Deep Networks in Diagnosis and Prognosis ApplicationsApplication of Artificial Intelligence in Battery Condition MonitoringImage ProcessingIoT-Based Time Series AnalysisRegression Prediction Models in Mechanical Engineering Applications
5 Years 5 Months
AICTE-QIP-PG Certificate Programme in Deep Learning, National Institute of Technology Karnataka (NITK), 2026.
Diploma in 3D Modelling and Analysis, CADD Centre, Bengaluru, 2011.
4 (Status: 2 Granted, 2 Published)
Vasudeva, S. T., Rao, S. S., Panambur, N. K., Shettigar, A. K., Mahabala, C., Kamath, P., Gowdru Chandrashekarappa, M. P., & Linul, E. (2022). Development of a Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals. Applied Sciences, 12(15), 7711.
Shrivathsa, T. V., Kalyan, C., Rao, S. S., Navin Karanth, P., Chakrapani, M., & Kamath, P. (2022). Development and performance evaluation of a coronary artery disease prediction system with transfer learned model based on single lead and multi-lead ECG & TMT-ECG signals. International Journal of Health Sciences, 6(S4), 8865–8890
Adiga, K., Herbert, M. A., Rao, S. S., Shettigar, A. K., Shrivathsa, T. V. (2024). Development of machine learning regression models for the prediction of tensile strength of friction stir processed AA8090/SiC surface composites. Materials Research Express, 11(7), 076517.
Adiga, K., Herbert, M.A., Rao, S.S., Shettigar, A., Shrivathsa, T.V., Tapariya, R. (2024). Comparison of Response Surface Methodology (RSM) and Machine Learning Algorithms in Predicting Tensile Strength and Surface Roughness of AA8090/B4C Surface Composites Fabricated by Friction Stir Processing. In: Venkata Rao, R., Taler, J. (eds) Advanced Engineering Optimization Through Intelligent Techniques. AEOTIT 2023. Lecture Notes in Electrical Engineering, vol 1226. Springer, Singapore.
Shrivathsa, T.V. et al. (2024). Predictive Intelligent System Development for Disease Classification in Diagnostic Applications. In: Venkata Rao, R., Taler, J. (eds) Advanced Engineering Optimization Through Intelligent Techniques. AEOTIT 2023. Lecture Notes in Electrical Engineering, vol 1226. Springer, Singapore.
Shetty, A. K., Abijeet, T. K., Machado, J. W., & Shrivathsa, T. V. (2017). Design and Analysis of Piston using Aluminium Alloys. International Journal of Innovative Research in Advanced Engineering, 4(04), 1-6.
Shrivathsa, T. V., Puneet, N. P., & BG, V. R. (2025). Interpretation study of quasi-static uni-axial compression analysis of cellular panels with machine learning application. Advances in Materials Research (AMR), 14(4), 309–326.
Keshavamurthy, R., Naveena, B. E., Gowda, P. V., & Shrivathsa, T. V. (2026). Performance evaluation of friction stir spot welding of Al 5754 and Al 6111 using machine learning approaches. Journal of Materials Engineering and Performance, 35(12), 11533–11551.
Naveena, B. E., Santhosh, K., Keshavamurthy, R., Shrivathsa, T. V., Ganesha, B. B., & Mahesh, B. R. (2026). Performance Comparison of Tree-Based and Neural Network Models for Wear Prediction in Coated and Uncoated Al6061. Results in Surfaces and Interfaces, Article 100788.
Presented the research paper entitled “Identification And Control Of The Noise Level Of The Gearbox With Application Of Attenuation Blankets” in the 2 nd International Conference on Mechanical Engineering: Researches and Evolutionary Challenges-2024, organized by the National Institute of Technology Warangal, Telangana.
Shrivathsa, T. V., & Amritha, U. R. (2026, March). Dynamic Sign Language Identification Using Mediapipe Landmarks and Random Forest Classifier. In 2026 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) (Vol. 4, pp. 1–6). IEEE.
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