G. Sathish Kumar

@sece.ac.in

Assistant Professor and Artificial Intelligence & Data Science
Sri Eshwar College of Engineering

G. Sathish Kumar


               Download Compact PDF  
https://researchid.co/saathhish

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Engineering
27

Scopus Publications

368

Scholar Citations

9

Scholar h-index

9

Scholar i10-index

Scopus Publications

  • An effective ECOLASSO with black widow optimization for feature selection and stagewise adaptive learning rate for disease prediction
    G. Vijaya, G. Sathish Kumar, G. Uma Maheshwari, M. Karthiga, S. Hemkiran, Seyed Jalaleddin Mousavirad, Ghanshyam G. Tejani
    Discover Artificial Intelligence, 2026
    Machine learning techniques are utilized for early detection of diseases, which can significantly enhance probabilities of positive treatment and existence. The traditional machine learning algorithms may be unable to predict outcomes with sufficient accuracy. In this work, an Effective ECOLASSO with Black Widow Optimization for Feature Selection and Stagewise Adaptive Learning Rate (ELBWOSALR) classifier is proposed for feature selection and prediction. The proposed work comprises two phases, in the first phase, Ecological similarity Least Absolute Shrinkage and Selection Operator (ECOLASSO) model is utilized to predict the best features from the dataset by removing the feature with smallest absolute regression coefficient from the feature set. A Black Widow Optimizer (BWO) is used to choose the subset of optimal features and to reduce local optima. In the second phase, Stagewise Adaptive Learning Rate (SALR) involves combining several weak learner classifiers into a strong ensemble classifier by adaptive learning rate. The key contribution of this work is the integration of ECOLASSO model with BWO for robust feature selection, combined with a SALR classifier. This hybridization addresses two critical challenges simultaneously: (i) ECOLASSO ensures sparsity and ecological similarity-driven selection of relevant features, while (ii) BWO prevents premature convergence and enhances global search efficiency. By coupling these with SALR, our model achieves superior accuracy and generalization compared to conventional classifiers. Lung cancer, breast cancer and heart disease datasets are used for experimentation. The ELBWOSALR classifier is compared with various classifier models such as Support Vector Classifier, Decision Tree Classifier, Random Forest Classifier, Logistic Regression, Extreme Gradient Boost Classifier, Gradient Boosting Classifier, K-Nearest Neighbors Classifier and CatBoost Classifier and the results are observed. The proposed ELBWOSALR classifier achieves accuracies of 98%, 97% and 91% with AUC values of 92%, 99% and 94% for lung cancer, breast cancer and heart disease datasets respectively.
  • An efficient optimized deep learning model for the risk prediction of arterial stiffness in diabetes patients
    A. Mohana Priya, P. Rajesh Kanna, S. Vanithamani, G. Sathish Kumar
    Biomedical Signal Processing and Control, 2025
  • SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence
    G. Sathish Kumar, E. Suganya, S. Sountharrajan, Balamurugan Balusamy, Adil O. Khadidos, Alaa O. Khadidos, Shitharth Selvarajan
    Scientific Reports, 2025
    Artificial Intelligence techniques are being used to analyse vast amounts of medical data and assist in the accurate and early diagnosis of diseases. The common brain related diseases are faced by most of the people which affects the structure and function of the brain. Artificial neural networks have been extensively used for disease prediction and diagnosis due to their ability to learn complex patterns and relationships from large datasets. However, there are some problems like over-fitting, under-fitting, vanishing gradient and increased elapsed time occurred in the course of data analysis and prediction which results in performance degradation of the model. Therefore, a complex structure perception is much essential by avoiding over-fitting and under-fitting. This empirical study presents a statistical reduction approach along with deep hyper optimization (SRADHO) technique for better feature selection and disease classification with reduced elapsed time. Deep hyper optimization combines deep learning models with hyperparameter tuning to automatically identify the most relevant features, optimizing model accuracy and reducing dimensionality. SRADHO is used to calibrate the weight, bias and select the optimal number of hyperparameters in the hidden layer using Bayesian optimization approach. Bayesian optimization uses a probabilistic model to efficiently search the hyperparameter space, identifying configurations that maximize model performance while minimizing the number of evaluations. Three benchmark datasets and the classifier models logistic regression, decision tree, random forest, K-nearest neighbour, support vector machine and Naïve Bayes are used for experimentation. The proposed SRADHO algorithm achieves 98.2% of accuracy, 97.2% of precision rate, 98.3% of recall rate and 98.1% of F1-Score value with 0.3% of error rate. The execution time for SRADHO algorithm is 12 s.
  • ZFNet and deep Maxout network based cancer prediction using gene expression data
    G. Vijaya, K. Ramesh, G. Sathish Kumar
    Biomedical Signal Processing and Control, 2025
  • Efficient Deep Learning-Based Approaches for Early Diagnosis of Diabetes Mellitus
    G. Priyanka, M. Nivaashini, M. Thenmozhi, G. Sathish Kumar
    Lecture Notes in Networks and Systems, 2025
  • Brain Tumour Detection using Convolutional Neural Network with Machine Learning Algorithm
    16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
  • Deep learning approach using modified DarkNet-53 for renal cell carcinoma grading
    G. Sathish Kumar, G. Uma Maheshwari, C. Selvan, M. Nagasuresh, D. Rasi, P. Swathypriyadharsini, Sathish Kumar Danasegaran
    International Journal of Bioinformatics Research and Applications, 2025
    Accurate and effective diagnostic procedures are required for appropriate treatment planning for renal cell carcinoma, the most frequent form of kidney cancer. Using fusion module, a network dubbed modified DarkNet (MDNet) was developed for image-based small-target detection. We built MDNet on top of a modified version of DarkNet-53, which itself a scale matching approach, to increase its speed and accuracy. By combining the results of several convolutional neural network (CNN) models, the ensemble structure improves classification accuracy. The effectiveness of a classification algorithm using kidney histopathology pictures dataset is measured in accuracy, precision, recall, sensitivity, specificity and F1-score. The results show that the ensemble deep learning method outperforms both standalone CNN models and more conventional machine learning techniques in RCC classification. Overall grade classification accuracy of 98.9%, a sensitivity of 98.2%, and a high classification specificity of 98.7%, in distinguishing tissues.
  • A synergistic framework for histopathologic cancer detection using Epicurve Search –PSB model with surrosec Optimizer
    P. Nancy, V Rajeshram, G. Sathish Kumar, P. Dhivya
    Biomedical Signal Processing and Control, 2024
  • Gene-Based Predictive Modelling for Enhanced Detection of Systemic Lupus Erythematosus Using CNN-Based DL Algorithm
    Jothimani Subramani, G. Sathish Kumar, Thippa Reddy Gadekallu
    Diagnostics, 2024
    Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune disease that presents with a diverse array of clinical signs and unpredictable disease progression. Conventional diagnostic methods frequently fall short in terms of sensitivity and specificity, which can result in delayed diagnosis and less-than-optimal management. In this study, we introduce a novel approach for improving the identification of SLE through the use of gene-based predictive modelling and Stacked deep learning classifiers. The study proposes a new method for diagnosing SLE using Stacked Deep Learning Classifiers (SDLC) trained on Gene Expression Omnibus (GEO) database data. By combining transcriptomic data from GEO with clinical features and laboratory results, the SDLC model achieves a remarkable accuracy value of 0.996, outperforming traditional methods. Individual models within the SDLC, such as SBi-LSTM and ACNN, achieved accuracies of 92% and 95%, respectively. The SDLC’s ensemble learning approach allows for identifying complex patterns in multi-modal data, enhancing accuracy in diagnosing SLE. This study emphasises the potential of deep learning methods, in conjunction with open repositories like GEO, to advance the diagnosis and management of SLE. Overall, this research shows strong performance and potential for improving precision medicine in managing SLE.
  • Photonic crystal based hour glass patch antenna for the detection of breast cancer
    R. Pandian, Sathish Kumar Danasegaran, S. Lalithakumari, G. Rajalakshmi, G. Sathish Kumar
    Optical and Quantum Electronics, 2024
  • Design and investigation of line-defected photonic crystal antenna for outstanding data transmission
    Sathish Kumar Danasegaran, Elizabeth Caroline Britto, S. Dhanasekaran, G. Rajalakshmi, S. Lalithakumari, A. Sivasangari, G. Sathish Kumar
    Radar and RF Front End System Designs for Wireless Systems, 2024
  • Multi-modal emotion recognition through adaptive normalization fusion with alpha Gaussian dropout in MCNN architecture
    M. Murugesan, P. Dhivya, P. Rajesh Kanna, G. Sathish Kumar
    Signal Image and Video Processing, 2024
  • Differential privacy scheme using Laplace mechanism and statistical method computation in deep neural network for privacy preservation
    G. Sathish Kumar, K. Premalatha, G. Uma Maheshwari, P. Rajesh Kanna, G. Vijaya, M. Nivaashini
    Engineering Applications of Artificial Intelligence, 2024
  • Data Privacy Preservation using Differential Privacy and Re-Identification Attacks
    G. Sathish Kumar, K Preethie, S Madhumitha, R Sushma, M. Nivaashini
    Proceedings of 2024 International Conference on Science Technology Engineering and Management Icstem 2024, 2024
  • Enhanced Heart Disease Classification: An Ensemble-Based Deep Learning Approach
    Nivaashini M, Sathish Kumar G, Uma Maheshwari G, Soundariya R S, Arun Kumar B, Vijaya G
    Proceedings 2024 IEEE International Conference on Signal Processing Informatics Communication and Energy Systems Harmonizing Signals Data and Energy Bridging the Digital Future Spices 2024, 2024
  • No more privacy Concern: A privacy-chain based homomorphic encryption scheme and statistical method for privacy preservation of user's private and sensitive data
    G. Sathish Kumar, K. Premalatha, G. Uma Maheshwari, P. Rajesh Kanna
    Expert Systems with Applications, 2023
  • STIF: Intuitionistic fuzzy Gaussian membership function with statistical transformation weight of evidence and information value for private information preservation
    G. Sathish Kumar, K. Premalatha
    Distributed and Parallel Databases, 2023
  • Convolution Neural Network with Unsupervised Machine Learning Approach for Feature Extraction and Brain Tumor Detection in Human beings
    G. Sathish Kumar, M. Nivaashini, G. Uma Maheshwari, D. Rasi, B. Magesh Kumar, R. Arun
    1st International Conference on Emerging Research in Computational Science Icercs 2023 Proceedings, 2023
  • Privacy preserving data mining – past and present
    G. Sathish Kumar, K. Premalatha
    International Journal of Business Intelligence and Data Mining, 2022
  • HYBRID FIREFLY META OPTIMIZATION FOR BIO MEDICAL IMAGE PROCESSING USING DEEP LEARNING
    P. Dhivya, T. Kumaresan, P. Subramanian, K. Gunasekaran, G. Sathish Kumar
    Journal of Pharmaceutical Negative Results, 2022
  • Securing private information by data perturbation using statistical transformation with three dimensional shearing[Formula presented]
    G. Sathish Kumar, K. Premalatha
    Applied Soft Computing, 2021
  • Design and simulation of handwritten recognition system
    D. Prabha Devi, R. Ramya, P.S. Dinesh, C. Palanisamy, G. Sathish Kumar
    Materials Today Proceedings, 2021
  • Data analytics for web structure mining in business website
    International Journal of Scientific and Technology Research, 2020
  • Bio-medical analysis of breast cancer risk detection based on deep neural network
    International Journal of Medical Engineering and Informatics, 2020
  • Automatic classification for preventing duplication of online multimedia data in secure cloud infrastructure
    E. Suganya, N. Aravindhraj, S. Sountharrajan, G. Sathish Kumar, C. Rajan
    International Journal of Advanced Intelligence Paradigms, 2020
  • Machine Learning-Based Sentiment Analysis of Twitter Data
    M. Karthiga, Sathish Kumar G., N. Aravindhraj, S. Priyanka
    Proceedings of the 2019 International Conference on Advances in Computing and Communication Engineering Icacce 2019, 2019
  • Secured cryptosystem using blowfish and RSA algorithm for the data in public cloud
    International Journal of Recent Technology and Engineering, 2019

RECENT SCHOLAR PUBLICATIONS

  • An effective ECOLASSO with black widow optimization for feature selection and stagewise adaptive learning rate for disease prediction
    G Vijaya, GS Kumar, GU Maheshwari, M Karthiga, S Hemkiran, ...
    Discover Artificial Intelligence , 2026
    2026
    Citations: 1
  • An efficient optimized deep learning model for the risk prediction of arterial stiffness in diabetes patients
    AM Priya, PR Kanna, S Vanithamani, GS Kumar
    Biomedical Signal Processing and Control 110, 108161 , 2025
    2025
    Citations: 3
  • Efficient Deep Learning-Based Approaches for Early Diagnosis
    G Priyanka, M Nivaashini, M Thenmozhi, GS Kumar
    Proceedings of International Conference on Data Analytics and Insights … , 2025
    2025
  • ZFNet and deep Maxout network based cancer prediction using gene expression data
    G Vijaya, K Ramesh, GS Kumar
    Biomedical Signal Processing and Control 100, 107038 , 2025
    2025
    Citations: 5
  • SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence
    GS Kumar, E Suganya, S Sountharrajan, B Balusamy, AO Khadidos, ...
    Scientific Reports 15 (1), 1245 , 2025
    2025
    Citations: 8
  • Deep learning approach using modified DarkNet-53 for renal cell carcinoma grading
    GS Kumar, GU Maheshwari, C Selvan, M Nagasuresh, D Rasi, ...
    International Journal of Bioinformatics Research and Applications 21 (1), 1-25 , 2025
    2025
  • A synergistic framework for histopathologic cancer detection using Epicurve Search–PSB model with surrosec Optimizer
    P Nancy, V Rajeshram, GS Kumar, P Dhivya
    Biomedical Signal Processing and Control 96, 106498 , 2024
    2024
    Citations: 5
  • Enhancing Parkinson’s Disease Prediction Using Deep Learning-Based Convolutional Neural Networks
    R Ramya, R (Ramya, C Ramesh, C (Ramesh, ...
    JOURNAL OF ELECTRICAL SYSTEMS 20 (5s), 1866-1874 , 2024
    2024
  • Gene-based predictive modelling for enhanced detection of systemic lupus erythematosus using CNN-based DL algorithm
    J Subramani, GS Kumar, TR Gadekallu
    Diagnostics 14 (13), 1339 , 2024
    2024
    Citations: 6
  • Data privacy preservation using differential privacy and re-identification attacks
    GS Kumar, K Preethie, S Madhumitha, R Sushma, M Nivaashini
    2024 International Conference on Science Technology Engineering and … , 2024
    2024
    Citations: 2
  • Photonic crystal based hour glass patch antenna for the detection of breast cancer
    R Pandian, SK Danasegaran, S Lalithakumari, G Rajalakshmi, GS Kumar
    Optical and Quantum Electronics 56 (5), 763 , 2024
    2024
    Citations: 17
  • Differential privacy scheme using Laplace mechanism and statistical method computation in deep neural network for privacy preservation
    GS Kumar, K Premalatha, GU Maheshwari, PR Kanna, G Vijaya, ...
    Engineering Applications of Artificial Intelligence 128, 107399 , 2024
    2024
    Citations: 96
  • Design and investigation of line-defected photonic crystal antenna for outstanding data transmission
    SK Danasegaran, EC Britto, S Dhanasekaran, G Rajalakshmi, ...
    Radar and RF front end system designs for wireless systems, 176-193 , 2024
    2024
    Citations: 4
  • Multi-modal emotion recognition through adaptive normalization fusion with alpha Gaussian dropout in MCNN architecture
    M Murugesan, P Dhivya, P Rajesh Kanna, G Sathish Kumar
    Signal, Image and Video Processing 18, 1779–1791 , 2023
    2023
    Citations: 12
  • STIF: Intuitionistic fuzzy Gaussian membership function with statistical transformation weight of evidence and information value for private information preservation
    GS Kumar, K Premalatha
    Distributed and Parallel Databases 41 (3), 233-266 , 2023
    2023
    Citations: 19
  • No more privacy Concern: A privacy-chain based homomorphic encryption scheme and statistical method for privacy preservation of user’s private and sensitive data
    GS Kumar, K Premalatha, GU Maheshwari, PR Kanna
    Expert Systems with Applications, 121071 , 2023
    2023
    Citations: 90
  • Applications in Automobile Industries: Warehouse, Logistics and Delivery Systems, Mobile Robots
    GS Kumar, DP Devi, R Ramya, PR Kanna
    Computational Intelligence in Robotics and Automation, 187-215 , 2023
    2023
  • HYBRID FIREFLY META OPTIMIZATION FOR BIO MEDICAL IMAGE PROCESSING USING DEEP LEARNING.
    P Dhivya, T Kumaresan, P Subramanian, K Gunasekaran, GS Kumar
    Journal of Pharmaceutical Negative Results 13 (4) , 2022
    2022
    Citations: 2
  • FPGA Implementation Of A Modified SMS4-BSK Cipher With Novel Efficient Xor Gate Design
    M Babu, GAS Kumar, K Sivachandar, K Kannan, J Gurumurthy, ...
    2022 7th International Conference on Communication and Electronics Systems … , 2022
    2022
    Citations: 1
  • A review analysis of attack detection using various methodologies in network security
    PR Kanna, S Gokulraj, K Karthik, G Vijaya, GS Kumar, G Rajeshkumar
    Journal of Pharmaceutical Negative Results 13 (4), 1599-1614 , 2022
    2022
    Citations: 7

MOST CITED SCHOLAR PUBLICATIONS

  • Differential privacy scheme using Laplace mechanism and statistical method computation in deep neural network for privacy preservation
    GS Kumar, K Premalatha, GU Maheshwari, PR Kanna, G Vijaya, ...
    Engineering Applications of Artificial Intelligence 128, 107399 , 2024
    2024
    Citations: 96
  • No more privacy Concern: A privacy-chain based homomorphic encryption scheme and statistical method for privacy preservation of user’s private and sensitive data
    GS Kumar, K Premalatha, GU Maheshwari, PR Kanna
    Expert Systems with Applications, 121071 , 2023
    2023
    Citations: 90
  • Automatic classification for preventing duplication of online multimedia data in secure cloud infrastructure
    E Suganya, N Aravindhraj, S Sountharrajan, G Sathish Kumar, C Rajan
    International Journal of Advanced Intelligence Paradigms 16 (3-4), 404-413 , 2020
    2020
    Citations: 23
  • Securing private information by data perturbation using statistical transformation with three dimensional shearing
    G Sathish Kumar, K Premalatha
    Applied Soft Computing 112 , 2021
    2021
    Citations: 21
  • Bio-medical analysis of breast cancer risk detection based on deep neural network
    M Nivaashini, RS Soundariya, N Aravindhraj, G Sathish Kumar
    International Journal of Medical Engineering and Informatics 12 (6), 529-541 , 2020
    2020
    Citations: 21
  • STIF: Intuitionistic fuzzy Gaussian membership function with statistical transformation weight of evidence and information value for private information preservation
    GS Kumar, K Premalatha
    Distributed and Parallel Databases 41 (3), 233-266 , 2023
    2023
    Citations: 19
  • Photonic crystal based hour glass patch antenna for the detection of breast cancer
    R Pandian, SK Danasegaran, S Lalithakumari, G Rajalakshmi, GS Kumar
    Optical and Quantum Electronics 56 (5), 763 , 2024
    2024
    Citations: 17
  • Multi-modal emotion recognition through adaptive normalization fusion with alpha Gaussian dropout in MCNN architecture
    M Murugesan, P Dhivya, P Rajesh Kanna, G Sathish Kumar
    Signal, Image and Video Processing 18, 1779–1791 , 2023
    2023
    Citations: 12
  • Design and simulation of handwritten recognition system
    D Prabha Devi, R Ramya, PS Dinesh, C Palanisamy, G Sathish Kumar
    Materials Today: Proceedings 45, 626-629 , 2021
    2021
    Citations: 10
  • SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence
    GS Kumar, E Suganya, S Sountharrajan, B Balusamy, AO Khadidos, ...
    Scientific Reports 15 (1), 1245 , 2025
    2025
    Citations: 8
  • A review analysis of attack detection using various methodologies in network security
    PR Kanna, S Gokulraj, K Karthik, G Vijaya, GS Kumar, G Rajeshkumar
    Journal of Pharmaceutical Negative Results 13 (4), 1599-1614 , 2022
    2022
    Citations: 7
  • Secured Cryptosystem using Blowfish and RSA Algorithm for The Data in Public Cloud
    G Sathish Kumar, K Premalatha, N Aravindhraj, M Nivaashini, M Karthiga
    International Journal of Recent Technology and Engineering (IJRTE) 7 (4s … , 2018
    2018
    Citations: 7
  • Gene-based predictive modelling for enhanced detection of systemic lupus erythematosus using CNN-based DL algorithm
    J Subramani, GS Kumar, TR Gadekallu
    Diagnostics 14 (13), 1339 , 2024
    2024
    Citations: 6
  • ZFNet and deep Maxout network based cancer prediction using gene expression data
    G Vijaya, K Ramesh, GS Kumar
    Biomedical Signal Processing and Control 100, 107038 , 2025
    2025
    Citations: 5
  • A synergistic framework for histopathologic cancer detection using Epicurve Search–PSB model with surrosec Optimizer
    P Nancy, V Rajeshram, GS Kumar, P Dhivya
    Biomedical Signal Processing and Control 96, 106498 , 2024
    2024
    Citations: 5
  • Design and investigation of line-defected photonic crystal antenna for outstanding data transmission
    SK Danasegaran, EC Britto, S Dhanasekaran, G Rajalakshmi, ...
    Radar and RF front end system designs for wireless systems, 176-193 , 2024
    2024
    Citations: 4
  • An efficient optimized deep learning model for the risk prediction of arterial stiffness in diabetes patients
    AM Priya, PR Kanna, S Vanithamani, GS Kumar
    Biomedical Signal Processing and Control 110, 108161 , 2025
    2025
    Citations: 3
  • Privacy preserving data mining-past and present
    GS Kumar, K Premalatha
    International Journal of Business Intelligence and Data Mining 21 (2), 149-170 , 2022
    2022
    Citations: 3
  • Machine Learning-Based Sentiment Analysis of Twitter Data
    M Karthiga, G Sathish Kumar, N Aravindhraj, S Priyanka
    2019 International Conference on Advances in Computing and Communication … , 2019
    2019
    Citations: 3
  • Data privacy preservation using differential privacy and re-identification attacks
    GS Kumar, K Preethie, S Madhumitha, R Sushma, M Nivaashini
    2024 International Conference on Science Technology Engineering and … , 2024
    2024
    Citations: 2