K D MOHANA SUNDARAM

@sietk.org

Assistant Professor, Department of Electronics and Communication Engineering
SIDDHARTH INSTITUTE OF ENGINEERING & TECHNOLOGY

K D MOHANA SUNDARAM


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https://researchid.co/kdmsundaram

RESEARCH, TEACHING, or OTHER INTERESTS

Engineering, Engineering, Engineering, Engineering
7

Scopus Publications

Scopus Publications

  • Lemon fruit classification by transfer learning technique: experimental investigation of convolutional neural network
    K.D. Mohana Sundaram, T. Shankar, N. Sudhakar Reddy
    International Journal of Information and Decision Sciences, 2025
  • Advancing Corporate Finance: A Multigranularity Approach to Bankruptcy Prediction
    A Suresh, Durairaj K, B Anandan, K. D. Mohana Sundaram, B. Ravi Babu, et al.
    2025 IEEE International Conference on Advanced Computing Technologies Icact 2025, 2025
  • Aero Dispense: Contactless QR Medicine Delivery
    Ratnakamala Petla, R L Krupakaran, Harathi Nimmala, K. D. Mohana Sundaram, K Elangovan, et al.
    2025 International Conference on Intelligent and Secure Engineering Solutions Cises 2025, 2025
  • MIL-BOT Sentinel: Integrating Real-Time Spying and Remote Bomb Diffusion in Battlefield Environments
    B. Ravibabu, K. D. Mohana Sundaram, S. V. Rajesh Kumar, P. M. Vijayan, S. Venkatakiran, et al.
    2025 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies Inspect 2025, 2025
  • Hybrid Filtering-Based Product Recommendation System Integrating GRU and BFGS Optimization
    A. Suresh, R. G. Kumar, D. Nagaraju, K D Mohana Sundaram, B Anandan
    IEEE International Conference on Electronic Systems and Intelligent Computing Icesic 2024 Proceedings, 2024
    It is more crucial than ever to handle crucial problems including data sparsity, cold-start problems, and the requirement for extremely precise forecasts in today’s ever changing recommendation system field. This work presents a hybrid filtering product recommendation system that integrates Gated Recurrent Units (GRU) with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization method to significantly enhance recommendation accuracy and performance. Standard techniques such content-based filtering and collaborative filtering can fail to offer reliable recommendations for large and dynamic datasets. Our hybrid model uses BFGS optimization to increase model accuracy and efficiency while utilizing GRU-based collaborative filtering to capture sequential user-item interactions. The GRU-BFGS model outperforms matrix factorization (MF) techniques in numerous areas, achieving an astounding accuracy of up to 92% on the Amazon customer review dataset. It also exhibits excellent recall, precision, and notable decreases in error metrics (MAE, MSE, and RMSE), which makes it a very successful method for providing individualized, accurate suggestions.
  • An efficient fruit quality monitoring and classification using convolutional neural network and fuzzy system
    K.D. Mohana Sundaram, T. Shankar, N. Sudhakar Reddy
    International Journal of Engineering Systems Modelling and Simulation, 2023
  • RETRACTION:A novel fuzzy pooling based modified ThinNet architecture for lemon fruit classification
    K.D. Mohana Sundaram, T. Shankar, N. Sudhakar Reddy
    Journal of Intelligent and Fuzzy Systems, 2022
    Computer vision functions like object detection, image segmentation, and image classification were recently getting advance due to Convolutional Neural Networks (CNNs). In the food and agricultural industries, image classification plays a critical role in quality control. CNNs are made up of layers that alternate between convolutional, nonlinearity, and feature pooling. In this article, we proposed Fuzzy Pooling, a novel pooling approach that works based on fuzzy logic, that can increase the accuracy of the CNNs by replacing the conventional pooling layer. This proposed Fuzzy Pooling was put to the test with CIFAR-10 and SVHN data sets on single layer CNN, and it outperformed previous pooling strategies by achieving 92% and 97% classification accuracy. This proposed Fuzzy Pooling layer was replaced the Max Pooling layer in the ThinNet architecture, and it was trained using the back propagation method. It was demonstrated experimentally on the Lemon fruit data set to classify the fruits into three categories such as Good, Medium, and Poor. In order to classify the lemon fruit into three categories, the 1000 Fully Connected layer in ThinNet architecture was replaced with three Fully Connected layers. The Modified ThinNet architecture called ThinNet_FP was trained with a learning rate of 0.001 and achieved 97% accuracy in classifying the images and outperformed previous CNN architectures when trained on the same data set.