Author
Contributions by role
Author 2
Muhammad Nawaz Khan
Department of Computer Science and Information Technology, University of Malakand, Chakdara, Pakistan
Summary
Edited Journals
IECE Contributions

Free Access | Research Article | 12 November 2024
Improving Effort Estimation Accuracy in Software Development Projects Using Multiple Imputation Techniques for Missing Data Handling
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
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