Anandhakrishnan Anandhakrishnan

@stc.ac.in

asst professor in computer science
Sree saraswathi thyagaraja college

Anandhakrishnan Anandhakrishnan


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https://researchid.co/anandhakrishnann

EDUCATION

Ph.d cse

RESEARCH INTERESTS

imageprocessing in python
6

Scopus Publications

143

Scholar Citations

3

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • A Cloud-Based Intrusion Detection Data Security BSO-ResNet for Malicious Packet Detection for Malicious Network Congestion in 6G Networks
    V Vivekanandan, S Pandiarajan, M Yogadharani, K Prema, T Anandhakrishnan, M Kannukkiniyal
    Journal of Multiscale Modelling, 2026
    The combination of Black Swan Optimization (BSO) and Residual Networks (ResNet) forms a robust framework for detecting malicious packets within network traffic. ResNet’s deep learning architecture learns intricate patterns through residual connections that mitigate the vanishing gradient problem, enabling effective training of deep models. BSO enhances this framework by optimizing feature selection, reducing redundancy, and improving detection accuracy through lower false positive (FP) and false negative (FN) rates. The resulting system supports two complementary deployment modes: An accuracy-oriented configuration, which leverages a richer optimized feature set and deeper residual learning for security-critical environments, and a latency-oriented configuration, which uses a lightweight feature subset to achieve faster execution and reduced computational overhead for real-time congestion-attack detection in resource-constrained 6G scenarios. This dual capability makes the BSO–ResNet integration suitable for both high-assurance cloud security and ultra-low-latency edge environments.
  • Machine Learning-Enhanced Calibration Algorithm for Drift Compensation in Long-Term Electrochemical Environmental Monitoring Networks
    VJ Chakravarthy, Vanapalli Yasaswini, Mohit Sanguri, Sindhusaranya Balraj, S. Jency, T. Anandhakrishnan
    Analytical Letters, 2026
  • Development of Efficient Deep Convolutional Neural Network for Prediction of Leaf Disease Identification in Agriculture
    T. Anandhakrishnan, G. Pandiya Rajan, S. Santhi, P. Harish, V. Kavin Kumar, S. Vidhya
    2024 1st International Conference on Data Computation and Communication Icdcc 2024, 2024
    Agriculture is the real backbone of an economic growth, plays a critical part in any country's economic growth and development. In many cases, agriculture production may be reduced, thereby reducing the nation's economy, because crops may be harmed by diseases. Diseases generally affect the leaves of the crop and should be identified early on so that the quality and quantity of the produce can be increased. The Deep convolutional neural network model is being implemented on a Kaggle benchmark dataset of tomato plant leaves. Gamma correction, images flipping, noise injection, scaling, color augmentation, & rotation were the five data augmentation methods used. Our experiments with two datasets, augmented and non-augmented, and found that data augmentation can improve model performance. The proposed model's training contains batch size, training epochs and dropouts. When using validation data, the suggested model outperforms other common learning algorithms. After extensive simulation, the proposed model has achieved 97.49% classification performance. The precision of the suggested task exceeds that of typical machine learning methodologies. Consistency and dependability are also examined for the suggested model.
  • Deep Convolutional Neural Networks for image based tomato leaf disease detection
    T. Anandhakrishnan, S.M. Jaisakthi
    Sustainable Chemistry and Pharmacy, 2022
  • Identification of tomato leaf disease detection using pretrained deep convolutional neural network models
    Scalable Computing, 2020
  • Internet of things in agriculture-survey
    T Anandhakrishnan, S. M Jaisakthi, Lohotsaurabh
    Journal of Computational and Theoretical Nanoscience, 2018

RECENT SCHOLAR PUBLICATIONS

  • A Cloud-Based Intrusion Detection Data Security BSO-ResNet for Malicious Packet Detection for Malicious Network Congestion in 6G Networks
    V Vivekanandan, S Pandiarajan, M Yogadharani, K Prema, ...
    Journal of Multiscale Modelling, 2640028 , 2026
    2026
  • Machine Learning-Enhanced Calibration Algorithm for Drift Compensation in Long-Term Electrochemical Environmental Monitoring Networks
    VJ Chakravarthy, V Yasaswini, M Sanguri, S Balraj, S Jency, ...
    Analytical Letters, 1-17 , 2026
    2026
    Citations: 1
  • Self-Organizing Networks (SON) for Efficient Load Balancing Using Deep Reinforcement Learning
    M Mohana, V Lathika, VP Singh, KN Babu, S Kumar
    2025 3rd International Conference on IoT, Communication and Automation … , 2025
    2025
  • Development of Efficient Deep Convolutional Neural Network for Prediction of Leaf Disease Identification in Agriculture
    T Anandhakrishnan, GP Rajan, S Santhi, P Harish, VK Kumar, S Vidhya
    2024 First International Conference on Data, Computation and Communication … , 2024
    2024
  • Deep Convolutional Neural Networks for image based tomato leaf disease detection
    T Anandhakrishnan, SM Jaisakthi
    Sustainable Chemistry and Pharmacy 30, 100793 , 2022
    2022
    Citations: 110
  • Identification of tomato leaf disease detection using pretrained deep convolutional neural network models
    T Anandhakrishnan, JSM Murugaiyan
    Scalable Computing: Practice and Experience 21 (4), 625-635 , 2020
    2020
    Citations: 24
  • Internet of Things in Agriculture-Survey
    T Anandhakrishnan, SM Jaisakthi
    Journal of Computational and Theoretical Nanoscience 15 (6-7), 2405-2409 , 2018
    2018
    Citations: 8

MOST CITED SCHOLAR PUBLICATIONS

  • Deep Convolutional Neural Networks for image based tomato leaf disease detection
    T Anandhakrishnan, SM Jaisakthi
    Sustainable Chemistry and Pharmacy 30, 100793 , 2022
    2022
    Citations: 110
  • Identification of tomato leaf disease detection using pretrained deep convolutional neural network models
    T Anandhakrishnan, JSM Murugaiyan
    Scalable Computing: Practice and Experience 21 (4), 625-635 , 2020
    2020
    Citations: 24
  • Internet of Things in Agriculture-Survey
    T Anandhakrishnan, SM Jaisakthi
    Journal of Computational and Theoretical Nanoscience 15 (6-7), 2405-2409 , 2018
    2018
    Citations: 8
  • Machine Learning-Enhanced Calibration Algorithm for Drift Compensation in Long-Term Electrochemical Environmental Monitoring Networks
    VJ Chakravarthy, V Yasaswini, M Sanguri, S Balraj, S Jency, ...
    Analytical Letters, 1-17 , 2026
    2026
    Citations: 1
  • A Cloud-Based Intrusion Detection Data Security BSO-ResNet for Malicious Packet Detection for Malicious Network Congestion in 6G Networks
    V Vivekanandan, S Pandiarajan, M Yogadharani, K Prema, ...
    Journal of Multiscale Modelling, 2640028 , 2026
    2026
  • Self-Organizing Networks (SON) for Efficient Load Balancing Using Deep Reinforcement Learning
    M Mohana, V Lathika, VP Singh, KN Babu, S Kumar
    2025 3rd International Conference on IoT, Communication and Automation … , 2025
    2025
  • Development of Efficient Deep Convolutional Neural Network for Prediction of Leaf Disease Identification in Agriculture
    T Anandhakrishnan, GP Rajan, S Santhi, P Harish, VK Kumar, S Vidhya
    2024 First International Conference on Data, Computation and Communication … , 2024
    2024