Education:
Mr. Ali Raza holds an impressive academic background, having completed his Master of Science (MS) in Computer Science at Khwaja Fareed University of Engineering and Information Technology (KFUEIT) with a remarkable CGPA of 3.93 in 2023. During his studies, KFUEIT achieved a ranking of #258 in the Asian University Rankings for Southern Asia, underscoring the institution’s reputation for academic excellence. Prior to this, he earned his Bachelor of Science (BS) in Computer Science from the same university, graduating with a CGPA of 3.47 in 2021. This solid educational foundation has equipped Mr. Raza with the necessary knowledge and skills to excel in the fields of computer science and machine learning, fostering his commitment to furthering research and innovation in technology.
Professional Experience:
Mr. Ali Raza has built a solid professional background in academia and industry, contributing to both teaching and software development. Currently, he serves as a Lecturer in the Department of Software Engineering at the University of Lahore, ranked #40 in the Asian University Rankings for Southern Asia, where he specializes in Object-Oriented Programming. Prior to this role, he was a Visiting Lecturer at Khwaja Fareed University of Engineering and Information Technology (KFUEIT) from 2021 to 2023, where he taught a wide range of courses, including Introduction to ICT, Programming Fundamentals, Database Systems, Machine Learning, Data Structures, and Algorithms. Complementing his academic roles, Mr. Raza gained valuable industry experience as a Full Stack Python Developer at BuiltinSoft from 2020 to 2022. In this role, he developed business web applications using Python Django and integrated machine learning frameworks, further enhancing his practical expertise in application development. This blend of academic and industry experience has equipped Mr. Raza with both a deep theoretical foundation and hands-on technical skills.
Research Interests:
Mr. Ali Raza’s research interests center on advancing methodologies in machine learning, cybersecurity, computer vision, and signal processing. He is particularly focused on leveraging machine learning algorithms to enhance network security, developing predictive models to detect cyber threats, and optimizing feature engineering for data-driven health risk analysis. Additionally, his work in computer vision, particularly using deep learning techniques, explores novel approaches for identifying genetic disorders from facial images, providing valuable tools in the field of medical diagnostics. His research contributions demonstrate a commitment to developing innovative, practical solutions that address complex challenges in technology and healthcare.
Conclusion:
Ali Raza’s strong academic background, extensive teaching and industry experience, and impactful research contributions make him a highly suitable candidate for the Best Researcher Award. His interdisciplinary approach, particularly in applying machine learning to pressing challenges in cybersecurity and healthcare, demonstrates a commitment to both innovation and societal impact. His work aligns well with the goals of the award, making him a deserving candidate for recognition.
Publication Top Noted:
A novel deep learning approach for deepfake image detection
- Authors: A. Raza, K. Munir, M. Almutairi
- Journal: Applied Sciences
- Volume: 12
- Issue: 19
- Article: 9820
- Year: 2022
- Citations: 80
Predicting employee attrition using machine learning approaches
- Authors: A. Raza, K. Munir, M. Almutairi, F. Younas, MMS Fareed
- Journal: Applied Sciences
- Volume: 12
- Issue: 13
- Article: 6424
- Year: 2022
- Citations: 77
Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction
- Authors: A. Raza, H.U.R. Siddiqui, K. Munir, M. Almutairi, F. Rustam, I. Ashraf
- Journal: Plos One
- Volume: 17
- Issue: 11
- Article: e0276525
- Year: 2022
- Citations: 63
A novel approach for polycystic ovary syndrome prediction using machine learning in bioinformatics
- Authors: S. Nasim, M.S. Almutairi, K. Munir, A. Raza, F. Younas
- Journal: IEEE Access
- Volume: 10
- Pages: 97610-97624
- Year: 2022
- Citations: 39
A performance overview of machine learning-based defense strategies for advanced persistent threats in industrial control systems
- Authors: M. Imran, H.U.R. Siddiqui, A. Raza, M.A. Raza, F. Rustam, I. Ashraf
- Journal: Computers & Security
- Volume: 134
- Article: 103445
- Year: 2023
- Citations: 29
Novel class probability features for optimizing network attack detection with machine learning
- Authors: A. Raza, K. Munir, M.S. Almutairi, R. Sehar
- Journal: IEEE Access
- Year: 2023
- Citations: 28
Effective feature engineering technique for heart disease prediction with machine learning
- Authors: A.M. Qadri, A. Raza, K. Munir, M.S. Almutairi
- Journal: IEEE Access
- Volume: 11
- Pages: 56214-56224
- Year: 2023
- Citations: 27
A novel methodology for human kinematics motion detection based on smartphones sensor data using artificial intelligence
- Authors: A. Raza, M.R. Al Nasar, E.S. Hanandeh, R.A. Zitar, A.Y. Nasereddin, et al.
- Journal: Technologies
- Volume: 11
- Issue: 2
- Article: 55
- Year: 2023
- Citations: 24
LogRF: An approach to human pose estimation using skeleton landmarks for physiotherapy fitness exercise correction
- Authors: A. Raza, A.M. Qadri, I. Akhtar, N.A. Samee, M. Alabdulhafith
- Journal: IEEE Access
- Year: 2023
- Citations: 22
A novel ensemble method for enhancing Internet of Things device security against botnet attacks
- Authors: A. Arshad, M. Jabeen, S. Ubaid, A. Raza, L. Abualigah, K. Aldiabat, H. Jia
- Journal: Decision Analytics Journal
- Volume: 8
- Article: 100307
- Year: 2023
- Citations: 21