Guhan

@acetcbe.edu.in

Associate Professor., Department of Computer Science and Engineering
akshaya college of engineering and technology

Guhan


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https://researchid.co/guhan

EDUCATION

M.E.,Ph.D

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science Applications, Artificial Intelligence, Computer Graphics and Computer-Aided Design
23

Scopus Publications

Scopus Publications

  • Weather Forecasting Using Machine Learning
    Guhan Thangavelu, Gowtham Ponnusamy, Keerthick Ravikumar, Mohamed Ashik Bajurulla, Mukhesh Ganesan
    Aip Conference Proceedings, 2025
    : A lot of businesses, especially those in the agriculture sector, depend a lot on certain weather patterns to run their operations. But the effects of climate change make earlier climate models outdated, so weather forecasts must constantly be improved. The ramifications of imprecise forecasts surpass their impact on enterprises; they also have an effect on individuals’ livelihoods and the country’s economy. By improving the forecasting of the weather, this project seeks to address these problems, with an emphasis on delivering trustworthy forecasts for remote locations. The strategy makes use of machine learning and data analysis methods, like using random forest classification to forecast weather
  • Automatic Diabetic Retinopathy Detection Using Vision Transformer
    Guhan Thangavelu, Dasanandini Soundararajan, Nishanthini Manikandan, Subash Reddy Murugapperumal, Vijay Baskar Vadivel
    Aip Conference Proceedings, 2025
  • IoT-Enabled Personalized Fitness Solutions for Home Workouts using Reinforcement Learning
    M. Pandi, T. Guhan, T Sivakumar, Aswathy R H
    Proceedings of the 7th International Conference on Intelligent Sustainable Systems Iciss 2025, 2025
    This research examines the integration of Reinforcement Learning (RL) and Internet of Things (IoT) technologies to create intelligent home exercise systems, addressing the increasing need for a virtual workout system. The technique employs RL algorithms to personalize exercises for each person and adapt them dynamically to facilitate achieving fitness goals. It employs IoT devices such as smart workout monitors and portable sensors for tracking human behavior, fingerprints, and ambient variables in real time. To attain optimum fitness outcomes, the proposed smart workout solution employs an automated feedback process through which RL techniques continuously track user interactions and adjust exercise parameters. It can be implemented by people of diverse fitness abilities, ensuring that anyone can discover beneficial workouts. The integration of IoT facilitates uninterrupted connectivity throughout devices, enhancing data transfer and the entire human interface. The device's primary features include continuous performance monitoring, developing individualized training regimens, and the automated adjustment of activity ranges. It examines the safety and protection challenges of collecting and processing private healthcare information in an integrated fitness environment. It aims to enhance digital workouts using RL and IoT adaptive and interactive personal training that transcends traditional static programs. Experimental findings indicate a 15% enhancement in exercise efficiency, a 20% rise in user engagement, and a 12% decrease in fatigue with implementing the proposed IoT-enabled RL fitness model.
  • Urinary Bladder Cancer Detection using U-NET and SVM Algorithm
    16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
  • Classification of Knee Osteoarthritis Using R-CNN Algorithm
    T. Guhan, RM. Abiraj, M. Muneshwaran, V. Sowndharya Lakshmi, M. Suriyanarayanan
    Proceedings of the International Conference on Multi Agent Systems for Collaborative Intelligence Icmsci 2025, 2025
    Age-related factors affecting the knee joints are the main cause of knee osteoarthritis (OA), a chronic disorder. It takes a lot of time to make an accurate diagnosis, which is usually done by medical specialists utilizing X-ray imaging. This study explores the use of explainable artificial intelligence (XAI) to improve the interpretability of deep learning (DL) models, specifically convolutional neural networks (R-CNNs), in order to automate the diagnosis of knee OA. To increase interpretability, the research employs a divide-and-conquer technique that moves from multi-class to binary classification using state-of-the-art pre-trained DL models to categorize instances of knee OA. Using Kellgren-Lawrence (KL) graded X-ray images, five refined DL models are assessed. Gradient-weighted Class Activation Mapping (GradCAM) is used for interpretability. According to the results, EfficientNetb7 can differentiate between normal and severe cases with a classification accuracy of above 90%. Its performance drops to 67% for other classes, though, highlighting how challenging it is to match medical professionals' diagnostic accuracy.
  • AN IMPLEMENTATION OF ENHANCED INCEPTION-RESIDUAL CONVOLUTIONAL NEURAL NETWORK IN LUNG CANCER PREDICTION
    Journal of Theoretical and Applied Information Technology, 2025
  • EMLARDE tree: ensemble machine learning based random de-correlated extra decision tree for the forest cover type prediction
    T. Guhan, N. Revathy
    Signal Image and Video Processing, 2024
  • Financial and economic analysis on serverless computing sytem services
    T. Guhan, G. Chandra Sekhar, N. Revathy, K. Baranidharan, H. Mickle Aancy
    Essential Information Systems Service Management, 2024
    In this chapter, the economic implications of serverless computing for enhancing human living experiences are exemplified. The infrastructure expenses, pay-per-use pricing models, and operational overhead have been elaborated. The cost efficiencies and allocation of resources towards innovation and growth initiatives have been achieved by eliminating the need for provisioning, managing, and scaling servers. The impact of serverless computing on business operations, market dynamics, and industry competitiveness is economically analyzed. However, the challenges (vendor lock-in, security concerns, and performance optimization complexities) have been considered.
  • RETRACTION:Long-term and short-term rainfall forecasting using deep neural network optimized with flamingo search optimization algorithm
    S. Vidya, Veeraraghavan Jagannathan, T. Guhan, Jogendra Kumar
    Journal of Intelligent and Fuzzy Systems, 2024
    Rainfall forecasting is essential because heavy and irregular rainfall creates many impacts like destruction of crops and farms. Here, the occurrence of rainfall is highly related to atmospheric parameters. Thus, a better forecasting model is essential for an early warning that can minimize risks and manage the agricultural farms in a better way. In this manuscript, Deep Neural Network (DNN) optimized with Flamingo Search Optimization Algorithm (FSOA) is proposed for Long-term and Short-term Rainfall forecasting. Here, the rainfall data is obtained from the standard dataset as Sudheerachary India Rainfall Analysis (IRA). Moreover, the Morphological filtering and Extended Empirical wavelet transformation (MFEEWT) approach is utilized for pre-processing process. Also, the deep neural network is utilized for performing rainfall prediction and classification. Additionally, the parameters of the DNN model is optimizing by Flamingo Search Optimization Algorithm. Finally, the proposed MFEEWT-DNN- FSOA approach has effectively predict the rainfall in different locations around India. The proposed model is implemented in Python tool and the performance metrics are calculated. The proposed MFEEWT-DNN- FSOA approach has achieved 25%, 26%, 25.5% high accuracy and 35.8%, 24.7%, 15.9% lower error rate for forecasting rainfall in Cannur at Kerala than the existing Map-Reduce based Exponential Smoothing Technology for rainfall prediction (MR-EST-RP), modular artificial neural networks with support vector regression for rainfall prediction (MANN-SVR-RP), and biogeography-based extreme learning machine (BBO-ELM) (BBO-ELM-RP) methods respectively.
  • Chronic Illness Detection using Gradient Boosting Algorithm
    15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
  • Stress Detection using CNN Algorithm
    15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
  • An Implementation of Smart Agricultural Communication System using IoT and Bigdata Analytics
    S. Raja, T. Guhan, M. Lalithambigai, P. Boopathi, S. S. Suganya, M. Umamaheswari
    2024 International Conference on Smart Technologies for Sustainable Development Goals Icstsdg 2024, 2024
  • Harnessing Advanced Digital (AI) Technologies for Environmental Applications: Carbon-Based Materials
    V. Jaganraja, Nurpatsha Seidakhmet, T. Guhan, Durai Vasanth R., N. Revathy, R. Premkumar
    Environmental Applications of Carbon Based Materials, 2024
  • Exploring the impact of pesticide resistance in agricultural pest management
    Global Nest Journal, 2024
  • Performance analysis of classifying the breast cancer images using KNN and naive bayes classifier
    B. Uma Maheswari, T. Guhan, Christopher Francis Britto, Adlin Sheeba, M. P. Rajakumar, Kumar Pratyush
    Aip Conference Proceedings, 2023
  • EGMPIP: Enhanced Geographic Multipath Routing using Gossip-Based Direct Neighbor Discovery Algorithm in Wireless Sensor Networks
    N. Senthilkumar, N. Revathy, T. Guhan
    2023 International Conference on Computer Communication and Informatics Iccci 2023, 2023
  • Evolutionary algorithm for target tracking adaptive pigeon inspired optimization based on energy proficient steering in wireless sensor networks
    Internet of Everything Smart Sensing Technologies, 2022
  • A Systematic BPCLSTM Algorithm for Concept Drift Detection Incorporated Sentiment Mining
    A. Uma Maheswari, N. Revathy, T. Guhan, B. Praveen, R. Magesh Kumar
    2022 International Conference on Computer Communication and Informatics Iccci 2022, 2022
  • IOT based Agriculture Monitoring System using Arduino UNO
    N. Revathy, T. Guhan, S. Nandhini, S. Ramadevi, R. Dhipthi
    2022 International Conference on Computer Communication and Informatics Iccci 2022, 2022
  • An Autonomous and Intelligent flame sensing extinguishing robot
    K. Anuradha, R. Prema, N. Revathy, T. Guhan, K. P. Uma
    2021 International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2021, 2021
  • EEDCHS-PSO: Energy-Efficient Dynamic Cluster Head Selection with Differential Evolution and Particle Swarm Optimization for Wireless Sensor Networks (WSNS)
    T. Guhan, N. Revathy, K. Anuradha, B. Sathyabama
    Advances in Intelligent Systems and Computing, 2021
  • Elitist streamlined sawtooth genetic algorithm (Sawtga) for anticipating the menace of coronary heart disease
    Oxidation Communications, 2021
  • Ovarian cancer disease prediction and categorization its level using hybrid classification approach
    Research Journal of Biotechnology, 2017