Lecturer, The University of Lahore, Pakistan
Ali Raza is a dedicated research scholar specializing in data science, known for his expertise in machine learning and deep learning applications. With a strong academic background and extensive professional experience in software development, he has contributed significantly to research in artificial intelligence and health informatics.
Profile
Google Scholar
📚 Education:
Ali completed his Bachelor of Science in Computer Science at KFUEIT after graduating from Iqra Degree College with a degree in Pre-Engineering. He further pursued his passion for computer science by earning a Master’s degree in Computer Science from KFUEIT, where his research focused on novel approaches in deep learning for image detection.
💼 Experience:
Ali’s professional journey includes roles as a Research Assistant at KFUEIT, where he published research articles on artificial intelligence. He has also worked as a Desktop App Developer at DexDevs Company and as a Full Stack Python Developer at BuiltinSoft Company, gaining expertise in business application development and machine learning frameworks.
🔬 Research Interests:
Ali’s research interests revolve around data science, particularly in machine learning model optimization, health informatics, and artificial intelligence applications in diverse domains such as pregnancy health analysis and network security.
🏆 Awards:
Ali has contributed significantly to research, evident from his publications and contributions as a peer reviewer for IEEE Access and PLOS ONE, highlighting his recognition in the academic community.
📄 Publications:
Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction, Plos one, 2022 (cited 46 times)
A novel deep learning approach for deepfake image detection, Applied Sciences, 2022 (cited 58 times)
Predicting employee attrition using machine learning approaches, Applied Sciences, 2022 (cited 44 times)
A novel methodology for human kinematics motion detection based on smartphones sensor data using artificial intelligence, Technologies, 2023 (cited 23 times)
Novel class probability features for optimizing network attack detection with machine learning, IEEE Access, 2023