Faculty Profile

Prof. Dr. Imran Khan

Professor

HOD, Department of Computing

imran.khan@thekit.edu.pk

(068) 111 000 155 Ext. 111

Dr. Imran Khan is a Professor and Head of the Department of Computing at The KIT. With a PhD in Computer Science from International Islamic University Islamabad and Kent State University, USA, he specializes in Natural Language Processing, Machine Learning, Deep Learning, Cybersecurity, and Blockchain. His extensive research contributions in AI, Big Data, and Information Security are published in leading journals. Passionate about technological innovation, Dr. Khan is committed to advancing research and mentoring the next generation of computing professionals.

Education

2017, PhD Computer Science, International Islamic University Islamabad + Kent State University, USA
2005, MS Computer Science, International Islamic University Islamabad
2002, MSc Computer Science, International Islamic University Islamabad
1998, BSc General Science, F.G Post Graduate College for Men H-8 Islamabad

Research Interests

Natural Language Processing
Information Retrieval
Machine Learning and Deep Learning
Data Mining
Artificial Intelligence (ML / DL)
Large Language Models
Information Security
Big Data
Blockchain
Cyber Security

Publications

2024
- Ashfaq, M., Khan, I., Alzahrani, A., Tariq, M.U., Khan, H. And Ghani, A., Accurate Wheat Yield Prediction Using Machine Learning and climate-NDVI Data Fusion. IEEE Access 12 (10.1109/ACCESS.2024.3376735), 40947 – 40961
- Akram A, Khan I, Rashid J, Saddique M, Idrees M, Ghadi YY, Algarn A. Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine. Computers, Materials & Continua. 2024 Jan 1;78(1). DOI
2023
- I. Khan, A. Ghani, S. M. Saqlain, M. U. Ashraf, A. Alzahrani and D. -H. Kim, Secure Medical Data Against Unauthorized Access using Decoy Technology in Distributed Edge Computing Networks. IEEE Access. DOI
- M. F. Afzal, I. Khan, M. J. Rashid, M. Saddique and Heba G. Mohamed, Binary Oriented Feature Selection for Valid Product Derivation in Software Product Line. CMC Computers, Materials and Continua, Vol.76, No.3, 3653-3670. DOI
- M. J. Rashid, I. Khan, I. A. Abbasi, M. R. Saeed, M. Saddique and M. Abbas, A Hybrid Deep Learning Approach to Classify the Plant Leaf Species. CMC Computers, Materials and Continua, Vol.76, No.2. DOI
2022
- Rashid, J., Khan, I., Ali, G., Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf using Hybrid Deep Learning Technique. CMC Computers, Materials and Continua, 74(1), 1235-1257. DOI
2021
- Rashid, J., Khan, I., Ali, G., Almotiri, S. H., AlGhamdi, M. A., & Masood, K. Multi-level deep learning model for potato leaf disease recognition. Electronics, 10(17), 2064. DOI
- Haseeb Ur Rahman, Anwar Ghani, Naved Ahmad, Imran Khan, Muhammad Bilal, Improving Network Eﬃciency In Wireless Body Area Network using Dual Forwarder Selection Technique. Personal and Ubiquitous Computing, Springer. DOI
2020
- Syed Muhammad Saqlain et al., Image Analysis using Human Body Geometry and Size Proportion Science for Action Classiﬁcation. Applied Sciences, MDPI, 10(16): 5453.
- Sher Afgun Raja Usmani et al., Context-based Adoption of Ranking and Indexing Measures for Cricket Team Ranks. CMC Computers, Materials and Continua, 65(2): 1113–1136.
- Shahwar Ali et al., An Efficient Cryptographic Technique Using Modified Diffie-Hellman in Wireless Sensor Network. International Journal of Distributed Sensor Networks, SAGE, 16(6):1-24. DOI
2019
- Anwar Ghani et al., Capacity Gain in Spread Spectrum Based Collaborative Communication with Unsynchronized Phase in Sensor Networks. Journal of Internet Technology, 20(3), 731 – 740.
- SM Saqlain et al., Fisher Score and Matthews Correlation Coefficient-based Feature Subset Selection for Heart Disease Diagnosis using Support Vector Machines. Knowledge and Information Systems, 58(1), 139-167.
2018
- Ahmed Fraz Baig et al., A Lightweight and Secure Two-Factor Anonymous Authentication Protocol for Global Mobility Networks. PLoS ONE, 13(4):e0196061.
2017
- SMS Shah et al., Feature Extraction through Parallel Probabilistic Principal Component Analysis for Heart Disease Diagnosis. Physica A: Statistical Mechanics and its Applications, 482, 796-807.
- MUA Robina Khatoon et al., A Comparative Study on Mixture of Gaussians for Object Segmentation. International Journal of Advanced and Applied Sciences, 4(6), 28-34.
2016
- Khan, I., Chaudhry, S. A., Sher, M., Khan, J. I., & Khan, M. K. An Anonymous and Provably Secure Biometric-Based Authentication Scheme Using Chaotic Maps for Accessing Medical Drop Box Data. The Journal of Supercomputing, 74(8), 3685-3703.
- Chaudhry, S.A. et al., A Provably Secure Anonymous Authentication Scheme for Session Initiation Protocol. Security and Communication Networks.
2013
- Khan, I., Alwarsh, M., Khan, J.I. A Comprehension Approach for Formalizing Privacy Rules of HIPAA for Decision Support. Proceedings of the 12th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA.