Academic Editor
Contributions by role
Editor 1
Abdur Rehman Sakhawat
School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
Summary
I am Abdur Rehman, a dedicated and accomplished professional who completed a Ph.D. in the School of Computer Science at NCBA&E in Lahore, Pakistan. My academic journey has been guided by a passion for computer science and a drive to make meaningful contributions to the field. Currently serving as an Associate Professor at NCBA&E, I have gained valuable experience in shaping the minds of future professionals while fostering an environment of academic excellence. In tandem with my academic role, I have also had the privilege of working as a Game Developer at GameObject in Lahore, Pakistan, accumulating more than 10 years of hands-on experience in the dynamic realm of game development. My research endeavors have been particularly focused on cutting-edge areas such as Smart City technologies, Healthcare, Machine Learning, Blockchain, Federated learning, and Network Security. Over the span of my career, I have produced a substantial body of work, comprising 15+ research articles published in reputable journals with notable impact factors, reflecting a cumulative impact factor of 70+. This research has enabled me to contribute to the advancement of knowledge and to address challenges that are central to our ever-evolving technological landscape.
Edited Journals
IECE Contributions

Open Access | Research Article | 30 March 2025
Uncovering COVID-19 Death Risk for Life on the Line with Machine Learning Precision
IECE Transactions on Neural Computing | Volume 1, Issue 1: 30-43, 2025 | DOI: 10.62762/TNC.2025.507897
Abstract
The global healthcare systems have faced unprecedented challenges due to the COVID-19 pandemic, necessitating innovative neural computing solutions to inform critical decision-making. In this study, we introduce a neural-inspired machine learning framework to predict COVID-19 mortality risk, utilizing a dataset comprising over one million records. We developed and evaluated a suite of advanced models—Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, Gradient Boost Classifier, and a neural ensemble-based Voting Classifier—to analyze the influence of demographics, symptoms, and preexisting conditions on mortality predictions. Through meticulous feature engineering... More >

Graphical Abstract
Uncovering COVID-19 Death Risk for Life on the Line with Machine Learning Precision