Girija Shankar Sahoo
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Girija Shankar Sahoo has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Alok Kumar Moharana1 shared publication
- Ish Kapila
Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, India
1 shared publication - Raj Kumar
Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India
1 shared publication - Sidhant Das
Chitkara Centre for Research and Development Chitkara University 174103 Himachal Pradesh India
1 shared publication - Suhas Ballal
Department of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University), Bengaluru, Karnataka, India
1 shared publication
Latest Publications
- Open Access
- 5 May 2024
- Published by Dewspublication
- Vol. 20 (S2)
Cardiovascular Disease Detection Using Sand Cat Swarm-driven AdaBoost Approach
Cardiovascular diseases (CVDs) constitute a major cause of death and disability worldwide and represent a major public health problem. Timely intervention and better patient outcomes depend on early identification of CVD. A novel Sand Cat Swarm-driven AdaBoost (SCS-AB) technique is presented for the diagnosis of CVD. The suggested strategy chooses useful features from intricate cardiovascular (CV) datasets by utilizing the shared knowledge of the SCS optimization technique. The SCS method, which addresses feature selection issues in CVD diagnosis, is motivated by the social behaviour of sand cats. It demonstrates a cooperative and self-organizing decision-making process. The AdaBoost incorporates the chosen features, improving its ability to distinguish between patients. The gathered dataset is used in this study's analysis of the suggested SCS-AB approach. The SCS-AB driven CVD prognosis is tested on the Python platform in this study. When contrasted with other standard approaches, the experimental results show that the suggested methodology is beneficial with regard to accuracy, sensitivity and specificity. The method has demonstrated its potential for practical clinical applications as well, offering a useful instrument for rapid CVD identification and risk evaluation.
- 5 May 2024
- Published by Dewspublication
- Vol. 20 (S2)
Open AccessCardiovascular Disease Detection Using Sand Cat Swarm-driven AdaBoost Approach
Cardiovascular diseases (CVDs) constitute a major cause of death and disability worldwide and represent a major public health problem. Timely intervention and better patient outcomes depend on early identification of CVD. A novel Sand Cat Swarm-driven AdaBoost (SCS-AB) technique is presented for the diagnosis of CVD. The suggested strategy chooses useful features from intricate cardiovascular (CV) datasets by utilizing the shared knowledge of the SCS optimization technique. The SCS method, which addresses feature selection issues in CVD diagnosis, is motivated by the social behaviour of sand cats. It demonstrates a cooperative and self-organizing decision-making process. The AdaBoost incorporates the chosen features, improving its ability to distinguish between patients. The gathered dataset is used in this study's analysis of the suggested SCS-AB approach. The SCS-AB driven CVD prognosis is tested on the Python platform in this study. When contrasted with other standard approaches, the experimental results show that the suggested methodology is beneficial with regard to accuracy, sensitivity and specificity. The method has demonstrated its potential for practical clinical applications as well, offering a useful instrument for rapid CVD identification and risk evaluation.