Munir Ahmad

Section 01

Academic Profile

Dr. Munir Ahmad (Senior Member, IEEE) is a distinguished professional with over 16 years of experience. He holds a Ph.D. in computer science from the School of Computer Science, National College of Business Administration and Economics, and a Master of Computer Science degree from the Virtual University of Pakistan. As the Executive Director/CIO at United International Group, Lahore, Pakistan, he has excelled in data management and resource optimization within multinational organizations. Munir Ahmad is renowned for his extensive research in sentiment analysis, AI applications in healthcare and animal facial identification. His expertise lies in data mining, big data, and artificial intelligence.

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

Open Access | Editorial | 17 March 2025

Neural Computing: A New Era of Intelligent Adaptation and Learning

by

Munir Ahmad

ICCK Transactions on Neural Computing | Volume 1, Issue 1: 1-10, 2025 | DOI: 10.62762/TNC.2025.125800

Abstract

The inaugural editorial of the IECE Transactions on Neural Computing (IECE-TNC) presents the revolutionary influence of neural computing that incorporates artificial intelligence (AI), machine learning (ML), and next-gen computation models in cognitive systems, robotics, and healthcare. Although there have been tremendous developments, some problems remain including computational scalability, model interpretability, ethical considerations, and data security. IECE-TNC is dedicated to resolving these issues by facilitating high-impact research, interdisciplinary collaboration, and real-world applications. The magazine covers the following trends such as federated learning, explainable deep lea... More >