Dr N Senthamarai

@srmist.edu.in

Associate Professor, Engineering & Technology
SRM Institute of Science & Technology

Dr N Senthamarai


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https://researchid.co/senthamn

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Networks and Communications, Computer Science, Artificial Intelligence, Information Systems
23

Scopus Publications

49

Scholar Citations

3

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Attention U-Net model and Vision transformer-based for segmenting and classifying kidney disease
    Dyagala Naga Sudha, N. Senthamarai
    Biomedical Signal Processing and Control, 2026
  • Quick Commerce and Consumer Behavior: Enhancing Augmented Customer Experiences
    Nivas Kumar, P. Bagirathi, Senthamarai Natarajan
    Revolutionizing Quick Commerce with AI Tools and Technologies, 2026
    This paper explores the relationship between quick commerce (Q-commerce) and consumer behavior to examine how enhanced experience design is transforming the ways individuals will decide to purchase. It examines what motivates the growth of Q-commerce (speed, convenience, and personalization) and how these characteristics influence consumers' attitudes. The paper also explores how innovations in AI, AR and VR are changing the customer experience and upending existing trends in online shopping. Additionally, the paper explores the challenges of supply chain constraints, strong data management, and privacy, as well as the prospects for moving toward eco-friendliness and digital progress. Q-commerce emerges from the evidence as a disruptive force in retail, and enterprises can use it to innovate and increase customer loyalty with highly personalized and interactive shopping experiences.
  • Cryptocurrency Price Prediction Using LSTM and ARIMA
    Anwesh Shaw, Arpit Pandey, Harshit Tripathi, N. Senthamarai
    Communications in Computer and Information Science, 2026
  • Cloud computing technologies and their importance in the textile industry
    Akhilesh Bharti, N. Senthamarai, Amit Kumar Tyagi
    Establishing AI Specific Cloud Computing Infrastructure, 2025
    With the introduction Cloud computing, it transformed the way organizations and businesses transfer, store, and manage data and applications. It reduces the need for on-site data centre and enables users to access resources on-demand from anywhere. Having many advantages, it also brings the challenge of privacy and security as all the data centres are managed by the Third party which are discussed in this paper with the brief discussion of cloud computing. This paper discusses the various models of cloud delivery and deployment, focusing on their importance in the textile manufacturing sector. Additionally, this paper introduces related concepts like fog computing, edge computing, distributed computing, and grid computing, which enhance traditional cloud computing. At last discussion is done on future research and scope of cloud computing in the field of textile and other.
  • Machine Learning Approaches for Early Diagnosis of Chronic Kidney Disease (CKD)
    Dyagala Naga Sudha, N. Senthamarai
    4th International Conference on Sentiment Analysis and Deep Learning Icsadl 2025 Proceedings, 2025
    A significant public health concern, CKD affects over 10% of the global population. To lessen the effects of the illness and enhance patient outcomes, CKD must be diagnosed and treated as soon as possible. Numerous academics are drawn to machine learning, which has been effectively used in a variety of industries, including banking, e-commerce, healthcare, and others. Predictive models for CKD have been developed recently using machine learning (ML) techniques. With a focus on the most pertinent studies released in the past ten years, this comprehensive study examines the state of the art in ML-based CKD prediction. The survey article addresses the training and testing data sets, difficulties, and constraints of the current CKD prediction models, and offers suggestions
  • Automatic Cloud Formation Using LLM
    Senthamarai N, Jeyaselvi M, Hemamalini V
    Icoicc 2025 3rd International Conference on Intelligent and Cloud Computing, 2025
    Introduces a groundbreaking IAC Code Generator for automated Terraform script creation. Empowers developers by treating infrastructure as a versioned, programmable artifact. Enhances efficiency, reduces time-to-market, ensures consistency, and promotes collaboration between development and operations teams. Features an intuitive interface, customizable templates, and integrates industry best practices for accessible and accelerated development cycles.
  • Measuring and Predicting Student’s Academic Performance Using ML
    Srijan Mohan, Gaurang Gujrati, N. Senthamarai
    Smart Innovation Systems and Technologies, 2025
  • Tornado Prediction and Tracking Using Cloud-Based Data Solutions
    Senthamarai N, Anoushka Sanjeev, Yashvardhan, Sai Prajwal Kacham
    International Conference on Innovations in Intelligent Systems Advancements in Computing Communication and Cybersecurity Isac3 2025, 2025
    Tornado prediction and tracking have been significantly enhanced by leveraging real-time data insights, which improve accuracy in forecasting and emergency response. This paper presents a novel approach to tornado prediction using cloud-based solutions powered by serverless technology for scalable and efficient meteorological data processing. The system processes critical meteorological parameters such as wind speed, atmospheric pressure, and temperature to enable accurate forecasting. Additionally, machine learning algorithms are integrated for real-time data analysis, allowing instant predictions and actionable insights. By transforming traditional weather analysis methodologies, this approach aims to minimize severe weather risks and enhance preparedness. Experimental evaluation of the proposed system demonstrated a prediction accuracy of 88%, which surpasses traditional statistical models and recent deep learning approaches. The serverless deployment architecture reduced processing latency by approximately 35% compared to conventional cloud-hosted frameworks. Furthermore, the integration of real-time visualization modules, including heatmaps and time-series risk graphs, significantly improved interpretability and supported faster decision-making during severe weather events. These outcomes validate the effectiveness of the TORNADYN algorithm and the cloud-based infrastructure in enhancing tornado forecasting capabilities.
  • Plant Leaf Disease Detection Using IoT and Deep Learning Approach
    N. Senthamarai, R. Srinivasan, M. Kavitha, R. Kavitha
    2025 International Conference on Cognitive Computing in Engineering Communications Sciences and Biomedical Health Informatics Ic3ecsbhi 2025, 2025
    Modern detecting innovations, with a specific spotlight on manageability related issues, are impacting the fate of farming as the globe turns out to be more interconnected. The majority of emerging countries' economies is based on agriculture. The agricultural industry is one of the most vital industries, and crops are a necessity for providing food to people all over the world. The agricultural sector depends heavily on early disease detection and recognition. Crop fields are susceptible to the outbreak of plant diseases, either as a result of spreading viruses and fungus or unfavorable weather conditions, due to the lack of trained labor and enough supporting infrastructure. Owners of community gardens that want to use affordable strategies for sustainable agriculture through a sustainable. This model's suggestion offers a practical means of identifying various ailments in a range of plant species. Using the recently created dataset, an improved variant of the best deep learning (DL) model, has been suggested. In this unique circumstance, think about the possible utilization of the Social Internet of Things for detecting and interchanges, deep learning for the identification of plant sicknesses, and crowd sourcing for the collection and grouping of pictures, drawing in ranchers and local area garden proprietors and specialists. Across data fusion and deep learning, the created structure can take utility of the information gathered and estimate with a certain level of accuracy when a plant would (or would not) contract a disease. The user application must be able to interface with the virtualization layer and must be able to assess the health of the plants using images, data from garden sensors, and Internet access. Take some photos of the plant to assess its health and to provide feedback on any photos of the plant that haven't yet been tagged for the classifier data set. In addition to the user's smart phone's photo, the diagnosis of a crop disease necessitates collecting all the information from the sensors owned by other users and keeping track of various environmental conditions in the garden. It consists of three steps: tuning the sampling frequency, sending a request for cultivation-related sensors, and aggregating the sensor data. Additionally, it aids in forecasting plant diseases based on previously observed environmental variables. Additionally, the model demonstrates here the effectiveness and accuracy of the deep learning model used for plant identification, as well as the benefits of utilizing Social IoT engineering. The majority of the suggested DL frameworks in the literature have decent detection performance on their own datasets, but the performance is subpar on other datasets. In reality, once data are provided for a specific plant illness, the pattern for anticipating a upcoming infection of the plant is prepared.
  • Audio Recognition System using Fourier Transform
    Senthamarai N, Mrinalini Vettri, Ishita Bhatnagar, Tajesh Nishad, Anshul Dalal
    2025 International Conference on Computing and Communication Technologies Iccct 2025, 2025
    This paper presents an audio recognition system aimed at improving robustness in noisy environments. Our approach employs a signature-based recognition methodology that utilizes minimal pre-processing through a Gaussian convolution alongside Fast Fourier Transform (FFT) to extract distinctive audio signatures. The system is designed to optimize performance in low Signal-to-Noise Ratio (SNR) conditions, demonstrating significant accuracy improvements compared to existing models. Notably, our system achieves an accuracy of 85.7% at a 50% SNR, substantially outperforming traditional methods that exhibit only 57% accuracy under similar conditions. Furthermore, the architecture exhibits linear scalability in both build initiation time and memory requirements relative to the size of audio samples, while matching times remain constant with respect to the database size.
  • A Federated Learning based Alzheimer’s Disease Prediction
    S Suchitra, N Senthamarai, M Jeyaselvi, RJ Poovaraghan
    Handbook on Federated Learning Advances Applications and Opportunities, 2024
  • Detection and Classification of Tuberculosis Disease Using Hybrid Deep Learning Method
    Eshwar Polisetty, Syed Sayeed, Obul Reddy Duttala, Senthamarai.N
    IEEE International Conference on Electronic Systems and Intelligent Computing Icesic 2024 Proceedings, 2024
  • Prediction Analysis for Diabetic Disease Using Machine Learning Model
    N. Senthamarai, Jafar Ali Ibrahim Syed Masood, M. Sathya, N. S. Kalyan Chakravarthy, Bharati Rathore, M Mohamed Kalith Oli
    Proceedings 2024 Oits International Conference on Information Technology Ocit 2024, 2024
  • Privacy preservation in wireless sensor network using energy efficient multipath routing for healthcare data
    Jafar Ali Ibrahim Syed Masood, M. Jeyaselvi, N. Senthamarai, S. Koteswari, M. Sathya, N.S. Kalyan Chakravarthy
    Measurement Sensors, 2023
  • Deep Learning Methods for Segmenting and Classifying Diabetic Retinopathy
    Dyagala Nagasudha, N. Senthamarai
    2023 1st International Conference on Advances in Electrical Electronics and Computational Intelligence Icaeeci 2023, 2023
  • Estimating the Risk of Diabetes Using Association Rule Mining Based on Clustering
    N. Senthamarai, M. Sathya, S. Jafar Ali Ibrahim, N. S. Kalyan Chakravarthy
    Image Based Computing for Food and Health Analytics Requirements Challenges Solutions and Practices Ibcfha, 2023
  • Design and Web Development of E-Voting System
    Mullapudi V Ramanatha Subrahmanya Kiran, S. Suchitra, K. Arthi, N. Senthamarai, M. Jayaselvi
    Proceedings of the 1st IEEE International Conference on Networking and Communications 2023 Icnwc 2023, 2023
  • Migration Prediction Approach for Predict the Overloaded and under Loaded Workload in Cloud Environment
    Senthamarai N
    International Journal of Computer Networks and Applications, 2022
  • Efficient resource utilization based on energy management in cloud data center
    , N. Senthamarai, M. Vijayalakshmi, , B. A. Vishnupriya, and
    International Journal of Engineering and Advanced Technology, 2019
  • Multicast utility maximization with load balancing in private cloud environment
    N Senthamarai, M Vijayalakshmi
    International Journal of Engineering and Technology Uae, 2018
  • Efficient NN spatial keyword search using Spatial Inverted (SI) index
    B.A. Vishnupriya, N. Senthamarai, S. Bharathi
    International Journal of Engineering and Technology Uae, 2018
  • Dynamic resource allocation based on energy consumption using hyped-up voltage frequency scaling model in cloud computing
    N. Senthamarai, M. Vijayalakshmi
    Journal of Computational and Theoretical Nanoscience, 2017
  • Dynamic VM allocation using adaptive map reduce algorithm in cloud computing
    Asian Journal of Information Technology, 2016

RECENT SCHOLAR PUBLICATIONS

  • Attention U-Net model and Vision transformer-based for segmenting and classifying kidney disease
    DN Sudha, N Senthamarai
    Biomedical Signal Processing and Control 120, 109914 , 2026
    2026
  • Cryptocurrency Price Prediction Using LSTM
    A Shaw, APH Tripathi, N Senthamarai
    Deep Sciences for Computing and Communications: Third International … , 2026
    2026
  • Tornado Prediction and Tracking Using Cloud-Based Data Solutions
    N Senthamarai, A Sanjeev, SP Kacham
    2025 International Conference on Innovations in Intelligent Systems … , 2025
    2025
  • Automatic Cloud Formation Using LLM
    N Senthamarai, M Jeyaselvi, V Hemamalini
    2025 International Conference on Intelligent and Cloud Computing (ICoICC), 1-6 , 2025
    2025
    Citations: 2
  • Audio Recognition System using Fourier Transform
    N Senthamarai, M Vettri, I Bhatnagar, T Nishad, A Dalal
    2025 International Conference on Computing and Communication Technologies … , 2025
    2025
    Citations: 1
  • Machine Learning Approaches for Early Diagnosis of Chronic Kidney Disease (CKD)
    DN Sudha, N Senthamarai
    2025 4th International Conference on Sentiment Analysis and Deep Learning … , 2025
    2025
  • Plant Leaf Disease Detection Using iot and Deep Learning Approach
    N Senthamarai, R Srinivasan, M Kavitha, R Kavitha
    2025 International Conference on Cognitive Computing in Engineering … , 2025
    2025
    Citations: 3
  • Prediction Analysis for Diabetic Disease Using Machine Learning Model
    N Senthamarai, JAIS Masood, M Sathya, NSK Chakravarthy, B Rathore, ...
    2024 OITS International Conference on Information Technology (OCIT), 240-245 , 2024
    2024
  • Cryptocurrency Price Prediction Using LSTM and ARIMA
    A Shaw, A Pandey, H Tripathi, N Senthamarai
    International Conference on Deep Sciences for Computing and Communications … , 2024
    2024
  • 12 Chapter A Federated Learning based
    S Suchitra, N Senthamarai, M Jeyaselvi
    Handbook on Federated Learning: Advances, Applications and Opportunities, 264 , 2023
    2023
  • A federated learning based alzheimer's disease prediction
    S Suchitra, N Senthamarai, M Jeyaselvi, RJ Poovaraghan
    Handbook on Federated Learning, 264-282 , 2023
    2023
    Citations: 2
  • Deep learning methods for segmenting and classifying diabetic retinopathy
    D Nagasudha, N Senthamarai
    2023 First International Conference on Advances in Electrical, Electronics … , 2023
    2023
    Citations: 5
  • Privacy preservation in wireless sensor network using energy efficient multipath routing for healthcare data
    JAIS Masood, M Jeyaselvi, N Senthamarai, S Koteswari, M Sathya, ...
    Measurement: Sensors 29, 100867 , 2023
    2023
    Citations: 26
  • Optimization of resource management for workload allocation in cloud computing
    N Senthamarai
    Proceedings of 3rd International Conference on Artificial Intelligence … , 2023
    2023
    Citations: 2
  • Design and Web Development of E-Voting System
    MVRS Kiran, S Suchitra, K Arthi, N Senthamarai, M Jayaselvi
    2023 International Conference on Networking and Communications (ICNWC), 1-5 , 2023
    2023
  • Estimating the risk of diabetes using association rule mining based on clustering
    N Senthamarai, M Sathya, SJA Ibrahim, NSK Chakravarthy
    Image Based Computing for Food and Health Analytics: Requirements … , 2023
    2023
    Citations: 4
  • Measuring and Predicting Student’s Academic Performance Using ML
    S Mohan, G Gujrati, N Senthamarai
    Congress on Smart Computing Technologies, 387-403 , 2022
    2022
  • Migration Prediction Approach for Predict the Overloaded and Under Loaded Workload in Cloud Environment
    S N
    international journal of computer networks and applications 9 (1), 51-59 , 2022
    2022
  • Migration Prediction Approach for Predict the Overloaded and Under Loaded Workload in Cloud Environment
    N Senthamarai
    International Journal of Computer Networks and Applications (IJCNA) 9 (1), 51-59 , 2022
    2022
  • Efficient Resource Utilization Based on Energy Management in Cloud Data Center
    S N
    International Journal of Engineering and Advanced Technology 8 (6), 340-344 , 2019
    2019

MOST CITED SCHOLAR PUBLICATIONS

  • Privacy preservation in wireless sensor network using energy efficient multipath routing for healthcare data
    JAIS Masood, M Jeyaselvi, N Senthamarai, S Koteswari, M Sathya, ...
    Measurement: Sensors 29, 100867 , 2023
    2023
    Citations: 26
  • Deep learning methods for segmenting and classifying diabetic retinopathy
    D Nagasudha, N Senthamarai
    2023 First International Conference on Advances in Electrical, Electronics … , 2023
    2023
    Citations: 5
  • Estimating the risk of diabetes using association rule mining based on clustering
    N Senthamarai, M Sathya, SJA Ibrahim, NSK Chakravarthy
    Image Based Computing for Food and Health Analytics: Requirements … , 2023
    2023
    Citations: 4
  • Plant Leaf Disease Detection Using iot and Deep Learning Approach
    N Senthamarai, R Srinivasan, M Kavitha, R Kavitha
    2025 International Conference on Cognitive Computing in Engineering … , 2025
    2025
    Citations: 3
  • Dynamic Resource Allocation Based on Energy Consumption Using Hyped-Up Voltage Frequency Scaling Model in Cloud Computing
    N Senthamarai, M Vijayalakshmi
    Journal of Computational and Theoretical Nanoscience 14 (4), 1837-1843 , 2017
    2017
    Citations: 3
  • Automatic Cloud Formation Using LLM
    N Senthamarai, M Jeyaselvi, V Hemamalini
    2025 International Conference on Intelligent and Cloud Computing (ICoICC), 1-6 , 2025
    2025
    Citations: 2
  • A federated learning based alzheimer's disease prediction
    S Suchitra, N Senthamarai, M Jeyaselvi, RJ Poovaraghan
    Handbook on Federated Learning, 264-282 , 2023
    2023
    Citations: 2
  • Optimization of resource management for workload allocation in cloud computing
    N Senthamarai
    Proceedings of 3rd International Conference on Artificial Intelligence … , 2023
    2023
    Citations: 2
  • Audio Recognition System using Fourier Transform
    N Senthamarai, M Vettri, I Bhatnagar, T Nishad, A Dalal
    2025 International Conference on Computing and Communication Technologies … , 2025
    2025
    Citations: 1
  • A Survey on Efficient Resource Allocation for Virtualized Energy Aware Live Migration in Cloud Computing
    L Arulmozhiselvan, N Senthamarai
    International Journal of Computer Science and Mobile Computing 5 (2), 125-131 , 2016
    2016
    Citations: 1
  • Attention U-Net model and Vision transformer-based for segmenting and classifying kidney disease
    DN Sudha, N Senthamarai
    Biomedical Signal Processing and Control 120, 109914 , 2026
    2026
  • Cryptocurrency Price Prediction Using LSTM
    A Shaw, APH Tripathi, N Senthamarai
    Deep Sciences for Computing and Communications: Third International … , 2026
    2026
  • Tornado Prediction and Tracking Using Cloud-Based Data Solutions
    N Senthamarai, A Sanjeev, SP Kacham
    2025 International Conference on Innovations in Intelligent Systems … , 2025
    2025
  • Machine Learning Approaches for Early Diagnosis of Chronic Kidney Disease (CKD)
    DN Sudha, N Senthamarai
    2025 4th International Conference on Sentiment Analysis and Deep Learning … , 2025
    2025
  • Prediction Analysis for Diabetic Disease Using Machine Learning Model
    N Senthamarai, JAIS Masood, M Sathya, NSK Chakravarthy, B Rathore, ...
    2024 OITS International Conference on Information Technology (OCIT), 240-245 , 2024
    2024
  • Cryptocurrency Price Prediction Using LSTM and ARIMA
    A Shaw, A Pandey, H Tripathi, N Senthamarai
    International Conference on Deep Sciences for Computing and Communications … , 2024
    2024
  • 12 Chapter A Federated Learning based
    S Suchitra, N Senthamarai, M Jeyaselvi
    Handbook on Federated Learning: Advances, Applications and Opportunities, 264 , 2023
    2023
  • Design and Web Development of E-Voting System
    MVRS Kiran, S Suchitra, K Arthi, N Senthamarai, M Jayaselvi
    2023 International Conference on Networking and Communications (ICNWC), 1-5 , 2023
    2023
  • Measuring and Predicting Student’s Academic Performance Using ML
    S Mohan, G Gujrati, N Senthamarai
    Congress on Smart Computing Technologies, 387-403 , 2022
    2022
  • Migration Prediction Approach for Predict the Overloaded and Under Loaded Workload in Cloud Environment
    S N
    international journal of computer networks and applications 9 (1), 51-59 , 2022
    2022