RAMESH SUNDER NAYAK

@canaraengineering.in

ASSOCIATE PROFESSOR, INFORMATION SCIENCE & ENGINEERING DEPARTMENT
CANARA ENGINEERING COLLEGE

RAMESH SUNDER NAYAK


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https://researchid.co/rsnresearch

RESEARCH INTERESTS

ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, BIG DATA, IMAGE PROCESSING
17

Scopus Publications

1422

Scholar Citations

11

Scholar h-index

12

Scholar i10-index

Scopus Publications

  • AI TalentSuite - Resume Enhancement, Interview Preparing and Exam monitoring system
    Ramesh Sunder Nayak, H. Manoj T. Gadiyar, Aniketh K, Darshan, Nishan, Varshith B A
    2025 World Skills Conference on Universal Data Analytics and Sciences Worldsuas 2025, 2025
    The rise of artificial intelligence (AI) has transformed the hiring and talent evaluation landscape, necessitating innovative solutions to streamline recruitment processes. This paper presents AI TalentSuite, a comprehensive system designed to enhance resumes, prepare candidates for interviews, and monitor exams effectively. The resume enhancement module leverages natural language processing (NLP) to optimize content, align skills with job requirements, and ensure ATS compatibility. The interview preparation feature uses AI-driven insights to simulate real-world interview scenarios, providing feedback on communication, posture, and response quality. The exam monitoring system integrates advanced proctoring tools, including facial recognition and anomaly detection, ensuring academic and professional assessment integrity. By addressing key challenges in talent acquisition and assessment, AI TalentSuite empowers both organizations and candidates, fostering a transparent, efficient, and merit-based evaluation ecosystem.
  • HireVue - Collaborative Code Editor
    H. Manoj T. Gadiyar, Ramesh Sunder Nayak, Amulya Jois, Rakshitha Shetty A, Tilak Shetty, Venkatesh R Kamath
    2025 World Skills Conference on Universal Data Analytics and Sciences Worldsuas 2025, 2025
    HireVue is a synchronous collaborative code editor designed particularly for remote technical interviews in an integrated environment where candidates and interviewers can simply interact with each other smoothly. It supports more than 80 programming languages and integrates to evaluate the problem-solving of candidates in real time through all three features: collaborative coding, video conferencing, and screen sharing. It is built from React.js, Vite, Node.js, and Express.js, ensuring a responsive frontend and robust backend. Security features include JWT-based authentication, automatic logout, and face tracking for monitoring. With Socket.io for real-time collaboration and WebRTC for video calls, HireVue enhances recruitment efficiency, scalability, and adaptability, making it ideal for interviews, education, and remote pair programming.
  • Land Pulse: Real-Time Agriculture Parameter Forecasting
    15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
  • Assisting Dementia Patients through IoT Device
    15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
  • Detection of Antibiotic Constituent in Aspergillus flavus Using Quantum Convolutional Neural Network
    Sannidhan M. S., Jason Elroy Martis, Ramesh Sunder Nayak, Sunil Kumar Aithal, Sudeepa K. B.
    International Journal of E Health and Medical Communications, 2023
    Treatment of influenza and its complications is a major challenge for healthcare systems. Pyrazine is one drug used in treating influenza. Aspergillic acid is major antibiotic constituent in pyrazine compounds mined from Aspergillus flavus' final stage. This stage of flavus is detected through color change forming a pale-yellow crystal structure. Detection of the same is complex and demands an experienced fraternity to continuously monitor the growth of fungus and identify its color change. However, researches proved that the task needs to be perfect and a tiny human error leads to a catastrophe in antibiotic creation. To avoid these flaws, druggists make a huge investment on costly equipment for accurate detection. To overcome these drawbacks, this article proposes a hybrid quantum convolutional neural network that predicts various stages of the fungus from the microscope's sample. To train the network, about 47,000 samples were poised under typical lab settings. The proposed system was tested in usual conditions and positively isolated the mature samples with 96% efficiency.
  • 3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks
    Javaria Amin, Muhammad Sharif, Eman Gul, Ramesh Sunder Nayak
    Complex and Intelligent Systems, 2022
    Wireless capsule endoscopy (WCE) might move through human body and captures the small bowel and captures the video and require the analysis of all frames of video due to which the diagnosis of gastrointestinal infections by the physician is a tedious task. This tiresome assignment has fuelled the researcher’s efforts to present an automated technique for gastrointestinal infections detection. The segmentation of stomach infections is a challenging task because the lesion region having low contrast and irregular shape and size. To handle this challenging task, in this research work a new deep semantic segmentation model is suggested for 3D-segmentation of the different types of stomach infections. In the segmentation model, deep labv3 is employed as a backbone of the ResNet-50 model. The model is trained with ground-masks and accurately performs pixel-wise classification in the testing phase. Similarity among the different types of stomach lesions accurate classification is a difficult task, which is addressed in this reported research by extracting deep features from global input images using a pre-trained ResNet-50 model. Furthermore, the latest advances in the estimation of uncertainty and model interpretability in the classification of different types of stomach infections is presented. The classification results estimate uncertainty related to the vital features in input and show how uncertainty and interpretability might be modeled in ResNet-50 for the classification of the different types of stomach infections. The proposed model achieved up to 90% prediction scores to authenticate the method performance.
  • Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization
    Asim Shahzad, Mudassar Raza, Jamal Hussain Shah, Muhammad Sharif, Ramesh Sunder Nayak
    Complex and Intelligent Systems, 2022
    White blood cells, WBCs for short, are an essential component of the human immune system. These cells are our body's first line of defense against infections and diseases caused by bacteria, viruses, and fungi, as well as abnormal and external substances that may enter the bloodstream. A wrong WBC count can signify dangerous viral infections, autoimmune disorders, cancer, sarcoidosis, aplastic anemia, leukemia, tuberculosis, etc. A lot of these diseases and disorders can be extremely painful and often result in death. Leukemia is among the more common types of blood cancer and when left undetected leads to death. An early diagnosis is necessary which is possible by looking at the shapes and determining the numbers of young and immature WBCs to see if they are normal or not. Performing this task manually is a cumbersome, expensive, and time-consuming process for hematologists, and therefore computer-aided systems have been developed to help with this problem. This paper proposes an improved method of classification of WBCs utilizing a combination of preprocessing, convolutional neural networks (CNNs), feature selection algorithms, and classifiers. In preprocessing, contrast-limited adaptive histogram equalization (CLAHE) is applied to the input images. A CNN is designed and trained to be used for feature extraction along with ResNet50 and EfficientNetB0 networks. Ant colony optimization is used to select the best features which are then serially fused and passed onto classifiers such as support vector machine (SVM) and quadratic discriminant analysis (QDA) for classification. The classification accuracy achieved on the Blood Cell Images dataset is 98.44%, which shows the robustness of the proposed work.
  • Brain tumor detection and classification using machine learning: a comprehensive survey
    Javaria Amin, Muhammad Sharif, Anandakumar Haldorai, Mussarat Yasmin, Ramesh Sundar Nayak
    Complex and Intelligent Systems, 2022
    Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.
  • Electroencephalogram for epileptic seizure detection using stacked bidirectional LSTM_GAP neural network
    D. K. Thara, B. G. Premasudha, Ramesh Sunder Nayak, T. V. Murthy, G. Ananth Prabhu, Naeem Hanoon
    Evolutionary Intelligence, 2021
  • Enhanced vascular and osseous information fusion: disagreement of quantitative and qualitative analysis
    Ayush Dogra, Bhawna Goyal, Sunil Agrawal, Urcun John Tanik, Sanjeev Kumar, Ramesh Sunder Nayak
    Neural Computing and Applications, 2020
  • Deep neural network assisted diagnosis of time-frequency transformed electromyograms
    A. Bakiya, K. Kamalanand, V. Rajinikanth, Ramesh Sunder Nayak, Seifedine Kadry
    Multimedia Tools and Applications, 2020
  • Developed Newton-Raphson based deep features selection framework for skin lesion recognition
    Muhammad Attique Khan, Muhammad Sharif, Tallha Akram, Syed Ahmad Chan Bukhari, Ramesh Sunder Nayak
    Pattern Recognition Letters, 2020
  • Stomach Deformities Recognition Using Rank-Based Deep Features Selection
    Muhammad Attique Khan, Muhammad Sharif, Tallha Akram, Mussarat Yasmin, Ramesh Sunder Nayak
    Journal of Medical Systems, 2019
  • A comprehensive evaluation of waste management systems
    Iioab Journal, 2016
  • Touch screen controlled defense robot: A comprehensive review
    Iioab Journal, 2016
  • Comparison of image restoration and segmentation of the image using neural network
    B. Sadhana, Ramesh Sunder Nayak, B. Shilpa
    Advances in Intelligent Systems and Computing, 2016
  • A study on IoT enabled smart store
    Iioab Journal, 2016

RECENT SCHOLAR PUBLICATIONS

  • Thermal Prediction in Dissimilar Joining
    P Meena, R Khan, A Gupta, S Khan, N Upadhyay, R Anant, S Srivastava, ...
    Advances in Materials and Manufacturing Technology: Select Proceedings of … , 2026
    2026
  • Weed‐Mediated Persistence of Meloidogyne enterolobii : Role of Bidens pilosa and Euphorbia heterophylla in Guava Wilt Management
    R Chandana, CP Manjula, TR Kavitha, D Chethan, S Rawat, S Kumar, ...
    Weed Biology and Management 25 (4), e70009 , 2025
    2025
  • AI TalentSuite–Resume Enhancement, Interview Preparing and Exam monitoring system
    RS Nayak, HMT Gadiyar
    2025 World Skills Conference on Universal Data Analytics and Sciences … , 2025
    2025
    Citations: 1
  • HireVue–Collaborative Code Editor
    HMT Gadiyar, RS Nayak, A Jois, T Shetty, VR Kamath
    2025 World Skills Conference on Universal Data Analytics and Sciences … , 2025
    2025
    Citations: 1
  • Pathogenic interaction and molecular characterization of Meloidogyne enterolobii infecting Lantana camara: A new host association from India
    R Chandana, R Nayak, CP Manjula, TR Kavitha, D Chethan, S Rawat, ...
    Physiological and Molecular Plant Pathology, 102852 , 2025
    2025
    Citations: 2
  • Thermal Prediction in Dissimilar Joining of P92 and AISI304L Steel Weld: FEM Analysis
    P Meena, R Khan, A Gupta, S Khan, N Upadhyay, R Anant, S Srivastava, ...
    International Conference on Advances in Materials and Manufacturing … , 2024
    2024
  • Lymphedema with dermatitis neglecta: a rare case report
    Z Shyma, S Farooqui, S Zulekha, VV Adarsh, J Martis, R Nayak
    2024
  • Detection of antibiotic constituent in aspergillus flavus using quantum convolutional neural network
    MS Sannidhan, JE Martis, RS Nayak, SK Aithal, KB Sudeepa
    International Journal of E-Health and Medical Communications (IJEHMC) 14 (1 … , 2023
    2023
    Citations: 68
  • Brain tumor detection and classification using machine learning: a comprehensive survey
    J Amin, M Sharif, A Haldorai, M Yasmin, RS Nayak
    Complex & intelligent systems 8 (4), 3161-3183 , 2022
    2022
    Citations: 582
  • Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization
    A Shahzad, M Raza, JH Shah, M Sharif, RS Nayak
    Complex & Intelligent Systems 8 (4), 3143-3159 , 2022
    2022
    Citations: 54
  • 3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks
    J Amin, M Sharif, E Gul, RS Nayak
    Complex & Intelligent Systems 8 (4), 3041-3057 , 2022
    2022
    Citations: 30
  • Brain tumor detection and classification using machine learning: a comprehensive survey. Complex Intell Syst 8: 3161–3183
    J Amin, M Sharif, A Haldorai, M Yasmin, RS Nayak
    2022
    Citations: 7
  • Brain tumor detection and classification using machine learning: a comprehensive survey. Complex & intelligent systems, 8 (4), 3161-3183
    J Amin, M Sharif, A Haldorai, M Yasmin, RS Nayak
    2022
    Citations: 3
  • Brain tumor detection and classification using machine learning: a comprehensive survey. Complex Intell Syst 8 (4): 3161–3183
    J Amin, M Sharif, A Haldorai, M Yasmin, RS Nayak
    2022
    Citations: 11
  • Electroencephalogram for epileptic seizure detection using stacked bidirectional LSTM_GAP neural network
    DK Thara, BG Premasudha, RS Nayak, TV Murthy, G Ananth Prabhu, ...
    Evolutionary Intelligence 14 (2), 823-833 , 2021
    2021
    Citations: 25
  • A novel nonintrusive decision support approach for heart rate measurement
    SL Fernandes, VP Gurupur, NR Sunder, N Arunkumar, S Kadry
    Pattern Recognition Letters 139, 148-156 , 2020
    2020
    Citations: 153
  • Enhanced vascular and osseous information fusion: disagreement of quantitative and qualitative analysis
    A Dogra, B Goyal, S Agrawal, UJ Tanik, S Kumar, RS Nayak
    Neural Computing and Applications 32 (20), 15885-15895 , 2020
    2020
    Citations: 2
  • Deep neural network assisted diagnosis of time-frequency transformed electromyograms
    A Bakiya, K Kamalanand, V Rajinikanth, RS Nayak, S Kadry
    Multimedia Tools and Applications 79 (15), 11051-11067 , 2020
    2020
    Citations: 63
  • Developed Newton-Raphson based deep features selection framework for skin lesion recognition
    MA Khan, M Sharif, T Akram, SAC Bukhari, RS Nayak
    Pattern Recognition Letters 129, 293-303 , 2020
    2020
    Citations: 167
  • Stomach deformities recognition using rank-based deep features selection
    MA Khan, M Sharif, T Akram, M Yasmin, RS Nayak
    Journal of medical systems 43 (12), 329 , 2019
    2019
    Citations: 71

MOST CITED SCHOLAR PUBLICATIONS

  • Brain tumor detection and classification using machine learning: a comprehensive survey
    J Amin, M Sharif, A Haldorai, M Yasmin, RS Nayak
    Complex & intelligent systems 8 (4), 3161-3183 , 2022
    2022.0
    Citations: 582
  • Developed Newton-Raphson based deep features selection framework for skin lesion recognition
    MA Khan, M Sharif, T Akram, SAC Bukhari, RS Nayak
    Pattern Recognition Letters 129, 293-303 , 2020
    2020.0
    Citations: 167
  • A novel nonintrusive decision support approach for heart rate measurement
    SL Fernandes, VP Gurupur, NR Sunder, N Arunkumar, S Kadry
    Pattern Recognition Letters 139, 148-156 , 2020
    2020.0
    Citations: 153
  • Segmentation and analysis of brain tumor using Tsallis entropy and regularised level set
    V Rajinikanth, SL Fernandes, B Bhushan, Harisha, NR Sunder
    Proceedings of 2nd International Conference on Micro-Electronics … , 2017
    2017.0
    Citations: 106
  • Stomach deformities recognition using rank-based deep features selection
    MA Khan, M Sharif, T Akram, M Yasmin, RS Nayak
    Journal of medical systems 43 (12), 329 , 2019
    2019.0
    Citations: 71
  • Detection of antibiotic constituent in aspergillus flavus using quantum convolutional neural network
    MS Sannidhan, JE Martis, RS Nayak, SK Aithal, KB Sudeepa
    International Journal of E-Health and Medical Communications (IJEHMC) 14 (1 … , 2023
    2023.0
    Citations: 68
  • Deep neural network assisted diagnosis of time-frequency transformed electromyograms
    A Bakiya, K Kamalanand, V Rajinikanth, RS Nayak, S Kadry
    Multimedia Tools and Applications 79 (15), 11051-11067 , 2020
    2020.0
    Citations: 63
  • Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization
    A Shahzad, M Raza, JH Shah, M Sharif, RS Nayak
    Complex & Intelligent Systems 8 (4), 3143-3159 , 2022
    2022.0
    Citations: 54
  • Report of the enquiry committee on grid disturbance in Northern Region on 30th july 2012 and in Northern, Eastern & North-Eastern Region on 31st July 2012
    AS Bakshi, A Velayutham, SC Srivastava, K Agrawal, R Nayak, S Soonee, ...
    New Delhi, India 3 , 2012
    2012.0
    Citations: 50
  • 3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks
    J Amin, M Sharif, E Gul, RS Nayak
    Complex & Intelligent Systems 8 (4), 3041-3057 , 2022
    2022.0
    Citations: 30
  • Electroencephalogram for epileptic seizure detection using stacked bidirectional LSTM_GAP neural network
    DK Thara, BG Premasudha, RS Nayak, TV Murthy, G Ananth Prabhu, ...
    Evolutionary Intelligence 14 (2), 823-833 , 2021
    2021.0
    Citations: 25
  • Brain tumor detection and classification using machine learning: a comprehensive survey. Complex Intell Syst 8 (4): 3161–3183
    J Amin, M Sharif, A Haldorai, M Yasmin, RS Nayak
    2022.0
    Citations: 11
  • Brain tumor detection and classification using machine learning: a comprehensive survey. Complex Intell. Syst.(2021)
    J Amin, M Sharif, A Haldorai, M Yasmin, RS Nayak
    Citations: 9
  • Brain tumor detection and classification using machine learning: a comprehensive survey. Complex Intell Syst 8: 3161–3183
    J Amin, M Sharif, A Haldorai, M Yasmin, RS Nayak
    2022.0
    Citations: 7
  • Brain tumor detection and classification using machine learning: a comprehensive survey. Complex & Intelligent Systems. 2021
    J Amin, M Sharif, A Haldorai, M Yasmin, RS Nayak
    Citations: 7
  • A study on IoT enabled smart store
    RS Nayak, SN Pai, A Nayak, AN Simha
    Iioab Journal 7 (2), 61-67 , 2016
    2016.0
    Citations: 5
  • Brain tumor detection and classification using machine learning: a comprehensive survey. Complex & intelligent systems, 8 (4), 3161-3183
    J Amin, M Sharif, A Haldorai, M Yasmin, RS Nayak
    2022.0
    Citations: 3
  • Intrusion detection system inside grid computing environment (IDS-IGCE)
    BB Kodada, R Nayak, R Prabhu, D Suresha
    International Journal of Grid Computing & Applications 2 (4), 27 , 2011
    2011.0
    Citations: 3
  • Pathogenic interaction and molecular characterization of Meloidogyne enterolobii infecting Lantana camara: A new host association from India
    R Chandana, R Nayak, CP Manjula, TR Kavitha, D Chethan, S Rawat, ...
    Physiological and Molecular Plant Pathology, 102852 , 2025
    2025.0
    Citations: 2
  • Enhanced vascular and osseous information fusion: disagreement of quantitative and qualitative analysis
    A Dogra, B Goyal, S Agrawal, UJ Tanik, S Kumar, RS Nayak
    Neural Computing and Applications 32 (20), 15885-15895 , 2020
    2020.0
    Citations: 2