Ms. Ifza Shad | Industrial Biotechnology | Best Researcher Award
PhD at University of Science and Technology of China | China
Ms. Ifza Shad is an emerging AI researcher specializing in computer vision, deep learning, and real-time object detection, with strong contributions to medical image analysis and intelligent automation. She completed her MS in Computer Science at Central South University, China, focusing on the development of real-time litter detection models for surface and aquatic environments, and previously earned a BS (Hons) in Computer Science from the University of Central Punjab, Pakistan, graduating as a gold medalist. Her professional experience includes serving as a Computer Vision Engineer at ITSOLERA Pvt, where she led research in medical image analysis for fracture detection and visual search systems for precision agriculture, and as a Data Analyst at Motive, USA, where she excelled in data annotation and analytics. Ifza has authored multiple research papers, including Deep Learning-Based Image Processing Framework for Efficient Surface Litter Detection (Journal of Radiation Research and Applied Sciences, 2025), Attention-Driven Sequential Feature Fusion Framework for Effective Brain Tumor Diagnosis (Significances of Bioengineering and Biosciences, 2025), and An Attention-Fused Architecture for Brain Tumor Diagnosis (Biomedical Signal Processing and Control, 2024). Her ongoing projects explore lightweight YOLO architectures for aquatic litter detection and driver distraction monitoring. With a growing Scopus profile demonstrating increasing academic visibility through 5 publications, citations, and an evolving h-index, she continues to advance AI-driven solutions that integrate sustainability, healthcare, and safety.
Profile: ORCID
Featured Publications
Shad, I. (2025). Deep learning-based image processing framework for efficient surface litter detection in computer vision applications. Journal of Radiation Research and Applied Sciences.
Shad, I. (2025). Attention-driven sequential feature fusion framework for effective brain tumor diagnosis. Significances of Bioengineering and Biosciences.
Shad, I., & Co-authors. (2024). An attention-fused architecture for brain tumor diagnosis. Biomedical Signal Processing and Control.
Shad, I. (2025). ALD-Yolov9c: Lightweight architecture for aquatic litter detection in dynamic environments. IEEE. (Submitted).
Shad, I. (2024). Overcoming misinformation: Advanced detection of fake news by integration of K-fold stacked ensemble. International Journal of Software Engineering and Knowledge Engineering (IJSEKE). (Under review).