Predictive Modelling of cardiovascular Disease Survival Using Mutual Information and Machine Learning Across Varying Sample Sizes Vijayalakshmi. Sarraju, Jaya Pal, Supreeti. Kamilya International Journal of Basic and Applied Sciences, 2025 In clinical data analytics, predicting survival outcomes for cardiovascular disease (CVD) is a challenging task with practical implications. Using three different datasets, this study investigates how sample size affects machine learning performance and generalizability. The methodology combines statistical sample-size analysis with mutual information gain, a filter-based, scalable, and domain-agnostic feature selection strategy, to identify clinically essential features. Mutual information gain measures the dependency between each predictor and the target variable, ensuring computational efficiency and applicability to large-scale data. Machine learning classifiers, support vector machines (SVMs) and logistic regression (LR), are employed to assess predictive performance across varying population sizes. Experimental results demonstrate that increasing the sample size improves model accuracy by up to 10%, recall by 5–8%, and maintains consistent specificity. Furthermore, to enhance clinical reliability, the models are evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), where SVM achieved an AUC of 0.965 and LR achieved 0.937, validating strong discriminatory power; Also, SHAP-based feature attribution is used to improve interpretability, identifying that larger sample sizes provide more stable and clinically meaningful explanations.
IoT-Enabled Methane Monitoring and LSTM-Based Forecasting System for Enhanced Safety in Underground Coal Mining Soumyadeep Paty, Arindam Biswas, Sonia Djebali, Guillaume Guerard, Supreeti Kamilya ACM Transactions on Internet of Things, 2025 Ensuring safety in the mining industry is a critical concern for a nation's industrial advancement. Industry 4.0, characterized by the integration of advanced technologies, is at the forefront of efforts to enhance mining practices. Coal seams contain a range of hydrocarbon gases, predominantly methane, which is released in significant quantities during mining operations. Effectively mitigating methane emissions is imperative. The inclusion of methane forecasting allows for the early identification of potential methane emissions, hence resulting in significance enhancement in mine safety. The research work is focused on real-time remote monitoring and cloud-based forecasting of methane levels in underground coal mines. An Industrial Internet of Things (IIoT) device is developed for data acquisition in underground coal mines, capturing essential parameters such as methane concentration, temperature, and humidity. The collected data are utilized to train a long short-term memory based multivariate forecasting model. The trained model is subsequently deployed in the cloud. The experiment is performed in a mine of Eastern Coalfields Limited, India. After the deployment of the proposed model, the developed IIoT device transmits real-time data, obtained from the mine, to the cloud. Based on the real-time data, our model conducts methane forecasting and communicates results back to the IIoT device. The device issues immediate alerts when methane levels surpass predefined thresholds. This ensures enhanced safety in mining operations by providing warnings for both current and forecasted methane concentrations. The forecasted methane concentrations, along with real-time data, are accessible through mobile applications and a web-based dashboard. The accuracy of the proposed model is measured by mean absolute error, mean absolute percentage error, and root mean square error, which demonstrate values of 156.95 ppm, 4.23%, and 191.53 ppm, respectively. A comparative study is performed where our model is evaluated against the multivariate multilayer perceptron, vector autoregression, and auto-regressive integrated moving average models. The comparative study demonstrates that our developed model outperforms the others, showing superior results.
INFERENTIAL STATISTICS-DRIVEN LOGISTIC REGRESSION MODEL FOR CARDIOVASCULAR DISEASE PREDICTION Vijayalakshmi Sarraju International Journal of Applied Mathematics, 2025 Cardiovascular diseases (CVDs) are widely recognisedas primary contributors to global mortality, necessitatingthe development of precise predictive models that provideclear explanations for early detection and facilitate effectiveinterventions. A logistic regression model incorporating inferential statistical methods into the analysis offers a solution for enhanced reliability and interpretability in predicting cardiovascular diseases using clinical data. The analysis employed correlation tests, combined with chi-squaretests and independent t-statistics, to identify key factors,including patient age, blood pressure, cholesterol levels, glucose measures, exercise activity, smoking habits, and alcohol intake. The model generalisation improved significantlyby using the Synthetic Minority Over-sampling Technique(SMOTE) to manage class imbalance problems. The implemented model attained exceptional scores with a 95.2% accuracy rate, 94.8% precision, 96.1% recall, 95.4% F1-score,and 97.2% AUC-ROC. Real-time Medical applications andreliability assessments rely on confusion matrix analysis,ROC curve examination, calibration plots, and feature importance assessment. The training loss curves verified thatconvergence occurred during the training process. The analytical results demonstrate that logistic regression performswell, generating computations that are both clinically usefuland statistically comprehensive, while promoting preventivecardiovascular healthcare
Identification of different types of rocks and ores using cellular automata assisted CNN models: an application of mining Industry S Paty, S Kamilya Natural Computing 25 (1), 14 , 2026 2026
Minimality, transitivity and sensitivity of non-uniform cellular automata S Kamilya, J Kari, K Paturi arXiv preprint arXiv:2605.22762 , 2026 2026
Assessing Cognitive Load Levels via TAR-Based EEG Analysis and Machine Learning Models MR Ansari, S Kamilya, S Anwar 2025 IEEE Silchar Subsection Conference (SILCON), 1-4 , 2025 2025
Light weight encryption technique: a cellular automaton based approach for securing health records: S. Anwar et al. S Anwar, P Pranav, S Kamilya Scientific Reports 15 (1), 23945 , 2025 2025
Cellular Automata Technology: 4th Asian Symposium, ASCAT 2025, Ranchi, India, March 6–8, 2025, Revised Selected Papers S Kamilya, S Das, E Formenti Springer Nature , 2025 2025
Subject-Specific Temporal Analysis of Cognitive Load Using fNIRS: A Machine Learning Approach S Kamilya, H Ritesh, C Shashank Vinod, S Karmakar, T Pal Intelligent Computing-Proceedings of the Computing Conference, 378-392 , 2025 2025
Quantum Dot Cellular Automata: Breaking Barriers in Electronics Circuitry for Tomorrow’s Technologies S Kamilya, S Paty Advances in Quantum Inspired Artificial Intelligence: Techniques and … , 2025 2025 Citations: 1
Circuitry for Tomorrow's Technologies S Kamilya, S Paty Advances in Quantum Inspired Artificial Intelligence: Techniques and … , 2025 2025
EchoCNN-Denoiser: a reservoir computing inspired deep learning model for enhanced synthetic aperture radar image despeckling SA Twinkle, S Kamilya, J Mukherjee Journal of Applied Remote Sensing 19 (2), 026501-026501 , 2025 2025 Citations: 4
Despeckling Images Using Elementary Cellular Automata S Aishwarya Twinkle, S Kamilya, J Mukherjee Asian Symposium on Cellular Automata Technology, 191-202 , 2025 2025
IoT-enabled methane monitoring and LSTM-based forecasting system for enhanced safety in underground coal mining S Paty, A Biswas, S Djebali, G Guerard, S Kamilya ACM Transactions on Internet of Things 6 (1), 1-29 , 2025 2025 Citations: 12
INFERENTIAL STATISTICS-DRIVEN LOGISTIC REGRESSION MODEL FOR CARDIOVASCULAR DISEASE PREDICTION V Sarraju, J Pal, S Kamilya International Journal of Applied Mathematics 38 (4s) , 2025 2025
A Deep Learning Technique for Real-Time Detection of Cognitive Load Using Optimal Number of EEG Electrodes S Karmakar, S Kamilya, C Koley, T Pal IEEE Transactions on Instrumentation and Measurement 74, 1-11 , 2024 2024 Citations: 9
Edge Preserving Multiplicative Noise Removal of SAR Images Through Convolutional Neural Network and Anisotropic Diffusion SA Twinkle, S Kamilya, J Mukherjee 2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), 1-4 , 2024 2024 Citations: 1
Impact of Variable Sample Size on the Efficiency of Support Vector Machines in Cardiovascular Disease Detection V Sarraju, J Pal, S Kamilya 2024 Second International Conference on Advances in Information Technology … , 2024 2024
SRS: Gender-based heart disease prediction using stratified random sampling approach V Sarraju, J Pal, S Kamilya AIP Conference Proceedings 3164 (1), 020005 , 2024 2024 Citations: 2
On Elementary Second Order Cellular Automata E Formenti, S Kamilya Asian Symposium on Cellular Automata Technology, 204-218 , 2024 2024 Citations: 1
Rock Image Classification Using CNN Assisted with Pre-processed Cellular Automata-Based Grain Detected Images S Paty, S Kamilya Asian Symposium on Cellular Automata Technology, 153-167 , 2024 2024
Water Informatics: Challenges and Solutions Using State of Art Technologies A Biswas, S Kamilya, SL Peng Springer Verlag, Singapore , 2024 2024 Citations: 1
Water Informatics S Kamilya, A Biswas, SL Peng 2024
MOST CITED SCHOLAR PUBLICATIONS
Real time detection of cognitive load using fNIRS: A deep learning approach S Karmakar, S Kamilya, P Dey, PK Guhathakurta, M Dalui, TK Bera, ... Biomedical Signal Processing and Control 80, 104227 , 2023 2023 Citations: 60
A study of chaos in cellular automata S Kamilya, S Das International Journal of Bifurcation and Chaos 28 (03), 1830008 , 2018 2018 Citations: 26
A study of chaos in non-uniform cellular automata S Kamilya, S Das Communications in Nonlinear Science and Numerical Simulation 76, 116-131 , 2019 2019 Citations: 23
IoT-enabled methane monitoring and LSTM-based forecasting system for enhanced safety in underground coal mining S Paty, A Biswas, S Djebali, G Guerard, S Kamilya ACM Transactions on Internet of Things 6 (1), 1-29 , 2025 2025 Citations: 12
A Deep Learning Technique for Real-Time Detection of Cognitive Load Using Optimal Number of EEG Electrodes S Karmakar, S Kamilya, C Koley, T Pal IEEE Transactions on Instrumentation and Measurement 74, 1-11 , 2024 2024 Citations: 9
Nilpotency and periodic points in non-uniform cellular automata S Kamilya, J Kari Acta Informatica 58 (4), 319-333 , 2021 2021 Citations: 9
SACAs:(Non-uniform) Cellular Automata that Converge to a Single Fixed Point. S Kamilya, S Adak, S Das, BK Sikdar Journal of Cellular Automata 14 , 2019 2019 Citations: 7
EchoCNN-Denoiser: a reservoir computing inspired deep learning model for enhanced synthetic aperture radar image despeckling SA Twinkle, S Kamilya, J Mukherjee Journal of Applied Remote Sensing 19 (2), 026501-026501 , 2025 2025 Citations: 4
Identification of rock images in mining industry: an application of deep learning technique S Paty, S Kamilya International Conference on Advances in Data Science and Computing … , 2022 2022 Citations: 4
SRS: Gender-based heart disease prediction using stratified random sampling approach V Sarraju, J Pal, S Kamilya AIP Conference Proceedings 3164 (1), 020005 , 2024 2024 Citations: 2
A Cellular Automaton-Based Technique for Estimating Mineral Resources. S Paty, S Kamilya Complex Systems 32 (2) , 2023 2023 Citations: 2
A cellular automata-based approach on assessment of thickness of stratified mineral deposits S Paty, S Adak, S Kamilya Asian Symposium on Cellular Automata Technology, 105-114 , 2023 2023 Citations: 2
Grade estimation of mineral resources: an application of cellular automata S Paty, S Kamilya Asian Symposium on Cellular Automata Technology, 45-54 , 2022 2022 Citations: 2
Simulation of Non-uniform Cellular Automata by Classical Cellular Automata and Its Application in Embedded Systems. S Kamilya, S Das, BK Sikdar Journal of Cellular Automata 16 , 2021 2021 Citations: 2
Quantum Dot Cellular Automata: Breaking Barriers in Electronics Circuitry for Tomorrow’s Technologies S Kamilya, S Paty Advances in Quantum Inspired Artificial Intelligence: Techniques and … , 2025 2025 Citations: 1
Edge Preserving Multiplicative Noise Removal of SAR Images Through Convolutional Neural Network and Anisotropic Diffusion SA Twinkle, S Kamilya, J Mukherjee 2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), 1-4 , 2024 2024 Citations: 1
On Elementary Second Order Cellular Automata E Formenti, S Kamilya Asian Symposium on Cellular Automata Technology, 204-218 , 2024 2024 Citations: 1
Water Informatics: Challenges and Solutions Using State of Art Technologies A Biswas, S Kamilya, SL Peng Springer Verlag, Singapore , 2024 2024 Citations: 1
Performance Analysis of Supervised Learning Algorithms on Different Applications V Sarraju, J Pal, S Kamilya CS & IT Conference Proceedings 12 (19) , 2022 2022 Citations: 1
Cellular automata: chaos, convergence and unification S Kamilya Ph. D. Thesis, Indian Institute of Engineering Science and Technology … , 2021 2021 Citations: 1
