Dr B Meenakshi

Scopus Publications

SmartVision: A Deep Learning and IoT-Based Framework for Automated Kitchen Hygiene Monitoring
T. Dhiliphan Rajkumar, B Meenakshi, Surarapu Ashish, L Rajesh Kannan, K Jeya Mithra
2026 International Conference on Emerging Technologies and Future Innovations Etfi 2026, 2026
A QoS-aware routing approach for Internet of Things-enabled wireless sensor networks in smart cities
D. Karunkuzhali, B. Meenakshi, Keerthi Lingam
Multimedia Tools and Applications, 2025
IoT-based plant disease detection using enhanced Elman spike neural network with Capuchin search optimisation algorithm
Danassou Karunkuzhali, Balasubramanian Meenakshi
International Journal of Bio Inspired Computation, 2025
In recent years, the internet of things (IoT) has gained attention for its transformative role in agriculture. A main challenge in agriculture is early identification of plant disease which is needed to prevent crop loss and ensure food preservative. Typical plant disease detection techniques are often time-consuming and labour-intensive, making it important to replace them with automated systems. Therefore, IoT-based plant disease detection using enhanced Elman spike neural network together with Capuchin search optimisation algorithm (IoT-PDD-OEESNN) is proposed in this paper for detecting potato plant. The input data is preprocessed using altered phase preserving dynamic range compression (APPDRC) filtering model for extracting the leaf region of the image and also eliminates the noise and blur image. The proposed IoT-PDD-OEESNN approach is implemented in Python using certain metrics. The IoT-PDD-OEESNN method attains better accuracy of 30.12%, 26.75% and lower computation time of 27.18%, 26.29%, and 29.56% when analysed with the existing methods.
Versa Watt Pioneering a Multi-Output SMPS Solution for Next-Gen Devices
B Meenakshi, Hiranya Karthika M, Kayathri Devi S, Lavanya J C, Sivaprasad R
2025 International Conference on Computing and Communication Technologies Iccct 2025, 2025
A novel approach to the design of multiple-output switched-mode power supplies (SMPS) is presented in this paper. By addressing critical design challenges such as high efficiency, robust fault tolerance, and dynamic load response, this work aligns the design of SMPS more closely with modern power electronics requirements. Using advanced control algorithms and optimized component selection, the proposed method minimizes cross-coupling between outputs while ensuring stable voltage regulation and minimal voltage ripple. The design framework incorporates fault isolation mechanisms, allowing individual channel faults to be detected and mitigated without impacting overall system functionality. Experimental prototypes demonstrate peak efficiency of 90 % and validate the system's reliability under real-world conditions. This approach shows that multiple-output SMPS can achieve both high performance and sustainability, aligning with global goals for energy efficiency and waste reduction. Future directions include further integration with renewable energy systems and scalability for industrial applications.
Deep Neural Networks for Advanced Medication Security in IoT-Enabled Smart Robotic Dispensing Cabinets
Prabhu V S, Gurumoorthi Gurulakshmanan, C. Chandravathi, D. P. Sangeetha, Vinoth Kumar V, B Meenakshi
2025 International Conference on Emerging Smart Computing and Informatics Esci 2025, 2025
Ensuring advanced medication security is of the utmost importance in smart robotic dispensing cabinets enabled by the Internet of Things (IoT). Strong pharmaceutical security is of the utmost importance in smart robotic dispensing cabinets that the IoT enables. To strengthen security measures in these systems, this research presents a new method that uses deep neural networks (DNNs). The proposed system improves the identification of potential medication mistakes and unauthorized access by using DNNs' anomaly detection and pattern recognition capabilities. A thorough security layer is created by incorporating DNN models into the IoT design of smart cabinets to address drug administration and dispensation weaknesses. Discovering security breaches or procedural problems entails training DNNs on large datasets to detect typical operating patterns and abnormalities. It shows that the strategy significantly improves security procedures and operational dependability in real-world conditions via simulations and validation experiments, proving its usefulness. Providing a strong answer to the problems of improving the effectiveness of drug administration in healthcare settings based on the IoT helps move healthcare technology forward. The technology improves pharmaceutical security and lays the groundwork for responsive and adaptable healthcare infrastructures. This study will make improving the dependability and safety of healthcare delivery systems possible.
Cloud-Powered SVM Solutions for Automated Monitoring and Intervention of ADHD Symptoms
R Puviarasi, G. Arunsankar, H. Azath, G. Divya, Audithan Sivaraman, B Meenakshi
2025 IEEE 2nd International Conference on Advances in Modern Age Technologies for Health and Engineering Science Amathe 2025 Proceedings, 2025
Diagnosing and managing attention deficit hyperactivity disorder (ADHD) are very challenging tasks. Automated symptom monitoring and intervention for ADHD is the focus of this project, which investigates the use of Support Vector Machine (SVM) algorithms hosted on the cloud. The SVM models are trained to identify patterns relevant to ADHD symptoms using data collected from wearable devices and digital self-reports. Due to the cloud’s architecture, data can be processed and analyzed in real-time, allowing prompt actions. With a sensitivity of $85 \%$ and a specificity of $92 \%$, the results show that the SVM models accomplish a high level of accuracy in symptom identification. The system provides personalized treatments, such as medication adherence reminders and mindfulness exercises, via continuous monitoring and are customized to individual symptom profiles. Longitudinal studies can show how symptoms develop and how well treatments work overtime. Improving ADHD treatment via providing timely interventions and personalized assistance is the goal of this cloudpowered SVM system. The algorithms will be fine-tuned, more data sources will be included, and clinical trials will be conducted to evaluate the system’s performance in real-world scenarios.
Predictive Analytics for Ear Health Management Using IoT and Ensemble Learning
S.K. Saravanan, Palaniraj Rajidurai Parvathy, R. Meenakshi, Prabu K, T. R. GaneshBabu, B Meenakshi
2025 International Conference on Intelligent Control Computing and Communications Ic3 2025, 2025
Proactive health monitoring solutions are becoming more important due to a growing population of older people. To better manage the ear health of the elderly, this research investigates ensemble learning methods within the domain of predictive analytics using the Internet of Things (IoT). It provides an ensemble learning approach that uses IoT capabilities to predict potential ear health problems in this population. It evaluates the efficacy of many ensembles learning approaches, including bagging, boosting, and stacking, using real-world sensor data acquired from IoT devices. Our results show that ensemble learning methods considerably improve prediction accuracy compared to standalone models. Ensemble learning overcomes the limitations of individual algorithms and maximizes their combined prediction potential by efficiently combining varied models. Our findings have important implications for the management of healthcare for the elderly, providing new opportunities to enhance early diagnosis and intervention techniques for ear health concerns via the use of predictive analytics provided by the IoT. It highlights the potential of ensemble learning approaches to improve predictive analytics in the senior healthcare sector, especially with IoT-driven solutions.
AI-Enhanced IoT Systems for Smart Predictive Maintenance in Steel Plants
S. Lourdu Jame, S. Suguna Mallika, Pramod Pandey, Bhavani R, L. M. Merlin Livingston, B Meenakshi
Proceedings of 5th International Conference on Trends in Material Science and Inventive Materials Ictmim 2025, 2025
This paper introduces a smart Internet of Things (IoT) system that uses artificial intelligence (AI) to improve operational efficiency and reduce downtime in steel plants through predictive maintenance. It addresses equipment failures in steel plants with deployment of an AI-driven IoT system using neural networks for facilitating accurate defect prediction and minimizing maintenance expenses. The system uses neural networks for continuous operation and proactive maintenance, which are essential in steel production processes to provide productivity and safety. Early problem detection and health monitoring are made possible by integrating IoT devices for real-time data gathering and cloud computing for scalable data processing in the proposed system. Anomalies and impending breakdowns may be foreseen using neural networks trained on time-series sensor data, paving the way for predictive maintenance. The solution improves equipment dependability, optimizes maintenance schedules, and decreases operating costs using historical data and machine learning algorithms. Results from real-world applications show that the system may enhance maintenance plans while reducing downtime in steel manufacturing facilities. The results highlight how technology may revolutionize maintenance procedures, gain insight into proactive equipment management, and help the steel sector progress in smart production.
SVM-based Predictive Modeling for Sustainable Solar Solutions in Off-Grid Areas
Giriprasad. S, S. Sangeetha, G Manikandan, G Bhuvaneswari, B Meenakshi, S. Sujatha
Proceedings of 5th International Conference on Trends in Material Science and Inventive Materials Ictmim 2025, 2025
The rising need for sustainable energy solutions in off-grid regions has prompted the investigation of creative predictive modeling methodologies. This research examines Support Vector Machine (SVM) techniques to enhance solar energy production and use. SVM, a powerful machine learning instrument, assesses diverse environmental variables, such as solar radiation, temperature, and humidity, to precisely forecast solar energy production. It presents a SVM model to forecast solar energy production in off-grid areas, optimizing resource distribution and improving sustainability with analysis of environmental variations and energy consumption data. The model improves decision-making for deploying solar systems in distant areas using historical data and real-time observations. The prediction skills of SVM enable the creation of customized solar energy systems that address the specific energy requirements of off-grid settlements. This technique enhances sustainability by optimizing solar installation efficiency, decreasing dependence on fossil fuels, and mitigating environmental consequences. The results indicate that SVM-based predictive modeling substantially enhances the dependability and efficiency of solar energy systems, making them more feasible for off-grid applications. It highlights the capacity of advanced machine learning methodologies to promote sustainable energy solutions, hence aiding the worldwide shift towards renewable energy sources.
Big Data Infrastructures Using Apache Storm for Real-Time Data Processing
Chitra Sabapathy Ranganathan, Rajeshkumar Sampathrajan, P L Kishan Kumar Reddy, Parkavi S, B Meenakshi, S. Sujatha
2025 International Conference on Intelligent Control Computing and Communications Ic3 2025, 2025
Apache Storm-based Big Data infrastructures strive to provide a scalable and fault-tolerant platform that will transform real-time data processing. The goal is to use Apache Storm's features to handle and analyze massive amounts of data in real-time, guaranteeing correct insights that are delivered promptly. The objective is to provide a solid foundation that can manage data moving at high speeds from many sources, allowing for analytics in real-time and continuous computing. The goal is to maximize throughput while reducing delay via architectural optimization and efficient data flow methods. The goal is to improve Apache Storm's capabilities and make sure data processes smoothly across dispersed systems by combining it with other big data technologies. The end goal is a robust system that can provide analytics on data in real-time, which will aid in decision-making across several industries, including banking, telecoms, and social media. Efficiency, scalability, and dependability in managing massive data streams are key considerations. The data flow topology dataset for 5 distinct components and 5 streams shows throughput values ranging from 2500 to 4100 tuples per second. Similarly, the fault tolerance and reliability dataset for 5 distinct nodes and 5 different intervals shows throughput values ranging from 700 to 1050 tuples per second. All these results are derived from real-time sensor data used for traffic monitoring.
Deep Q-Network-Powered Optimization of Urban Public Transit for Sustainable Mobility and Efficiency
V G Sivakumar, Sridhar Raj Sankara Vadivel, Anita Titus, R Krishnaswamy, Jay Kant Pratap Singh Yadav, B Meenakshi
Proceedings of 5th International Conference on Soft Computing for Security Applications Icscsa 2025, 2025
AI-Enabled Muscle Oxygen Sensors for Personalized Exercise Intensity Monitoring and Recovery Analysis
K. Balachander, K. Balasubadra, Bharat Tidke, Sree Southry S, B Meenakshi, S. Murugan
2025 5th International Conference on Advances in Electrical Computing Communication and Sustainable Technologies Icaect 2025, 2025
Predictive Analysis of Chronic Migraine Symptoms Detection and Management Using IoT and ML
S. Srinivasan, Bharat Tidke, Ahmed Shahebaaz, E. Punarselvam, N. Gnanavel, B Meenakshi
2025 IEEE 2nd International Conference on Advances in Modern Age Technologies for Health and Engineering Science Amathe 2025 Proceedings, 2025
Cloud-Enabled ML Techniques for PTSD Assessment and Management
S. Srinivasan, Pramod Pandey, Sindhu Boianapalli, S. Rajes Kannan, Ms.J.S.Jenin, B Meenakshi
Proceedings 2025 5th International Conference on Expert Clouds and Applications Icoeca 2025, 2025
Dietary Patterns Analysis for Obesity Prevention with IoT and LSTM Networks
G. Arunsankar, D. Antony Joseph Rajan, Barkathulla. A, T. Jansirani, Audithan Sivaraman, B Meenakshi
International Conference on Intelligent Systems and Computational Networks Iciscn 2025, 2025
Time-Series Forecasting of Solar Power Generation using RNN and IoT Data
K. Thinakaran, B. Bharathi, K. Sangeetha, R. Anitha, R. Vinodha, B Meenakshi
Proceedings of International Conference on Modern Sustainable Systems Cmss 2025, 2025
Enhancing cyber security in WSN using optimized self-attention-based provisional variational auto-encoder generative adversarial network
B. Meenakshi, D. Karunkuzhali
Computer Standards and Interfaces, 2024
Voice-Activated Email System: Enhancing Accessibility with Speech-to-Text Integration
Srinivasan A, Meenakshi B, Swathiha R R
2024 International Conference on Smart Technologies for Sustainable Development Goals Icstsdg 2024, 2024
Leakage Detection Monitoring in Underground Pipes Using ZigBee in Wireless Sensor Networks
E. Sudhakar, J. Thamilselvi, N Logeswari, B. Meenakshi, K. Subhashini
2024 International Conference on Communication Computing and Internet of Things Ic3iot 2024 Proceedings, 2024
Speed and Torque Optimization of Motor Drive Through Intelligent Control Approaches
Hanumaji Kantari, M. Vadivel, Pavithra Jagadeesan, T. J. Nagalakshmi, B. Meenakshi, S. Velmurugan
2024 International Conference on Automation and Computation Autocom 2024, 2024
Cloud Based Intelligent Nutrient Management System for Precision Agriculture Using CNN
M. Shafiya Banu, Arshadh Ariff Mohamed Abuthahir, R. Mohandas, D. Antony Joseph Rajan, B. Meenakshi, N. Mohankumar
2nd International Conference on Sustainable Computing and Smart Systems Icscss 2024 Proceedings, 2024
Predictive Analysis of Airborne Contaminant Spread with Cloud-Connected Sensors and Deep Learning Model
Chitra Sabapathy Ranganathan, Jothi M, V. Prasanna Srinivasan, P. Sathyanathan, B Meenakshi, S. Sujatha
3rd International Conference on Communication Control and Intelligent Systems Ccis 2024, 2024
Automated Detection and Prediction of Hospital-Acquired Infections Using IoT and Linear Regression
Cheepurupalli Raghuram, Subramani Roychoudri, C. Viswanathan, C. Chandru Vignesh, Geetha Ponnaian, B. Meenakshi
2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things Icoici 2024 Proceedings, 2024
Sustainable Crop Protection Using IoT-Enabled Drone Spraying with Support Vector Machine Analysis
K. Lalitha, M. Xavier Suresh, R. Selvakumar, Prathik A, J. M. Sunitha, B. Meenakshi
2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things Icoici 2024 Proceedings, 2024
Data-Driven with IoT Sensing and Deep Learning Model for Dynamic Skin Cancer Diagnosis
Dr. S. Mohan Kumar, Dr. P. Epsiba, Dr. R. Thenmozhi, N. Malathi, Dr. T. S. Balaji Damodhar, Dr. B. Meenakshi
2nd International Conference on Self Sustainable Artificial Intelligence Systems Icssas 2024 Proceedings, 2024
Enhancing Data Security in Cloud-Based Soil Carbon Sequestration Monitoring Systems Using Random Forest Regression
D. Chandrakala, Chitra Sabapathy Ranganathan, E. Swarnalatha, R. C. Karpagalakshmi, B. Meenakshi, S. Srinivasan
2024 International Conference on Intelligent Systems for Cybersecurity Iscs 2024, 2024
Comparative Study of Automatic Driving Assist Systems in Internal Combustion and Electric Vehicles
R. Sivaprasad, D P K Hari, R S Surej, Devesh J S, Prathibanandhi K, Meenakshi B
4th International Conference on Power Energy Control and Transmission Systems Harnessing Power and Energy for an Affordable Electrification of India Icpects 2024, 2024
A Gradient Boosting Algorithm to Predict Energy Consumption for Home Applications
V G Sivakumar, N. Arunfred, N Anusha, C. Balakrishnan, B. Meenakshi, S. Sujatha
2024 2nd International Conference on Computer Communication and Control Ic4 2024, 2024
Agriculture Yield Estimation Using Machine Learning Algorithms
Ramakrishnan Raman, Hanumaji Kantari, Atul A Gokhale, K. Elangovan, B. Meenakshi, S. Srinivasan
2024 International Conference on Automation and Computation Autocom 2024, 2024
Wind Power Forecasting with Support Vector Machines using Sparrow Search Algorithm
V. Vidya Lakshmi, S Giriprasad, S P Vimal, Hanumaji Kantari, B Meenakshi, S. Srinivasan
2024 2nd International Conference on Computer Communication and Control Ic4 2024, 2024
Optimizing Hospital Stay Duration Prediction using IoT and Linear Regression Techniques
D. Sethuraman, D.V.N. Bharathi, Aarthy. S.T, Boopathy K, M. Rajmohan, B Meenakshi
4th International Conference on Sustainable Expert Systems Icses 2024 Proceedings, 2024
Decision Trees for Eco-Friendly Green Supply Chains IoT-Enabled Environmental Footprint Reduction
M Tamilselvi, S. Edson Nirmal Christopher, S. Priyadarshini, R. Lumina Julie, B. Meenakshi, P. Anitha
2024 10th International Conference on Smart Computing and Communication Icscc 2024, 2024
IoT-Enhanced Forest Fire Monitoring and Notification System
A. Lekha Shri, R. R. Swathiha, M. Mohamed Ismail, M. Krithiga, B. Meenakshi, M. Kathir
Proceedings 2024 5th International Conference on Image Processing and Capsule Networks Icipcn 2024, 2024
Efficient Remote Rehabilitation and Physical Therapy Management using Decision Trees and IoT Technology
A. Sairam, Raveendra N Amarnath, Parthiban Veda, R. Priyadharshini, T. R. GaneshBabu, B Meenakshi
2024 1st International Conference on Innovations in Communications Electrical and Computer Engineering Icicec 2024, 2024
Efficient Thermal Pollution Management in Water Bodies through Cloud-Based Monitoring and XGBoost
K. K. Manivannan, M. Maria Sampoornam, V. Sumathi, S. Parveen Banu, T. Tholhappiyan, B. Meenakshi
2024 Asian Conference on Intelligent Technologies Acoit 2024, 2024
Robotic Restroom Hygiene Solutions with IoT and Recurrent Neural Networks for Clean Facilities
A. Kathija Nasreen, M. Shenbagapriya, Senthil Kumar Seeni, Parthiban Veda, B. Meenakshi, S. Murugan
7th International Conference on Inventive Computation Technologies Icict 2024, 2024
Automotive CAN-Based Intelligent Collision Avoidance System using Machine Learning and Cloud Computing
P. Santhuja, C. Selvi, C. Jehan, S. Aghalya, B Meenakshi, K Mathivanan
Proceedings of the 2024 10th International Conference on Communication and Signal Processing Iccsp 2024, 2024
Cloud-Enabled Predictive Vehicle Health Monitoring and Maintenance with LSTM Networks for Time-Series Analysis
Viyyapu Lokeshwari Vinya, K. K. Manivannan, S. K. Manivannan, C. Sathiyamoorthy, M. Jagadeeswaran, B. Meenakshi
2024 1st International Conference on Innovations in Communications Electrical and Computer Engineering Icicec 2024, 2024
Efficient Congestion Management Through IoT - Driven Road User Charging Systems with Reinforcement Learning
A. Sahaya Anselin Nisha, N. Venkatesvara Rao, G. Venkatesh, Sree Southry S, S. Murugan, B. Meenakshi
2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things Icoici 2024 Proceedings, 2024
Data-Driven IoT Smart Refrigerators for Medication Storage with Deep Learning Analytics
U. Kavitha, Manjula Pattnaik, S. Jayaprakash, Yuvaraj S, Geetha Ponnaian, B. Meenakshi
2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things Icoici 2024 Proceedings, 2024
Blockchain-enabled joint trust (RF-AOA-HTSA) algorithm-based multiobjective clustering protocol for wireless sensor network
B. Meenakshi, D. Karunkuzhali, Syed Muqthadar Ali
Concurrency and Computation Practice and Experience, 2023
Enhanced Elman spike neural network for cluster head based energy aware routing in WSN
B. Meenakshi, D. Karunkuzhali
Transactions on Emerging Telecommunications Technologies, 2023
Wireless Sensor Networks for Disaster Management and Emergency Response using SVM Classifier
B. Meenakshi, A. Vanathi, B. Gopi, S. Sangeetha, L. Ramalingam, S. Murugan
2023 2nd International Conference on Smart Technologies for Smart Nation Smarttechcon 2023, 2023
Artificial Intelligence Enhanced - Plant and Soil Monitoring Systems
B Meenakshi, B Sumathy, Aouthithiye Barathwaj SR Y, Haariharan N C, Krishnakanth L, M. Soumya
2023 Intelligent Computing and Control for Engineering and Business Systems Iccebs 2023, 2023
Automated Sensing Cleaner
Meenakshi B., Logeswari N, Sivaprasad R, Brindha J, Janani N, Gokula Subashree N
2023 Intelligent Computing and Control for Engineering and Business Systems Iccebs 2023, 2023
Improvement of power quality in grid system based on zeta converter integrated with PV supply
B. Meenakshi, Murugananth Gopal Raj, C. Pradip, N. Saju
Aip Conference Proceedings, 2022
OQR-SC: An Optimal QoS Aware Routing Technique for Smart Cities Using IoT Enabled Wireless Sensor Networks
D. Karunkuzhali, B. Meenakshi, Keerthi Lingam
Wireless Personal Communications, 2022
An Adaptive Fuzzy C Means with Seagull Optimization Algorithm for Analysis of WSNs in Agricultural Field with IoT
D. Karunkuzhali, Balasubramanian Meenakshi, Keerthi Lingam
Wireless Personal Communications, 2022
Animal Intrusion Detection and Ranging system using YOLOv4 and LoRa
B Meenakshi, Aouthithiye Barathwaj SR Y, Haariharan N C, Krishnakanth L, Abishek J
3rd International Conference on Power Energy Control and Transmission Systems Icpects 2022 Proceedings, 2022
Designing and Commisioning of Heat Pumps for Hybrid Heating and Cleaning of Coaching Equipments Using Solar Panels
B. Meenakshi, R. Sivaprasad, T Porselvi, V Sunandha, T Janani
2022 International Conference on Communication Computing and Internet of Things Ic3iot 2022 Proceedings, 2022
Voice Based Motile Video Surveillance System with ViolenceDetection
J. Shalini Priya, S. Thennarasu, M. Hemalatha, M. Karthikeyan, B. Meenakshi, R. Sivaprasad
3rd International Conference on Power Energy Control and Transmission Systems Icpects 2022 Proceedings, 2022
Intelligent Wireless Charging Station for EVs with Integrated PV Power Grid System
Porselvi T, Srividhya S, Kurinjimalar L, Meenakshi B, Sivaprasad R, Sai Ganesh CS
3rd International Conference on Power Energy Control and Transmission Systems Icpects 2022 Proceedings, 2022
Automatic Object and Crack Detecting System Using IoT
B Meenakshi, R Sivaprasad, J Abishek, M Soumya, S Eniyan, M Harish
3rd International Conference on Power Energy Control and Transmission Systems Icpects 2022 Proceedings, 2022
GSM Based Power Meter Billing with Load Control
Malini V, Vaishnav M, Venkateshwaran A, Vikas S, Prathibanandhi K, B. Meenakshi
3rd International Conference on Power Energy Control and Transmission Systems Icpects 2022 Proceedings, 2022
Design and Analysis of Fifteen Level Inverter for Renewable Applications
L. Kurinjimalar, S. Pooja, R. Sivaprasad, B. Meenakshi, J. Shalini Priya, T. Porselvi
3rd International Conference on Power Energy Control and Transmission Systems Icpects 2022 Proceedings, 2022
PV Based Multilevel Inverter with 15 Levels using POD Control Method
Porselvi T, Praveena D, Shalini Priya J., Hemalatha M, Meenakshi B, Thennarasu S
2022 International Conference on Communication Computing and Internet of Things Ic3iot 2022 Proceedings, 2022
Grid connected single-stage multiport inverter with two independent source
B. Meenakshi, T. Porselvi, M Deepa, R. SivaPrasad, L. Kurinjimalar, V Selvapriya
3rd International Conference on Power Energy Control and Transmission Systems Icpects 2022 Proceedings, 2022
Modified Bridgeless Landsman Converter Fed EV Battery Charger Power Factor Improvement
R. Sivaprasad, T. Mahalakshmi, B. Meenakshi, T. Porselvi, L Kurinjimalar, J. RManasa
3rd International Conference on Power Energy Control and Transmission Systems Icpects 2022 Proceedings, 2022
Aegis Surtout
Sivaprasad R, Shriranjani. J, Abinaya. R, K Prathiba Nandhi, B. Meenakshi, Shalinipriya J
3rd International Conference on Power Energy Control and Transmission Systems Icpects 2022 Proceedings, 2022
Design and Development of PV Powered Intergraded DC-DC Converter Fed Multilevel Inverter for Standalone Application
K. Prathibanandhi, B. Meenakshi, T. Porselvi, R. Azhagumurugan, V. Chithra, V. Malini
2022 International Conference on Future Trends in Smart Communities Icftsc 2022, 2022
Intelligent MPPT Control for Wind Energy Conversion Systems Based on Reinforcement Learning
Arun Kumar Pallekonda, S. Muthukrishnan, B. Meenakshi, R. Saravanan
Journal of Physics Conference Series, 2021
Fault Detection in the Wind Farm Turbine Using Machine Learning Based on SVM Algorithm
S Muthukrishnan, Arun Kumar Pallekonda, R Saravanan, B Meenakshi
Journal of Physics Conference Series, 2021
The Defense Against Jamming Attack in Cognitive Radio Networks: Energy Efficiency Management Perspective
Bhuvana Suganthi D, R. Jaichandran, P. Shyamala Bharathi, B. Meenakshi, A. Anushya, V. Brindha Devi
Microprocessors and Microsystems, 2021
Automated Rocker Rover for Terrain Surface Surveillance
R. Vallikannu, B. Meenakshi, Venkata Subhash, Basha, Chandra KiranKumar
Lecture Notes in Networks and Systems, 2021
Development of prototype spectacles for the blind with digital to voice conversion device
B. Meenakshi, C. Singaravel, E. Vignesh
Proceedings of the International Conference on Power Energy Control and Transmission Systems Icpects 2018, 2018
Enhanced digital energy meter
F. Reni Clenitiaa, E. Ilakya, G. S. Preetha, B. Meenakshi
6th International Conference on Computation of Power Energy Information and Communication Iccpeic 2017, 2017
Design and analysis of standard structure converter for solar photovoltaic applications
M. Yamuna, B. Meenakshi
2016 International Conference on Computation of Power Energy Information and Communication Iccpeic 2016, 2016
Analysis and design of DC-DC/AC non isolated cuk converter using sliding mode controller
P. Nagalakshmi Kanna, B. Meenakshi
IEEE International Conference on Circuit Power and Computing Technologies Iccpct 2015, 2015
Traffic free routing using path load factor in Wireless Sensor Networks
B. Meenakshi, P. Anandhakum
Information Technology Journal, 2013
Cluster based time division multiple access scheduling scheme for zigbee wireless sensor networks
Meenakshi
Journal of Computer Science, 2012
Lifetime extension of wireless sensor network by selecting two cluster heads and hierarchical routing
B. Meenakshi, P. Anandhakumar
ACM International Conference Proceeding Series, 2012
Fuzzy based energy efficient clustering technique for ZigBee (802.15.4) sensor networks
European Journal of Scientific Research, 2012

Dr B Meenakshi

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