Summary

Tahir Sher pursuing his PhD in Artificial Intelligence from Korea University, Seoul Campus, Korea. He has completed an MS in Data Science from Air University and a bachelor's degree in mathematics from the International Islamic University, Islamabad, where he graduated with a gold medal and received both distinction and position certificates. With extensive teaching experience across various institutions, Mr. Sher specializes in delivering courses such as Calculus, Ordinary and Partial Differential Equations, Numerical Analysis, Linear Algebra, and Statistical \& Mathematical Methods for Data Science. His students hail from Pakistan and allied countries. As a research scholar in the Explainable AI Research Group at Air University, his areas of interest include the Internet of Things (IoT), Social IoT, Machine Learning, Deep Learning, Natural Language Processing (NLP), Data Analysis, Time Series Analysis, Mathematical Modeling for Decision-Making, Social Media Analysis, Federated Learning, and Computer Vision. Committed to advancing interdisciplinary research, Mr. Tahir Sher strives to bridge the fields of computer science and human-centered disciplines.

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