Taviti Naidu Gongada
Assistant Professor, Operations & supply chain, GSB, VSP
Dr. G. Taviti Naidu is from Visakhapatnam, Andhra Pradesh. He has a Ph.D. from Andhra University and a Post Doctoral Fellowship (PDF) from ICSSR, New Delhi. He is qualified for UGC NET and SET for lectureship. Papers were presented in prestigious institutes like IIM-A, IIM-I, and Andhra University. His research interests are predictive data analytics, fraud detection & security, sentiment analysis, data mining, Health care analytics, and operations research to solve complex business problems.
Research Publications
- Taviti Naidu Gongada, et.al., (2024), “Optimizing Resource Allocation in Cloud Environments using Fruit Fly Optimization and Convolutional Neural Networks” International Journal of Advanced Computer Science and Applications (IJACSA), 15(5), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01505119
- Taviti Naidu Gongada, Vijayalakshmi (2024) “Leveraging Machine Learning for Enhanced Cyber Attack Detection and Defence in Big Data Management and Process Mining” International Journal of Advanced Computer Science and Applications(IJACSA), 15(2), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150266
- Sandhyavani, K. v., Naidu, G. T., Kumar. A, & Dutta, G. (2021). Cyclone Hudhud at Rashtriya Ispat Nigam Limited, Visakhapatnam. In Cyclone Hudhud at Rashtriya Ispat Nigam Limited, Visakhapatnam. https://doi.org/10.4135/9781529753028
- Alijoyo, F. A., Gongada, T. N., Kaur, C., (2024). Advanced hybrid CNN-Bi-LSTM model augmented with GA and FFO for enhanced cyclone intensity forecasting. Alexandria Engineering Journal, 92. https://doi.org/10.1016/j.aej.2024.02.062
- Gongada, T. N., et.al., (2023). E-Commerce Trend Analysis and Management for Industry 5.0 using User Data Analysis. International Journal of Intelligent Systems and Applications in Engineering, 11(11).
Authored Books
- Gongada, T. N., (2021), ''Research Methodology for Management'', Scientific International Publishing
Expertise
- Statistical Analysis, Mathematical Modeling, and Quantitative Decision-Making techniques.
- Analyzing the data through SPSS and R-software,
- Development and implementation of optimization models.