S. Deivasigamani
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
S. Deivasigamani has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- C. Senthilpari
Faculty of Engineering, Cyberjaya Campus, Multimedia University, Cyberjaya, Malaysia
2 shared publications - Hin Yong Wong
Multimedia University
2 shared publications C. Senthilpari
Faculty of Engineering; Cyberjaya Campus, Multimedia University; Malaysia
1 shared publication- G. Narmadha
Department of Electrical and Electronics Engineering, Sethu Institute of Technology, Virudhunagar 626106, India
1 shared publication - Perumbilavil Kaithamanakallam Rajesh
Former Dean, Head of Medical Education, Faculty of Medicine, AIMST University
1 shared publication - Prakash A1 shared publication
Latest Publications
- Retraction
- 4 July 2022
- Published by Springer Nature in Journal of Ambient Intelligence and Humanized Computing
- Vol. 14 (S1) , 649
Retraction Note to: Machine learning method based detection and diagnosis for epilepsy in EEG signal
- 4 July 2022
- Published by Springer Nature in Journal of Ambient Intelligence and Humanized Computing
- Vol. 14 (S1) , 649
RetractionRetraction Note to: Machine learning method based detection and diagnosis for epilepsy in EEG signal
- Open Access
- 28 February 2022
- Published by Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication (BEIESP) in International Journal of Engineering and Advanced Technology
- Vol. 11 (3) , 51-57
Computer Aided Automatic Detection and Diagnosis System of Wound and Ulcer Care for Diabetic Patient
The diabetic wound healing process is a complex task under the category of B40 classification and below. The medical expenses are high in private wound specialist organizations compared to government hospitals. This article designed a computer-aided automatic detection and classification method for wound and ulcer care for diabetic patients using image processing techniques by Edge detection, colour scale of tissues, wound area calculation, and percentage calculation with GUI. The system results, Combination of edge detection methodology and 2-D boundary technique and design with the significant three tissues classification which is harmful and required immediate medical responses are Granulation, Fibrin and Necrotic values are used for wound area determination. The result of the system will help the patient immediately, which is classified as high or less severity.
- 28 February 2022
- Published by Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication (BEIESP) in International Journal of Engineering and Advanced Technology
- Vol. 11 (3) , 51-57
Open AccessComputer Aided Automatic Detection and Diagnosis System of Wound and Ulcer Care for Diabetic Patient
The diabetic wound healing process is a complex task under the category of B40 classification and below. The medical expenses are high in private wound specialist organizations compared to government hospitals. This article designed a computer-aided automatic detection and classification method for wound and ulcer care for diabetic patients using image processing techniques by Edge detection, colour scale of tissues, wound area calculation, and percentage calculation with GUI. The system results, Combination of edge detection methodology and 2-D boundary technique and design with the significant three tissues classification which is harmful and required immediate medical responses are Granulation, Fibrin and Necrotic values are used for wound area determination. The result of the system will help the patient immediately, which is classified as high or less severity.
- Retracted
- 3 March 2020
- Published by Springer Nature in Journal of Ambient Intelligence and Humanized Computing
- Vol. 12 (3) , 4215-4221
RETRACTED ARTICLE: Machine learning method based detection and diagnosis for epilepsy in EEG signal
- 3 March 2020
- Published by Springer Nature in Journal of Ambient Intelligence and Humanized Computing
- Vol. 12 (3) , 4215-4221
RetractedRETRACTED ARTICLE: Machine learning method based detection and diagnosis for epilepsy in EEG signal
- 27 December 2016
- Published by Wiley in International Journal of Imaging Systems and Technology
- Vol. 26 (4) , 277-283
Classification of focal and nonfocal EEG signals using ANFIS classifier for epilepsy detection
- 27 December 2016
- Published by Wiley in International Journal of Imaging Systems and Technology
- Vol. 26 (4) , 277-283
Classification of focal and nonfocal EEG signals using ANFIS classifier for epilepsy detection