ICCK

Irshad Khalil

Department of Health Science and Technology, Gachon Advanced Institute for Health Sciences and Technology GAIHST, Gachon University, Incheon 21936, Korea

Section 01

Academic Profile

Irshad Khalil was born in Malakand, Pakistan in 1992. He received his bachelor’s degree in computer science from University of Malakand, Pakistan, in 2013. He received MS degree in Image Processing from University of Malakand, Pakistan, in 2016. Now he is a Ph.D. scholar at Gachon University, Incheon 21936, Korea, and his current research interests include image processing, medical imaging, Internet of Medical Things and Deep learning, AI.

Section 02

Editorial Roles

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

Section 03

ICCK Publications

Free Access | Research Article | 12 November 2024 | Cited: Crossref logo  3 , Scopus 3
Improving Effort Estimation Accuracy in Software Development Projects Using Multiple Imputation Techniques for Missing Data Handling
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: Crossref logo  10 , Scopus 12
Enhancing Ocular Health Precision: Cataract Detection Using Fundus Images and ResNet-50
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