Nidhi Gupta Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Professor
faculty
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Biography and Research Information
OverviewAI-generated summary
Dr. Nidhi Gupta's research focuses on the application of machine learning and deep learning techniques to image processing and computer vision tasks. Her work has explored advancements in areas such as automated weapon detection, leveraging both classical machine learning and deep learning methods, including specific architectures like Yolov7-DarkVision and YOLOv5.
Her investigations extend to medical imaging, with publications on brain tumor detection and analysis using explainable deep learning, as well as COVID-19 detection from CT scan images utilizing transfer learning. Dr. Gupta has also applied these technologies to practical systems, such as developing an automated electronic meter reading system and a real-time firearm detection system. Her expertise encompasses programming languages like Python and MATLAB, and she has a proven track record with 77 publications and 588 citations, maintaining an h-index of 13.
Metrics
- h-index: 13
- Publications: 78
- Citations: 607
Selected Publications
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Seeing Through the Mask: AI-Generated Text Detection with Similarity-Guided Graph Reasoning (2025)
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CAMFeND: Credibility-Aware Multimodal Fake News Detection with Rotational Attention (2025)
Collaboration Network
Top Collaborators
- A comprehensive study towards high-level approaches for weapon detection using classical machine learning and deep learning methods
- Robust weapon detection in dark environments using Yolov7-DarkVision
- WeaponVision AI: a software for strengthening surveillance through deep learning in real-time automated weapon detection
- COVID-19 Detection from CT Scan Images using Transfer Learning Approach
- Deep Learning Based Automated Electronic Meter Reading System using YOLOv5 Architecture
Showing 5 of 7 shared publications
- WeaponVision AI: a software for strengthening surveillance through deep learning in real-time automated weapon detection
- Real-Time Firearm Detection System Utilizing Deep Learning and Super-Resolution CNNs
- Enhancing Video Surveillance with Deep Learning-Based Real-Time Handgun Detection and Tracking
- A comprehensive study towards high-level approaches for weapon detection using classical machine learning and deep learning methods
- Robust weapon detection in dark environments using Yolov7-DarkVision
- Metaverse-Driven Advances in Immersive VR Experiences for Behaviour Analysis and Skill Development
- Securing Serverless Environments: A Blockchain-Based Approach for Licensing and Payment Verification
- Adherence to Trust Section 17 Leave Policy at High Dependency Unit and an Open Rehab Unit
- Survey of Mental Health Professionals’ Knowledge and Skills in Managing Substance Misuse in Patients Admitted on a Mental Health High Dependency Unit
- Survey of Techniques for Clustering and Classification of ECG using WEKA
- Line Segmentation from Unconstrained Handwritten Text Images using Adaptive Approach
- Simulation of Autonomous Car using Deep Learning
- Simulation of Autonomous Car using Deep Learning
- Simulation of Autonomous Car using Deep Learning
- Simulation of Autonomous Car using Deep Learning
- Computational Study of Energy Consumptions in Residential Building for Predicting Effectiveness of Various Energy Conservation Steps
- A Comprehensive Study on Machine Learning Approaches for Emotion Recognition
- JMCD Dataset for Brain Tumor Detection and Analysis Using Explainable Deep Learning
- JMCD Dataset for Brain Tumor Detection and Analysis Using Explainable Deep Learning
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