Postdoctoral Research Fellow | Shandong University | China
Dr. Sheharyar Khan is a highly skilled researcher specializing in the Diagnosis of Infectious Diseases, demonstrating outstanding contributions through his academic and scientific achievements. His education in Computer Science and Software Engineering has been instrumental in developing innovative computational models and AI-based frameworks that enhance the Diagnosis of Infectious Diseases through data analysis, prediction models, and intelligent diagnostic systems. Professionally, Dr. Khan has held roles as a Postdoctoral Research Fellow, IT Specialist, and Lecturer, where he applied advanced technologies to improve the Diagnosis of Infectious Diseases by integrating machine learning and computational techniques. His research focuses on using cloud computing, big data, and ensemble learning to improve precision in the Diagnosis of Infectious Diseases. His numerous peer-reviewed publications, recognized in leading journals, contribute to cutting-edge advancements in healthcare technology and the Diagnosis of Infectious Diseases. Dr. Khan has received multiple honors and awards, including international scholarships and distinctions for his exceptional research performance related to the Diagnosis of Infectious Diseases. His technical expertise spans machine learning, deep learning, IoT, and data-driven software applications that strengthen real-time Diagnosis of Infectious Diseases. With strong analytical and programming skills, he continues to drive impactful scientific progress. Overall, Dr. Sheharyar Khan embodies a blend of innovation, academic excellence, and leadership in the Diagnosis of Infectious Diseases field, maintaining a Google Scholar profile of 113 Citations, 7 h-index, and 6 i10-index.
Profiles: Google Scholar | ORCID
Featured Publications
1. Ahmed, U., Jiangbin, Z., Almogren, A., Khan, S., Sadiq, M. T., Altameem, A., & others. (2024). Explainable AI-based innovative hybrid ensemble model for intrusion detection. Journal of Cloud Computing, 13(1), 150.
2. Khan, S., Jiangbin, Z., Irfan, M., Ullah, F., & Khan, S. (2024). An expert system for hybrid edge to cloud computational offloading in heterogeneous MEC–MCC environments. Journal of Network and Computer Applications, 225, 103867.
3. Khan, S., Zheng, J., Khan, S., Masood, Z., & Akhter, M. P. (2023). Dynamic offloading technique for real-time edge-to-cloud computing in heterogeneous MEC–MCC and IoT devices. Internet of Things, 24, 100996.
4. Khan, S., Jiangbin, Z., Ullah, F., Akhter, M. P., Khan, S., Awwad, F. A., & Ismail, E. A. A. (2024). Hybrid computing framework security in dynamic offloading for IoT-enabled smart home system. PeerJ Computer Science, 64.
5. Afridi, A., Wahab, A., Khan, S., Ullah, W., Khan, S., Islam, S. Z. U., & Hussain, K. (2021). An efficient and improved model for power theft detection in Pakistan. Bulletin of Electrical Engineering and Informatics, 10(4), 1828–1837.