Nikitha Shruthi Ramasetti

In this thesis work, the heart rate (HR) and blood pressure (BP) were estimated from the raw PPG signals using deep neural networks architecture. The deep neural network framework was developed, which involves convolutional neural network (CNN) and long short-term (LSTM) models using PyTorch. The proposed model was trained and implemented to measure the patient’s heart rate, systolic and diastolic BP from the PPG signals. The model was evaluated on the unseen test datasets using regressions metrics.

Evaluation of welding research data, which are collected experimentally. Software development in the programming language Python with appropriate tools- Git/GitHub, automated API documentation, CI/CD pipelines. Creation and updating of software documentation via readthedocs.org. Creation of tutorials for the developed software using Sphinx.

Understanding and analyzing the data within the company’s database system. Methods to initialize parameters to build the proposed model. Build and train the convolutional neural network (CNN) model to extract high-level features from the raw DNA sequences. Recognizing the patterns and predicting the sequences based on the label (production time) Evaluation of the model performance using measuring metrics and testing on the unseen datasets.

Financial Management, Production and Operational Management, Quantitative Methods and Operational Research, Managerial Economics, Business Mathematics and Analytics, Information Technology for Business, Entrepreneurship & New Venture Creation, Strategic Management, Business Marketing.

Data Structures with C, Analysis and Design of Algorithms, C++, Software Engineering, System Software, Operating Systems, Database Management System, Unix System Programming, Compiler Design, Programming the Web, Data Mining, JAVA and J2EE, System Modelling & Simulation and Software Testing.