Assoc. Prof. Dr. Ammar Oad | Computer Vision | Research Excellence Award
Professor | Shaoyang University | China
Assoc. Prof. Dr. Ammar Oad is an accomplished researcher in Artificial Intelligence with strong expertise in deep learning, computer vision, cybersecurity, and intelligent data-driven systems. His research focuses on designing advanced algorithms for image analysis, object detection, multimodal learning, cross-modal retrieval, and secure AI frameworks capable of addressing modern challenges in threat detection and autonomous systems. Dr. Oad’s scientific contributions span AI-powered fake news detection, plant disease identification using explainable AI, blockchain-enabled cybersecurity mechanisms, sustainable smart grid prediction models, and intelligent pattern recognition. His research impact is reflected in Scopus metrics of 382 citations across 374 documents with an h-index of 9, and Google Scholar metrics of 573 citations, h-index 10, and i10-index 12, demonstrating strong visibility and influence within the scientific community. His work regularly appears in reputable journals such as IEEE Access, Optik, Electronics (MDPI), and leading materials science journals through interdisciplinary collaborations. Dr. Oad also contributes to the academic community as an editorial board member and scientific reviewer for several high-impact journals. His research interests include deep neural architectures, Gaussian mixture models, ensemble learning, blockchain security frameworks, and energy-efficient AI systems for smart cities. By integrating machine learning with cybersecurity principles, he aims to develop intelligent, robust, and transparent AI solutions capable of safeguarding digital infrastructures while advancing the state of automated recognition and decision-making technologies. His growing body of research reflects innovation, rigor, and a commitment to addressing real-world AI challenges.
Profile
Scopus | ORCID | Google Scholar
Featured Publications
Oad, A., Farooq, H., Zafar, A., Akram, B. A., Zhou, R., & Dong, F. (2024). Fake news classification methodology with enhanced BERT. IEEE Access, 12, 164491–164502.
Oad, A., Abbas, S. S., Zafar, A., Akram, B. A., Dong, F., Talpur, M. S. H., & Uddin, M. (2024). Plant leaf disease detection using ensemble learning and explainable AI. IEEE Access, 12, 156038–156049.
Oad, A., Ahmad, H. G., Talpur, M. S. H., Zhao, C., & Pervez, A. (2023). Green smart grid predictive analysis to integrate sustainable energy of emerging V2G in smart city technologies. Optik, 272, 170146.
Oad, A., Razaque, A., Tolemyssov, A., Alotaibi, M., Alotaibi, B., & Zhao, C. (2021). Blockchain-enabled transaction scanning method for money laundering detection. Electronics, 10(15), 1766.
Li, Y., Liu, W., Pang, X., Oad, A., Liang, D., Zhang, X., Tang, B., Fang, Z., Shi, Z., & Chen, J. (2024). Microwave dielectric properties, Raman spectra and sintering behavior of low loss La7Nb3W4O30 ceramics with rhombohedral structure. Ceramics International.