretical RVV studies and practical step-by-step hardware implementation.
Authors - Lalitha R, Husna Sarirah Husin, Suriana Ismail, Nikitha S, Kavya Darshini S, Pooja M Abstract - The data from Tamil Nadu government MSME programs is a treasure trove, but the information is fragmented and scattered in different kinds of documents. Consequently, it becomes a task for both the public and the analysts to process the data and get important insights. The paper introduces LKD-RAG, an explainable hybrid retrieval-augmented generation (RAG) system that relies on LLMs and KGs to make natural language queries possible on the data of these schemes collected from different sources. In the initial phase, the LLM started autonomously to discover entities, relations, and attributes, which eventually led to the creation of structured triples that signify factual statements (subject-predicate-object). The knowledge represented by these triples was loaded into Neo4j, thereby producing a MSME Scheme KG that is specific to the domain. Also, a document embedding layer was set up with SentenceTransformer ("all-MiniLM-L6-v2") that made it possible to do semantic retrieval of supporting textual evidence. When a query is made, Gemini decodes the person’s inquiry, finds relevant KG subgraphs and text embeddings, and constructs a response that is grounded on the evidence. The subgraph that corresponds to the answer is shown to the user, so the user can check what knowledge the model is relying on for its reasoning. Thus, the process facilitates transparency and the use of explainable AI (XAI) in policy analytics. The results of the experiments indicate that the hybrid RAG model not only has the ability to generate factually accurate responses but also to provide interpretation through different Tamil Nadu MSME programs.
Authors - Krashn Kumar Tripathi, Sachin B. Jadhav Abstract - In digital world, cyber-attacks are becoming more sophisticated and popular. The conventional intrusion detection models are not adequate in challenging threat escapes. Importantly, the major reason for increasing demand in the networks, unauthorized access is increasing their interests in these areas. Various network environments and organizations are tackling numerous of attacks on their network at frequent times. Traditionally, various manual methods are used for intrusion detection such as packet and flow analysis, traffic log reviewers and monitoring the security. Nevertheless, the manual techniques for such type of the detections takes too much time and also the result obtained is not up to the mark, so due to this it is difficult to predict all types of attacks and intrusions for network security. To overcome these issues, several conventional researches have concentrated on intrusion detection models to offer effective security to the networks. Conversely, it results with accuracy and speed lacks. For enhancing the intrusion detection, research make use of a Deep Learning (DL) Unravelled Spatial Features in Multilayer Perceptron with Gradient Jacobian Matrix. Gaussian Activation is used to enhance the Intrusion detection system for an effective classification. In the proposed research work we are using the RT-IoT dataset and the final efficiency has been analyzed by using various parameters like overall correctness, actually correct, correctly identified by the model,and the balance between the both values of recall and precision (Harmonic Mean). Furthermore, the current work and the proposed model is developed to contribute to avoid the different cyber threats by timely identifying such type of intrusion in the networks.
Authors - Maulana Amirul Adha, Maulana Paramaditya Ananta, Bayu Suhendry, Ria Rahma Nida, Eka Dewi Utari, Nur Athirah Sumardi Abstract - The challenge of generating accurate and contextually complete mod-els and prompts in Model-Driven Engineering (MDE) using Large Language Models (LLMs) is based on the current limitations in understanding the complex structured data. The significance of this issue lies at the heart of modern software development where MDE has taken the lead to advance development in the field moving towards with the aim of automating manual processes. To increase this automation, the application of LLMs holds the potential to reduce the manual effort and reduce human error involved in the process. To address this, we pro-pose a context-based prompt generation framework that integrates the techniques of Retrieval-Augmented Generation (RAG) with LLMs such as GPT-4 and CodeLlama to produce prompts that are contextually accurate and sound. Along with these LLMs, tools like FAISS, LangChain, and PlantUML are also em-ployed to produce detailed and structurally accurate UML models and prompt to enhance MDE understandability. In summary, the proposed framework aims to improve the accuracy and completeness of model generation by providing a con-textually correct prompt with a high level of accuracy and enhances the interpret-ability and ability of trust in AI-generated artifacts, creating the way for more efficient, automated, and user-friendly MDE processes.
Authors - Pierre Buys, Tevin Moodley Abstract - This paper presents a real-time chessboard state detection system that leverages computer vision and deep learning to automate a digital representation of a physical chess game. Traditional digitization systems either require manual input or specialized equipment. However, the proposed system addresses this problem by capturing a chess game in real time through the use of a smartphone camera. Detected piece positions are mapped to standard board coordinates and translated into Forsyth-Edwards Notation (FEN), enabling seamless integration with existing chess engines for analysis and move suggestions. The system works by firstly localizing the chessboard via Canny edge detection as well as a Hough transform. Thereafter, multi-class object detection is addressed by developing a two-stage R-CNN model alongside a single-stage YOLO model, allowing for a comparative evaluation of their respective methodologies and performance. The described system achieves a localization precision of 98.77% per board coordinate, whilst the two-stage R-CNN and single-stage YOLO models achieve a piece detection accuracy of 83.62% and 99.47%, respectively.
Authors - Hardik Modi, Mayur Makwana, Sagarkumar Patel, Dharmendra Chauhan, Siddhi Patel, Dhara Soni, Malvi Patel Abstract - Early and accurate detection of brain tumors is a critical requirement in modern clinical diagnostics, as it directly affects treatment planning, disease prognosis, and patient survival rates. The rapid increase in the availability and complexity of medical imaging data has intensified the need for reliable computer-aided diagnosis (CAD) systems to assist radiologists in consistent and precise tumor identification. Among various CAD techniques, medical image segmentation plays a pivotal role in differentiating abnormal tumor tissue from healthy brain structures in diagnostic images. This paper presents an automated brain tumor detection framework based on medical image analysis, implemented using a MATLAB-based graphical user interface. The proposed system processes Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans through a structured processing pipeline that includes image acquisition, noise reduction, contrast enhancement, feature-based segmentation, and tumor region visualization. The segmentation methodology is designed to accurately localize tumor boundaries while minimizing false-negative detections, which is a crucial requirement for clinical decision-making. The developed interface enables interactive visualization of segmented regions, allowing efficient analysis without the need for extensive computational expertise. The proposed framework offers a user-friendly and computationally efficient platform that reduces reliance on manual interpretation and improves diagnostic repeatability across clinical environments. The novelty of this work lies in the seamless integration of automated tumor detection, structured segmentation techniques, and real-time visual interpretation within a unified MATLAB-based environment, providing a practical and accessible CAD solution without dependence on complex hardware or deep learning infrastructures. Experimental observations indicate that the system enhances analysis efficiency and supports medical professionals in making faster, more reliable, and time-effective diagnostic decisions.
Authors - Najera R. Umpar Abstract - Artificial Intelligence (AI), as a technology, has the potential to change the manner in which organizations are run in the world. However, small and medium-sized enterprises (SMEs) in the Philippines have unique limitations in the use of AI in running the business. The study aims to explore the perceptions of SME managers in the Philippines on the use of AI, with particular reference to the limitations and facilitators in the use of the technology in the business environment. In this study, the researcher interviewed five SME managers from different sectors, including retail, manufacturing, and service sectors. The researcher used thematic analysis to identify the commonalities in the decisions made by the SME managers on the use of AI in the business environment. The study revealed the perceptions of the SME managers on the use of AI in the business environment in the Philippines, with the limitations and facilitators in the use of the technology in the business environment. The study provides practical insights that can guide strategies aimed at strengthening AI readiness and responsible adoption among SMEs in the Philippines.
Authors - Nilay Shah, Darsh Pandya, Nisarg Patel, Rudra Shah, Umang Shah, Dhaval Patel, Priteshkumar Prajapati Abstract - Early and accurate detection of brain tumors is a critical requirement in modern clinical diagnostics, as it directly affects treatment planning, disease prognosis, and patient survival rates. The rapid increase in the availability and complexity of medical imaging data has intensified the need for reliable computer-aided diagnosis (CAD) systems to assist radiologists in consistent and precise tumor identification. Among various CAD techniques, medical image segmentation plays a pivotal role in differentiating abnormal tumor tissue from healthy brain structures in diagnostic images. This paper presents an automated brain tumor detection framework based on medical image analysis, implemented using a MATLAB-based graphical user interface. The proposed system processes Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans through a structured processing pipeline that includes image acquisition, noise reduction, contrast enhancement, feature-based segmentation, and tumor region visualization. The segmentation methodology is designed to accurately localize tumor boundaries while minimizing false-negative detections, which is a crucial requirement for clinical decision-making. The developed interface enables interactive visualization of segmented regions, allowing efficient analysis without the need for extensive computational expertise. The proposed framework offers a user-friendly and computationally efficient platform that reduces reliance on manual interpretation and improves diagnostic repeatability across clinical environments. The novelty of this work lies in the seamless integration of automated tumor detection, structured segmentation techniques, and real-time visual interpretation within a unified MATLAB-based environment, providing a practical and accessible CAD solution without dependence on complex hardware or deep learning infrastructures. Experimental observations indicate that the system enhances analysis efficiency and supports medical professionals in making faster, more reliable, and time-effective diagnostic decisions.
Authors - D.K. Chaturvedi, Tipu Sultan Abstract - A real-time operating system (RTOS) should be able to recover from interruptions. Since RTOS systems are used in safety-critical environments, this function is essential for ensuring system availability and reliability. However, while many of the current anomaly detection techniques can detect faults, they do not provide any means for recovery. Therefore, in this paper, I propose a self-repairing RTOS framework that utilizes reinforcement learning (RL) to automatically select the best course of action to take when an anomalous event arises. I propose a Q-Learning agent that learns to recover from six types of common faults, including: sensor degradation, stuck sensor, priority inversion, memory leaks, sporadic overloads, and task starvation. The framework is built on FreeRTOS, and the agent utilizes an 8-dimensional state space and the six different types of recovery options available for each fault. The overall success rate of the system was 99.2 % after 5,000 training episodes, with average success rates of 98.0 % and 99.9 % when handling individual faults. The RL agent completely prevented system crashes and returned the system to normal operation within an average of 0.06 ms after an interruption occurred. The training results provide strong evidence that the model learned to operate effectively and consistently, with its success rate improving from 97.0 % during early training stages to 100 % after training was completed. Therefore, this study demonstrates a practical, production-ready method to implement autonomous fault recoveries in RTOSs in automotive applications. To our knowledge, this is the first successful implementation of RL for autonomous, self-repairing behaviors in this area.
Authors - Jason Elroy Martis, Ronith, Anvitha Rao, Vignesh Salian, Apoorva Shetty, Philomina Princiya Mascarenhas Abstract - The task of recovering high-level architectures from embedded software systems is error-prone and difficult, and state-of-the-art methods still rely on static analysis or heuristics and lack explainability. To address these challenges, an explainable and automated method for recovering high-level architectural diagrams directly from source code is suggested. Specifically, this method begins with the generation of function call graphs at the function level via static analysis and functions grouping into domain-agnostic component classes, generating a component graph. Components are then augmented with semantic attributes learned via CodeBERT embeddings, facilitating a light graph convolutional network (light GCN) model for learning-component interactions reflecting structure and semantics. Methods for explainability via gradients are incorporated for emphasizing prominent components and edges, helping in developer understanding, validation, and tuning of predicted architectures. The performance of this method on several embedded projects showed accuracy as high as 91.87%, precision of 96.48%, recall of 86.90%, and an F1-score of 91.44%. Use cases have shown successful extraction and interpretation of critical paths, bottlenecks, and unusual architectures and highlight explainable insights that enable efficient analysis and thus make it a highly significant progress in explainable AI for embedded software.
Authors - Nazia Sultana, Kumar P K Abstract - This research details the design and implementation of the AI-Driven Penalty Performance Analysis System, a desktop application aimed at bridging the technological divide in football analytics. The system focuses particularly on environmental and situational influences, such as crowd size, match context, and time of day, on penalty outcomes. The system employs a robust data pipeline and a comparative evaluation of multiple machine learning classifiers to predict the likelihood of penalty kick success. Using a dataset of professional penalties, we engineered novel features such as a ‘PressureIndex‘ to quantify situational fac tors. A suite of models, including Logistic Regression, K-Nearest Neighbours, Decision Tree, Random Forest, and Gradient Boosting, was trained and evalu ated. The optimal Gradient Boosting model achieved an accuracy of 79.1% and an AUC-ROC score of 0.87. A critical contribution is the integration of Explain able AI (XAI) using SHapley Additive exPlanations (SHAP), which transforms the system from a predictive ’black box’ into a transparent, diagnostic tool. This provides coaches and players with actionable, data-driven insights, validating the system’s potential to democratize advanced sports analytics.
Authors - Ankita Manohar Walawalkar, Chun-Wei Remen Lin, Suman Kumar, Ming-Yen Wang Abstract - The growing dependence on digital platforms for service discovery has revealed a substantial visibility gap for local businesses and independent service providers. Skilled professionals, in-cluding electricians, beauticians, bakers, tutors, mechanics, tailors, and photographers, frequently encounter challenges in reaching potential customers due to limited marketing expertise, financial barriers, and the lack of an integrated digital marketplace. This study introduces SkillBizz, a mo-bile platform intended to connect local service providers and businesses with nearby users through a community-driven, location-aware interface. The application features a scrollable home feed that prioritizes services and businesses based on geographical proximity, allowing users to refine their results using filters such as service category, budget range, distance, and popularity. Service providers can promote their offerings through multimedia posts that highlight services, offers, and announcements, while users engage through familiar social media features, including likes, comments, saves, and shares. By facilitating free and organic visibility without reliance on paid advertising, SkillBizz aims to support local entrepreneurship and foster trust-based service discovery. The proposed platform aims to create a digital marketplace that seeks to enhance com-munity engagement, improve service accessibility, and promote sustainable economic growth. In a short survey, students rated the app’s ease of navigation and overall usefulness highly, with an average satisfaction score of 4.5/5, indicating strong acceptance and positive user experience. Shop owners noted that the app provides an easy way to share product updates, promotions, and service news directly with local customers, with 80% expressing interest in continued usage due to time-saving benefits and improved customer reach.
Authors - Karuna A. Katakadhond, Manohar Madgi Abstract - Groundnut being a major oilseed crops, contributes to nearly 10% of the total value of produce from agricultural crops in India. Several researches indicate that disease infestations at different stages of crop growth can lead to 30-70% of yield reduction and significant economic losses. This challenge can be addressed by using Artificial Intelligence (AI) based smart monitoring and recommendation systems through early detection, identification, and prediction of crop diseases. The primary objective of the study is to develop an AI driven smart monitoring framework capable of detecting, identifying, and predicting biotic and abiotic factors responsible for major disease occurrences in groundnut plants. Additionally, the systems goal is to provide an effective and efficient recommendation system for sustainable agriculture from an integrated and practical perspective with its technical and economic performance to the farmers for managing the field level infestations. This includes prediction of diseases and timely recommendation of plant protection chemicals which may reduce the yield loss and enhance the productivity of the crop.
Authors - Usman Ali, Ghulam Mohayud Din, Sajid, Ayesha Ali, Munawar Hussain, Muhammad Mujeeb Akbar Abstract - The proliferation of misinformation on social media poses significant social, political and economic risks. This research proposes an AI-based fake news detection system that leverages deep learning (BERT and LSTM) and Explainable Artificial Intelligence (XAI) frameworks to classify online fake news as Fake or True. The proposed architecture processes textual data through Natural Language Processing (NLP) techniques for semantic and contextual analysis. To ensure Interpretability, SHAP and LIME is Integrated to visualize the rationale behind classification results. The system was trained using balanced datasets augmented through SMOTE, achieving over 95% accuracy. A web-based interface was developed to facilitate real-time text and URL verification, providing confidence scores and explanations. This approach minimizes human intervention, enhances transparency and explainable frameworks yields an accurate and trust-worthy tool for combating misinformation.
Authors - Suphawatchara Malanond, Pongsarun Boonyopakorn Abstract - In the food supply industry, differentiating between cultivated and weedy rice is crucial since the latter interferes with production and competes for essential resources. This research utilizes the YOLOv8 object detection model to automate the classification of rice grains to improve the separation process. The dataset was gathered during the harvesting phase and annotated utilizing a typical bounding-box methodology. Multiple configurations were evaluated with different model sizes (nano, small, medium) and training epochs. The optimal results attained a precision of 0.845, a recall of 0.779, and a mAP@50 of 0.822. These findings indicate that YOLOv8 enables near real-time identification at the grain level, diminishing dependence on manual verification. The study yielded a lightweight prototype developed to demonstrate and reflect the application of the trained model for rapid, image-based screening by non-technical users. The significance of the study lies in its support for more effective rice quality management and its contribution to strengthening food security and sustainable agriculture.
Authors - Wongpanya S. Nuankaew, Parichat Janjom, Khwanchiwa Khumdaeng, Rattiyaporn Laemchat, Thapanapong Sararat, Pratya Nuankaew Abstract - Communication has been a topic as ancient as man and at the same time so important that, over time, various forms have been cre- ated to facilitate it, among which stand out: mail, telephony, telegrams, and fax, to name a few. Nowadays many people use instant messaging applications to communicate with each other by feeling that their con- versations are protected. However, that feeling could not be further from reality and should not be taken lightly, since there are always groups focused on taking advantage of the vulnerability of this kind of applica- tions, resulting in users’ privacy being compromised. In this paper, we present the development of an instant messaging application that inte- grates a novel key establishment protocol based on a quantum-resistant algorithm. Our application employs cutting-edge lattice-based crypto- graphic techniques, ensuring robust security against quantum attacks while maintaining operational efficiency. Obtained results show the ap- plication’s viability by offering a practical solution to safeguard mobile communication in the impending quantum era.
Authors - Rashmi Shivanadhuni, Martha Sheshikala Abstract - The rapid expansion of QR-code payment systems has positioned QRIS as a key component of Indonesia’s national digital payment infrastructure. While prior studies have largely focused on initial adoption, limited empirical evidence explains the factors that sustain long-term usage of QR-code payments in mobile banking. This study investigates the determinants of sustained QRIS adoption by examining the roles of perceived usefulness, perceived ease of use, trust, and perceived security, with user satisfaction as a mediating variable. Using a quantitative approach, survey data were collected from QRIS users of mobile banking applications and analyzed using Structural Equation Modeling (SEM). The results indicate that perceived usefulness, trust, and perceived security significantly enhance user satisfaction, which in turn strongly predicts sustained adoption of QRIS in mobile banking. Perceived ease of use shows a weaker direct effect, suggesting that post-adoption behavior is driven more by value realization and trust than by usability alone. These findings contribute to ICT and fintech literature by highlighting user satisfaction as a critical post-adoption mechanism for sustaining engagement with national digital payment systems. Practically, the study offers insights for policymakers, banks, and system designers to strengthen the long-term viability of QR-based payment infrastructures through trust-building and value-enhancing strategies.
Authors - Suman Kumar, Yeneneh Tamirat Negash, Ankita Manohar Walawalkar, Ming-Yen Wang Abstract - The backbone of modern data infrastructure which demands strategies to ensure data availability and uptime is Cloud Storage. This paper provides a complete overview of redundancy models and storage techniques that are used to maintain data availability and uptime in cloud storage systems. It covers core redundancy methods like data replication, erasure coding, Raid and disk-level redundancy, multi-cloud redundancy and hybrid models. This paper also provides storage techniques that support data availability like distributed file systems and object storage platforms for scalability and flexible access. Additionally, the paper also presents a literature review of key research findings and compares models that demonstrates substantial improvements in reliability and storage efficiency. It also covers the challenges related to computational complexity and monitoring precision. By synthesizing theoretical and practical perspectives, this research guides the design of cloud storage solution which balance availability, cost and recovery objectives and also help stakeholders to meet stringent service level agreements in increasingly heterogeneous and large-scale cloud infrastructure.
Authors - Massoud Moslehpour, Suman Kumar, Hanif Rizaldy, Ankita Manohar Walawalkar, Thanaporn Phattanaviroj 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 - Chaitra Sai Chakravarthi Ganapaneni, Rishik Reddy Cheruku, Venkata Karthik Chamarthi, Venkata Sasidhar Kommu, Malathi P Abstract - Academic websites function as institutional interfaces connecting universi-ties with multiple stakeholder groups. Many institutions face challenges in developing web presences that address usability, accessibility, and stakeholder needs simultaneously. Existing frameworks address isolated dimensions without providing integrated guidance. This research proposes a conceptual design framework for academic websites that integrates Web Con-tent Accessibility Guidelines (WCAG) 2.1 Level AA standards with Nor-man's design principles. The framework consists of four core segments (In-terface Design, Content Accessibility, Technical Performance, User Experience) and four modular add-ons categories (Career and Job Opportunities, Student Projects Showcase, Alumni Community, Industry Collaboration). Framework validation employed dual evaluation methods to ensure both conceptual soundness and stakeholder relevance. Expert judgment assessment (n=5) achieved complete agreement on conceptual soundness. Quantitative user assessment (n=450) across six stakeholder groups showed that framework components achieved good performance levels (mean scores 3.58 to 3.70) and add-ons features received high priority classifications (mean scores 3.62 to 3.80). The framework contributes systematic integration of accessibility standards with design principles and provides guidance for institutions developing academic websites.
Authors - Amulya Saxena, Pratibha Joshi, Adwitiya Sinha Abstract - Global food security and hunger mitigation is one of the major challenges ahead of us. The global population specifically from underdeveloped countries are quite vulnerable to climate change and its impact in abnormal weather conditions and related bad crop leading to food shortages. In today’s globalised world, where a disruption in food supply chain has its own impact on potentially everyone in the planet is a mounting challenge to surpass. The advent of Artificial Intelligence, specifically Computer Vision techniques prove to be extremely helpful in identifying the data pattern of the images of the cultivated land, its anomalies and is insightful in giving the challenges of farming such as affect of bad weather, bad crop prediction, crop distribution etc. The availability of high-quality geospatial data from the satellites such as Sentinel 1/2, Landsat is extremely helpful for advanced ML techniques to provide timely predictions so that a corrective action can be taken in time. This study focuses on an AI-driven approach that predicts land where Rice will be produced vs. no crop land using satellite optical data and its variates, radar logs, weather data and location information.
Authors - Arin Bansal, Pranshu CBS Negi Abstract - The research provides a description of WaveTrust, which is a trust-conscious and energy-efficient routing protocol that is applied to Underwater Wireless Sensor Networks (UWSNs) based on reinforced Q-learning and trust assessment. Neutral trust and network deployment initiate the protocol. During the process of routing data in real time, monitoring of the behavior by the nodes is required with respect to four metrics namely Packet Forwarding Ratio, Energy Behavior Consistency, Latency Observance and Link Quality Indicator. The calculation of the trust is performed according to the direct and indirect observations and makes it possible to determine malicious nodes. Q-learning routing strategy The routing strategy uses weighted rewards according to energy, trust and latency in updating paths such that it favors nodes with high-trust and high-Q-value. The nodes dynamically revise the trust and Q-values about the received feedback during transmission of data. The sink node keeps on broadcasting the global updates of the updated trust thresholds and routing updates. The simulation outcomes have indicated that WaveTrust is better than T-AODV, FuzzyTrust on the basis of packet delivery ratio, detection accuracy, energy consumption, routing overhead and an apparent strength on the capability to work in dynamic and resource limited underwater setting. This creates the impression that WaveTrust is quite flexible protocol and has the capability of providing secure and energy efficient routing in UWSNs.
Authors - Harita Venkatesan Abstract - Fusion-based multimodal models typically assume full modality availability at inference, an assumption that often fails in real-world settings. When a modality is missing, common strategies such as zerovector masking or unimodal fallback can lead to unstable predictions. We propose CORE, an embedding-level framework that completes multimodal representations by integrating original and cross-modally reconstructed embeddings in a fusion-consistent manner prior to fusion. CORE employs lightweight bidirectional cross-modal imagination networks with a cycle-consistency constraint to preserve shared semantic structure across modalities. The model is trained with stochastic modality dropout, enabling unified inference under complete and incomplete modality configurations. Experiments on a multimodal MRI–text classification task for lumbar spine analysis demonstrate that CORE yields more stable predictions than zero-vector masking under severe modality absence, while maintaining comparable performance when all modalities are present.
Authors - Latha N. R., Pallavi G B, Shyamala G., Abubakar Mohammedshafee Matte, Aditya Dinesh Netrakar, Akshara Singa, Akshata Hosmani Abstract - Tourism has become a strategic pillar in China’s transition toward a service-oriented economy, the world cultural heritage sites play an important role in promoting cultural–tourism integration in both China and global. The Dazu Rock Carvings is located in Chongqing, well known by their unique synthesis of Buddhist, and Taoist ideas and their wonderful stone-carving artistry. Recently, the Dazu site received growing number in tourist arrivals and tourism-related revenue due to the regional rapid development as well as the strategic support; however, compared with other outstanding heritage destinations such as the Mogao Grottoes, the reception capacity, product diversity, brand influence, and market performance of Dazu still remain relatively weak. This study adopts a mixed qualitative–quantitative case study design. Data are collected from official tourism statistics and cultural heritage management reports published by national and local authorities in between 2018-2024. Descriptive analysis is used to explore the trends in tourist arrivals, tourism revenue, and related industrial effects. Based on the findings, the study identifies key dimensisons on sustainable development and proposes a marketing path centered on cultural IP empowerment, industrial ecosystem construction, and digital technology-driven innovation, offering practical guidance for similar heritage destinations.
Authors - Deepa V, Atul Anilkumar, Sheena Susan Andrews Abstract - Organizations are rapidly embedding artificial intelligence (AI), including generative AI, into core business functions, but making AI sustainable across environmental, social, and economic dimensions is still challenging, especially when data governance is weak. Public estimates suggest data centres consumed roughly 415 TWh of electricity in 2024 and may rise toward ~945 TWh by 2030 under a base-case trajectory, while reported AI-related incidents reached a new high in 2024. In parallel, industry signals point to fast enterprise adoption of GenAI and ongoing leakage of sensitive information through tools that are not properly governed. Taken together, these patterns increase sustainability risks that are often data-mediated in practiceshaped by data quality and representativeness, provenance and documentation, access control, privacy protections, and end-to-end lifecycle management. Although data governance is widely seen as “foundational” to responsible AI, the concrete mechanisms linking governance capabilities to sustainable AI outcomes, and the ways to measure them, remain dispersed across data management, AI governance, and sustainability research. This paper consolidates peer-reviewed research, public standards, and open industry evidence to position data governance as an operational, measurable capability for Sustainable AI, one that converts sustainability goals into decision rights, lifecycle controls, and auditable outcomes. It contributes: (i) a capability-based taxonomy of data governance tailored to AI lifecycles; (ii) six evidence-grounded impact pathways showing how governance mechanisms influence outcomes (quality and fairness; documentation and auditability; privacy and security; interoperability and reuse; lifecycle stewardship; and sustainability instrumentation); and (iii) the Sustainable AI Data Governance Impact Model (SAI-DGIM), accompanied by testable hypotheses (H1–H8) and a KPI-oriented measurement framework that can be validated using survey constructs, system telemetry, and governance artifacts. For practitioners, the model offers a practical roadmap to embed governance controls directly into AI delivery workflows and treat sustainability metrics as release criteria, not just retrospective reporting. For researchers, it provides aligned constructs, hypotheses, and measurement guidance to rigorously assess how organizational data governance shapes Sustainable AI outcomes at scale.
Authors - Nhat Ho Minh, Long Le Pham Tien, Kien Nguyen Trung, An Pham Nam, Trong Nhan Phan Abstract - The fast increase in the number of unstructured digital documents in academic, industrial, and personal fields has generated an urgent requirement to have intelligent systems to read, arrange and structure document automatically. Traditional document organization methods have traditionally been heavily based on either manual intervention or rule-based methods, neither scalable nor efficient nor error free. The current paper is a multimodal AI architecture to assist document under-understanding and structuring that uses large language models (LLMs) and vision language models to handle heterogeneous document types. The suggested framework does semantic metadata extraction, classification of documents as well as structural organization of textual and visual documents. It uses a modular three-layer design, including an AI processing layer, service oriented backend, and cross platform user interfaces. The system is also developed to support secure functioning in the offline mode, which guarantees the privacy of data and the low-latency processing. The effectiveness of the pro-posed frame-work has been proved through experimental assessment, as it will be seen that classifying documents and categorizing images are very precise. The findings show that multimodal AI is remarkably better in document understanding and automation than traditional systems.
Authors - S M Mazharul Hoque Chowdhury, Ruth West, Stephanie Ludi Abstract - The prediction of liver disease through clinical data analysis faces difficulties because current machine learning methods fail to handle class imbalance and produce incorrect probability assessments. The existing supervised and ensemble methods use fixed decision thresholds together with heuristic weighting methods which results in biased predictions that compromise their ability to achieve balanced performance. The research introduces CAL-WE++ which serves as a Calibration- Weighted Ensemble system that uses an MCC-Optimized Threshold to forecast liver disease. The system employs five-fold stratified cross-validation without data leakage to produce out-of-fold probability results. The model weights are determined by evaluating both the model's ability to distinguish between outcomes (measured through ROC-AUC) and its accuracy in predicting probabilities (assessed through Expected Calibration Error ECE). The Matthews Correlation Coefficient (MCC) serves as the optimization method to determine the final classification threshold which helps to solve class imbalance problems. The Indian Liver Patient Dataset (583 records; 416 diseased, 167 non-diseased) experiments show that CAL-WE++ achieves a mean cross-validation MCC of 0.3474 and a test MCC of 0.4487 which exceeds the performance of baseline classifiers. The model achieves a ROC-AUC score of 0.8140 and a PR-AUC score of 0.9272 while maintaining a low ECE value of 0.0774 which demonstrates strong ability to distinguish between different outcomes and accurate probability assessments. The CAL-WE++ framework offers medical professionals a decision-making system that maintains balance between multiple criteria while delivering dependable outcomes for medical datasets with unequal class distributions.
Authors - Nidhi Pruthi, Rajiv Singh, Swati Nigam Abstract - Automatic Speech Recognition (ASR) systems have achieved remarkable progress through deep learning and Transformer-based architectures, demonstrating near-human accuracy on clean audio. However, their performance degrades significantly under challenging conditions and specialized domains. This comprehensive study evaluates leading commercial ASR APIs—Google Cloud Speech-to-Text, Microsoft Azure Speech Service, AssemblyAI, Deepgram, OpenAI Whisper, Speechmatics, and others—across multiple dimensions: general speech recognition, low-quality forensic-like audio, domain-specific mathematical notation, and personalized speaker adaptation. Results demonstrate 100% accuracy on clean audio for leading systems (Deepgram, Speechmatics, Webkit SpeechRecognition), but dramatic performance degradation to 10− 81% word error rates on forensic-like audio. Analysis of domain-specific challenges reveals that none of the tested commercial ASR systems natively support direct transcription of mathematical symbols and Greek letters into structured symbolic output (e.g., LaTeX). The study identifies critical limitations in robustness, modularity, and domain adaptation, while highlighting promising customization mechanisms including custom vocabularies, language models, and post-processing integration. Performance improvements through speaker personalization ranged from 3% for natural voices to 10% for synthetic voices. Despite notable advances in end-to-end and Transformer-based approaches, ASR systems remain unsuitable for forensic applications and specialized domains without substantial customization and post-processing. Future research must address low-resource performance, linguistic diversity, robustness in extreme noise, and the integration of Large Language Models for semantic understanding. This paper synthesizes recent advances and critical gaps, providing a roadmap for advancing ASR technology in specialized and challenging acoustic environments.
Authors - G Naga sree suma, A. Kamala kumari Abstract - The existence of a growing social media has created complex cyber systems in which vast quantities of interactions constitute substantial issues regarding misinformation, privacy invasion, deception of identities, and destructive behavioural tendencies. The regularity of involvement in this type of big systems requires sophisticated systems that are able to judge the motive of the user, content validity and suspicious activities within real time. Overall interest will be to develop a universal trust calculation system that will be more secure and effective in ensuring privacy and increasing the accuracy of suspicious or malicious users in social sites. The proposed Multi-Layer Federated Trust Framework algorithm is a combination of peer-based user reputation scoring, feature-based content authenticity detection, federated trust indicators aggregation, and anomaly detection with the help of behavioural anomalies. These approaches cooperate with secure aggregation and decentralized learning in removing the uncoded information exposure and enable the computation of trust at scale. The proposed algorithm is experimentally confirmed, and the obtained results are 95.2, 94.1, 93.5, and 93.8, corresponding to a minimum latency of 65 ms and a privacy preservation score of 0.98. The general results indicate a viable and holistic response that adds to secure interactions, blocks malicious acts and encourages trust in the actual social media settings.
