OF-SMED: An optimal foreground detection method in surveillance system for traffic monitoring. - K. SuganyaDevi; N. Malmurugan; R. Sivakumar (CyberSec, 2012)

OFGM-SMED: An Efficient and Robust Foreground Object Detection in Compressed Video Sequences. - K. SuganyaDevi; N. Malmurugan (Eng. Appl. Artif. Intell., 2014)

A study on various methods used for video summarization and moving object detection for video surveillance applications. - A. Senthil Murugan; K. SuganyaDevi; A. Sivaranjani; P. Srinivasan 003 (Multim. Tools Appl., 2018)

Secure cloud-based e-learning system with access control and group key mechanism. - Kanimozhi Sakthivel; Arputharaj Kannan; K. SuganyaDevi; Selvamani Kadirvelu (Concurr. Comput. Pract. Exp., 2019)

Enhancing and Classifying Traffic Signs Using Computer Vision and Deep Convolutional Neural Network. - Satish Kumar Satti; K. Suganya Devi; Prasenjit Dhar; P. Srinivasan 003 (MIND (1), 2020)

H2K - A robust and optimum approach for detection and classification of groundnut leaf diseases. - K. SuganyaDevi; P. Srinivasan 003; Sivaji Bandhopadhyay (Comput. Electron. Agric., 2020)

Automatic method for classification of groundnut diseases using deep convolutional neural network. - M. P. Vaishnnave; K. SuganyaDevi; P. Ganeshkumar (Soft Comput., 2020)

Image classifiers and image deep learning classifiers evolved in detection of Oryza sativa diseases: survey. - N. V. Raja Reddy Goluguri; K. Suganya Devi; Nagesh Vadaparthi (Artif. Intell. Rev., 2021)

A machine learning approach for detecting and tracking road boundary lanes. - Satish Kumar Satti; K. Suganya Devi; Prasenjit Dhar; P. Srinivasan 003 (ICT Express, 2021)

Rice-net: an efficient artificial fish swarm optimization applied deep convolutional neural network model for identifying the Oryza sativa diseases. - N. V. Raja Reddy Goluguri; K. Suganya Devi; P. Srinivasan 003 (Neural Comput. Appl., 2021)

Energy Efficient Data Gathering using Spatio-temporal Compressive Sensing for WSNs. - K. Sekar; K. Suganya Devi; P. Srinivasan 003 (Wirel. Pers. Commun., 2021)

Diagnosis Evaluation and Interpretation of Qualitative Abnormalities in Peripheral Blood Smear Images - A Review. - K. Suganya Devi; G. Arutperumjothi; P. Srinivasan 003 (Health Informatics, 2021)

Detail Study of Different Algorithms for Early Detection of Cancer. - Prasenjit Dhar; K. Suganya Devi; Satish Kumar Satti; P. Srinivasan 003 (Health Informatics, 2021)

R-ICTS: Recognize the Indian cautionary traffic signs in real-time using an optimized adaptive boosting cascade classifier and a convolutional neural network. - Satish Kumar Satti; K. SuganyaDevi; Srinivasan P. 003 (Concurr. Comput. Pract. Exp., 2022)

Detecting potholes on Indian roads using Haar feature-based cascade classifier, convolutional neural network, and instance segmentation. - Satish Kumar Satti; K. Suganya Devi; Prasenjit Dhar; P. Srinivasan 003 (Soft Comput., 2022)

Indian Cautionary Traffic Sign (ICTS) data-set. - Satish Kumar Satti; Suganya Devi K (IEEE DataPort, 2022)

Unified approach for detecting traffic signs and potholes on Indian roads. - Satish Kumar Satti; K. SuganyaDevi; Prasad Maddula; N. V. Vishnumurthy Ravipati (J. King Saud Univ. Comput. Inf. Sci., 2022)

Qualitative Abnormalities of Peripheral Blood Smear Images Using Deep Learning Techniques. - G. Arutperumjothi; K. Suganya Devi; C. Rani; P. Srinivasan 003 (Intell. Autom. Soft Comput., 2023)

Recognizing the Indian Cautionary Traffic Signs using GAN, Improved Mask R-CNN, and Grab Cut. - Satish Kumar Satti; Suganya Devi K; Srinivasan P. 003 (Concurr. Comput. Pract. Exp., 2023)

EEEDCS: Enhanced energy efficient distributed compressive sensing based data collection for WSNs. - K. Sekar; Suganya Devi K; Satish Kumar Satti; P. Srinivasan 003 (Sustain. Comput. Informatics Syst., 2023)

HPKNN: Hyper-parameter optimized KNN classifier for classification of poikilocytosis. - Prasenjit Dhar; Suganya Devi Kothandapani; Satish Kumar Satti; Srinivasan Padmanabhan (Int. J. Imaging Syst. Technol., 2023)

An optimal deep learning model for recognition of hidden hazardous weapons in terahertz and millimeter wave images. - J. Jayachitra; Suganya Devi K; S. V. Manisekaran; Satish Kumar Satti (Earth Sci. Informatics, 2023)

CNN-RSVM: a hybrid approach for classification of poikilocytosis using convolutional neural network and radial kernel basis support vector machine. - Prasenjit Dhar; K. Suganya Devi; P. Srinivasan 003 (Comput. methods Biomech. Biomed. Eng. Imaging Vis., 2023)

An hybrid soft attention based XGBoost model for classification of poikilocytosis blood cells. - Prasenjit Dhar; K. Suganya Devi; Satish Kumar Satti; P. Srinivasan 003 (Evol. Syst., 2024)

Terahertz video-based hidden object detection using YOLOv5m and mutation-enabled salp swarm algorithm for enhanced accuracy and faster recognition. - J. Jayachitra; K. Suganya Devi; S. V. Manisekaran; Satish Kumar Satti (J. Supercomput., 2024)

Classification of Epileptic Seizures Disorder Using CWT and Deep Learning Algorithm from Electroencephalograms. - S. Alagesan; R. Murugan; Suganya Devi K; Ramanujam Elangovan (KST, 2025)

Efficient detection and partitioning of overlapped red blood cells using image processing approach. - Prasenjit Dhar; K. Suganya Devi; Satish Kumar Satti; P. Srinivasan 003 (Innov. Syst. Softw. Eng., 2025)

An explainable deep learning-based panoptic segmentation for brain tumor diagnosis. - Berlin Shaheema S; Naresh Babu Muppalaneni; K. Suganya Devi (Neural Comput. Appl., 2025)