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    Author 2
    Irshad Khalil
    Department of Health Science and Technology, Gachon Advanced Institute for Health Sciences and Technology GAIHST, Gachon University, Incheon 21936, Korea
    Summary
    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.
    Edited Journals
    IECE Contributions
    Articles
    2
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    Free Access | Research Article | 12 November 2024
    Improving Effort Estimation Accuracy in Software Development Projects Using Multiple Imputation Techniques for Missing Data Handling
    by
    Shahida Hayat
    ,
    Wajahat Akbar
    ,
    Tariq Hussain
    ,
    Muhammad Inam Ul Haq
    ,
    Altaf Hussian
    ,
    Irshad Khalil
    ,
    Muhammad Nawaz Khan
    ,
    Samsonova Diana
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 190-202, 2024 | DOI:10.62762/TIS.2024.751418
    Abstract
    The challenge of accurately estimating effort for software development projects is critical for project managers (PM) and researchers. A common issue they encounter is missing data values in datasets, which complicates effort estimation (EE). While several models have been introduced to address this issue, none have proven entirely effective. The Analogy-Based Effort Estimation (ABEE) model is the most widely used approach, relying on historical data for estimation. However, the common practice of deleting cases or cells with missing observations results in a reduction of statistical power and negatively impacts the performance of ABEE, leading to inefficiencies and biases. This study employ... 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
    Enhancing Ocular Health Precision: Cataract Detection Using Fundus Images and ResNet-50
    by
    Irshad Khan
    ,
    Wajahat Akbar
    ,
    Abdullah Soomro
    ,
    Tariq Hussain
    ,
    Irshad Khalil
    ,
    Muhammad Nawaz Khan
    ,
    Abdu Salam
    IECE 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 cases due to poor living condition, 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 are categorized into normal,moderate,... More >
    Graphical Abstract
    Enhancing Ocular Health Precision: Cataract Detection Using Fundus Images and ResNet-50