VIRENDRA KUMAR SWARNKAR

@bhartiuniversity.org

PROFESSOR IN COMPUTER SCIENCE & ENGINEERING
BHARTI VISHWAVIDYALAYA, DURG

VIRENDRA KUMAR SWARNKAR


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https://researchid.co/swarnkarvirendra

EDUCATION

 PH.D (COMPUTER SCIENCE & ENGINEERING) FROM KALINGA UNIVERSITY, NAVA RAIPUR IN THE YEAR 2021.
 M.TECH IN COMPUTER SCIENCE AND ENGINEERING SPECIALIZATION IN SOFTWARE ENGINEERING FROM RUNGTA COLLEGE OF ENGINEERING AND TECHNOLOGY, BHILAI AFFILIATED TO CSVTU, BHILAI WITH 8.34 CPI IN THE YEAR 2014.
 B.E. (INFORMATION TECHNOLOGY) FROM RUNGTA COLLEGE OF ENGINEERING AND TECHNOLOGY, BHILAI AFFILIATED TO PTRSU, RAIPUR WITH 7.27 CPI IN THE YEAR 2007.
 HSC FROM MDAV SCHOOL, DURG, CHHATTISGARH IN THE YEAR 2003.
 SSC FROM MDAV SCHOOL, DURG, CHHATTISGARH IN THE YEAR 2001.

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Engineering, Computer Science, Multidisciplinary
8

Scopus Publications

113

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Analyzing Student Academic Outcomes in the Digital Learning Era through Data Mining
    Geeta Zunjani, Virendra Kumar Swarnkar
    Lecture Notes in Networks and Systems, 2025
  • Advancing Underwater Image Quality Enhancement Through Hybrid Deep Learning Architectures
    Ghanshyam Sahu, Virendra Kumar Swarnkar
    Lecture Notes in Networks and Systems, 2025
  • Enhancing Ear Detection for Biometric Systems Using YOLOv9
    Niraj K. Nagrale, Vinay Kumar Jain, Virendra Kumar Swarnkar
    Communications in Computer and Information Science, 2025
  • Machine learning models for early detection of pest infestation in crops: A comparative study
    Suman Kumar Swarnkar, Yogesh Kumar Rathore, Virendra Kumar Swarnkar
    Smart Agriculture Harnessing Machine Learning for Crop Management, 2024
    Pest infestations pose a significant threat to agricultural productivity and food security, necessitating timely and accurate detection methods. This study investigates the efficacy of various machine-learning models in the early detection of pest infestations in crops. We employ a comparative approach, analyzing the performance of models such as decision trees, random forests, support vector machines (SVM), convolutional neural networks (CNN), and long short-term memory (LSTM) networks. The dataset utilized includes multispectral images and environmental parameters collected from diverse agricultural settings, ensuring a robust evaluation across different crop types and growth stages. Each model’s performance is assessed based on accuracy, precision, recall, and F1-score, with additional consideration given to computational efficiency and scalability. Our findings reveal that deep-learning models, particularly CNNs and LSTMs, demonstrate superior accuracy in detecting early signs of pest infestation compared to traditional machine-learning algorithms. However, trade-offs in computational requirements and implementation complexity are discussed. The study also explores the potential integration of these models into real-time monitoring systems, offering practical insights for farmers and agronomists aiming to adopt advanced technological solutions in pest management. This research underscores the transformative potential of machine learning in enhancing agricultural resilience and productivity, paving the way for future innovations in precision agriculture.
  • Detection of Skin Cancer Types in Dermoscopy Images with Gradient Boosting
    Leelkanth Dewangan, Kirti Gupta, Virendra Kumar Swarnkar, B. PruthviRaj Goud, Shivangi Rao
    International Conference on Artificial Intelligence for Innovations in Healthcare Industries Icaiihi 2023, 2023
    This study develops a computer-based system for classifying using gradient booster algorithms that addresses the urgent demand for precise and prompt skin cancer diagnosis. Dermoscopy pictures were gathered and prepared for the extraction of features using an interpretivist strategy. A large dataset with a variety of skin lesions was used for testing and training purposes. The gradient amplifier model performed better than other models at classifying different types of skin cancer, with an F1-score of 0.92 as well as an accuracy of 94.5%. The effectiveness of the suggested technique was underlined by comparison with initial models. A graphical representation of the classification findings, including a confusion matrix and ROC curves, gave intuitive understandings of the model's discriminatory abilities. An examination of the feature importance indicated the crucial characteristics influencing accurate classification. Future research is advised to investigate different ensemble methods, incorporate multisensory data sources, and perform real-time therapeutic validations. This study highlights the possibilities potential models to be an important tool in skincare, enhancing patient care and providing early management in the identification of skin cancer
  • Classification of Thyroid Cancer Subtypes With Imagenet Pretrained CNNS
    Ashwini Gavade, Mahesh V. Shitole, Vaishali Pendse, Virendra Kumar Swarnkar, J. Somasekar, Rajesh Kumar Manik
    International Conference on Artificial Intelligence for Innovations in Healthcare Industries Icaiihi 2023, 2023
    This study applies interpretivism, a deductive method, and a descriptive design to prepared Convolutional Neural Networks (for identifying thyroid cancer subgroups from histopathology pictures. Utilized is secondary data made up of several annotated photos. Strong subtype categorization performance is displayed by the fine-tuned CNN, which achieves high accuracy, preciseness, and recall. Key diagnostic aspects are clarified by interpretability metrics like Grad-CAM as well as Layer-wise Resonance Propagation. Potential discrepancies in forecasts are addressed by bias analysis as well as fairness measures. Comparing performance to traditional approaches reveals superiority. A prospective clinical verification study, research into cutting-edge deep learning structures, and dataset expansion are suggested. Future research will focus on molecular markers along with multi-modal interfacing. This study improves the diagnosis of thyroid cancer by providing an accurate method for subtype differentiation
  • The Role of AI in Healthcare Policy Development and Management
    Sapna Yadav, Pravin Mane, Virendra Kumar Swarnkar, Shital kumar Rawandale, Alaknanda S. Patil, Kiran S. Katke, Niyati Bhat
    International Conference on Artificial Intelligence for Innovations in Healthcare Industries Icaiihi 2023, 2023
    This study looks at how artificial intelligence (AI) is incorporated into healthcare policy, with an emphasis on how it affects administrative procedures, treatment optimization, especially clinical choice support. Using a deductive methodology and interpretive thinking as a philosophy, an exploratory design with secondary data collecting was used. In the present-day environment, AI is being used more frequently in healthcare legislation, especially within medical settings. Clinical decision-making systems driven by AI have shown to offer a great promise for lowering diagnostic mistakes and increasing treatment precision. Improving patient outcomes through individualized AI-driven treatments for treatment improvement appears promising. But there are issues with algorithm prejudices as well as information privacy, among other ethical and legal issues. Establishing thorough guidelines, ongoing education, and auditing procedures are among the suggestions that are made. In order to guarantee fair and efficient integration of health care legislation, future research should prioritize longitudinal research and improving artificial intelligence algorithms
  • Optimized Convolution Neural Network (OCNN) for Voice-Based Sign Language Recognition: Optimization and Regularization
    Suman Kumar Swarnkar, Asha Ambhaikar, Virendra Kumar Swarnkar, Upasana Sinha
    Lecture Notes in Networks and Systems, 2022

RECENT SCHOLAR PUBLICATIONS

  • AI-driven society in India in the next decade (2026–2036)
    DG Dr. Virendra Kumar Swarnkar
    International Journal of Advanced Engineering and Technology 10 (1), 25-31 , 2026
    2026
  • Next-Generation Stress Recognition through Intelligent Machine Learning on Physiological Data
    K Yadav, VK Swarnkar
    2025 2nd International Conference on Artificial Intelligence for Innovations … , 2025
    2025
  • UNDERWATER IMAGE QUALITY ENHANCEMENT USING HYBRIDE MODEL (CNN-GAN)
    G Sahu, B Vishwavidyalaya, C Durg, VK Swarnkar, G Zunzani, ...
    2025
  • Artificial Immune Mechanisms for Proactive Threat Detection in Computing Systems
    DVKS Rohini Mudliyar
    International Journal for Multidisciplinary Research 7 (5), 1-20 , 2025
    2025
  • DESIGN AND IMPLEMENTATION OF ARTIFICIAL IMMUNE SYSTEM FOR SAFETY OF COMPUTERS
    DVKS ROHINI MUDLIYAR
    International Journal for Multidisciplinary Research 7 (5), 1-16 , 2025
    2025
  • Multi-Scale Deep Neural Networks for Efficient and High-Quality Image Super-Resolution
    PK Tamrakar, VK Swarnkar
    International Conference on Advances and Applications in Artificial … , 2025
    2025
  • Analyzing Student Academic Outcomes in the Digital Learning Era through Data Mining
    G Zunjani, VK Swarnkar
    International Conference on Soft Computing: Theories and Applications, 169-177 , 2024
    2024
  • Machine learning models for early detection of pest infestation in crops: A comparative study
    SK Swarnkar, YK Rathore, VK Swarnkar
    Smart Agriculture, 147-162 , 2024
    2024
    Citations: 18
  • Self-Supervised Multi-Scale Deep Learning Framework for Unpaired Image Super-Resolution
    DVKS Prashant Kumar Tamrakar
    International Journal of Intelligent Systems and Applications in Engineering … , 2024
    2024
  • Enhancing Ear Detection for Biometric Systems Using YOLOv9
    NK Nagrale, VK Jain, VK Swarnkar
    International Conference on Paradigm Shifts in Communication, Embedded … , 2024
    2024
  • A Comprehensive Review of Stress Detection Using Physiological Signals and Machine Learning Approaches
    VKSW Karuna Yadav
    Advances in Nonlinear Variational Inequalities 27 (4), 668-686 , 2024
    2024
  • Advancing Underwater Image Quality Enhancement Through Hybrid Deep Learning Architectures
    G Sahu, VK Swarnkar
    International Conference on Artificial Intelligence on Textile and Apparel … , 2024
    2024
  • Deep Learning Approaches for Ear Biometrics: A Novel Approach
    VKS Niraj K. Nagrale
    International Journal of Scientific Research in Engineering and Management … , 2024
    2024
  • The Role of AI in Healthcare Policy Development and Management
    S Yadav, P Mane, VK Swarnkar, S Rawandale, AS Patil, K
    2023 International Conference on Artificial Intelligence for Innovations in … , 2024
    2024
    Citations: 3
  • Classification of Thyroid Cancer Subtypes With Imagenet Pretrained CNNS
    A Gavade, MV Shitole, V Pendse, VK Swarnkar, J Somasekar, Rajesh
    2023 International Conference on Artificial Intelligence for Innovations in … , 2024
    2024
    Citations: 3
  • Detection of Skin Cancer Types in Dermoscopy Images with Gradient Boosting
    L Dewangan, K Gupta, VK Swarnkar, BPR Goud, S Rao
    2023 International Conference on Artificial Intelligence for Innovations in … , 2024
    2024
    Citations: 2
  • Advancements in Underwater Image Enhancement via Deep Convolutional Neural Networks: A Comprehensive Study
    VKS Ghanshyam sahu
    IJISAE 12 (21), 4905-4915 , 2024
    2024
  • HYDROGEN FUELLED E-BIKE
    DAA Virendra Kumar Swarnkar, Dr Suman Kumar Swarnkar
    IN Patent 6,335,711 , 2024
    2024
  • RESPIRATION MONITORING DEVICE
    DRVKS RUPESH BALKRISHN YADAV
    2024
  • Predictive Analysis of Student Academic Performance in the Pandemic period : A Data Mining Approach
    GZ Virendra Kumar Swarnkar
    Journal of Electrical Systems 20 (11s), 3899-3907 , 2024
    2024

MOST CITED SCHOLAR PUBLICATIONS

  • Optimized convolution neural network (OCNN) for voice-based sign language recognition: optimization and regularization
    SK Swarnkar, A Ambhaikar, VK Swarnkar, U Sinha
    Information and Communication Technology for Competitive Strategies (ICTCS … , 2021
    2021
    Citations: 67
  • Machine learning models for early detection of pest infestation in crops: A comparative study
    SK Swarnkar, YK Rathore, VK Swarnkar
    Smart Agriculture, 147-162 , 2024
    2024
    Citations: 18
  • A survey on crime detection and prediction techniques
    V Gendre, NK Chandrakar, LKP Bhaiya, VK Swarnkar
    Int J Res Appl Sci Eng Technol 10, 119-122 , 2022
    2022
    Citations: 7
  • BIG DATA SECURITY ENHANCEMENT BASED INTRUSION DETECTION SYSTEM USING K-MEAN CLUSTERING OF DECOMPOSITED FEATURES
    VK Swarnkar, A Ambhaikar, SK Swarnkar
    IN Patent 202,121,004,801 , 2021
    2021
    Citations: 4
  • The Role of AI in Healthcare Policy Development and Management
    S Yadav, P Mane, VK Swarnkar, S Rawandale, AS Patil, K
    2023 International Conference on Artificial Intelligence for Innovations in … , 2024
    2024
    Citations: 3
  • Classification of Thyroid Cancer Subtypes With Imagenet Pretrained CNNS
    A Gavade, MV Shitole, V Pendse, VK Swarnkar, J Somasekar, Rajesh
    2023 International Conference on Artificial Intelligence for Innovations in … , 2024
    2024
    Citations: 3
  • An Implementation of Efficient Text Data Compression
    V Swarnkar, K Satao
    International Journal of Engineering and Advanced Technology (IJEAT) ISSN … , 2013
    2013
    Citations: 3
  • Detection of Skin Cancer Types in Dermoscopy Images with Gradient Boosting
    L Dewangan, K Gupta, VK Swarnkar, BPR Goud, S Rao
    2023 International Conference on Artificial Intelligence for Innovations in … , 2024
    2024
    Citations: 2
  • Efficient Crime Analysis Based on Hybrid Approach by Combining Dynamic Time Wrapping Algorithm with K-Means Clustering Approach.
    V Gendre, NK Chandrakar, LKP Bhaiya, VK Swarnkar
    International Journal for Research in Applied Science & Engineering … , 2022
    2022
    Citations: 2
  • Using Neural Network Techniques to Detect Malicious URL for Preventing Ransomwares
    S Yadav, N Sharma, LKP Bhaiya, VK Swarnkar
    International Journal for Research in Applied Science & Engineering … , 2022
    2022
    Citations: 1
  • Iris Recognition System Using Adaptive Neuro Fuzzy Inference System Classifier
    V Verma, SK Swarnkar, V Swarnkar
    2017
    Citations: 1
  • Review of Recommendation System for Web Application
    VS Preeti Bhole1, Neetish Kumar Chandrakar2
    International Journal of Science and Research (IJSR) 6 (Issue 1), 314-317 , 2017
    2017
    Citations: 1
  • A Survey on Performance of different Text Editor
    VK Swarnkar, KJ Satao
    Jul , 2013
    2013
    Citations: 1
  • AI-driven society in India in the next decade (2026–2036)
    DG Dr. Virendra Kumar Swarnkar
    International Journal of Advanced Engineering and Technology 10 (1), 25-31 , 2026
    2026
  • Next-Generation Stress Recognition through Intelligent Machine Learning on Physiological Data
    K Yadav, VK Swarnkar
    2025 2nd International Conference on Artificial Intelligence for Innovations … , 2025
    2025
  • UNDERWATER IMAGE QUALITY ENHANCEMENT USING HYBRIDE MODEL (CNN-GAN)
    G Sahu, B Vishwavidyalaya, C Durg, VK Swarnkar, G Zunzani, ...
    2025
  • Artificial Immune Mechanisms for Proactive Threat Detection in Computing Systems
    DVKS Rohini Mudliyar
    International Journal for Multidisciplinary Research 7 (5), 1-20 , 2025
    2025
  • DESIGN AND IMPLEMENTATION OF ARTIFICIAL IMMUNE SYSTEM FOR SAFETY OF COMPUTERS
    DVKS ROHINI MUDLIYAR
    International Journal for Multidisciplinary Research 7 (5), 1-16 , 2025
    2025
  • Multi-Scale Deep Neural Networks for Efficient and High-Quality Image Super-Resolution
    PK Tamrakar, VK Swarnkar
    International Conference on Advances and Applications in Artificial … , 2025
    2025
  • Analyzing Student Academic Outcomes in the Digital Learning Era through Data Mining
    G Zunjani, VK Swarnkar
    International Conference on Soft Computing: Theories and Applications, 169-177 , 2024
    2024