Abdur Rehman Sakhawat

Section 01

Academic Profile

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.

Section 02

Editorial Roles

This user currently does not serve as an editor for any ICCK journals.

Section 03

ICCK Publications

Open Access | Research Article | 24 June 2025 | Cited:

4 , 3

Multi-Source Information Fusion for Anomaly Detection in Smart Grids Using Federated Learning

by

Munir Ahmad

,

Abdur Rehman

Chinese Journal of Information Fusion | Volume 2, Issue 2: 157-170, 2025 | DOI: 10.62762/CJIF.2025.220738

Abstract

The wide-ranging expansion of smart grid networks has resulted in insurmountable difficulties that must be overcome to ensure the security and reliability of crucial energy infrastructures. The information system can be subjected to threats such as cyber-attacks or hardware malfunctioning resulting in a data integrity compromise which implies that the system will consequently not operate correctly. Anomaly detection methods that are relying on centralized data aggregation are problematic to the issues of data privacy and scalability resulting from such approaches. In this paper, we present a completely distinct approach that is based on federated learning that is employed in anomaly detectio... More >

Graphical Abstract

Multi-Source Information Fusion for Anomaly Detection in Smart Grids Using Federated Learning

Open Access | Research Article | 16 May 2025

Exploring the Frontiers of Neural Computing: Innovations, Architectures, and Applications in Intelligent Systems

by

Abdur Rehman

,

Munir Ahmad

ICCK Transactions on Neural Computing | Volume 1, Issue 2: 65-77, 2025 | DOI: 10.62762/TNC.2025.168636

Abstract

Neural computing, as an influential factor of artificial intelligence, is an industry that has managed to achieve an extensive array of innovations. This paper presents an overview of the recent advancements in the field of neural computing, which are focused on state-of-the-art architectures, novel computational paradigms, and their applications in intelligent systems. The paper traces the development of neural networks, from the original artificial neural network (ANN) through deep learning models and on to neuromorphic computing. In other words, the main points of emphasis are breakthroughs in hardware acceleration, hybrid models, and bio-inspired computing, which are responsible for inte... More >

Graphical Abstract

Exploring the Frontiers of Neural Computing: Innovations, Architectures, and Applications in Intelligent Systems