Summary

Dr. Abdul Rehman is a Distinguished Research Professor at the Human Data Science Lab at Jeonju University, South Korea. He previously served as a Postdoctoral Research Associate at the Hyper-Connectivity Convergence Technology Research Center, Kyungpook National University (KNU), South Korea, and as an Assistant Professor at the University of Central Punjab, Pakistan. His academic journey spans multiple prestigious institutions, reflecting a deep commitment to research, innovation, and education. Dr. Rehman earned his bachelor’s degree in Mathematics from the International Islamic University, Islamabad, Pakistan, before pursuing an Integrated Ph.D. in Computer Science and Engineering at KNU, Daegu, South Korea. His research interests include IoT, Social IoT, Industrial IoT, Data Science (AI, ML, DL), Social Networking, Small-World Problems, Smart Cities, Data Analysis, Mathematical Modeling for Decision Making, and Brain Mapping. Dr. Rehman has received several prestigious accolades, including the KINGS Scholarship Award for his Integrated Ph.D. studies at KNU and the Outstanding Researcher Award from the School of Computer Science and Engineering, KNU. An active contributor to the global research community, Dr. Rehman serves as a Guest Editor and Editorial Board Member for various international journals, including Digital Technologies Research and Applications. He has also played key roles in several international conferences, serving as a Session Chair at ICAIIC 2024, ICOT 2023 & IIHMSP 2023, ICAIIC 2023, ICSCA 2023, and CAIML 2022, among others. Additionally, he was the Publication Chair for ICOT 2020. His extensive involvement in academic conferences, editorial roles, and research collaborations underscores his dedication to advancing knowledge in his field. More details about his work and contributions can be found on his personal website: https://sites.google.com/view/drrehman/home.

Edited Journals

ICCK Contributions


Open Access | Review Article | 17 November 2025
A Systematic Literature Review of Text-to-SQL: Performance, Challenges, and Limitations
ICCK Transactions on Advanced Computing and Systems | Volume 2, Issue 1: 1-24, 2025 | DOI: 10.62762/TACS.2025.497935
Abstract
This literature review examines the state of Text-to-SQL technology, which translates natural language queries into SQL. It analyzes rule-based, neural, and hybrid approaches, assessing their strengths and weaknesses, and surveys commonly used datasets, benchmarks, and evaluation metrics. The study identifies research gaps concerning generalization, scalability, and interpretability, and suggests integrating user feedback and domain knowledge. To better understand the implementation and potential improvements of machine learning in this domain, we conducted a systematic literature review (SLR) of publications from 2015 to 2023. From 439 gathered papers, 23 were identified as highly relevant.... More >

Graphical Abstract
A Systematic Literature Review of Text-to-SQL: Performance, Challenges, and Limitations

Open Access | Research Article | 04 October 2025
Transforming Citation Networks into Insights: Mapping Scholarly Influence with Advanced Graph Models
ICCK Transactions on Advanced Computing and Systems | Volume 1, Issue 4: 238-257, 2025 | DOI: 10.62762/TACS.2025.939169
Abstract
The growing role of citation relations in identifying research impact has spurred much investigation on assessing the most cited papers and their roles within datasets. Due to the richness of the CORA dataset, this study selects highly cited papers and measures the results of node classification, as well as the H-index of research articles. Besides, it explores the correlations and robustness with regard to the nodes by computing their chances and studying their connections. To these ends, linear transformation was utilized for mapping low-level node features to high-level, and the Graph Attention Networks (GAT) for node classification. The study was able to find highly cited papers and com... More >

Graphical Abstract
Transforming Citation Networks into Insights: Mapping Scholarly Influence with Advanced Graph Models