Qassim University, Saudi Arabia.
Dr. Saleh Albahli is a highly accomplished academic and researcher specializing in Digital Transformation, Data Science, and Artificial Intelligence. Currently an Associate Professor and Vice-Dean of Information Technology Deanship at Qassim University, he is known for spearheading transformative digital initiatives, leading enterprise architecture projects, and contributing to cutting-edge research in machine learning and deep learning. His work is globally recognized, ranking him among the top 2% of scientists in AI research worldwide.
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🎓 Education
Dr. Saleh Albahli holds a Ph.D. in Computer Science with distinction from Kent State University, USA (2016), showcasing his expertise in advanced computational methodologies and research excellence. He earned a Master’s degree in Information Technology with distinction from The University of Newcastle, Australia (2010), highlighting his dedication to mastering cutting-edge IT solutions. His academic journey began with a Bachelor’s degree in Computer Science from King Saud University, Saudi Arabia (2004), laying a strong foundation for his accomplished career in technology and innovation.
đź’Ľ Experience
Dr. Saleh Albahli has built an illustrious career, currently serving as an Associate Professor in the Department of IT at Qassim University since 2020, where he contributes to advancing education and research. Concurrently, he holds dual leadership roles as Vice-Dean of IT Deanship and Director of Enterprise Architecture & Digital Transformation at Qassim University, spearheading transformative initiatives to enhance technological frameworks and drive digital innovation.
Previously, Dr. Albahli gained international experience as a Senior System Analyst at Cleveland Clinic, USA (2015–2016), where he developed cutting-edge systems to optimize healthcare operations. He also served as a Lecturer at Kent State University, USA (2015–2016), imparting knowledge and fostering academic growth. Earlier in his career, he worked as an Oracle Developer and Apps DBA at Riyadh Bank and Integrated Telecom Company in Saudi Arabia (2005–2007), honing his technical expertise in database systems and enterprise applications.
🔬 Research Interests
Digital Transformation and its integration with enterprise architecture
Machine Learning and Deep Learning Pipelines
Big Data Analytics, Data Governance, and Predictive Analytics
Artificial Intelligence Applications in healthcare and business
Process Optimization in technology-driven environments
🏆 Awards & Recognitions
Ranked among the top 2% of scientists globally in AI research (2022)
First Place in Digital Transformation (Qiyas) – Qassim University (2022, 2023)
ISO certifications in 22301, 20000, and 27001 for excellence in IT management
đź“š Selected PublicationsÂ
Efficient Hyperparameter Tuning for Predicting Student Performance with Bayesian Optimization
Albahli, S.
Multimedia Tools and Applications, 2024, 83(17), pp. 52711–52735.
This study introduces a Bayesian optimization approach to enhance hyperparameter tuning for predictive models in educational datasets, achieving improved accuracy and efficiency. (Citations: 4)
MedNet: Medical Deepfakes Detection Using an Improved Deep Learning Approach
Albahli, S., Nawaz, M.
Multimedia Tools and Applications, 2024, 83(16), pp. 48357–48375.
This paper presents MedNet, a novel deep learning framework tailored to detect medical deepfakes, ensuring the integrity of critical healthcare data. (Citations: 4)
Opinion Mining for Stock Trend Prediction Using Deep Learning
Albahli, S., Nazir, T.
Multimedia Tools and Applications, 2024.
Leveraging deep learning techniques, this research focuses on sentiment analysis to predict stock trends, demonstrating robust performance metrics. (Citations: 0)
An Improved DenseNet Model for Prediction of Stock Market Using Stock Technical Indicators
Albahli, S., Nazir, T., Nawaz, M., Irtaza, A.
Expert Systems with Applications, 2023, 232, 120903.
This work proposes enhancements to DenseNet architectures for stock market predictions based on technical indicators, achieving notable predictive accuracy. (Citations: 10)
A Circular Box-Based Deep Learning Model for the Identification of Signet Ring Cells from Histopathological Images
Albahli, S., Nazir, T.
Bioengineering, 2023, 10(10), 1147.
This open-access study develops a circular box-based deep learning model for the accurate detection of signet ring cells in histopathological images, aiding cancer diagnosis.