Dr. R. Manivannan
@stanley.edu.in
Professor, Computer Science and Engineering
Stanley College of Engineering and Technology for Women
R. Manivannan was born in Tamilnadu, India, in 1977. He received B.E. Degree in CSE from the University of Madras, Chennai, India in 1999, the M.E. Degree in CSE from the Sathyabama University, Chennai, India in 2005 and the Ph. D Degree in CSE from Dr. M.G.R. Educational and Research Institute University, Chennai, India in 2014. In 1999, he joined in the Department of CSE, Ranipet Institute of Technology, as a Lecturer. In 2000, he joined in the Department of CSE, C. Abdul Hakeem College of Engineering and Technology, Tamilnadu, where he was a Lecturer, became an Assistant Professor in 2005, an Associate Professor in 2011 and a Professor in 2014. Since September 2015, he has been with the Department of CSE, Stanley College of Engineering and Technology for WoWomen, Abids, Hyderabad, India. His current research interests include Data Mining, Cloud Computing, Image Processing, Artificial Intelligence, Natural Language Processing & Machine Learning.
EDUCATION
Ph.D - Educational and Research Institute (2014)
M.E. - Sathyabama Institute of Science and Technology (2005)
B.E.- Adhiyamaan College of Engineering (1999)
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Engineering, Computer Science Applications, Computer Vision and Pattern Recognition
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
R. Manivannan, Y. V. S. S. Pragathi, and Uday Kumar Kanike
IGI Global
The integration of image processing methods with IoT devices in healthcare has revolutionized patient health checking procedures, enabling continuous monitoring, automated image analysis, and personalized treatments. This improves diagnostic precision, prompt action, and favorable patient outcomes. Wearable health trackers and medical equipment collect real-time vital sign data, enabling informed judgments. IoT device integration also enables remote access, virtual consultations, and follow-ups, boosting patient engagement and treatment compliance. However, issues like data security, interoperability, and infrastructure must be resolved for successful deployment. Future directions include advancements in 6G networks, AI integration, augmented reality, and data fusion methods.
Rathish Manivannan and M Amsaprabhaa
IEEE
The machine learning along with computer vision has helped widely in classification of sports videos. Deep learning techniques are also being used to perform research in this domain. The video frames are the most significant components of the sports classification system. There are many models that have been used to classify sports videos. The objective of this work is to develop a classifier for different sports activity recognition using video data with high accuracy and probability. Sports dataset has been used from Kaggle [10]. This framework has been created for applications related to sports, object detection, game identification, recognition, analysis, players’ tracking, and performance. This framework consists of various intermediary processes. At first, preprocessing has been carried out in this framework by converting input sports video into video frames. Then skeletonization has been carried out using computer vision. Finally, feature extraction and classification has been done using the Yolov5 model that has been trained with the dataset. Accuracy and loss graphs have been generated using prediction metrics for this model to evaluate the accuracy of this framework. The accuracy of the framework is more than 90% according to the results we have obtained.
Srinivasu Badugu and R. Manivannan
Springer Nature Singapore
Sumera, K. Vaidehi, and R. Manivannan
Springer Nature Singapore
Vaidehi K. and Manivannan R.
IGI Global
Handwritten character/symbol recognition is an important area of research in the present digital world. The solving of problems such as recognizing handwritten characters/symbols written in different styles can make the human job easier. Mathematical expression recognition using machines has become a subject of serious research. The main motivation for this work is both recognizing of the handwritten mathematical symbol, digits and characters which will be used for mathematical expression recognition. The system first identifies the contour in handwritten document segmentation and features extracted are given into SVM classifier for classification. GLCM and Zernike Moments are the two different feature extraction techniques used in this work. SVM with RBF kernel is used for classification. Zernike Moment features overperforms than GLCM. Zernike Moment achieves 97.89% accuracy and GLCM achieves 87.61% accuracy.
R. Sathya, R. Manivannan, and K. Vaidehi
Springer Nature Singapore
Srinivasu Badugu and R. Manivannan
Springer Science and Business Media LLC
R. Sugumar, A. Rajesh, and R. Manivannan
Springer Singapore
B Srinivasu and R Manivannan
American Scientific Publishers
R Sugumar, A Rajesh, and R Manivannan
American Scientific Publishers
R. Manivannan and S.K. Srivatsa
Science Alert