Muhammad Saadi
Senior Research Fellow, Department of Computer Science, School of Science and Technology · Nottingham Trent University
Biography
Muhammad Saadi is currently working as a Senior Research Fellow at the Department of Computer Science, Nottingham Trent University, UK. Before that, he was an Associate Professor and Head of Electrical Engineering Department, Faculty of Engineering, University of Central Punjab. He received his Ph.D. from the Department of Electrical Engineering, Chulalongkorn University, Thailand. During his Ph.D., he was associated with NECTEC and KMUTNB as a research assistant and lecturer respectively. Furthermore, he also worked as a technical content writer at Aimagin. Mr. Saadi has completed his Bachelor's Degree from the National University of Computer and Emerging Sciences, Pakistan in year 2007. Before pursuing his Master's Degree from National University of Malaysia (UKM) in year 2008, he has worked as Network Engineer in Mobilink, Pakistan. He has worked in University of Management and Technology, Pakistan for two years as a Lecturer in Electrical Engineering Department.
Education
Chulalongkorn University, Thailand Dec 2011 - Jan 2016 • Doctor of Philosophy in Electrical Engineering • Course Work CGPA: 3.90 • Thesis Title: Development of Indoor Localization Techniques for Visible Light Communication National University of Malaysia, Malaysia June 2008 - Aug 2009 • Master of Engineering in Communication and Computer • Secured 1st Position in University • Course Work CGPA: 3.78 • Thesis Title: Development of Total Electron Content Map using Global Positioning System Data National University of Computer and Emerging Sciences, Pakistan Aug 2003 - Dec
Recent Google Scholar Publications
- Optimized LED Placement for Indoor Visible Light Communication: Trade-offs Between Uniform Coverage and High Throughput
- Integer linear programming for optimizing drone-based delivery routes
- BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning (vol 22, 7409, 2022)
- Correction: Okey et al. BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning. Sensors 2022, 22, 7409
- Lightbioptimum: An Intrusion Detection System Based on Bio‐Inspired Algorithm for VANET
Links
- ORCID https://orcid.org/0000-0001-7901-7435
- Google Scholar https://scholar.google.com/citations?user=rfoHcHoAAAAJ
- Scopus https://www.scopus.com/authid/detail.uri?authorId=https%3A%2F%2Fwww.scopus.com%2Fauthid%2Fdetail.uri%3FauthorId%3D55638443900
- Personal Weblink https://sites.google.com/site/muhammadsaadi