[email protected]
Academic Editor
Author
Contributions by role
Author 3
Editor 1
ORCID
0000-0002-5240-0984
Munir Ahmad
University College, Korea University, Seoul, 02841, Republic of Korea
Summary
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.
Edited Journals
ICCK Transactions on Neural Computing
ICCK Contributions
Articles
3
Edited
1
Open Access | Research Article | 24 June 2025
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 >