Muhammad Khalil Afzal

Also published under:Muhammad Hammad Afzal, Muhamamd Khalil Afzal, Muhammad Afzal, M. Khalil Afzal, M. Afzal

Affiliation

Department of Computer Science, COMSATs University Islamabad, Wah Campus, Islamabad, Pakistan

Topic

Neural Network,Energy Trading,Gated Recurrent Unit,Long Short-term Memory,Machine Learning,Machine Learning Models,Prediction Accuracy,Renewable Energy Sources,Service Quality,Supply And Demand,Time Slot,Traffic Congestion,Traffic Flow,Traffic Prediction,Training Time,Accuracy Of Model,Achievable Rate,Achievable Rate Of User,Adaptive Quality Of Service,Adaptive System,Amount Of Electricity,Artificial Intelligence-based Algorithm,Autonomous Vehicles,Baseline Methods,Big Data Platform,Block Error Rate,Block Length,Boost Converter,Bottom Of Page,Bounding Box,Bounding Box Regression,Capacity Of Users,Channel State,Class Probabilities,Client Participation,Closed-loop Control,Closed-loop System,Cognitive Framework,Communication Network,Confidence Score,Configuration Parameters,Consumer Demand,Consumer Preferences,Control Design,Conventional Converter,Convolution Operation,Correlation Score,Data Privacy,Dc Output Voltage,Deep Learning,

Biography

Muhammad Khalil Afzal [SM'16] ([email protected]) is an assistant professor in the Department of Computer Science, COMSATS University Islamabad Wah Campus, Pakistan. He received his Ph.D. degree in information and communication engineering from Yeungnam University, Korea in 2014. He is an associate editor of Elsevier FGCS and IEEE Access, and lead guest editor for issues in IEEE Communication Magazine and Transactions for Emerging Telecommunications Technologies (ETT). His research interest includes wireless sensor networks, Smart Cities, 5G, and IoT.