Dr. Faisal Saeed is an ai research scientist specializing in computer vision, deep learning, and intelligent manufacturing, with a strong research portfolio built through advanced academic training and international research appointments. He earned his master’s combined Ph.d. in computer science from Kyungpook National University, South Korea, where his work focused on transformer-based architectures for industrial small-object detection, culminating in the thesis feature enhanced assignment-based detection transformer for industrial small object detection. His academic contributions include 21 documents, a growing research footprint of 738 citations, and an h-index of 10, reflecting the global impact of his work across ai-driven automation, defect detection, and predictive maintenance. Professionally, he has served as a university research assistant and later as a postdoctoral fellow in both South Korea and China, contributing to deep learning theory, medical image analysis, multimodal ai, Object Detection and industrial visual inspection systems. His research integrates digital twins, time-series forecasting, and transformer models to advance intelligent manufacturing and robotics. Committed to bridging theoretical innovation with real-world applications, Dr. Saeed continues to publish influential work, secure funding for emerging ai research, and contribute to the scientific community through teaching, collaboration, and cutting-edge industrial ai development.
Profile: Scopus | Google Scholar
Featured Publications
Shah, H. A., Saeed, F., Yun, S., Park, J. H., Paul, A., & Kang, J. M. (2022). A robust approach for brain tumor detection in magnetic resonance images using finetuned EfficientNet. IEEE Access, 10, 65426–65438.
Saeed, F., Paul, A., Rehman, A., Hong, W. H., & Seo, H. (2018). IoT-based intelligent modeling of smart home environment for fire prevention and safety. Journal of Sensor and Actuator Networks, 7(1), 11.
Saeed, F., Paul, A., Karthigaikumar, P., & Nayyar, A. (2020). Convolutional neural network based early fire detection. Multimedia Tools and Applications, 79(13), 9083–9099.
Saeed, F., Ahmed, M. J., Gul, M. J., Hong, K. J., Paul, A., & Kavitha, M. S. (2021). A robust approach for industrial small-object detection using an improved faster regional convolutional neural network. Scientific Reports, 11(1), 23390.
Rehman, A., Rathore, M. M., Paul, A., Saeed, F., & Ahmad, R. W. (2018). Vehicular traffic optimisation and even distribution using ant colony in smart city environment. IET Intelligent Transport Systems, 12(7), 594–601.