Kanagaraj Narayanasamy

@jjcoll.in

Assistant Professor
J.J. College of Arts and Science Autonomous Sivapuram Pudukkottai Tamil Nadu

Kanagaraj Narayanasamy


               Download Compact PDF  
https://researchid.co/kanagaraj.n.in

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Signal Processing
10

Scopus Publications

146

Scholar Citations

5

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • Efficient Architecture Integrating Wav2Vec 2.0 and Transformer Decoding for Low-Resource Environments
    Kanagaraj Narayanasamy, Sathish Kumar L, Saravanan D, M. Maragatharajan
    3rd International Conference on Electronics and Renewable Systems Icears 2025 Proceedings, 2025
    This article introduces a cutting-edge Automatic Speech Recognition (ASR) architecture that combines a Transformer-based decoder optimised with weak supervision with Wav2Vec 2.0 for self-supervised feature extraction. By using high-level representations of raw audio and optimising with poorly tagged data, the architecture overcomes the difficulties posed by a lack of annotated datasets. Improved transcription accuracy is demonstrated by the model's notable decrease in Word Error Rate (WER), which goes from 15.6% with baseline RNN models to 7.2%. Furthermore, the system's computational efficiency is demonstrated by the 25% reduction in training time. The outcomes demonstrate that the suggested framework is a reliable option for ASR tasks in low-resource environments, attaining a fair compromise between computational cost, data needs, and performance.
  • Efficient Deer Hunting Optimization Algorithm Based Spectrum Sensing Approach for 6G Communication Networks
    R. Pandi Selvam, Kanagaraj Narayanasamy, M. Ilayaraja
    AI Enabled 6g Networks and Applications, 2023
    Next-generation sixth generation (6G) networks have the ability to fulfil the high client side requirements. The transitions from 5G to 6G networks take place as the 6G network is anticipated to integrate terrestrial, aerial, and maritime communication into a robust network. It enables the user to accomplish a fast network with maximum reliability. But the restricted accessibility of the spectrum is a major issue in improving the user experience. So, recent technologies such as cognitive radios (CRs) and cooperative spectrum sensing are essential in the design of upcoming networks. Optimum utilization and organization of the accessible spectrum are important to enhance network efficiency. Therefore, this paper presents a novel efficient deer hunting optimization algorithm based spectrum sensing approach (EDHO-SSA) for 6G communication networks. The presented EDHO-SSA technique mainly intends to manage the availability of spectrums that exist in the 6G networks. The EDHO-SSA technique is based on the hunting nature of the deers. It also derives an objective function to define the performance of SS including distinct parameters such as energy and throughput. The experimental result analysis of the EDHO-SSA technique is carried out, and the results are assessed with respect to various measures. The experimental results reported the improved outcomes of the EDHO-SSA technique over the other techniques.
  • CNN-based Deep Learning Approach for MRI-based Brain Tumor Detection
    Kanagaraj Narayanasamy, Elangovan, Sathish Kumar L, M Maragatharajan, D. Deepa
    Proceedings of the 4th International Conference on Smart Electronics and Communication Icosec 2023, 2023
    Conventional brain tumor prediction method is a time-consuming and inaccurate process that involves manually evaluating a large number of MRI (magnetic resonance imaging) scan pictures in search of signs of brain cancer. This might have an effect on the patient's medical care. It may take a long time to analyse a large volume of scan data sets comprising brain pictures. Normal brain tissue and brain cancer tissue seem quite similar under the microscope, making it difficult to distinguish between the two during segmentation. A very accurate automated method for detecting cancer is crucial. This research study has developed a novel method that uses an algorithm to analyse 2D MRI human brain scans and detect brain Tumors. Combining conventional classification algorithms with deep learning methods, the approach makes use of a convolutional neural network (CNN). The model was successfully trained using a dataset of magnetic resonance imaging (MRI) scan images, including Tumors of varied sizes, locations, and intensities. Here, a SVM classifier (Support Vector Machine) and several activation methods are also used to check the results, including Softmax, RMSProp, and sigmoid. Our research showed that a CNN could attain an accuracy of 99.87%, which is higher than the present state-of-the-art findings. The CNN -based approach is meant to aid doctors in the precise identification of brain cancers in MRI scans. This innovation might greatly accelerate the pace at which patients are helped.
  • Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification
    Janjhyam Venkata Naga Ramesh, T. Abirami, T. Gopalakrishnan, Kanagaraj Narayanasamy, Mohamad Khairi Ishak, Faten Khalid Karim, Samih M. Mostafa, Alaa Allakany
    IEEE Access, 2023
    Medical image analysis for pancreatic cancer (PC) classification and recognition is a vital domain of research and medical practices. PC is challenging to diagnose and treat; medical imaging approaches aid early diagnosis to analyse and treat, and employ of medical imaging approaches are support early diagnosis, correct analysis, and treatment planning. Computed Tomography (CT) scans are generally utilized to detect and classify PCs. Deep learning (DL) approaches have demonstrated the ability to support the diagnosis and detection of several medical conditions, containing PC. Convolutional Neural Networks (CNNs) are a kind of DL approach generally employed for image analysis that is trained to automatically learn and extract features in medical images. So, this study purposes a new Sparrow Search Algorithm with Stacked Deep Learning based Medical Image Analysis for Pancreatic Cancer Detection and Classification (SSASDL-PCDC) technique on CT images. The purpose of the study is to design an SSASDL-PCDC technique to achieve improved pancreatic cancer detection performance. In addition, the SSASDL-PCDC technique applies Harris Hawks Optimization (HHO) with a densely connected networks (DenseNet) model for the feature extraction process. Moreover, convolutional neural network with bi-directional long short-term memory (CNN-BiLSTM) approach was utilized for PC detection and classification. Furthermore, Sparrow Search Algorithm (SSA) is used to adjust the hyperparameter values of the CNN-BiLSTM technique. To evaluate the effectiveness of the SSASDL-PCDC technique, extensive experiments were executed on a comprehensive database of pancreatic CT images. The simulation outcome value depicted that the SSASDL-PCDC technique with maximum sensitivity of 99.26%, specificity of 99.26%, and accuracy of 99.26%.
  • Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model
    Thavavel Vaiyapuri, Akshya Jothi, Kanagaraj Narayanasamy, Kartheeban Kamatchi, Seifedine Kadry, Jungeun Kim
    Cancers, 2022
    Osteosarcoma is one of the aggressive bone tumors with numerous histologic patterns. Histopathological inspection is a crucial criterion in the medical diagnosis of Osteosarcoma. Due to the advancement of computing power and hardware technology, pathological image analysis system based on artificial intelligence (AI) were more commonly used. But classifying many intricate pathology images by hand will be challenging for pathologists. The lack of labeling data makes the system difficult to build and costly. This article designs a Honey Badger Optimization with Deep Learning based Automated Osteosarcoma Classification (HBODL-AOC) model. The HBODL-AOC technique’s goal is to identify osteosarcoma’s existence using medical images. In the presented HBODL-AOC technique, image preprocessing is initially performed by contrast enhancement technique. For feature extraction, the HBODL-AOC technique employs a deep convolutional neural network-based Mobile networks (MobileNet) model with an Adam optimizer for hyperparameter tuning. Finally, the adaptive neuro-fuzzy inference system (ANFIS) approach is implemented for the HBO (Honey Badger Optimization) algorithm can tune osteosarcoma classification and the membership function (MF). To demonstrate the enhanced classification performance of the HBODL-AOC approach, a sequence of simulations was performed. The extensive simulation analysis portrayed the improved performance of the HBODL-AOC technique over existing DL models.
  • A Novel Direction-Based Routing Approach for Underwater Wireless Sensor Networks
    S. Alagu, K. Santhi, K. Thinakaran, Kanagaraj Narayanasamy, V Pandimurugan, Sathish L Kumar
    Proceedings IEEE International Conference on Advances in Computing Communication and Applied Informatics Accai 2022, 2022
    Submarine Wireless Sensor Networks (WSNs) or Under Water Sensor Networks (UWSNs) can exploit natural undersea infrastructure and collect science in watery conditions. Features of underwater MSN (Mobile Sensor Networks) which is the bandwidth of low connectivity, delayed propagation, moving node, and the maximum likelihood of fault vary greatly from terrestrial WSN. In mobile under WSN, therefore, communication systems that are both energy-efficient and cost-effective are critical. This article provides a routing strategy for resolving these issues in upcoming WSNs, in which a fuzzy logic inference system is applied to determine which sensors should route packages to their target. The protocol proposed in the analysis for mobile UWSNs is compared to a relevant routing system. The testing findings confirm that the suggested work is successful and feasible.
  • Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment
    Ashit Kumar Dutta, Jenyfal Sampson, Sultan Ahmad, T. Avudaiappan, Kanagaraj Narayanasamy, Irina V. Pustokhina, Denis A. Pustokhin
    Computers Materials and Continua, 2022
    Intelligent Transportation Systems (ITS) have become a vital part in improving human lives and modern economy. It aims at enhancing road safety and environmental quality. There is a tremendous increase observed in the number of vehicles in recent years, owing to increasing population. Each vehicle has its own individual emission rate; however, the issue arises when the emission rate crosses a standard value. Owing to the technological advances made in Artificial Intelligence (AI) techniques, it is easy to leverage it to develop prediction approaches so as to monitor and control air pollution. The current research paper presents Oppositional Shark Shell Optimization with Hybrid Deep Learning Model for Air Pollution Monitoring (OSSO-HDLAPM) in ITS environment. The proposed OSSO-HDLAPM technique includes a set of sensors embedded in vehicles to measure the level of pollutants. In addition, hybridized Convolution Neural Network with Long Short-Term Memory (HCNN-LSTM) model is used to predict pollutant level based on the data attained earlier by the sensors. In HCNN-LSTM model, the hyperparameters are selected and optimized using OSSO algorithm. In order to validate the performance of the proposed OSSO-HDLAPM technique, a series of experiments was conducted and the obtained results showcase the superior performance of OSSO-HDLAPM technique under different evaluation parameters.
  • AlexNet approach for early stage Alzheimer's disease detection from MRI brain images
    L. Sathish Kumar, S. Hariharasitaraman, Kanagaraj Narayanasamy, K. Thinakaran, J. Mahalakshmi, V. Pandimurugan
    Materials Today Proceedings, 2021
  • Symmetric cryptographic framework for network security
    , Kanagaraj Narayanasamy, Padmapriya Arumugam, and
    International Journal of Innovative Technology and Exploring Engineering, 2019
    In this rapidly developing digital environment, a single cryptographic algorithm becomes inefficient and incapable to hold the secrecy of data. A symmetric cryptographic framework is proposed which provides the platform for using the existing and future algorithms in a secured manner. In this research paper, totally six algorithms have been taken into the framework, two algorithms for text, three algorithms for image, and one algorithm for video. The algorithms are grouped into the proposed symmetric encryption framework which provides better network security for the adopted environment. Cryptanalysis and brute force attack have been done to assess the strength of the algorithms incorporated in the framework. Character repetition frequency and brute force attack are analyzed for text encryption algorithms. Mean values, Entropy measure, Differential attack and brute force attack are analyzed and used to assess the reliability of the image and video encryption algorithms. The framework is designed in such a way to adopt the existing and future algorithms. The proposed framework provides a bridge to achieve quality, upgradability, maintainability, and longer usability in applied applications..
  • i-TSS: An image encryption algorithm based on transposition, shuffling and substitution using randomly generated bitmap image
    Kanagaraj Narayanasamy, Padmapriya Arumugam
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2016

RECENT SCHOLAR PUBLICATIONS

  • Efficient Architecture Integrating Wav2Vec 2.0 and Transformer Decoding for Low-Resource Environments
    K Narayanasamy, D Saravanan, M Maragatharajan
    2025 International Conference on Electronics and Renewable Systems (ICEARS … , 2025
    2025.0
  • Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification
    JVN Ramesh, T Abirami, T Gopalakrishnan, K Narayanasamy, MK Ishak, ...
    IEEE Access 11, 111927-111935 , 2023
    2023.0
    Citations: 22
  • CNN-based Deep Learning Approach for MRI-based Brain Tumor Detection
    K Narayanasamy, M Maragatharajan, D Deepa
    2023 4th International Conference on Smart Electronics and Communication … , 2023
    2023.0
    Citations: 1
  • Efficient Deer Hunting Optimization Algorithm Based Spectrum Sensing Approach for 6G Communication Networks
    R Pandi Selvam, K Narayanasamy, M Ilayaraja
    AI‐Enabled 6G Networks and Applications, 111-129 , 2023
    2023.0
    Citations: 11
  • Design of a honey badger optimization algorithm with a deep transfer learning-based osteosarcoma classification model
    T Vaiyapuri, A Jothi, K Narayanasamy, K Kamatchi, S Kadry, J Kim
    Cancers 14 (24), 6066 , 2022
    2022.0
    Citations: 24
  • Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment.
    AK Dutta, J Sampson, S Ahmad, T Avudaiappan, K Narayanasamy, ...
    Computers, Materials & Continua 72 (1) , 2022
    2022.0
    Citations: 13
  • A Novel Direction-Based Routing Approach for Underwater Wireless Sensor Networks
    S Alagu, K Santhi, K Thinakaran, K Narayanasamy, V Pandimurugan, ...
    2022 International Conference on Advances in Computing, Communication and … , 2022
    2022.0
    Citations: 1
  • AlexNet approach for early stage Alzheimer’s disease detection from MRI brain images
    LS Kumar, S Hariharasitaraman, K Narayanasamy, K Thinakaran, ...
    Materials Today: Proceedings 51, 58-65 , 2022
    2022.0
    Citations: 69
  • Efficient road side framework placement using VANET for reducing network delays
    DV Babu, LS Kumar, PS Priyadarsini, K Narayanasamy, V Pandimurugan
    Annals of the Romanian Society for Cell Biology 25 (5), 706-720 , 2021
    2021.0
    Citations: 1
  • INTELLIGENT AND EFFECTIVE CROP YIELD PREDICTION USING MACHINE LEARNING TECHNIQUES FOR CLOUD-BASED BIG DATA ANALYTICS
    K Narayanasamy, RP Selvam, PP Selvi, K Selvan, M Ilayaraja
    Turkish Journal of Physiotherapy and Rehabilitation 32, 3 , 2021
    2021.0
    Citations: 1
  • dAN EFFICIENT DSDV ROUTING IN MOBILE NETWORKS THROUGH SYMMETRIC CRYPTOGRAPHIC METHOD
    C Anusha, EL Lydia, T Pavani, CU Kumari, M Ilayaraja, K Narayanasamy
    Journal of Critical Reviews 7 (10), 1883-1891 , 2020
    2020.0
    Citations: 1
  • DETECTION OF AGRO BASED PRODUCTS QUALITY USING CONVOLUTIONAL NEURAL NETWORKS
    BV Rani, AM Rao, EL Lydia, CU Kumari, K Narayanasamy, M Ilayaraja
    Journal of Critical Reviews 7 (10), 1788-1795 , 2020
    2020.0
  • Determining Fake Reviews using Comparative Opinion Mining
    S Shajahan, K Narayanasamy
    International Journal of Pure and Applied Mathematics 118 (7), 681-684 , 2018
    2018.0
  • Distributed Computing and Internet Technology: 12th International Conference, ICDCIT 2016, Bhubaneswar, India, January 15-18, 2016, Proceedings
    N Bjorner, S Prasad, L Parida
    Springer , 2016
    2016.0
    Citations: 2
  • i-Tee–An Image Encryption Algorithm Based on Multilevel Encryption Using a Randomly Generated Bitmap Image
    K Narayanasamy, P Arumugam
    Australian Journal of Basic and Applied Sciences 10 (2), 150-155 , 2016
    2016.0
  • i -TSS: An Image Encryption Algorithm Based on Transposition, Shuffling and Substitution Using Randomly Generated Bitmap Image
    K Narayanasamy, P Arumugam
    International Conference on Distributed Computing and Internet Technology … , 2015
    2015.0
  • AUTOMATED MACHINE LEARNING BASED AERIAL IMAGE CLASSIFICATION USING UNMANNED AERIAL VEHICLES
    N Gnanasankaran, K Narayanasamy, AW Nasir, S Manochitra, ...
    Turkish Journal of Physiotherapy and Rehabilitation 32, 3 , 0
  • Symmetric Cryptographic Framework for Network Security
    K Narayanasamy, P Arumugam

MOST CITED SCHOLAR PUBLICATIONS

  • AlexNet approach for early stage Alzheimer’s disease detection from MRI brain images
    LS Kumar, S Hariharasitaraman, K Narayanasamy, K Thinakaran, ...
    Materials Today: Proceedings 51, 58-65 , 2022
    2022.0
    Citations: 69
  • Design of a honey badger optimization algorithm with a deep transfer learning-based osteosarcoma classification model
    T Vaiyapuri, A Jothi, K Narayanasamy, K Kamatchi, S Kadry, J Kim
    Cancers 14 (24), 6066 , 2022
    2022.0
    Citations: 24
  • Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification
    JVN Ramesh, T Abirami, T Gopalakrishnan, K Narayanasamy, MK Ishak, ...
    IEEE Access 11, 111927-111935 , 2023
    2023.0
    Citations: 22
  • Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment.
    AK Dutta, J Sampson, S Ahmad, T Avudaiappan, K Narayanasamy, ...
    Computers, Materials & Continua 72 (1) , 2022
    2022.0
    Citations: 13
  • Efficient Deer Hunting Optimization Algorithm Based Spectrum Sensing Approach for 6G Communication Networks
    R Pandi Selvam, K Narayanasamy, M Ilayaraja
    AI‐Enabled 6G Networks and Applications, 111-129 , 2023
    2023.0
    Citations: 11
  • Distributed Computing and Internet Technology: 12th International Conference, ICDCIT 2016, Bhubaneswar, India, January 15-18, 2016, Proceedings
    N Bjorner, S Prasad, L Parida
    Springer , 2016
    2016.0
    Citations: 2
  • CNN-based Deep Learning Approach for MRI-based Brain Tumor Detection
    K Narayanasamy, M Maragatharajan, D Deepa
    2023 4th International Conference on Smart Electronics and Communication … , 2023
    2023.0
    Citations: 1
  • A Novel Direction-Based Routing Approach for Underwater Wireless Sensor Networks
    S Alagu, K Santhi, K Thinakaran, K Narayanasamy, V Pandimurugan, ...
    2022 International Conference on Advances in Computing, Communication and … , 2022
    2022.0
    Citations: 1
  • Efficient road side framework placement using VANET for reducing network delays
    DV Babu, LS Kumar, PS Priyadarsini, K Narayanasamy, V Pandimurugan
    Annals of the Romanian Society for Cell Biology 25 (5), 706-720 , 2021
    2021.0
    Citations: 1
  • INTELLIGENT AND EFFECTIVE CROP YIELD PREDICTION USING MACHINE LEARNING TECHNIQUES FOR CLOUD-BASED BIG DATA ANALYTICS
    K Narayanasamy, RP Selvam, PP Selvi, K Selvan, M Ilayaraja
    Turkish Journal of Physiotherapy and Rehabilitation 32, 3 , 2021
    2021.0
    Citations: 1
  • dAN EFFICIENT DSDV ROUTING IN MOBILE NETWORKS THROUGH SYMMETRIC CRYPTOGRAPHIC METHOD
    C Anusha, EL Lydia, T Pavani, CU Kumari, M Ilayaraja, K Narayanasamy
    Journal of Critical Reviews 7 (10), 1883-1891 , 2020
    2020.0
    Citations: 1
  • Efficient Architecture Integrating Wav2Vec 2.0 and Transformer Decoding for Low-Resource Environments
    K Narayanasamy, D Saravanan, M Maragatharajan
    2025 International Conference on Electronics and Renewable Systems (ICEARS … , 2025
    2025.0
  • DETECTION OF AGRO BASED PRODUCTS QUALITY USING CONVOLUTIONAL NEURAL NETWORKS
    BV Rani, AM Rao, EL Lydia, CU Kumari, K Narayanasamy, M Ilayaraja
    Journal of Critical Reviews 7 (10), 1788-1795 , 2020
    2020.0
  • Determining Fake Reviews using Comparative Opinion Mining
    S Shajahan, K Narayanasamy
    International Journal of Pure and Applied Mathematics 118 (7), 681-684 , 2018
    2018.0
  • i-Tee–An Image Encryption Algorithm Based on Multilevel Encryption Using a Randomly Generated Bitmap Image
    K Narayanasamy, P Arumugam
    Australian Journal of Basic and Applied Sciences 10 (2), 150-155 , 2016
    2016.0
  • i -TSS: An Image Encryption Algorithm Based on Transposition, Shuffling and Substitution Using Randomly Generated Bitmap Image
    K Narayanasamy, P Arumugam
    International Conference on Distributed Computing and Internet Technology … , 2015
    2015.0
  • AUTOMATED MACHINE LEARNING BASED AERIAL IMAGE CLASSIFICATION USING UNMANNED AERIAL VEHICLES
    N Gnanasankaran, K Narayanasamy, AW Nasir, S Manochitra, ...
    Turkish Journal of Physiotherapy and Rehabilitation 32, 3 , 0
  • Symmetric Cryptographic Framework for Network Security
    K Narayanasamy, P Arumugam