Wajahat Akbar

Section 01

Academic Profile

Wajahat Akbar is a PhD student in the School of Electronic and Control Engineering at Chang'an University Xi'an, China. He received his BS degree in Computer Science from Khushal Khan Khattak University Karak in 2019. He further pursued his academic journey at the same university and received his MS degree in Computer Science (Gold Medalist), specializing in Artificial Intelligence in 2023. He was honored with the Youth Talent Award and held a merit scholarship during his academic pursuits. His research interests span a diverse range, encompassing Artificial Intelligence, Deep Learning, Natural Language Processing (NLP), Computer Vision, Computer Networks, and Network Security, with a focus on healthcare applications.

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 | 15 May 2025 | Cited:

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FuzzDL-HeartPredict: Heart Attack Risk Prediction Using Fuzzy Logic and Deep Learning

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ICCK Transactions on Advanced Computing and Systems | Volume 1, Issue 2: 63-77, 2025 | DOI: 10.62762/TACS.2025.794425

Abstract

Across the globe, heart diseases rank as the top cause of death, with their incidence steadily rising. However, early detection before a cardiac event (e.g., cardiac arrest) remains a significant challenge. Although the healthcare sector possesses extensive data on heart disease, the effective use of this data for timely detection is essential to protect from such events. This paper proposes an innovative approach using fuzzy logic (FL), convolutional neural network (CNN) models, and feature selection to more accurately assess the risk of heart attacks. Our study also emphasizes the importance of data preprocessing, including data transformation, cleaning, and normalization, to facilitate th... More >

Graphical Abstract

FuzzDL-HeartPredict: Heart Attack Risk Prediction Using Fuzzy Logic and Deep Learning

Free Access | Research Article | 12 November 2024 | Cited:

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Improving Effort Estimation Accuracy in Software Development Projects Using Multiple Imputation Techniques for Missing Data Handling

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ICCK Transactions on Intelligent Systematics | Volume 1, Issue 3: 190-202, 2024 | DOI: 10.62762/TIS.2024.751418

Abstract

Intelligent project management systems rely on high-quality historical data for accurate automated decision-making, yet missing data in software project repositories remains a persistent challenge that degrades intelligent estimation performance. This study proposes an Intelligent Decision Support Framework (IDSF) for software development effort estimation (SDEE) that integrates Multiple Imputation (MI) as a critical data quality enhancement layer within the Analogy-Based Effort Estimation (ABEE) model. The framework is evaluated on the ISBSG dataset by systematically comparing six imputation strategies. Results demonstrate that the MI-enhanced framework achieves competitive and more stable... More >

Graphical Abstract

Improving Effort Estimation Accuracy in Software Development Projects Using Multiple Imputation Techniques for Missing Data Handling

Free Access | Research Article | 29 October 2024 | Cited:

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Enhancing Ocular Health Precision: Cataract Detection Using Fundus Images and ResNet-50

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ICCK Transactions on Intelligent Systematics | Volume 1, Issue 3: 145-160, 2024 | DOI: 10.62762/TIS.2024.640345

Abstract

Cataracts are a leading cause of blindness in Pakistan, contributing to more than 54% of blindness cases in Pakistan, primarily due to poor living conditions, nutritional deficiencies, and limited healthcare access. Early detection is critical to avoid invasive treatments, but current diagnostic approaches often identify cataracts at advanced stages. This paper presents an advanced,automated cataract detection system using deep learning specifically the ResNet-50 architecture, to address this gap. The model processes fundus retinal images curated from diverse datasets, classified by ophthalmologic experts through a rigorous three-stage process. By leveraging the ResNet-50 model, cataracts ar... More >

Graphical Abstract

Enhancing Ocular Health Precision: Cataract Detection Using Fundus Images and ResNet-50