Index

Predictive Modeling for Pre-Conditioning in Vehicles

Machine Learning approach for hiring demand forecasting in Large Scale Organizations

In the field of human resources management, the ability to forecast hiring demand with precision is critical for optimizing workforce planning and talent acquisition strategies. As organizations become increasingly complex, traditional forecasting methods, such as simple time series models or heuristic approaches, often fall short of capturing the multifaceted nature of hiring dynamics. In large multinational corporations, forecasting hiring demand requires the consideration of various factors, including macroeconomic indicators, organizational structure, and workforce fluctuations. This thesis proposes the development of a sophisticated machine learning workflow to enhance the accuracy and reliability of hiring demand predictions.

Wearable Virtuosity: Try-On Any Outfit, Virtually

Generalizable X-Ray View Synthesis

Development of a Foundational Feature Extraction Model for Medical X-Ray Images

Image Quality Assessment using Generative AI

Text Generation in Alzheimer’s Disease

Improving Few Shot Classification in Chest X-rays

Evaluate methods to improve few-shot classification on CXRs. This is a rarely studied area for medical domains.

Evaluating State of the Art Scene Text Recognition Methods for Recognizing Text on Tombstones

Investigating Target Class Influence on Neural Network Compressibility for Edge Deployment in Avian Species Identification