V BALAJI

@rmkec.ac.in

PROFESSOR-ELECTRONICS AND COMMUNICATION ENGINEEIRNG
RMK Engineering College

V BALAJI


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https://researchid.co/balaji_301180
Dr. V. Balaji received his B.TECH. degree in Electronics and Communication Engineering and M.TECH. degree in Applied Electronics from Bharath University, Chennai, India, and his Ph.D. in Information and Communication Engineering. He is currently serving as an Associate Professor in the Department of Electronics and Communication Engineering, Easwari Engineering College, Chennai, India.

He has over 22 years of teaching and research experience. His research interests include Computer Networking, Image Processing, and Machine Learning. Dr. Balaji has authored and co-authored several research papers published in reputed national and international journals and conferences.

He is a Life Member of the Indian Society for Technical Education (ISTE) and a Member of the Institute of Electrical and Electronics Engineers (IEEE). He is also actively involved in mentoring students, guiding innovative projects, and promoting research-driven learning in emerging areas of engineering and technology.

EDUCATION

B.Tech[ECE], M.Tech[Applied Electronics], Ph.D.

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Networks and Communications, Information Systems, Multidisciplinary
29

Scopus Publications

171

Scholar Citations

7

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • Fog enabled approach for IoT
    Radha Prabhakaran, S. Preethi, Paarvathi, B. Prashanthi, V. Balaji
    Aip Conference Proceedings, 2026
  • Detecting cyber attacks in vehicle networks using improved LSTM based optimization methodology
    C. Jayasri, V. Balaji, C. M. Nalayini, S. Pradeep
    Scientific Reports, 2025
    The growing adoption of intelligent transportation systems and connected vehicle networks has raised significant cybersecurity concerns due to their vulnerability to cyberattacks such as spoofing, message tampering, and denial-of-service. Traditional intrusion detection systems struggle to cope with the dynamic and high-volume nature of vehicular data, often leading to high false positives and limited adaptability. To address this problem, this study proposes an enhanced deep learning-based optimization framework for detecting cyberattacks in vehicle networks. The methodology employs the UNSW-NB15 dataset, with data preprocessed using Maximum-Minimum Normalization. Feature extraction is performed using the Discrete Fourier Transform (DFT), capturing frequency-domain patterns indicative of anomalies. Detection is executed through an Improved Long Short-Term Memory (ILSTM) model, whose parameters are optimized using the Crocodile Optimization Algorithm (COA), aiming to maximize classification accuracy. Experimental results demonstrate that the proposed ILSTM-COA model significantly outperforms existing techniques, achieving 98.9% accuracy and showing notable improvements across sensitivity, specificity, and other performance metrics. This model offers a robust, scalable, and real-time solution for safeguarding vehicular networks against evolving cyber threats.
  • An Efficient and Secured Data Transmission in Vanet Using LBK Interfaced AI-Optimized Routing Algorithm
    V Balaji, J. Afsal, K.K. Santhosh, C. Andreana
    2024 International Conference on Recent Advances in Electrical Electronics Ubiquitous Communication and Computational Intelligence Raeeucci 2024, 2024
    The vehicular network, which is supported by attacker-less authorization, is an assembly of automobiles and other components that may connect to a variety of sensors. Vehicle-to-vehicle Ad hoc Networks (VANETs) can broadcast Emergency Messages (EMs) ahead of time between cars, therefore mitigating traffic-related accidents. Current authentication techniques prioritize security or the lightweight feature. However, confidentiality throughout an authorization process is crucial. Furthermore, existing techniques suffer from the infrastructure-less situation since they rely on a trusted authority to verify the authenticity of a communication node. This paper uses the GWO-based ANFIS to propose a lightweight, secure, privacy-preserving, and less vulnerable to attacks authentication mechanism for VANETs. The Artificial Neural Network (ANN) or Adaptive Neuro-Fuzzy Inference System (ANFIS) is a kind of Artificial Intelligence (AI). Neural network and fuzzy logic concepts are combined by ANFIS, which may leverage both inside a single framework. The suggested approach, known as ANFIS- Grey Wolf Optimization (GWO), is divided into two phases. Using the training set, GWO is employed in the first step to learn the ANFIS parameters. In the meantime, the performance of the suggested ANFIS-GWO approach is assessed using the testing set in the second stage. Furthermore, a thorough performance study using the current systems for authorization demonstrates that the suggested algorithm has lower compute, communication, and energy costs than the existing schemes. NS2 software is used to implement this project. ANFIS-GWO yielded an accuracy comparison of 91.5% and an energy consumption of 115J.
  • Advanced Day-Ahead Photovoltaic Power Prediction through RNN Modeling: Enhancing Forecast Accuracy for Improved Grid Stability
    S V Dharani Kumar, Thamizharasan R, S. Karthick, Deepak Arumugam, K. Buvaneswari, V. Balaji
    Proceedings of the 9th International Conference on Communication and Electronics Systems Icces 2024, 2024
    The day-ahead photovoltaic (PV) power forecasting is very important in the management and integration of the PV systems into today's complex power systems especially given the increasing trend in the adoption of the renewable energy-based power systems. The efficiency of PV power output forecast not only improves the stability of the power grid, but also plays a significant role in energy scheduling and the pursuit of the net-zero emission goal. There are certain issues which vary from time to time in the case of solar energy and hence there are certain drawbacks attached with it, for instance predicting of power forecast is one of the major drawback of solar energy production. It has been found that existing approaches are problematic because they are either less accurate or not very adaptive, and also because they are complex to compute, and consequently less reliable, which impacts the potential of energy systems to operate efficiently. In response to these challenges, we introduce an RNN-based model for day-ahead PV power prediction in this paper. By OLS, the proposed model builds on the successful implementation of RNNs in modeling temporal characteristics and nonlinear trends of historical power data to improve the accuracy of the forecast. The RNN model is also capable of learning from other influencing factors since the dataset adopted in this paper embraces diverse and intricate parameters in the analysis of the solar irradiance and other environmental conditions. The results shown here indicate returns that clearly places the RNN-based model ahead of traditional approaches to forecasting as well as DL structures besides the four-fold validation of performances with high scores in MAE, MSE, and MAPE.
  • Design and Implementation of Renewable Solar Energy in Smart Cities
    D. Priya Matharasi, V. Balaji, S. Nalini Jayanthi, R. Manimegalai, S. Raja, Leema Nelson
    8th International Conference on Electronics Communication and Aerospace Technology Iceca 2024 Proceedings, 2024
    Solar energy is a clean, accessible, and unconsumable energy source. With the necessary infrastructure, a solar PV system delivers variable output based on solar irradiance. As power shortages are unavoidable in today’s modern civilization, which saw an increase in electrical energy use after the industrial revolution. Therefore, all industrialized countries, as well as many emerging ones like India, have been working on green energy initiatives to produce gadgets that are not only energy-efficient but also significantly reduce greenhouse gas emissions. The end result is smart cities that revive the environment while keeping the nation’s economy and technology advancements moving at the same speed. Using photovoltaic (PV) systems in solar energy projects is one such important move that the world is considering.
  • Design of Super-Lift Luo-Converter with of Buck Converters for Electric Vehicle Applications
    S. Gomathi, Ammal Dhanalakshmi, V. Janakiraman, D. Sheela, V Balaji, M J Carmel Mary Belinda
    Proceedings of the 5th International Conference on Smart Electronics and Communication Icosec 2024, 2024
    An appropriate control system is incorporated into the newly introduced (Super lift Luo Converter) SLBC, which features a versatile output voltage ranges,a simple design, and includes a suitable control system. This structure achieves low ripple in its output voltage by avoiding the use of electromagnetic components to create a dual output. An analysis is performed by comparing the recommended converter with comparable configurations to highlight the advantages that converters offer. The effectiveness of SLBC converters is evaluated both with and without filters. The filter system demonstrates superior performance compared to the other system, effectively minimizing the occurrence of ripples. It produce a high power output while minimizing ripples and enhancing stability.
  • Energy-Efficient Optimization Using WSN Framework for Enhanced Smart Agriculture
    K. Malarkodi, V. Janakiraman, D. Sheela, Carmel Mary Belinda M J, V Balaji, R. Rajeswari
    Proceedings of the 9th International Conference on Communication and Electronics Systems Icces 2024, 2024
    Wireless Sensor Networks (WSNs) has revolutionized farming techniques, especially in the field of smart agriculture. This innovative approach enhances both the quantity and quality of crop yields while minimizing labor and resource input. By deploying WSNs across agricultural fields, farmers can automate various monitoring and management tasks, such as operating water pumps and valves. These networks consist of multiple relay sensors and agricultural field sensors that continuously monitor critical parameters like soil moisture, temperature, irrigation status, and pH levels. Each sensor is responsible for data collection, energy management, and ensuring reliable data delivery. Despite the advantages, WSNs face challenges such as high energy consumption, connectivity issues, and latency, all of which can adversely affect crop yields. This paper proposes an IoT-WSN framework designed to mitigate these issues and enhance the functionality of agricultural smart lands. In proposed system, sensors in the agricultural field first gather pertinent data and then select a group of cluster heads through a Multi-Standard Selection These cluster heads are vital for structuring the network and ensuring effective data gathering. Relay sensors are crucial for transmitting data securely from the field to a base station. To optimize this data relay process, utilize a Linear Integer Programming Method (LIPM). Our simulations and analytical results demonstrate that this framework effectively prolongs the lifespan of sensors while ensuring that data is transmitted accurately and promptly from agricultural fields to end users. This improvement not only enhances the efficiency of agricultural operations but also contributes to sustainable farming practices by optimizing resource usage and reducing manual intervention.
  • Energy Efficient Cluster Head Selection and Routing Algorithm using Hybrid Firefly Glow-Worm Swarm Optimization in WSN
    S. Bharathiraja, S. Selvamuthukumaran, V. Balaji
    Ksii Transactions on Internet and Information Systems, 2023
    The Wireless Sensor Network (WSN), is constructed out of teeny-tiny sensor nodes that are very low-cost, have a low impact on the environment in terms of the amount of power they consume, and are able to successfully transmit data to the base station.The primary challenges that are presented by WSN are those that are posed by the distance between nodes, the amount of energy that is consumed, and the delay in time.The sensor node's source of power supply is a battery, and this particular battery is not capable of being recharged.In this scenario, the amount of energy that is consumed rises in direct proportion to the distance that separates the nodes.Here, we present a Hybrid Firefly Glow-Worm Swarm Optimization (HF-GSO) guided routing strategy for preserving WSNs' low power footprint.An efficient fitness function based on firefly optimization is used to select the Cluster Head (CH) in this procedure.It aids in minimising power consumption and the occurrence of dead sensor nodes.After a cluster head (CH) has been chosen, the Glow-Worm Swarm Optimization (GSO) algorithm is used to figure out the best path for sending data to the sink node.Power consumption, throughput, packet delivery ratio, and network lifetime are just some of the metrics measured and compared between the proposed method and methods that are conceptually similar to those already in use.Simulation results showed that the proposed method significantly reduced energy consumption compared to the state-of-the-art methods, while simultaneously increasing the number of functioning sensor nodes by 2.4%.Proposed method produces superior outcomes compared to alternative optimization-based methods.
  • Smart Crop Production System Using Machine Learning Techniques
    V. Balaji, Aishwarya D, Bramhani Ch, Mahima Kumari
    Proceedings of 8th IEEE International Conference on Science Technology Engineering and Mathematics Iconstem 2023, 2023
    The development of Agricultural activities in current fast internet world is enhanced a lot. Farmers started utilising the Android applications to gather information regarding the crop price economic growth contents related to make effective farming etc. Forming is a unique category of providing information related to smart agriculture in terms of analysis data that measure the input and provide customised results based on forming time amount of water resources to be used cost of the crop updates environmental data changes in the soil moisture and related pesticides available in market even more information accurately. The presented system utilised wireless sensor network developed with node configuration technique using each computing. The system comprises and analysis module using relative regression algorithm. Input parameters or gathered from various input sensors collectively used here for environmental measurement soil moisture measurement abnormality in plants and humidity are temperature etc. Based on the presented input parameters a Smart relativity regression Model (SRR) is used to predict the given data with respect to historical measurements done and make a final prediction of further farming activities. This information or transfer to the IoT nodes to update in the mobile application.
  • Recommendation learning system model for children with autism
    V. Balaji, S. Kanaga Suba Raja
    Intelligent Automation and Soft Computing, 2022
    Autism spectrum disorder (ASD), is a neurological developmental disorder. It affects how people communicate and interact with others, as well as how they behave and learn. The symptoms and signs appear when a child is very young. Derived with increased usage of machine learning procedure in the medicinal analysis investigations. In this paper, our objective is to find out the most significant attributes and automate the process using classification techniques and pattern clustering using K-means clustering. We have analyzed ASD datasets of children towards determining the best performance of classifier for these binary datasets considering recall, precision, accuracy and classification errors. For this purpose only we use classifier along with a training model with the deep learning technique. In this deep neural network the different types of patterns have been obtained, and then the entire autism dataset has been trained using this classification technique. After that, the group of patterns has been formed using K- means clustering technique. Then these grouped patterns have been sent to the stochastic gradient descent (SGD) for obtaining the classification with enhanced accuracy, and after this, the regression process is done, then by this classified output, the recommendation learning system model is given for autism affected children. The need for recommendation for ASD is due to slow functioning of the brain and change in personal characteristic of the affected children, so it is necessary for recommendation learning model in ASD.
  • Space Constrained Fast Association Rule Mining with Optimal Support and Confidence Threshold Using Grammatical Evolution: An Effective Nudge in Policymaking
    Tina Susan Thomas, V Balaji
    Iranian Journal of Information Processing and Management, 2022
  • Satellite Image Processing for Water Detection
    Kanaga Suba Raja S, Kumar R S, Balaji V, Usha Kiruthika S
    Ecs Transactions, 2022
  • New approach to optimize groundwater usage using multi-criteria decision making techniques
    P Aparna, V Abhinaya, D Anbarasi, V Balaji
    Journal of Physics Conference Series, 2021
  • Sensor based learning device for children with autism
    S. Kanaga Suba Raja, V. Balaji
    Materials Today Proceedings, 2021
  • An IoT-Based System for Supporting Children with Autism Spectrum Disorder
    Kanaga Suba Raja. S, Balaji. V, Usha Kiruthika. S, C.J. Raman
    3rd IEEE International Virtual Conference on Innovations in Power and Advanced Computing Technologies I Pact 2021, 2021
  • A Novel Fraud Detection Scheme for Credit Card Usage Employing Random Forest Algorithm Combined with Feedback Mechanism
    Usha Kiruthika, S. Kanaga Suba Raja, C.J. Raman, V. Balaji
    Icpects 2020 IEEE 2nd International Conference on Power Energy Control and Transmission Systems Proceedings, 2020
  • E-Agriculture for Direct Marketing of Food Crops Using Chatbots
    Usha Kiruthika, S. Kanaga Suba Raja, V. Balaji, C.J. Raman
    Icpects 2020 IEEE 2nd International Conference on Power Energy Control and Transmission Systems Proceedings, 2020
  • Smart assistance for asperger syndrome using raspberry pi
    , Dr. S.Kanaga Suba Raja*, Mr.V. Balaji*, , S. Aparna, and
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Detection of tuberculosis in chest X-rays using U-net architecture
    , S. Usha Kiruthika*, Dr. S.Kanaga Suba Raja, , Mr.V. Balaji, , Mr. S. S. L. Durai Arumugam, , Dr. C.J. Raman, and
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Software engineering techniques for block chain technology
    Computer Science, Engineering, Sridevi Women’s Engineeirng College, Hyderabad, India, Dr.T.K.S. Rathish Babu, Dr.V. Balaji, Electronics, Communication Engineering, KCG College of Technology, Karapakkam, Chennai, India, Dr.V Anantha Krishana, Computer Science, Engineering, Sridevi Women’s Engineeirng College, Hyderabad, India
    International Journal of Recent Technology and Engineering, 2019
  • Design of ZigBee based wireless sensor network for early flood monitoring and warning system
    V. Balaji, A. Akshaya, N. Jayashree, T. Karthika
    Proceedings 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development Tiar 2017, 2017
  • Autonomous mobile navigation robot for agricultural purpose
    International Journal of Applied Engineering Research, 2015
  • Smart robot for disaster detection using zigbee technology
    International Journal of Applied Engineering Research, 2015
  • Performance of wavelet based medical image fusion on FPGA using high level language C
    K. Umapathy, V. Balaji, V. Duraisamy, S. S. Saravanakumar
    Jurnal Teknologi, 2015
  • Performance analysis of energy management controller for stand alone solar power generation system using soft computing techniques
    Balaji, V., Sekar, K., Duraisamy, V., Uma, S., Raghavendran, T. S.
    Jurnal Teknologi, 2015
  • Enhancing varying overhead ad hoc on demand distance vector with artificial ants
    V. Balaji, N. Umapathy, V. Duraisamy, K. Umapathy, P. Venkatesan, S. Saravanakumar
    Jurnal Teknologi, 2015
  • Ant Optimized Link Quality for Ad Hoc on Demand Distance Vector
    V. Balaji, V. Duraisamy
    Wireless Personal Communications, 2014
  • Congestion prediction in wireless network using gene expression programming technique
    Journal of Theoretical and Applied Information Technology, 2014
  • Varying overhead ad hoc on demand vector routing in highly mobile ad hoc network
    Balaji
    Journal of Computer Science, 2011

RECENT SCHOLAR PUBLICATIONS

  • A gated temporal attention based intra prediction framework for robust deepfake video detection
    JE Clapten, JE (Clapten, V Balaji, V (Balaji
    SCIENTIFIC REPORTS 15 (1) , 2025
    2025
  • Detecting cyber attacks in vehicle networks using improved LSTM based optimization methodology
    C Jayasri, V Balaji, CM Nalayini, S Pradeep
    Scientific Reports 15 (1), 19141 , 2025
    2025
    Citations: 10
  • Energy-Efficient Optimization Using WSN Framework for Enhanced Smart Agriculture
    K Malarkodi, V Janakiraman, D Sheela, CMB MJ, V Balaji, R Rajeswari
    2024 9th International Conference on Communication and Electronics Systems … , 2024
    2024
    Citations: 7
  • Design and Implementation of Renewable Solar Energy in Smart Cities
    DP Matharasi, V Balaji, SN Jayanthi, R Manimegalai, S Raja, L Nelson
    2024 8th International Conference on Electronics, Communication and … , 2024
    2024
    Citations: 3
  • Advanced Energy Consumption Forecasting in Smart Grids Using Quantum Boltzmann Machines
    P Velayutham, VS Balaji, N Krissh Shankaran
    2024 Global Conference on Communications and Information Technologies (GCCIT … , 2024
    2024
    Citations: 1
  • Design of Super-Lift Luo-Converter with of Buck Converters for Electric Vehicle Applications
    S Gomathi, A Dhanalakshmi, V Janakiraman, D Sheela, V Balaji, ...
    2024 5th International Conference on Smart Electronics and Communication … , 2024
    2024
    Citations: 5
  • High Speed Neural Network MPPT Algorithm For DFIG Based Wind Energy Conversion System
    RT Kumar, V Balaji, K Sakthidhasan, S Gomathi, R Sreedhar, N Janaki
    2024 7th International Conference on Circuit Power and Computing … , 2024
    2024
    Citations: 1
  • PSO Optimized RBFNN Classifier For Lung Cancer Identification System
    V Balaji, RJ Malar, KS Kavin, SR Rose, TR Permila, B Pandian
    2024 7th International Conference on Circuit Power and Computing … , 2024
    2024
  • A Homomorphic Encryption Compiler for Blood Pressure Analysis
    V Balaji, S Kannan, M Belwal
    2024 Third International Conference on Electrical, Electronics, Information … , 2024
    2024
    Citations: 1
  • Neural Style Transfer: A Comparative Study
    M Suryamritha, V Balaji, S Kannan, T Singh, M Sharma
    2024 15th International Conference on Computing Communication and Networking … , 2024
    2024
  • Emotion Categorization on Multiclass Textual Data Using ML and Ensemble Techniques
    S Kannan, M Suryamritha, V Balaji, M Venugoapalan
    2024 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 1-7 , 2024
    2024
    Citations: 1
  • An Efficient and Secured Data Transmission in VANET Using LBK Interfaced AI-Optimized Routing Algorithm
    V Balaji, J Afsal, KK Santhosh, C Andreana
    2024 International Conference on Recent Advances in Electrical, Electronics … , 2024
    2024
    Citations: 2
  • Implementing a Novel Blockchain Algorithm for Enhanced Data Integrity in Cloud Systems
    V Balaji, S Ambika, S Varshadevi, RT Rajasekaran, J Sangeetha
    2024 International Conference on Trends in Quantum Computing and Emerging … , 2024
    2024
  • Utilizing Advanced Machine Learning Techniques for Effective Prediction and Control of Pandemic Outbreaks
    RT Rajasekaran, H Shanmugavalli, P Jayalakshmi, S Ambika, V Balaji
    2024 International Conference on Trends in Quantum Computing and Emerging … , 2024
    2024
  • Revelatory Insights into Parkinson’s: Hand Gestures Deciphering with Mobilenet SSD
    VS Balaji, K Sangeetha, VA Anand, JA Velayutharaj
    International Conference on Computer, Communication, and Signal Processing, 3-15 , 2024
    2024
    Citations: 3
  • Precision exercise monitoring through advanced body language detection using computer vision
    VS Balaji, K Sangeetha, PSA Ganapathy, MS Banu, SD Kumar
    International Conference on Computer, Communication, and Signal Processing … , 2024
    2024
    Citations: 2
  • Bike Crash Detection using Internet of Things
    V Balaji, SU Kiruthika, T Srivathsan, M Surendhar
    AIP Conference Proceedings 2802 (1), 090001 , 2024
    2024
  • FAST FUEL: A Cost Effective Automated Real Time Embedded System
    S Kannan, S Kirubakaran, J Swarna Latha, Jaiganesh, V Balaji, ...
    International Conference on Innovations in Bio-Inspired Computing and … , 2023
    2023
  • Enhancing Cryptanalysis of DES Encryption using Neural Networks and Firefly Algorithms
    V Balaji, V Krishnamurthy
    2023 International Conference on Evolutionary Algorithms and Soft Computing … , 2023
    2023
  • Analyzing hand gestures using object detection and processing it into local language
    K Sangeetha, VS Balaji, P Kamalesh, PSA Ganapathy
    International Conference on Advances in Artificial Intelligence and Machine … , 2023
    2023
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Performance of wavelet based medical image fusion on FPGA using high level language C
    K Umapathy, V Balaji, V Duraisamy, SS Saravanakumar
    Jurnal Teknologi (Sciences & Engineering) 76 (12) , 2015
    2015
    Citations: 28
  • Enhancing varying overhead ad hoc on demand distance vector with artificial ants
    V Balaji, N Umapathy, V Duraisamy, K Umapathy, P Venkatesan, ...
    Jurnal Teknologi 77 (28), 39-42 , 2015
    2015
    Citations: 24
  • Performance analysis of energy management controller for stand alone solar power generation system using soft computing techniques
    V Balaji, K Sekar, V Duraisamy, S Uma, TS Raghavendran
    Jurnal Teknologi 76 (6) , 2015
    2015
    Citations: 21
  • Varying overhead ad hoc on demand vector routing in highly mobile ad hoc network
    V Balaji, V Duraisamy
    Journal of Computer Science 7 (5), 678 , 2011
    2011
    Citations: 12
  • Detecting cyber attacks in vehicle networks using improved LSTM based optimization methodology
    C Jayasri, V Balaji, CM Nalayini, S Pradeep
    Scientific Reports 15 (1), 19141 , 2025
    2025
    Citations: 10
  • Improved content based image retrieval using SMO and SVM classification technique
    C Ramesh BabuDurai, V Balaji, V Duraisamy
    European Journal of Scientific Research 69 (4), 560-564 , 2012
    2012
    Citations: 8
  • Energy-Efficient Optimization Using WSN Framework for Enhanced Smart Agriculture
    K Malarkodi, V Janakiraman, D Sheela, CMB MJ, V Balaji, R Rajeswari
    2024 9th International Conference on Communication and Electronics Systems … , 2024
    2024
    Citations: 7
  • Improved AODV based on link quality metrics
    V Balaji, V Duraisamy
    International Journal of Advances in Engineering & Technology 5 (1), 269 , 2012
    2012
    Citations: 7
  • Design of Super-Lift Luo-Converter with of Buck Converters for Electric Vehicle Applications
    S Gomathi, A Dhanalakshmi, V Janakiraman, D Sheela, V Balaji, ...
    2024 5th International Conference on Smart Electronics and Communication … , 2024
    2024
    Citations: 5
  • Energy Efficient Cluster Head Selection and Routing Algorithm using Hybrid Firefly Glow-Worm Swarm Optimization in WSN.
    S Bharathiraja, S Selvamuthukumarn, V Balaji
    KSII Transactions on Internet and Information Systems 17 (8), 2140-2157 , 2023
    2023
    Citations: 5
  • Ant optimized link quality for ad hoc on demand distance vector
    V Balaji, V Duraisamy
    Wireless personal communications 79 (1), 763-771 , 2014
    2014
    Citations: 5
  • Cluster based packet loss prediction using tcp ack packets in wireless network
    V Balaji, V Duraisami
    IJCSE) International Journal on Computer Science and Engineering 2 (07 … , 2010
    2010
    Citations: 4
  • Cluster Based Packet Loss Prediction using Packet lost Segment in Wireless Network
    UN Balaji V, Duraisamy V
    IEEE International Conference on Computational Intelligence and Computing … , 2010
    2010
    Citations: 4
  • Design and Implementation of Renewable Solar Energy in Smart Cities
    DP Matharasi, V Balaji, SN Jayanthi, R Manimegalai, S Raja, L Nelson
    2024 8th International Conference on Electronics, Communication and … , 2024
    2024
    Citations: 3
  • Revelatory Insights into Parkinson’s: Hand Gestures Deciphering with Mobilenet SSD
    VS Balaji, K Sangeetha, VA Anand, JA Velayutharaj
    International Conference on Computer, Communication, and Signal Processing, 3-15 , 2024
    2024
    Citations: 3
  • Analyzing hand gestures using object detection and processing it into local language
    K Sangeetha, VS Balaji, P Kamalesh, PSA Ganapathy
    International Conference on Advances in Artificial Intelligence and Machine … , 2023
    2023
    Citations: 3
  • An implementation of area and power efficient digital FIR filter for hearing aid applications
    VS Balaji, HN Upadhyay
    Optoelectronics and Advanced Materials-Rapid Communications 9 (May-June 2015 … , 2015
    2015
    Citations: 3
  • An Efficient and Secured Data Transmission in VANET Using LBK Interfaced AI-Optimized Routing Algorithm
    V Balaji, J Afsal, KK Santhosh, C Andreana
    2024 International Conference on Recent Advances in Electrical, Electronics … , 2024
    2024
    Citations: 2
  • Precision exercise monitoring through advanced body language detection using computer vision
    VS Balaji, K Sangeetha, PSA Ganapathy, MS Banu, SD Kumar
    International Conference on Computer, Communication, and Signal Processing … , 2024
    2024
    Citations: 2
  • Advanced Energy Consumption Forecasting in Smart Grids Using Quantum Boltzmann Machines
    P Velayutham, VS Balaji, N Krissh Shankaran
    2024 Global Conference on Communications and Information Technologies (GCCIT … , 2024
    2024
    Citations: 1