Sivasubramanyam Medasani
Research Subjects
Sivasubramanyam Medasani has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Bala Srinivas Peteti
Department of ECE, Aditya University, Surampalem, India
2 shared publications - Chandrasekhar Rao Jetti
Department of ECE, Bapatla Engineering College (A), Bapatla, India
2 shared publications - J. Prasanth Kumar
Department of ECE, Ramachandra College of Engineering, Eluru, India
2 shared publications - Muppidi Venkata Sudhakar
Department of ECE, Lakireddy Bali Reddy College of Engineering, Mylavaram, India
2 shared publications - Padmaja Nimmagadda
Department of ECE, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India
2 shared publications - Satish Kumar M
Department of ECE, S.R.K.R. Engineering College (Autonomous), Bhimavaram, India
2 shared publications
Latest Publications
- Open Access
- 27 September 2026
- Published by Springer Nature in Cluster Computing
- Vol. 29 (14) , 804
Correction: A novel external crossed stubs for high isolation characteristics of a 4-element wide-band MIMO antenna for 5G Sub-6 GHz application
- 27 September 2026
- Published by Springer Nature in Cluster Computing
- Vol. 29 (14) , 804
Open AccessCorrection: A novel external crossed stubs for high isolation characteristics of a 4-element wide-band MIMO antenna for 5G Sub-6 GHz application
- 30 June 2026
- Published by Springer Nature in Cluster Computing
- Vol. 29 (7) , 446
A novel external crossed stubs for high isolation characteristics of a 4-element wide-band MIMO antenna for 5G Sub-6 GHz application
- 30 June 2026
- Published by Springer Nature in Cluster Computing
- Vol. 29 (7) , 446
A novel external crossed stubs for high isolation characteristics of a 4-element wide-band MIMO antenna for 5G Sub-6 GHz application
- 7 May 2025
- Published by Edtech Publishers (OPC) Private Limited in International Journal of Scientific Research in Engineering and Management
- Vol. 09 (05) , 1-9
Fraud Detection in Financial Transactions Using Machine Learning Techniques
—Through integrating data warehousing, data visualization, information retrieval, and stream processing analytics, this project seeks to create a strong fraud detection framework for financial institutions. Significant volumes of transactional data are efficiently processed and stored, allowing for rapid retrieval and in-depth analysis. To detect trends and irregularities, advanced machine learning models such as Random Forest, Decision Tree, Logistic Regression & Naïve Bayes are used. To evaluate these models, we use precision, recall, F1-score, and AUC-ROC. To help ensure practical deployment in financial environments, the system places a strong emphasis on scalability and real-time processing. This improves fraud prevention and strengthens confidence in economic systems. Keywords: K-Means, Financial Transactions, Random Forests, Decision Trees, Ensemble Learning, Logistic Regression, Fraud Detection, and Classification.
- 7 May 2025
- Published by Edtech Publishers (OPC) Private Limited in International Journal of Scientific Research in Engineering and Management
- Vol. 09 (05) , 1-9
Fraud Detection in Financial Transactions Using Machine Learning Techniques
—Through integrating data warehousing, data visualization, information retrieval, and stream processing analytics, this project seeks to create a strong fraud detection framework for financial institutions. Significant volumes of transactional data are efficiently processed and stored, allowing for rapid retrieval and in-depth analysis. To detect trends and irregularities, advanced machine learning models such as Random Forest, Decision Tree, Logistic Regression & Naïve Bayes are used. To evaluate these models, we use precision, recall, F1-score, and AUC-ROC. To help ensure practical deployment in financial environments, the system places a strong emphasis on scalability and real-time processing. This improves fraud prevention and strengthens confidence in economic systems. Keywords: K-Means, Financial Transactions, Random Forests, Decision Trees, Ensemble Learning, Logistic Regression, Fraud Detection, and Classification.