Real-Time Video Text Spotting with OpenCV and OCR Powered by Deep Learning
OpenCV; Real-Time Processing; Video Processing Text Detection
The identification of text in video places huge challenges, and translating them into target form demands high-level expert skills in computer vision and deep learning. This system can grab and supervise the real-time video process of text elements by utilizing OpenCV for text recognition and extraction. The proposed model was employed diverse machine translation models to guarantee high-quality results for translation. Based on wide testing and assessment, the goal is to make the apporach fast and precise, offering valuable tool for instructors, content creators, and overall users. This novel approach solves problems relating to language difficulty problems. Key elements include video processing, text detection and recognition, and machine translation. In addition to these essential functionalities, sophisticated preprocessing methods are applied to make text stand out from diverse backgrounds to render high performance in diverse environments. Deep learning algorithms improves the accuracy of text detection, especially for occluded or distorted characters. Finally, the cloud-based translation service offers real-time multilingual support to enable maximal adaptability to user needs. For the first time, this innovative technology finally enables streamlining access to the content and facilitates cross-cultural communication in multimedia contexts with nigh-guaranteed linguistic barriers broken down. 2025 IEEE.
Mary, Sahaaya Arul; Dakshinamurthi, Vidyabharathi; Tamilselvi, M.; Seshaiah, M.A.; Abinaya, A.; Syed Husain, S.
2025 IEEE 6th Global Conference for Advancement in Technology, GCAT 2025;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/GCAT66372.2025.11368436 https://www.scopus.com/pages/publications/105034262457?origin=resultslist
Mary S.A., Christ University, School of Engineering and Technology, Department of Aiml & Data Science, Karnataka, Bangalore, India; Dakshinamurthi V., Sona College of Technology, Department of Computer Science and Engineering, Salem, India; Tamilselvi M., Roever Engineering College, Department of Computer Science and Engineering, Tamilnadu, Perambalur, India; Seshaiah M.A., Velammal Engineering College, Department of Ece, Surapet, Chennai, India; Abinaya A., Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology, Department of Ece, Avadi, Tamilnadu, Chennai, India; Syed Husain S., K. Ramakrishnan College of Engineering, Dept. of Ece, Tamil Nadu, Tiruchirappalli, 621112, India
A Lightweight LCDECG Model for Cardiovascular Diagnostics Using ECG Features
Cardiovascular Disease (CVD); Deep learning; Electrocardiogram (ECG); Feature fusion; Lightweight models; MobileNetV2; Real-time diagnosis; Statistical descriptors
Cardiovascular disease (CVD) is among the leading causes of death around the world, requiring accurate and reliable diagnostics, and early detection. This project aims at the development of an efficient and accurate, lightweight model to classify heart rhythms based on an ECG.. In this paper, we propose the LCDECG (Lightweight Cardiac Diagnostic ECG) model, which integrates deep morphological feature extraction from ECG with clinically relevant handcrafted features. With MobileNetV2 used as a feature extractor and statistical descriptors of ECG signals, both the pathways are combined at the feature level for multi-class classification of cardiac conditions. Experiments conducted on the Dataset demonstrate better classification performance with 97.8% accuracy, 96.4% precision, 97.1% recall, and 96.7% F1-score over traditional neural networks alone or only statistical methods. The model is able to achieve the desired results as it only utilizes 2.43M parameters in its architecture, and therefore is amenable to real-time deployment in resource-scarce environments. Its use is advantageous for facilitating timely and early detection, which is necessary to improve patient survival and reduce healthcare costs through preventative treatment. Current ECG readings are based on manual assessment by trained cardiologists, which can be time-consuming and potentially subjective, depending on several professionals in the medical field evaluating the tracing. Due to the increased incidence of cardiovascular disease globally, and the limited number of professionals, particularly in developing countries, there is even greater need for automated convenient and trustworthy ECG tracing for diagnostic support. 2025 IEEE.
Nitin Joshua, M.; Kumar, Pawan; George, Jossy
https://doi.org/10.1109/GCAT66372.2025.11368610 https://www.scopus.com/pages/publications/105034258168?origin=resultslist
Nitin Joshua M., Christ (Deemed to be University), India; Kumar P., Christ (Deemed to be University), India; George J., Christ (Deemed to be University), India
Bridging Traditional NLP and Deep Learning: Comparative Study on Text Categorization Performance
DistilBERT; LoRA; Machine Learning; NLP; PEFT; Semantic Embeddings; Text Classification; Transformer Models
Text categorization is an important area of Natural Language Processing (NLP) that is used to automatically organize textual information into a set of specific categories. This study is a comparative study of models that use statistical features and models that use transformers, using the example of DistilBERT-base-uncased fine-tuned and LoRA (Parameter-Efficient Fine-Tuning, PEFT). The data extracted on the Kaggle site is presented in the form of labeled text samples of five classes Business, Entertainment, Sport, Tech, and Politics. Conventional models such as Logistic Regression, Random Forest and XGBoost were trained on manually crafted word-level features (word count, mean word length and punctuation ratio) and had precisions up to 94.7%. Comparatively, the given DistilBERT-LoRA model used semantic embeddings to find the contextual dependencies and managed to reach the total accuracy of 97, precision of 97, and the recall of 96. The training and validation loss curves showed the stable convergence without overfitting, and the confusion matrix showed the consistent performance at all the classes with minimum misclassification. Comparative analysis indicated that semantic embeddings are much better than statistical models because they enhance contextual perception and strength of classification. The findings confirm the effectiveness and scalability of the LoRA-based fine-tuning, which offers an efficient but lightweight strategy in the context of real-world settings to achieve high-performance text categorization. 2025 IEEE.
Srinivas, T. Aditya Sai; Shivajyothi, Aenugu; Pal Pandian, P.; Nandini, M. Raja; Periyasamy, Rajeswari; Kaliappan, S.
2025 IEEE 3rd Global Conference on Wireless Computing and Networking, GCWCN 2025;
https://doi.org/10.1109/GCWCN66157.2025.11448361 https://www.scopus.com/pages/publications/105037312319?origin=resultslist
Srinivas T.A.S., Ravindra College of Engineering for Women, Department of Computer Science and Engineering, Andhra Pradesh, Kurnool, 518452, India; Shivajyothi A., Geethanjali College of Engineering and Technology, Department of Computer Science and Engineering, Telangana, Medchal, 501301, India; Pal Pandian P., Christ University, Robotics and Mechatronics, School of Engineering and Technology, Bangalore, India; Nandini M.R., Mallareddy Engineering College for Women, Dept of CSE, Telangana, Hyderabad, 500100, India; Periyasamy R., Dayananda Sagar College of Engineering, Department of Electronics and Telecommunication Engineering, Karnataka, Bangalore, 560078, India; Kaliappan S., Lovely Professional University, Division of Research and Development, Punjab, Phagwara, 144411, India
Enhancing Stock Market Price Prediction with Advanced Machine Learning Techniques: A Comparative Study
Machine Learning; price prediction; Stock Market Prediction; Yahoo Finance data
The non-linearity and intrinsic volatility of financial markets make accurate stock price prediction an important but challenging undertaking. This research proposes a Gated Recurrent Unit (GRU)-based model to forecast the stock prices of Tata Consultancy Services (TCS) using 18 years of historical data sourced from Yahoo Finance, comprising features such as Date, Open, High, Low, Close, Adjusted Close, and Volume. The methodology includes data preprocessing steps such as feature selection using Recursive Feature Elimination (RFE), normalization with standard scaling, and data splitting into 70% training and 30% testing sets. The proposed GRU model was evaluated and benchmarked against existing models including Long Short-Term Memory (LSTM), Linear Regression (LR), and Decision Tree (DT), using performance metrics such as Root Mean Squared Error (RMSE) and R2 score. Experimental outcomes revealed that the GRU model achieved the best performance with an RMSE of 0.045, outperforming LSTM (38.19), LR (8.66), and DT (5.22). The study's findings have important implications for algorithmic trading and well-informed investment choices, since the GRU model effectively captures temporal trends in stock data while minimizing prediction mistakes. 2025 IEEE.
Jain, Yogesh Kumar; Kumar Mannepalli, Praveen; Kumar, Santosh; Malhotra, Amit; Choudhary, Deepak Kumar; Saharan, Mohit
2025 International Conference on Next Generation of Green Information and Emerging Technologies, GIET 2025;
https://doi.org/10.1109/GIET65294.2025.11234804 https://www.scopus.com/pages/publications/105030995215?origin=resultslist
Jain Y.K., School of Management, IILM University, Greater Noida, India; Kumar Mannepalli P., Chandigarh University, Department of Computer Science and Engineering, Mohali, India; Kumar S., ERA University, Computer Science, Uttar Pradesh, Lucknow, India; Malhotra A., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Ghaziabad, India; Choudhary D.K., Muzaffarpur Institute of Technology, Department of Computer Science and Engineering, Bihar, Muzaffarpur, India; Saharan M., Vidya University, Meerut, India
The Impact of AI Tools on Enhancing EFL Learners' Engagement in Higher Education Using HubSVM Models
Artificial Intelligence (AI); Blended Learning (BL); English Foreign Language (EFL); Feature Scaling (FS); Support Vector Machine (SVM)
BL has become prevalent in higher education as a means of delivering information, managing activities, and executing lessons, thanks in large part to the proliferation of COVID-19 and other technological developments in education. By combining online and offline learning, BL encourages students to be more engaged and flexible than in a typical classroom setting. Engaged learners are crucial for psychometric analysis; they are like energy in action, full of life, focus, and determination. By encouraging mental and physical exertion towards studying, it significantly improves EFL students' involvement in higher education. Using MinMax for feature scaling and the HubSVM, which, similar to the L1-norm SVM, allows automatic feature selection, this study analyses and improves engagement. By highlighting highly connected features, HubSVM improves the selection process and makes computing the complete solution path easier. The results show that when dealing with highly correlated variables, HubSVM performs better than L1-norm SVM. The suggested classifier outperforms the competition with an accuracy of 95.65%. The results show that the concept works well to make BL settings more engaging for students. This research helps make higher education more engaging for EFL learners by incorporating modern machine learning techniques, which means they will have a better, more effective learning experience. 2025 IEEE.
Nadeem Pasha, K.; Antar, Dina; Shakir, Alakbarova Tamara; Sharan, Mudita; Farheen, Syeda Fatima; Mallick, Mohamed
2025 Global Conference in Emerging Technology, GINOTECH 2025;
https://doi.org/10.1109/GINOTECH63460.2025.11076951 https://www.scopus.com/pages/publications/105013621590?origin=resultslist
Nadeem Pasha K., Hkbk College of Engineering, Department of Mechanical Engineering, Karnataka, India; Antar D., American University of Ras Al Khaimah, School of Arts & Sciences, Department of Humanities and Social Sciences, United Arab Emirates; Shakir A.T., Alakbarova Tamara Shakir Azerbaijan Technological University, Department Automation and Information Technologies, Azerbaijan; Sharan M., Christ (Deemed to be University), School of Sciences, Department of Computer Science and Engineering, Delhi, India; Farheen S.F., Vardhaman College of Engineering, Department of English, Telangana, Hyderabad, India; Mallick M., Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology, Department of Cse, Tamil Nadu, Chennai, India
EEG Emotion Recognition Using PSO-Based Feature Selection and Convolutional Neural Networks
EEG signals have become a promising source for emotion recognition due to their ability to capture the brain's electrical activity connected with different emotional conditions. In this work, a novel approach is proposed that integrates Particle Swarm Optimization (PSO)-based feature selection with Convolutional Neural Networks (CNNs) for improved EEG emotion classification. The method with the preprocessing of a notch filter to eliminate noise and enhance the quality of the EEG signals. Key features, including Magnitude Squared Coherence Estimate (MSCE) and Power Spectral Density (PSD), are extracted to capture essential frequency-domain information. PSO is employed to optimize the selection of features, reducing dimensionality while preserving the most relevant and informative attributes for emotion recognition. The optimized feature was subsequently passed to a CNN classifier, which improves the model's capability to accurately differentiate between different emotional states. This study is implemented using Python software to analyze emotion, and the effectiveness of the proposed approach is assessed using the EEG Brainwave dataset. Experimental results demonstrate that the proposed approach delivers an accuracy of 92.6% and a precision of 91%, highlighting its effectiveness in real-time, high-precision emotion recognition from EEG data. 2025 IEEE.
Raji, V.; Maheswari, S.; Kanmani, P.; Kavin, K.S.; Janice, J. Benisha; Lawrence, Jinsha
https://doi.org/10.1109/GINOTECH63460.2025.11077080 https://www.scopus.com/pages/publications/105013619510?origin=resultslist
Raji V., S.K.P. Engineering College, Department of Information Technology, Tiruvannamalai, India; Maheswari S., Nandha Engineering College, Department of Computer Science and Engineering(IoT), Tamilnadu, Erode, 638052, India; Kanmani P., Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Kengeri Campus, Bangalore, 560074, India; Kavin K.S., Government College of Engineering, Department of Electrical and Electronics Engineering, Tamil Nadu, Tirunelveli, India; Janice J.B., Mar Ephraem College of Engineering and Technology, Department of Computer Science and Engineering, Elavuvilai, India; Lawrence J., Karpagam Academy of Higher Education (Deemed to be University), Department of Computer Science and Engineering, Tamil Nadu, Coimbatore, India
An Intelligent Approach for Breast Cancer Diagnosis Using Fuzzy Logic and Extreme Learning Machine
African Vultures Optimization Algorithm (AVOA); Breast Cancer (BC); Deoxyribonucleic Acid (DNA); Genetic Algorithm (GA); Kalman filter (KF)
The long-term prognosis and mortality rates can be improved with early identification of breast cancer. The time-consuming and expensive procedures of mammography, MRI, ultrasound, CT, PT, and biopsy have been the subject of much research; nevertheless, these approaches are not suitable for younger women and can be rather expensive. This study employed cutting-edge image processing to improve early breast cancer detection. The researchers utilised anisotropic filtering to reduce background noise in medical images after picking mammograms at random from the Digital Database for Screening Mammography. The use of morphology-based feature extraction allowed for autonomous and accurate categorisation after mass segmentation using a genetic algorithm with recurrent thresholding. By merging a KF with an ELM enhanced with an AV, a new model named KF-av-elm improves diagnostic accuracy. Medical imaging noise and estimating errors are both significantly reduced by the combination method. Their accuracy rating of 98.28% allowed them to outperform other approaches. The KF-av-elm model appears to be a reliable, efficient, and effective diagnostic tool; its adoption may lead to better identification and outcomes for breast cancer patients. 2025 IEEE.
Singh, Davinder Paul; Rani, Rozy; Sindhu, V.; Revathi, V.M.; Kosalairaman, T.; Paul, P. Mano
2025 Global Conference on Information Technology and Communication Networks, GITCON 2025;
https://doi.org/10.1109/GITCON65266.2025.11377847 https://www.scopus.com/pages/publications/105035604073?origin=resultslist
Singh D.P., Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gujarat, Gandhinagar, India; Rani R., Applied Sciences, Chandigarh Group of Colleges Jhanjeri, Chandigarh Engineering College, Punjab, Mohali, India; Sindhu V., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Revathi V.M., Department of Mathematics, Bannari Amman Institute of Technology, Sathyamangalam, Erode, India; Kosalairaman T., Department of Information Technology, V. S. B. Engineering College, Tamilnadu, Karur, India; Paul P.M., Department of CSE, Artificial Intelligence, Dayananda Sagar Academy of Technology and Management, Karnataka, Udayapura, India
Leveraging Machine Learning to Predict Revenue-Generating Sessions in E-Commerce Platforms
Customer Behavior Analysis; Online Shopping Behavior; Predictive Analytics; Purchase Intention Prediction
Due to the rapid growth of e commerce, develops effective predictive models of online shopper behavior has become important. The goal of this study is to use dataset of online shopping sessions to predict purchase intentions based on session characteristics, user behavior and site metrics. This research aims to apply machine learning and deep learning models to predict online purchasing intentions to assist businesses to improve their strategies of maximizing conversion rates. Using a dataset having numerical and categorical features, features like page views, session duration, bounce rates etc., and the presence of some special days near the user session, we used. We evaluated nine models, including the traditional methods: Logistic Regression, Decision Tree, Naive Bayes, ensemble methods: Random Forest, Gradient Boosting, XGBoost, and more advanced ones like Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Neural Networks. Then, key metrics including Accuracy, Precision, Recall, F1 Score and ROC AUC were used to asses each model. We find that ensemble models perform best (ROC AUC = 0.9245) with Gradient Boosting performing best, with XGBoost and Random Forest close behind. With a competitive ROC AUC of 0.9000, neural networks showed strong potential, but fell slightly behind in recall compared with ensemble methods. Logistic Regression and Decision Tree were simpler models that did not achieve as strongly in predictive accuracy as more complex model; however they provided a baseline insight. Through this analysis, ensemble models and deep learning showed to be very efficient to predict online purchase intentions and provide actionable insights to optimize e-commerce platforms. 2025 IEEE.
Yadav, Sangeeta; Ranka, Monica; Ojha, Shikha; Malhotra, Amit; Basha, Md Shaik Amzad
2025 International Conference on Information, Implementation, and Innovation in Technology, I2ITCON 2025;
https://doi.org/10.1109/I2ITCON65200.2025.11210485 https://www.scopus.com/pages/publications/105026287123?origin=resultslist
Yadav S., New Delhi Institute of Management, Ndim, New Delhi, India; Ranka M., Dayananda Sagar College of Arts, Science and Commerce, Bangalore, India; Ojha S., Ifim college, Bangalore, India; Malhotra A., School of Commerce, Finance and Accountancy, Christ (Deemed to be University), Ghaziabad, India; Basha M.S.A., Gitam School of Business, Gandhi Institute of Technology and Management (Deemed to be University), Hyderabad, India
Bridging the Rural Digital Divide: Machine-Learning-Driven Predictive Modeling of Digital Literacy Program Outcomes
Digital Literacy; Educational Outcomes Prediction; Machine Learning; Predictive Modeling; Rural Education
The research project performed multiple regression model evaluations to assess how effective digital literacy schemes are in rural education settings. Training program achievements relied on predicted educational proficiency scores while program evaluation relied on both comprehensive participant demographic details and process training statistics. Our study examined numerous regression approaches from basic Linear Regression forms through advanced Random Forest and Gradient Boosting models and concluded with complex methods including Stacking and XGBoost. The research analyzed prediction accuracy and model explanatory power using Mean Squared Error (MSE) and Rsquared (R2) values during the evaluation process. Multiple feature applications were the best fit for the deterministic ensemble techniques which exhibited superior performance but alternatively different analytical models displayed stable prediction results. This research proposes educational method advancement through machine learning approaches capable of creating custom solutions targeting rural user requirements. This study delivers key information to stakeholders in its combined study of digital education enhancements and sophisticated learning evaluation data analysis techniques. 2025 IEEE.
Krishnan, Divya R.; Yadav, Suneetha; Biswas, Pritha; Basha, Md Shaik Amzad; Prathiba, L.
https://doi.org/10.1109/I2ITCON65200.2025.11210709 https://www.scopus.com/pages/publications/105026312898?origin=resultslist
Krishnan D.R., Christ (Deemed to be University), Department of Professional Studies, Bengaluru, India; Yadav S., Rgm College of Engineering and Technology, Nandyal, India; Biswas P., Christ (Deemed to be University), Department of Professional Studies, Bengaluru, India; Basha M.S.A., Gitam School of Business, Gandhi Institute of Technology and Management (Deemed to be University), Hyderabad, India; Prathiba L., Ashoka Women Engineering College (Autonomous), Kurnool, India
Prioritized QoS Enforcement in Smart Healthcare IoT Using Adaptive Deep Q-Network-Based Traffic Decision System
adaptive systems; healthcare IOT; network optimization; quality of service; reinforcement learning
Healthcare IoT systems have been plagued with significant challenges with regard to maintaining an optimum QoS due to the dynamic conditions of the network, diverse device capabilities, and stringent real-time constraints imposed by patient monitoring-type applications. Traditional QoS mechanisms are basically static; they do not take into account changes within the network. Hence, service delivery experiences degradation, with attendant risk to patients' safety. As a solution, this research proposes an adaptive QoS approach employing Deep Q-Network (DQN) reinforcement learning algorithms to dynamically control resource allocation and traffic prioritization in healthcare IoT networks. This system involves multi-agent reinforcement learning architecture where continuous state-action space mapping is utilized for adjusting bandwidth allocation, latency management, and packet prioritization automatically based on network conditions and the criticality levels of applications in real-time. Experimentally, the solution has attained an accuracy of 94.7 percent in QoS prediction, an 87.3 percent reduction in average latency to critical healthcare applications, 91.2 percent improvement in network throughput utilization, and an 89.6 percent success rate in adhering to service level agreements in peak traffic conditions. Through reinforcement learning-based decision making, the adaptive QoS mechanism dynamically accommodates the requirements of healthcare IoT, ensuring reliable service delivery while optimizing the usage of network resources. 2025 IEEE.
Srivastava, Shubhi; Gupta, Roopesh; Banerjee, Somnath; Vijayan, Alpha; Thacker, Chintan; Vasmatkar, Abhijit
2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2025;
https://doi.org/10.1109/IACIS65746.2025.11210968 https://www.scopus.com/pages/publications/105023639181?origin=resultslist
Srivastava S., New Horizon College of Engineering, Department of Information Science and Engineering, Bengaluru, India; Gupta R., SCES's Indira School of Business Studies, Department of Marketing, Pune, India; Banerjee S., Sun Prairie, United States; Vijayan A., Christ University, Department of Artificial Intelligence Machine Learning & Data Science (AIML&DS), Bengaluru, India; Thacker C., Parul institute of Engineering and Technology, Parul University, Faculty of Engineering and Technology, Department of Computer science and Engineering, P.O.Limda, India; Vasmatkar A., Symbiosis Law School, Symbiosis International (Deemed University), Faculty of Law, Pune, India
Attainments of Mission Statements and Programme Educational Objectives in Outcome Based Education for a Degree Programme
course outcome; Outcome-based education; programme educational objectives; programme gap analysis; programme outcome
The outcome-based education (OBE) assesses the outcomes of the students in the form of attainments. The attainments are of a programme which can be undergraduate, postgraduate or diploma. The OBE are assessed in the form of attainments of programme outcomes (POs), programme educational objectives (PEOs) and attainment of mission statements. The attainments are calculated on the basis of direct and indirect tools. The outcomes of theory and lab courses, projects, and placements are considered direct tools. Indirect tools include several surveys like alumni, curriculum, exit, etc. The attainments are finally calculated by combining direct and indirect tools. The attainments of different batches are compared and assessed. The entire attainment process is covered in this research article, and the attainment of a certain batch is shown by the attainments of PEOs and POs. 2025 IEEE.
Rajak, Akash; Gupta, Praveen Kumar; Mishra, Siddheshwari Dutt; Kumar, Amit; Verma, Ankit; Vidushi
2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025;
https://doi.org/10.1109/IATMSI64286.2025.10984504 https://www.scopus.com/pages/publications/105007418919?origin=resultslist
Rajak A., Kiet Group of Institutions, Delhi-NCR, Ghaziabad, India; Gupta P.K., Bennett University, Greater Noida, India; Mishra S.D., Mriirs, Faridabad, India; Kumar A., Kiet Group of Institutions, Delhi-NCR, Ghaziabad, India; Verma A., Kiet Group of Institutions, Delhi-NCR, Ghaziabad, India; Vidushi, Christ (Deemed to be University), India
Advancements in EEG and EMG Signals for Motor Imagery Classification and Artifact Removal: A Comprehensive Review and Analysis
Brain-Computer Interfaces (BCIs); EEG signals; EMG signals; Motor imagery classification; Motor rehabilitation; Neurotechnology
An essential noninvasive method for assessing brain electrical activity and gaining important knowledge about how the brain functions is electroencephalography (EEG). Understanding the brain's reactions to particular sensory, cognitive, or motor events requires understanding event-related potentials (ERPs), which are derived from EEG. By displaying variations in frequency content across time, time- frequency analysis improves ERP interpretation. Each of the five EEG frequency bands - delta, theta, alpha, beta, and gamma - has a unique clinical significance and is linked to different physiological and cognitive processes. In order to improve motor control and rehabilitation, this work focuses on the development of NeuroMotor Fusion approaches, which integrate EEG and Electromyography (EMG) signals for motor imagery classification. It looks at new developments in the classification of motor imagery and investigates cutting edge methods such as VR motor priming and brain-computer interfaces (BCIs). The study also discusses the difficulties in removing artifacts from EEG and EMG signals, using hybrid techniques to reduce ocular and muscular artifacts. The study produced a 96.2% accuracy rate in motor function enhancement using the ShallowFBCSPNet model architecture and the MOABBDataset "BNCI2014-001". These findings show that NeuroMotor Fusion has a great deal of promise for use in neurological disease support, individualized motor skill training, and rehabilitation. 2025 IEEE.
Kushwah, Piyush; Tiwari, Vibha; Shukla, Sunil Kumar; Tiwari, Akshra
https://doi.org/10.1109/IATMSI64286.2025.10984968 https://www.scopus.com/pages/publications/105007435740?origin=resultslist
Kushwah P., Madhav Institute of Technology & Science, Centre for Artificial Intelligence, Gwalior, India; Tiwari V., Madhav Institute of Technology & Science, Centre for Artificial Intelligence, Gwalior, India; Shukla S.K., Madhav Institute of Technology & Science, Centre for Artificial Intelligence, Gwalior, India; Tiwari A., Christ University, School of Sciences, Delhi, India
Corporate Credit Rating Assessment for Financial Risk and Regulatory Compliance
Corporate credit rating; Credit risk modelling; Financial risk assessment; Multimodal learning; Regulatory compliance; Temporal graph transformer
Accurate corporate credit rating is crucial to financial risk management and regulation but the current models tend to use narrow data modalities, fail to consider time and relational relationships and have weak probabilistic calibration. These constraints make them less effective in detecting the risk of default and under pinning decision-making that is in line with the regulator. The objective of this study was to formulate and test a multimodal model with a time-dependent credit rating system to incorporate financial, textual, market and relational information. The publicly available corporate financial statements, market time series data, text disclosures and inter-firm relational information were used to conduct an experimental study. Baseline logistic regression, a hybrid XGBoost with FinBERT embeddings model, and a proposed Temporal Heterogeneous Graph Transformer with cross-modal fusion were implemented and compared using discrimination, calibration, and computational efficiency metrics. The model proposed had the best predictive performance up to a ROC-AUC of 0.903 and PR-AUC of 0.482 which is better than the baseline (0.761) and hybrid (0.842) models. Calibration analysis revealed more correspondence with observed default frequencies, and confusion matrices revealed that the number of true default detection improved as 64 (baseline) to 158. Ablation and Pareto analysis was used to verify that multimodal fusion and temporal graph modelling were the major sources of performance improvements. These findings indicate that the combination of multimodal, temporal, and relational data has a significant positive effect on the accuracy and reliability of credit ratings and provides an institutional and supervisory-appropriate credit risk evaluation framework to the regulator. 2026 IEEE.
Manjunatha, M.K.; Karimikonda, Harinadh; Dwivedi, Yogita; Komatiguntala, Dhanalakshmi
Proceedings of the 4th IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2026;
https://doi.org/10.1109/IATMSI68868.2026.11465453 https://www.scopus.com/pages/publications/105036965879?origin=resultslist
Manjunatha M.K., Visvesvaraya Technological University, Centre for PostGraduate Studies, Department of Management Studies, Mysuru, India; Karimikonda H., School of Management Studies (MBA), Sreenidhi Institute of Science and Technology, Telangana, Hyderabad, India; Dwivedi Y., Jaypee Business School, Jaypee Institute of Information Technology, Noida, India; Komatiguntala D., School of Business and Management, Christ University, Banglore, India
It-Driven Governance Models for Social Impact and Community Development
Community Development; Differential privacy; Explainable AI; Federated Neuro-Symbolic Governance (FNSG); India Stack; Neuro-Symbolic AI; Open Government Data (OGD); Secure Aggregation
Local development planning in India is impaired by fragmented administrative datasets, limited citizen participation, and opaque decision processes despite the availability of India Stack primitives and Open Government Data (OGD) such as NFHS and MGNREGA. This study aimed to design, implement, and evaluate Federated Neuro-Symbolic Governance (FNSG), a privacy-preserving cross-silo framework that couples neural concept learners with symbolic rule synthesis, to deliver interpretable district-level decision support. FNSG operates in a hierarchical federated topology (district clients, state aggregator) using NFHS-5, MGNREGA, census/SECC proxies, and geospatial layers. Local clients train neural concept encoders, extract symbolic rule candidates, and transmit clipped, DP-noised updates plus anonymized rule evidence for secure aggregation and global rule distillation. In pilot experiments (K=20 districts, geographic holdout) FNSG achieved mean test AUC 0.84 versus centralized 0.88 and FedAvg 0.80, Precision@100 0.69, and produced concise global rules with fidelity ? 0.72. Privacy - utility sweeps indicate acceptable utility at ? ? 2 (AUC ? 0.82), with degradation at ? ? 1; convergence required ? 110 rounds and per-epoch client time ? 18 s on CPU (? 8 s on GPU). FNSG balances utility, interpretability, and data sovereignty. Policy implications include staged pilots, capacity building for district data centers, mandatory audit logs, and India Stack-mediated consent and integration. 2026 IEEE.
Chacko, Elizabeth; Chowdhury, Piyali Roy; Bommisetti, Ravi Kumar; Gupta, Yogita; Puvvada, Sudhakara Rao; Painoli, Girish Kumar
https://doi.org/10.1109/IATMSI68868.2026.11465472 https://www.scopus.com/pages/publications/105036947582?origin=resultslist
Chacko E., School of Business and Management, Christ University, Bangalore, India; Chowdhury P.R., School of Business and Management, Christ University, Bangalore, India; Bommisetti R.K., Akal University, Department of Commerce, Punjab, Bathinda, India; Gupta Y., Symbiosis Centre for Distance Learning, Department of Academics, Pune, India; Puvvada S.R., Bapatla Engineering College, Department of Management, Bapatla, India; Painoli G.K., School of Commerce and Management, Sanjivani University, Maharashtra, Ahilyanagar, India
Posture Classification Using a Hybrid Deep Learning Model
Class Imbalance; Deep Learning; Feature Engineering; Hybrid Model; Posture Detection; Radon Transform; ResNet50; Transfer Learning
Automated posture detection is a critical task in ergonomics and healthcare, yet it presents significant challenges for standard computer vision models, particularly in handling class imbalance and understanding geometric constraints. This paper proposes an enhanced hybrid deep learning model that synergizes the feature extraction power of a pre-trained ResNet50 architecture with engineered geometric features derived from the Radon Transform and pre-calculated joint angles. Our approach utilizes a dual-balancing strategy, combining data upsampling with a custom weighted loss function, to effectively address the problem of underrepresented classes. By processing visual and geometric data streams in parallel and fusing them within a deep architecture, our model achieves a holistic understanding of the subject's posture. The fine-tuned model demonstrates strong performance on an unseen test set, achieving a final accuracy of 92% for wrist posture and 92% for neck posture. Crucially, it attains a robust F1-score of 0.74 for the challenging 'Bad Wrist Posture' minority class, a significant improvement compared to the ResNet50-only baseline (F1=0.24) and achieves excellent ROC-AUC scores of 0.9859 for wrist and 0.9838 for neck, proving the efficacy of our hybrid, dual-balancing methodology for realworld application. 2026 IEEE.
George, Geniets M.; Ummesalma, M.; Parihar, Ruchi Singh
https://doi.org/10.1109/IATMSI68868.2026.11465994 https://www.scopus.com/pages/publications/105036962349?origin=resultslist
George G.M., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India; Ummesalma M., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India; Parihar R.S., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India
FINNET: A Hierarchical Graph Learning Framework for Adaptive Cross-Market Financial Risk Prediction
Dynamic Embeddings; Explanatory AI; Graph Neural Networks in Finance; Multi-Scale Learning; Systemic Risk; Transfer Learning
Systemic financial risk emerges from complex multi-scale interactions among entities, sectors, and markets. We introduce FINNET, a hierarchical graph neural network framework that models these vertical dependencies through volatility-aware adaptive pooling. Our approach features: (1) a tri-scale graph structure capturing entity, sector, and market dynamics; (2) dynamic embeddings combining static features with time-varying signals; (3) transfer learning for emerging markets; and (4) transparent risk decomposition for regulatory compliance. Validated on 58,432 financial entities across three continents, FINNET achieves 0.891 AUC with only 3.8% performance degradation during crises, while providing early warnings 15 days before failures. 2026 IEEE.
Anandu, P.S.; Gogi, Vyshali J.; Azarudheen, S.
https://doi.org/10.1109/IATMSI68868.2026.11466022 https://www.scopus.com/pages/publications/105036968997?origin=resultslist
Anandu P.S., Christ (Deemed to be University), Department of Data Science, Bangalore Central Campus, India; Gogi V.J., Christ (Deemed to be University), Department of Data Science, Bangalore Central Campus, India; Azarudheen S., Christ (Deemed to be University), Department of Data Science, Bangalore Central Campus, India
AI-Driven Early Diagnosis of Acute Liver Failure: A Machine Learning Perspective
Acute Liver Failure; Feature Extraction; Liver Disease; Machine Learning
The liver performs a valuable role in operating proper metabolism. This organ in the human body is responsible for maintaining and preserving overall health and well-being. However, when it fails to function optimally, it can cause severe and significant health complications. Liver diseases are multifactorial conditions that can be challenging to diagnose and treat. Early detection of any disease is beneficial for effective treatment and diagnosis of patients' conditions. Machine Learning algorithms create a great platform for analyzing medical data that helps improve disease detection procedures. This paper aims to get a better understanding of ML algorithms for detecting diseases associated with the liver. The paper tries to explore various machine learning techniques for predicting accurate liver diseases. It uses various parameters as symptoms and calculates ALF (Acute Liver Failure) based on the parameters and ALF predicts in-case the person is suffering from a Liver disease or not. Accuracy was calculated with various ML techniques i.e. Logistic Regression Classification, KNN Classification, Decision Tree, Random Forest and Support Vector Machine. Among all these, Logistic Regression was found to be most effective in identifying and predicting the outcome of the dataset compared to other algorithms. SVM has a higher cross-validation score but Accuracy, precision and recall are very low thus, cannot select this model. 2025 IEEE.
Shoran, Preety; Yadav, Meenakshi; Saxena, Esha; Khare, Akhilendra; Harizan, Subash
2025 IEEE International Conference on Computer, Electronics, Electrical Engineering and their Applications, IC2E3 2025;
https://doi.org/10.1109/IC2E365635.2025.11167356 https://www.scopus.com/pages/publications/105018450164?origin=resultslist
Shoran P., Christ University, School of Sciences, India; Yadav M., Galgotia College of Engineering & Technology, Department of Information Technology, Gr. Noida, India; Saxena E., Guru Tegh Bahadur Institute of Technology, Department of Computer Science & Engineering, Delhi, India; Khare A., Galgotia University, Department of Computer Science & Engineering, Greater Noida, India; Harizan S., Galgotia Univesity, Department of Computer Science & Engineering, Greater Noida, India
Real-Time Football Match Analysis with Region-Independent Player Tracking Using Deep Learning
ball detection; ByteTrack; Frames; Full frame detection; Homography; Multi object tracking; Yolov8
This project explores the application of AI in analyzing football games by tracking players across the entire video frame. Unlike traditional methods that focus on limited areas, the system here uses YOLO for detecting players everywhere in the frame and ByteTrack to follow them throughout the match. The goal is to get a clearer picture of each player's movement, particularly their speed and distance covered. Manual methods or GPS-based tools often fall short in providing quick, reliable data, especially in real-time scenarios. This study compensates for camera motion and adjusts for different viewpoints to get more accurate tracking results. As a way to test player identity consistency, the system randomly assigns popular player names to different tracking IDs. Experiments on public match videos show that the system can keep track of players even during zoom-ins, crowding, or partial visibility. Code snippets show how the model works in practice. Our results show that using full-frame AI tracking gives coaches more detailed tactical insights and helps them develop more effective strategies. 2025 IEEE.
Goswami, Jai; Shoran, Preety; Shanbhog, Manjula
https://doi.org/10.1109/IC2E365635.2025.11167431 https://www.scopus.com/pages/publications/105018462669?origin=resultslist
Goswami J., CHRIST University, Computer Science, Bengaluru, India; Shoran P., CHRIST University, Computer Science, Bengaluru, India; Shanbhog M., CHRIST University, Computer Science, Bengaluru, India
Customer Evaluation of Internet Banking Services: Analysing South Indian Bank's Digital Banking Experience
Christ University; Customer Satisfaction; Digital Banking Retention; Internet Banking; Regression Analysis; South Indian Bank
This research intends to assess the valuation of Christ University's customers on internet banking services provided by South Indian Bank. The research objectives are to evaluate the impact of efficiency, security, ease of use, reliability and social influence on customer satisfaction in the context of internet banking. Consumers of Christ University were selected to answer in the survey to give their view and experience toward the internet banking services offered by the South Indian Bank. The strategies used in data analysis incorporated regression analysis to realize correlation between such factors and extent of customer satisfaction. The research offers huge information on how it is possible to enhance the levels of customer satisfaction specifically in internet banking services of South Indian Bank. Therefore, the study underpins an importance of enhancing digital banking platforms to fulfil the need and expectation of customers. The study is also beneficial for other financial institution with the aim at improving the level of customer satisfaction and ability to retain the clients in the realm of digital banking. 2025 IEEE.
https://doi.org/10.1109/IC2E365635.2025.11167549 https://www.scopus.com/pages/publications/105018465669?origin=resultslist
Athiban Vetrivel A., CHRIST (Deemed to Be University), India; Shoran P., CHRIST (Deemed to Be University), India