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    Wajahat Akbar
    School of Electronic and Control Engineering at Chang'an University Xi'an 710064, China
    Summary
    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.
    Edited Journals
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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