Dr. Muhammad Waqas Nadeem
Assistant Professor
Waqas.nadeem@ucp.edu.pk
Ext:
Ext:
Dr. Muhammad Waqas Nadeem has 4 years of experience in academia. He holds a Ph.D. in Computer Science from Universiti Tunku Abdul Rahman (UTAR), Malaysia. He completed his M.Phil. Degree in computer science from the University of Management and Technology, Lahore, and his BS Degree from Lahore Garrison University. He has published numerous research articles in various international journals and conferences. His potential research areas include Network Security, FinTech Security, SDN Security, IoT Security, Artificial Intelligence, Machine Learning, and Deep Learning.
| Doctor of Philosophy | Computer Science | Universiti Tunku Abdul Rahman (UTAR), Kampar, Perak, Malaysia |
| Master of Science | Computer Science | University of Management & Technology, Lahore, Pakistan |
| Bachelor of Science | Computer Science | Lahore Garrison University, Lahore, Pakistan |
| Lecturer | Lahore Garrison University | September 2017- May 2021 |
| 1 | Toward Secure Software-Defined Networks Using Machine Learning: A Review, Research Challenges, and Future Directions. 2023 Computer Systems Science & Engineering Vol.47, No.2 WoS |
| 2 | Detecting and Mitigating Botnet Attacks in Software-Defined Networks using Deep Learning Techniques. 2023 IEEE Access Vol.11 WoS |
| 3 | Deep learning for diabetic retinopathy analysis: A review, research challenges, and future directions. 2022 Sensors Vol. 22, No. 18 WoS |
| 4 | A fusion-based machine learning approach for the prediction of the onset of diabetes. 2021 Healthcare Vol. 9, No. 10 WoS |
| 5 | DDoS Detection in SDN using Machine Learning Techniques. 2021 Computers, Materials & Continua Vol.71, No1 WoS |
| 6 | Osteoporosis prediction for trabecular bone using machine learning: a review. 2021 Computers, Materials & Continua Vol.61, No.1 WoS |
| 7 | Fusion-Based Machine Learning Architecture for Heart Disease Prediction. 2021 Computers, Materials & Continua Vol,67, No.2 WoS |
| 8 | Bone age assessment empowered with deep learning: a survey, open research challenges and future directions. 2020 Diagnostics Vol.10, No.10 WoS |
| 9 | Brain tumor analysis empowered with deep learning: A review, taxonomy, and future challenges. Brain sciences. 2020 Brain sciences Vol.10, No.2 WoS |
| 10 | A survey, taxonomy, and open research challenges. 2020 IEEE Access Vol.8 WoS |