Dr. Parul Arora’s research focuses on model development for the application related to pattern recognition. She works mainly in the field of information set theory based model development. Her studies on information set theory based feature extraction and classification models have helped understand how human behavioral traits like human gait and physical modalities like palmprint and Iris can identify and verify humans in surveillance applications and many more. She has also worked on fabric defect patterns detection, Cardio-vascular diseases detection, skin cancer detection and seizure detection. Her research also covers robust image forgery detection, ensuring safety and long-term stability. Dr. Arora applied deep learning for applications in health informatics and medical image analysis, where customized wide residual networks (HRB-D) for skin lesion segmentation and sentiment analysis models for depression detection from social media data have both achieved state-of-the-art accuracy, advancing early diagnosis tools and demonstrating the applicability of AI across domains. She has also explored image processing and machine learning for automated photoluminescence detection. She has presented her research at IEEE international conferences and has received recognition for her technical contributions. Through the Giant project scheme at DRID, where she is the Co-Principal Investigator, she is working with EEG signals acquired from BioAMP and analyzing them for Wireless System Design. Her work demonstrates practical value in varied domains—from surveillance and authentication to medical diagnostics.