Dr. M. Sumathi

@smgacw.org

Associate Professor and Head, PG and Research Department of Computer Science
Sri Meenakshi Government Arts College for Women(A), Madurai - 2

Dr. M. Sumathi


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https://researchid.co/msumathi

EDUCATION

Completed Ph.D from Madurai Kamaraj University in 2010

RESEARCH INTERESTS

IMAGE PROCESSING
BIG DATA
SOFTWARE ENGINEERING
55

Scopus Publications

402

Scholar Citations

11

Scholar h-index

12

Scholar i10-index

Scopus Publications

  • AI-powered detection and classification of harmful algal blooms (HABs) using a Volterra Convolutional Neural Network (VCNN) and advanced image processing techniques
    R. Mahalakshmi Priya, J. I. Christy Eunaicy, T. S. Urmila, C. Jayapratha, J. Naveen Ananda Kumar, et al.
    Iran Journal of Computer Science, 2026
  • OPTIMISED DEEP LEARNING FOR ORAL CANCER CLASSIFICATION
    Chellasamy Sulochana, Mahadevan Sumathi
    Acta Polytechnica, 2026
    Oral cancer detection is essential, especially in areas with high occurrence rates, is essential for better early diagnosis and individualised treatment plans. SV-OnionNet is a deep learning framework presented in this article that aims to improve the classification accuracy of oral cancer diagnosis. While maintaining important structural details, the method lowers noise in medical pictures by integrating an adaptive Non-Linear Means (NLM) filter. Spatial features are improved by the Label-Guided Attention (LGA) module, which guarantees constant labelling and improves feature extraction. By enabling accurate pixel-level segmentation of lesions, Seg-UNet provides increased classification reliability. The Support Vector Machines (SVM) deep learning classification model used in the SV-OnionNet architecture preserves spatial relationships for improved feature learning, replacing traditional fully linked layers (LKN). The Competitive Search Optimization (CSO) algorithm fine-tunes model parameters, therefore optimising feature selection and classification. The evaluation on the Mouth and Oral Diseases dataset demonstrated exceptional accuracy, precision, recall, and specificity, with the proposed classification achieving a 99.94% accuracy. These findings emphasise the effectiveness of SV-OnionNet in improving the diagnostic accuracy and reliability. The study highlights the potential of integrating deep learning techniques with optimisation strategies to advance oral cancer detection. Future research will focus on expanding datasets and exploring additional optimisation methods to further improve the classification performance.
  • Machine Learning and Deep Learning Algorithms and Cognitive Approach for VR, AR Model Building
    R. Mahalakshmi Priya, J. Naveen Ananda Kumar, C. Jayapratha, T.S. Urmila, M. Sumathi
    Virtual Reality and Augmented Reality with 6g Communication, 2025
  • PollenMorph AI: quantum contours based segmentation and deep learning for pollen recognition using microscopic images
    Mahalakshmi Priya Rajendran, Sumathi Mahadevan
    Iran Journal of Computer Science, 2025
  • Impact of Microorganisms on Food Spoilage and Human Health: A Comprehensive Review of Advances in Identification using Image Processing and Artificial Intelligence Techniques
    Mahalakshmi Priya R, Sumathi M
    International Research Journal of Multidisciplinary Scope, 2025
    Food spoilage and human health are greatly affected by microorganisms, such as bacteria, algae, fungi, and protozoa. While traditional identification methods are reliable, they are often laborious and time-consuming. In recent years, artificial intelligence (AI) and image processing have made significant progress in identifying and classifying microorganisms quickly and accurately. In this review, we will examine image processing and artificial intelligencebased techniques for identifying and classifying microorganisms relevant to human health and food spoilage, comparing their effectiveness to traditional methods and assessing their impact on food safety. Bacteria, algae, fungi, and protozoa are the four major groups of microorganisms examined in this review. A review of applications in food safety, clinical microbiology, and environmental monitoring is presented in this paper. It examines how bacteria, yeast, and molds cause food spoilage and examines their mechanisms of action. Furthermore, the article highlights common foodborne illnesses and the health consequences of eating contaminated food. The paper also discusses advances in identifying spoilage-causing microorganisms, with a particular emphasis on artificial intelligence (AI) and image processing. With modern techniques, microbial contamination can be detected more accurately and efficiently, thus improving food safety. Finally, the review concludes by analyzing current challenges and future directions in the field, emphasizing the need for continued innovation in microbial detection methods. In the review, rapid detection of foodborne pathogens is highlighted, as well as automated spoilage monitoring. This technology has the potential to revolutionize food safety practices and clinical microbiology, so it must continue to be developed and validated.
  • Fungal Species Classification Using MixNet-Lite: A Lightweight Deep Learning Approach
    R. Mahalakshmi Priya, M. Sumathi
    Proceedings of 2025 IEEE International Conference on Contemporary Computing and Communications Inc4 2025, 2025
  • Bridging AI and Ecology: CILNN and XAI for Acoustic Based Prediction of Dangerous Wild Animals
    Govindaprabhu GB, Sumathi M, Sharan Neyvasagam, Naveen Ananda Kumar J
    International Research Journal of Multidisciplinary Scope, 2025
    In habitats that are encroaching on humans, human-wildlife conflict is an increasing global challenge. There is a significant risk of human injury and retaliatory action being taken if humans encounter dangerous animals. This work presents a novel approach to automated detection and classification of dangerous animals using audio signals, with a focus on model interpretability. This work introduces the Convolutional Interconnected Layer Neural Network (CILNN), a deep learning architecture designed to effectively process and classify animal vocalizations. Our method leverages a comprehensive set of audio features, including Mel-frequency cepstral coefficients (MFCCs) and spectral characteristics, optimized through SHAP-based feature selection. The CILNN incorporates interconnected layers and attention mechanisms to enhance feature extraction and model performance. It evaluates proposed approach on a diverse dataset of vocalizations from five dangerous animal species: bears, bison, cheetahs, elephants, and wild boars. Experimental results demonstrate that the CILNN outperforms traditional machine learning models such as Random Forests and Decision Trees in classification accuracy and robustness. Crucially, it employs Explainable AI (XAI) techniques, including SHAP values and decision tree visualizations, to interpret the decision-making processes of both our CILNN (90.6% accuracy) and other models. This interpretability analysis provides insights into feature importance and model behavior, enhancing trust and understanding in the classification process. Our work contributes to wildlife monitoring and human-wildlife conflict mitigation by offering an efficient, accurate, and interpretable method for acoustic-based animal detection
  • RETRACTED ARTICLE: Fuzzy assisted fog and cloud computing with MIoT system for performance analysis of health surveillance system (Journal of Ambient Intelligence and Humanized Computing, (2021), 12, 3, (3423-3436), 10.1007/s12652-020-02156-y)
    S. Selvakanmani, M. Sumathi
    Journal of Ambient Intelligence and Humanized Computing, 2024
  • Safeguarding Humans from Attacks Using AI-Enabled (DQN) Wild Animal Identification System
    Govindaprabhu GB, Sumathi M
    International Research Journal of Multidisciplinary Scope, 2024
    Without advanced artificial intelligence (AI) technologies, monitoring and identifying wildlife has become increasingly difficult. To examine AI-driven methodologies for wild animal identification, this work uses a diverse dataset of annotated images with human, domestic and wild animal annotations. Convolutional Neural Networks (CNNs), AlexNet, and Deep Q-Learning (DQN) models are developed and compared by combining sophisticated preprocessing techniques such as dynamic color space conversion and day-night image translation. The models are evaluated on accuracy, precision, recall, F1-score, and mean percent error (MPE) loss metrics for classifying diverse species. The DQN model achieves the best performance with 79.5% accuracy, 0.78 precision, 0.84 F1-score, and 0.24 MPE loss. These findings demonstrate AI's potential to support conservation efforts by enabling accurate and automated wildlife monitoring. The comparative assessment of different models and factors influencing performance provides methodological insights to guide future research toward robust and generalizable AI solutions for biodiversity and habitat management.
  • Enhancing Oral Cancer Diagnosis: IAWMF based Preprocessing in RGB and CT Images
    C. Sulochana, M. Sumathi
    2024 International Conference on Recent Advances in Electrical Electronics Ubiquitous Communication and Computational Intelligence Raeeucci 2024, 2024
    Globally, there are over 350,000 cases of oral cancer, mainly oral squamous cell carcinomas that arise in the mouth and tongue tissues. For metastatic cases, the survival rate drops dramatically from 83% to only 65% if detected early. It is challenging, however, to identify oral lesions and precancerous conditions in their earliest stages when treatment can be most effective. CT scans and intraoral RGB photography are critical imaging methods for screening and diagnosing oral cancers. It is difficult to detect oral cancer accurately with these imaging techniques due to artifacts, noise, and poor lesion visibility. In current CAD methods, preprocessing pipelines for oral cavity images across RGB and CT are not robust or tailored. An automated, adaptive, and multimodal preprocessing pipeline is presented in this study to reduce this gap by facilitating early detection of oral cancer from RGB photographs and CT scans. A region-of-interest cropping technique is used to focus on diagnostically significant areas, specialized noise filters are used to reduce noise while maintaining tissue features, and adaptive histogram equalization is used to normalize contrast dynamically across images and highlight lesions. Oral cavity images in RGB and CT formats were used to evaluate the proposed techniques. As compared to original images, the results demonstrated significant improvements in image quality, noise reduction, lesion conspicuity, and feature visibility. Among the noise filters assessed, the Iterative Adaptive Weighted Median Filter (IAWMF) performed best with PSNR 33, SNR 28.2817, and RMSE 5.7088, which indicated the filter's effectiveness for high fidelity denoising. The median filter was also capable of reducing noise effectively with PSNR of 26.7701, SNR of 22.0518, and RMSE of 11.696. Image quality was the poorest when using Total Generalized Variation. A tailored preprocessing pipeline shows promise in aiding early diagnosis and treatment of oral cancer.
  • Watershed Segmentation with Gradient Vista and SOBEL-Crafted Contours for Analyzing EMDS-6 Microbial Environmental Dataset
    R Mahalakshmi Priya, M. Sumathi
    Proceedings of Inc4 2024 2024 IEEE International Conference on Contemporary Computing and Communications, 2024
  • An Integrated Approach to Bacteria Structure Detection using Frangi-Thresholding Segmentation and its Impact on Analysis
    R. Mahalakshmi Priya, M. Sumathi
    2024 International Conference on Recent Advances in Electrical Electronics Ubiquitous Communication and Computational Intelligence Raeeucci 2024, 2024
  • Ethno medicine of Indigenous Communities: Tamil Traditional Medicinal Plants Leaf detection using Deep Learning Models
    G.B. Govindaprabhu, M. Sumathi
    Procedia Computer Science, 2024
  • An Elephant Identification Emissary: A Technological Odyssey in Elephant Recognition with IP & AI Solutions
    G B Govindaprabhu, M. Sumathi
    Proceedings of Inc4 2024 2024 IEEE International Conference on Contemporary Computing and Communications, 2024
  • Machine Learning for Plankton Species Identification and Classification: A New Era in Marine Ecology
    R.Mahalakshmi Priya, R. Barani, M. Sumathi
    Proceedings of the 5th International Conference on Inventive Research in Computing Applications Icirca 2023, 2023
  • Segmentation and Sentiment Word Categorization Using Feature Extraction—A Novel ASFW Framework
    S. Ashika Parvin, M. Sumathi
    Lecture Notes in Electrical Engineering, 2022
  • Challenges of Sentiment Analysis - A Survey
    S.Ashika Parvin, M. Sumathi, C. Mohan
    Proceedings of the 5th International Conference on Trends in Electronics and Informatics Icoei 2021, 2021
  • Nuances of data pre-processing and its impact on business
    M. Sumathi, S.Ashika Parvin
    Proceedings 5th International Conference on Intelligent Computing and Control Systems Iciccs 2021, 2021
  • Fuzzy assisted fog and cloud computing with MIoT system for performance analysis of health surveillance system
    S. Selvakanmani, M. Sumathi
    Journal of Ambient Intelligence and Humanized Computing, 2021
  • Survey on data security in cloud environment
    International Journal of Advanced Research in Engineering and Technology, 2020
  • Id based adaptive-key signcryption for data security in cloud environment
    International Journal of Advanced Research in Engineering and Technology, 2020
  • Energy efficient algorithm for high speed packet data transfer on smartphone environment
    International Journal of Engineering and Advanced Technology, 2019
  • Building a Data Mining Based Software Reliability Estimation Model
    A. R. Visagan, M. Sumathi, G. Sujatha
    1st IEEE International Conference on Advances in Information Technology Icait 2019 Proceedings, 2019
  • Adaptive PET/CT fusion using empirical wavelet transform
    R. Barani, M. Sumathi
    Advances in Intelligent Systems and Computing, 2018
  • Salinity sensor using photonic crystal fiber
    D. Vigneswaran, N. Ayyanar, Mohit Sharma, M. Sumathi, Mani Rajan M.S., et al.
    Sensors and Actuators A Physical, 2018

RECENT SCHOLAR PUBLICATIONS

  • AI-powered detection and classification of harmful algal blooms (HABs) using a Volterra Convolutional Neural Network (VCNN) and advanced image processing techniques
    R Mahalakshmi Priya, JI Christy Eunaicy, TS Urmila, C Jayapratha, ...
    Iran Journal of Computer Science 9 (1), 20 , 2026
    2026
    Citations: 1
  • Machine Learning and Deep Learning Algorithms and Cognitive Approach for VR, AR Model Building
    RM Priya, JNA Kumar, C Jayapratha, TS Urmila, M Sumathi
    Virtual Reality and Augmented Reality with 6G Communication, 169-195 , 2025
    2025
  • Bridging AI and Ecology: CILNN and XAI for Acoustic Based Prediction of Dangerous Wild Animals
    G GB, DM Sumathi, J Kumar
    International Research Journal of Multidisciplinary Scope 6 (01), 10.47857 , 2025
    2025
    Citations: 1
  • Fungal Species Classification Using MixNet-Lite: A Lightweight Deep Learning Approach
    RM Priya, M Sumathi
    2025 IEEE International Conference on Contemporary Computing and … , 2025
    2025
  • Bridging ai and ecology: Cilnn and xai for acoustic based prediction of dangerous wild animals
    GB Govindaprabhu, M Sumathi, S Neyvasagam, NAJ Kumar
    International Research Journal of Multidisciplinary Studies 6 (1), 1280-1298 , 2025
    2025
    Citations: 5
  • Impact of Microorganisms on food spoilage and human health: a comprehensive review of advances in identification using image processing and artificial intelligence techniques
    RM Priya, M Sumathi
    Int. Res. J. Multidiscipl. Scope 6 (1), 1299-1316 , 2025
    2025
    Citations: 3
  • An integrated approach to bacteria structure detection using Frangi-thresholding segmentation and its impact on analysis
    RM Priya, M Sumathi
    2024 International Conference on Recent Advances in Electrical, Electronics … , 2024
    2024
    Citations: 5
  • Safeguarding Humans from Attacks Using AI-Enabled (DQN) Wild Animal Identification System
    G GB, DM Sumathi
    Available at SSRN 5244356 , 2024
    2024
    Citations: 1
  • An Elephant Identification Emissary: A Technological Odyssey in Elephant Recognition with IP & AI Solutions
    GB Govindaprabhu, M Sumathi
    2024 IEEE International Conference on Contemporary Computing and … , 2024
    2024
    Citations: 1
  • Watershed segmentation with gradient vista and SOBEL-crafted contours for analyzing EMDS-6 microbial environmental dataset
    RM Priya, M Sumathi
    2024 IEEE International Conference on Contemporary Computing and … , 2024
    2024
    Citations: 3
  • Safeguarding humans from attacks using AI-enabled (DQN) wild animal identification system
    GB Govindaprabhu, M Sumathi
    Int. Res. J. Multidiscip. Scope 5 (3), 285-302 , 2024
    2024
    Citations: 14
  • Ethno medicine of indigenous communities: Tamil traditional medicinal plants leaf detection using deep learning models
    GB Govindaprabhu, M Sumathi
    Procedia Computer Science 235, 1135-1144 , 2024
    2024
    Citations: 18
  • A Novel Approach to Classify Sentiments on Different Datasets Using Hybrid Approaches of Sentiment Analysis
    SA Parvin, M Sumathi, R Barani
    Indian Journal of Science and Technology 16 (44), 3962-3970 , 2023
    2023
    Citations: 2
  • Machine Learning for Plankton Species Identification and Classification: A New Era in Marine Ecology
    RM Priya, R Barani, M Sumathi
    2023 5th International Conference on Inventive Research in Computing … , 2023
    2023
    Citations: 2
  • Segmentation and Sentiment Word Categorization Using Feature Extraction—A Novel ASFW Framework
    S Ashika Parvin, M Sumathi
    Proceedings of Third International Conference on Communication, Computing … , 2022
    2022
  • Hybrid Genetic Algorithm with K-Means for Detection of Brain Tumor
    M Sumathi, C Mohan, S Pandikumar, SB Sethupandian
    Design Engineering, 17248-17256 , 2021
    2021
  • SPI Transactional Database Using Secure Elastic Cloud Access with OOB.
    M Sumathi, NGS Parameswaran
    Turkish Online Journal of Qualitative Inquiry 12 (9) , 2021
    2021
  • Challenges of sentiment analysis-a survey
    SA Parvin, M Sumathi, C Mohan
    2021 5th International Conference on Trends in Electronics and Informatics … , 2021
    2021
    Citations: 11
  • Nuances of data pre-processing and its impact on business
    M Sumathi, SA Parvin
    2021 5th International Conference on Intelligent Computing and Control … , 2021
    2021
    Citations: 4
  • Anomaly Detection Using PSO In Cloud Integrated IoT Devices Using MDGAN
    NGSP M.Sumathi
    International Journal of Aquatic Science 12 (03) , 2021
    2021

MOST CITED SCHOLAR PUBLICATIONS

  • Prediction of stock market price using hybrid of wavelet transform and artificial neural network
    SK Chandar, M Sumathi, SN Sivanandam
    Indian journal of Science and Technology 9 (8), 1-5 , 2016
    2016
    Citations: 80
  • Forecasting gold prices based on extreme learning machine
    KC Sivalingam, S Mahendran, S Natarajan
    International Journal of Computers Communications & Control 11 (3), 372-380 , 2016
    2016
    Citations: 74
  • Forecasting of foreign currency exchange rate using neural network
    SK Chandar, M Sumathi, SN Sivanandam
    International Journal of Engineering and Technology 7 (1), 99-108 , 2015
    2015
    Citations: 27
  • Ethno medicine of indigenous communities: Tamil traditional medicinal plants leaf detection using deep learning models
    GB Govindaprabhu, M Sumathi
    Procedia Computer Science 235, 1135-1144 , 2024
    2024
    Citations: 18
  • Design of algorithm for vehicle identification by number plate recognition
    P Vijayalakshmi, M Sumathi
    2012 Fourth International Conference on Advanced Computing (ICoAC), 1-6 , 2012
    2012
    Citations: 17
  • Qualitative evaluation of pixel level image fusion algorithms
    M Sumathi, R Barani
    International Conference on Pattern Recognition, Informatics and Medical … , 2012
    2012
    Citations: 17
  • Safeguarding humans from attacks using AI-enabled (DQN) wild animal identification system
    GB Govindaprabhu, M Sumathi
    Int. Res. J. Multidiscip. Scope 5 (3), 285-302 , 2024
    2024
    Citations: 14
  • Survey On Data Security In Cloud Environment
    MS T.SUJITHRA
    International Journal of Advanced Research in Engineering and Technology 11 … , 2020
    2020
    Citations: 14
  • Id Based Adaptive-Key Signcryption For Data Security In Cloud Environment
    MS T.SUJITHRA
    International Journal of Advanced Research in Engineering and Technology 11 … , 2020
    2020
    Citations: 12
  • Effective features of remote sensing image classification using interactive adaptive thresholding method
    T Balaji, DM Sumathi
    arXiv preprint arXiv:1401.7743 , 2014
    2014
    Citations: 12
  • PCA based classification of relational and identical features of remote sensing images
    T Balaji, M Sumathi
    International Journal of Engineering and Computer Science 3 (7), 7221-7228 , 2014
    2014
    Citations: 12
  • Challenges of sentiment analysis-a survey
    SA Parvin, M Sumathi, C Mohan
    2021 5th International Conference on Trends in Electronics and Informatics … , 2021
    2021
    Citations: 11
  • Relational features of remote sensing image classification using effective k-means clustering
    T Balaji, M Sumathi
    International Journal of Advancements in Research & Technology 2 (8), 103-107 , 2013
    2013
    Citations: 9
  • Foreign exchange rate forecasting using Levenberg-Marquardt learning algorithm
    SK Chandar, M Sumathi, SN Sivanandam
    Indian Journal of Science and Technology 9 (8), 1-5 , 2016
    2016
    Citations: 7
  • GA-based optimization of tapering windows for artifact reduction in Fourier electron magnetic resonance images
    M Sumathi, MC Krishna, R Murugesan
    International Journal of Computational Intelligence and Applications 8 (02 … , 2009
    2009
    Citations: 7
  • Neural network based forecasting of foreign currency exchange rates
    SK Chandar, M Sumathi, SN Sivanandam
    International Journal on Computer Science and Engineering 6 (6), 202 , 2014
    2014
    Citations: 6
  • Bridging ai and ecology: Cilnn and xai for acoustic based prediction of dangerous wild animals
    GB Govindaprabhu, M Sumathi, S Neyvasagam, NAJ Kumar
    International Research Journal of Multidisciplinary Studies 6 (1), 1280-1298 , 2025
    2025
    Citations: 5
  • An integrated approach to bacteria structure detection using Frangi-thresholding segmentation and its impact on analysis
    RM Priya, M Sumathi
    2024 International Conference on Recent Advances in Electrical, Electronics … , 2024
    2024
    Citations: 5
  • Evaluation of Spatial and Transform Fusion methods for Medical Images using Normalized Non-Reference Quality Metrics
    R Barani, M Sumathi
    International Journal of Computer Applications 143 (13), 21-28 , 2016
    2016
    Citations: 5
  • Design of algorithm for detection of hidden objects from Tera hertz images
    P Vijayalakshmi, M Sumathi
    IOSR J. Comput. Eng 13 (2), 25-32 , 2013
    2013
    Citations: 5