Rate My Professor Rafiullah Khan

Dr. Khan's research focuses on user privacy, machine learning, Internet of Things (IoT), and sentiment analysis. Notable projects include "Identifying Vulnerabilities in Privacy Protocols Using Machine Learning Techniques," funded by Macquarie University. His prolific publication record features works such as "Detecting malicious code variants using convolutional neural network (CNN) with transfer learning" (PeerJ Computer Science, 2025), "Stacked LSTM Model for Contextual Correlation Detection Among Multiple Emotions" (IEEE Access, 2025), "Remote diagnosis and triaging model for skin cancer using EfficientNet and extreme gradient boosting" (Complexity, 2021, cited 76 times), "Acute Myeloid Leukemia (AML) detection using AlexNet model" (Complexity, 2021, cited 73 times), "NN-QuPiD Attack: Neural Network-Based Privacy Quantification Model for Private Information Retrieval Protocols" (Complexity, 2021), "Quantification of PIR protocols privacy" (2017 International Conference on Communication, Computing and Digital Systems), and "EEWMP: An IoT-Based Energy-Efficient Water Management Platform for Smart Irrigation" (Scientific Programming, 2021, cited 75 times), among over 35 publications. With more than 562 citations on Google Scholar, his contributions have advanced privacy-preserving techniques in web search, medical diagnostics via deep learning, and IoT applications. Dr. Khan is recognized as an HEC-approved PhD supervisor since 2020, has delivered keynote speeches, served as Web Chair for the 20th International Conference on Advanced Data Mining and Applications (2024) hosted by Macquarie University, and reviews for leading journals including IEEE Access, Scientific Reports, and ACM Transactions on the Web.