Engineering, Electrical and Electronic Engineering, Energy Engineering and Power Technology, Renewable Energy, Sustainability and the Environment
27
Scopus Publications
Scopus Publications
Tuna Swarm Algorithm Based Robust Node Localization Scheme in Wireless Communication Networks JK Periasamy, Shrabani Mallick, Sridevi Chitti, S Sivasakthi, Bindu KV, Ezudheen Puliyanjalil International Research Journal of Multidisciplinary Scope, 2025 Node Localization (NL) in wireless sensor networks (WSNs) is main procedure for defining physical matches such as longitude, latitude, and altitude of Sensor Nodes (SNs) organized in a provided region. Exact NL is very essential for numerous WSN applications like surveillance, asset tracking and environmental monitoring. Localization models involve GPS hardware, anchor nodes with recognized locations or algorithms that influence distance dimensions and connectivity designs amongst SN in order to evaluate their places. Trustworthy NL improves exactness and efficiency of data collection and study in WSNs, finally boosting up the network's performance and quality information it offers. This article introduces a tuna swarm algorithm-based node localization (TSA-RNL) technique in WSN. The major aim of the TSA-RNL model to focus unknown nodes in WSN. TSA-RNL technique is developed for enhancing the localization accuracy in the WSN. The TSA, stimulated from the collective nature of tuna fish, optimizes the localization process by iteratively refining node positions. Over wide simulation and experimentation, we estimate the TSA-RNL model performance and establish its authority in gaining great accurateness node localization in WSNs. The methods provide potential advantages for many applications that based on specific node positioning, environmental monitoring, data fusion and target tracking that contributing to the development of WSN methodology.
ENHANCEMENTS IN ECO-FRIENDLY ENERGY SOLUTIONS FOR IMPROVED FUEL QUALITY Oxidation Communications, 2025
Comparative analysis of explainable machine learning models for cardiovascular risk stratification using clinical data and shapley additive explanations Komal Kumar Napa, Rajkumar Govindarajan, S. Sathya, J. Senthil Murugan, Bindu Kolappa Pillai Vijayammal Intelligence Based Medicine, 2025 Heart disease remains a leading cause of mortality globally, demanding timely and reliable diagnostic support in clinical settings. This study proposes an interpretable machine learning framework that leverages Random Forest (RF) models integrated with SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP) to predict heart disease risk using clinical data. By addressing the limitations of “black-box” predictive systems, the framework ensures transparency and trust in decision-making. Multiple machine learning classifiers were benchmarked, with RF demonstrating high performance among the classifiers under analysis in terms of accuracy and interpretability. A streamlined, user-friendly graphical interface was developed using Streamlit to facilitate real-time risk assessment, feature-level explanations, and actionable clinical insights. The system incorporates electronic health records, utilising preprocessing and imputation strategies to enhance model robustness. Experimental evaluations demonstrated that the proposed method strikes a balance between good predictive accuracy and interpretability, making it suitable for integration into clinical workflows. This work contributes to the advancement of explainable AI in healthcare engineering, supporting clinicians in early diagnosis and preventive care for cardiovascular conditions. • An interpretable machine learning framework using Random Forest is proposed for heart disease prediction. • SHAP and Partial Dependence Plots are integrated for transparent clinical feature explanations. • KNN imputation improves data quality in Electronic Health Records for robust model training. • A real-time Streamlit GUI enables interactive risk prediction and visual explanation for clinicians. • The model achieves strong predictive performance (81.3 % accuracy) while ensuring explainability.
NOVEL APPROACHES FOR INCREASING ENERGY EFFICIENCY IN PLASMA-ASSISTED CATALYTIC CONVERTERS Journal of Environmental Protection and Ecology, 2025
Exploring Feature Relationships in Brain Stroke Data Using Polynomial Feature Transformation and Linear Regression Modeling Sitanaboina S L Parvathi, Aruna Devi B, Gururaj L Kulkarni, Sangeetha Murugan, Bindu Kolappa Pillai Vijayammal, Neha Journal of Machine and Computing, 2024 A Cerebral vascular accident, commonly known as a stroke, is a pathological condition that impacts the brain due to the rupture of capillaries. It occurs when there is a disturbance in the typical blood circulation and essential physiological processes of the brain. Stroke prediction plays a crucial role in early diagnosis and intervention, potentially improving patient outcomes. This paper proposes a machine learning model that leverages polynomial feature transformation and linear regression modeling for stroke prediction. The model addresses the challenge of capturing non-linear relationships between features and the target variable while maintaining interpretability. The proposed approach involves preprocessing data by separating categorical and numerical features, applying one-hot encoding to categorical features, and generating polynomial features up to the second degree for numerical features. This tailored preprocessing is facilitated by a Column Transformer. For model development, a machine learning pipeline is constructed, splitting the data into training and testing sets. Despite utilizing polynomial features, linear regression is employed as the final model, allowing for the capture of both linear and non-linear relationships while maintaining interpretability. This work contributes to stroke prediction by offering a balanced approach that considers model complexity and interpretability, showcasing the potential of linear regression with polynomial features for accurate predictions and insights into feature-target relationships. The proposed model exhibited superior performance compared to other existing models, achieving a remarkable testing accuracy of 99.2%.
Consistency, local stability, and approximation of Shapash explanation Tsehay Admassu Assegie, Bommy Manivannan, Komal Kumar Napa, Bindu Kolappa Pillai Vijayammal, Rajkumar Govindarajan, Sangeetha Murugan, Atinkut Molla Mekonnen Telkomnika Telecommunication Computing Electronics and Control, 2024 Consistency, scalability, and local stability properties ensure that a model or method produces reliable and predictable outcomes. The Shapash helps users understand how the model makes its decisions. With machine learning (ML) system, healthcare experts can identify individuals at higher risk and implement interventions to reduce the occurrence and severity of disease. ML had achieved higher prediction accuracy even though the accuracy of their prediction depends on the quality and quantity of the data used for training. Despite the wider application and higher accuracy of different ML for disease prediction, the explanation of their predictive outcome is much more important to the healthcare professional, the patient, and even their developers. However, most of the ML systems do not explain their outcomes. To address the explainability issue various techniques such as local model agnostic explanation (LIME), and shapley additive explanation (SHAP) have been proposed over the recent years. Furthermore, the consistency, local stability, and approximation of the explanation remained one of the research topics in ML. This study investigated the consistency, stability, and approximation of LIME and SHAP in predicting heart disease (HD). The result suggested that LIME and SHAP generated a similar explanation (distance=0.35), compared to the active coalition of variable (ACV) explanation (distance=0.43).
A Multi-Objective Approach with Modified Particle Swarm Optimization and Hybrid Energy Systems Vijayammal, Bindu Kolappa Pillai, Cherukupalli, Kumar, Jayaraman, Ramesh, Kannan, Elango Tehnicki Vjesnik, 2024 Designing a photovoltaic (PV) power grid involves intricate considerations, focusing on sizing the PV system and strategically optimizing its placement. Intelligent multi-objective optimization techniques are crucial for addressing the complexity of this task, seeking an optimal solution that balances various objectives such as maximizing energy production, minimizing costs, and ensuring system reliability. In this research, we have selected Modified Particle Swarm Optimization (MPSO) as a suitable multi-objective optimization technique. The primary objective of this optimization is to maximize the energy generated by the PV system, involving the minimization of installation costs, including expenses associated with solar panels, batteries, and related equipment. The optimization technique aims to determine the capacity of the PV system, considering factors such as energy demand, available space, and budget constraints. The ultimate goal is to achieve maximal energy production while adhering to specified budget and space limitations. Optimizing the placement of solar panels is crucial for maximizing energy production. This optimization process takes into account various factors, including shading, panel orientation, tilt angle, and spacing between panels. Utilizing optimization algorithms, the aim is to identify the most effective configuration that ensures the highest energy production. The final step involves implementing the selected PV system design, considering practical installation considerations and regulatory requirements. This comprehensive approach ensures that the designed PV power grid not only meets energy production goals but also considers real-world constraints and compliance with relevant regulations. Through the use of a Hybrid Energy System (HES) with a 15 kW PV scheme and a modest bank, maximum investments for the user and a reduction in carbon influence of more than half can be achieved. This outcome was observed across all four sites evaluated in this research, involving two building types.
MCHeRA: An Efficient Multi Cloud Heuristic Resource Allocation using Grey Wolf Optimization P. Gandhimathi, S. Kiruthiga, BIndu K V, Shanthi H J, V. Velmurugan, Sangeethaa SN Proceedings of the 5th International Conference on Data Intelligence and Cognitive Informatics Icdici 2024, 2024 Cloud computing is a cutting-edge technology that has gained immense popularity in the present era. The burgeoning demand for services has necessitated the development of a framework for efficiently allocating resources to incoming requests. This approach not only enhances the network's efficiency but also reduces costs significantly. The convergence of cloud and edge computing has emerged as a burgeoning field of study in the computing industry over the past few years. The exponential rise in the number of customers and requests for cloud data centers (CDCs) has highlighted the critical need for robust servers and energy-efficient mechanisms. As CDCs expand to meet growing demands, they face significant challenges related to energy consumption, environmental impact, and operational costs. Employing suitable algorithms for resource allocation in CDCs is crucial in order to minimize energy consumption. The objective of this research endeavor was to devise an ingenious approach for dynamically allocating resources in cloud networks by leveraging the power of the Gray Wolf Optimization algorithm. The proposed work performs Multi-Cloud Heuristic Resource Allocation (MCHeRA) using Grey Wolf Optimization (GWO) algorithm and produces 61% of computational resource utilization, 79% of CPU utilization, 64% memory utilization, 74% bandwidth utilization. The algorithm that has been put forth is meticulously assessed and contrasted with its counterparts. The outcome of the simulation manifests the remarkable efficacy of the novel MCHeRA in comparison to alternative algorithms.
Improving State of Charge Estimation for Lithium-Ion Batteries through Optimized CNN Models Indradeep Kumar, Madhavi Dasari, Chaitanya Danamaraju, Bindu K V, V. Mohanavel, Joshuva Arockia Dhanraj Proceedings 2024 5th International Conference on Mobile Computing and Sustainable Informatics Icmcsi 2024, 2024 Lithium-ion (Li-ion) batteries are the preferred choice for electric vehicles (EVs) because of their extended lifespans, low self-discharge rates, high voltage, and high energy density. A well-functioning Battery Management System (BMS) is critical to the efficient operation of an EV. The State of Charge (SoC) is an important statistic that reflects the remaining charge in the battery, and its exact assessment is essential for BMS and improving EV efficiency, which extends the battery's life and decreases the probability of catastrophic failure. However, SOC estimation is complicated and affected by numerous unknowns, such as battery age and external temperature. In this study, we estimated SOC using a Convolutional Neural Network (CNN) model. To improve the CNN architecture, this study has applied three different optimization algorithms: Particle Swarm Optimization (PSO), Elephant Search Algorithm (ESA), and Equilibrium Optimization (EO). Sensor data from lithium-ion batteries were carefully processed. The processed dataset was then supplied to the CNN and three optimized CNN models. These models were tested using error, R2, and time metrics to identify the optimal technique. CNN-ESA outperformed the other CNN models in SOC estimation, with the lowest error rates and the highest R2 value of 0.9987. This simulation result demonstrates the effectiveness of applying ESA to improve CNN architectures for better Li-ion battery SOC estimates. It enhances the efficiency and lifespan of EVs.
Nonlinear Dynamics in Distributed Ledger Blockchain and analysis using Statistical Perspective Purnendu Bikash Acharjee Communications on Applied Nonlinear Analysis, 2024 More and more in healthcare is blockchain technology applied for safe and open data storage. Still, it is understudied how deeply regression analysis combined with nonlinear dynamics into distributed ledger systems performs. This kind of approach may help to increase data transfer efficiency and help storage management in blockchain systems. Data speed and storage efficiency restrictions make current blockchain systems difficult to handle for large amounts of healthcare data. Conventional methods find poor data retrieval and transfer due to the great complexity and nonlinear characteristics of healthcare data. Combining nonlinear dynamics with deep regression analysis, this paper proposes a fresh approach for maximizing data transfer and storage in blockchain systems. Inspired by nonlinear dynamics ideas, a deep regression model aimed at maximizing block storage and forecast data transmission requirements was assessed on a simulated healthcare dataset using a distributed ledger system with 1,000 blocks and a 500 GB total dataset size. Performance criteria covered transmission efficiency and storage consumption. The proposed technique improved data transmission efficiency by thirty percent over current techniques. Another clear improvement was using storage; block size needs fell 25%. The best model, according to numerical research, lowered an average transmission time from 120 to 84 minutes and storage overhead from 200 to 150 GB.
Mobile Charging Port for Electric Vehicles C Jeeva, Lalit Kumar Sagar, Bindu K V, Abinaya R, Narmatha M, Shanmuga Priya M 4th International Conference on Power Energy Control and Transmission Systems Harnessing Power and Energy for an Affordable Electrification of India Icpects 2024, 2024
Modelling, simulation and PQ analysis of PWM DC –AC converter International Journal of Applied Engineering Research, 2015
Modelling, analysis and simulation of solar photovoltaic module using perturb and observe algorithm International Journal of Applied Engineering Research, 2015
A novel multi source electrical energy system Journal of Electrical Engineering, 2012
A novel voltage mitigation method for high power applications S. S. Darly, P. Vanaja Ranjan, A. Kannan, K.V. Bindu, B. Justus Rabi Proceedings of the International Conference on Recent Advances in Space Technology Services and Climate Change 2010 Rsts and Cc 2010, 2010
