Ms. Ifza Shad | Computer Vision | Research Excellence Award

University of Central Punjab | Pakistan

Ms. Ifza Shad is a computer vision and artificial intelligence researcher whose work focuses on real-time object detection, medical image analysis, deep learning optimization, and multimodal perception models for complex environments. Her research integrates advanced machine learning architectures, including YOLO-based detectors, attention-driven fusion networks, and lightweight deep learning frameworks designed for resource-efficient deployment in dynamic real-world scenarios. She has contributed to cutting-edge studies in aquatic and surface litter detection, brain tumor diagnosis, protective workwear recognition, and driver-behavior monitoring systems, demonstrating a strong emphasis on safety, healthcare, and environmental sustainability. Her interdisciplinary approach merges computer vision, robotics, and large-scale data processing, allowing her to design algorithms that address challenges in automation, public health, and smart systems. She has authored impactful publications in reputable international journals indexed in Scopus and Web of Science, with her research widely cited and accessible on Google Scholar. Her scholarly record includes peer-reviewed articles, collaborative projects with international researchers, and contributions to academic seminars and conferences. She continues to advance innovative detection models and AI-driven solutions, aiming to enhance real-time decision support systems through robust, interpretable, and computationally efficient algorithms. Her research output reflects a growing citation count, supported by Scopus metrics, Google Scholar indices, and document-level analytics, emphasizing her active role in the global scientific community and her contribution to emerging intelligent systems.

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ORCID

Featured Publications

Shad, I., Zhang, Z., Asim, M., Al-Habib, M., Chelloug, S. A., & Abd El-Latif, A. (2025). Deep learning-based image processing framework for efficient surface litter detection in computer vision applications. Journal of Radiation Research and Applied Sciences, 18(2), 101534.

Shad, I., Bilal, O., & Hekmat, A. (2025). Attention-driven sequential feature fusion framework for effective brain tumor diagnosis. Significances of Bioengineering & Biosciences, 7(3).

Hekmat, A., Zhang, Z., Khan, S. U. R., Shad, I., & Bilal, O. (2024). An attention-fused architecture for brain tumor diagnosis. Biomedical Signal Processing and Control, 101, 107221.