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    Volume 1, Issue 3 (Online First) - Table of Contents
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    Citations: 0, 0   |   Viewed: 1371, Download: 235
    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 | Review Article | 09 November 2024
    Comprehensive Evaluation of Artificial Intelligence Applications in Forensic Odontology: A Systematic Review and Meta-Analysis
    by
    Muhammad Salman Khan
    ,
    Urooj Afridi
    ,
    Muhammad Jamal Ahmed
    ,
    Babar Zeb
    ,
    Irfan Ullah
    ,
    Muhammad Zain Hassan
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 176-189, 2024 | DOI:10.62762/TIS.2024.818917
    Abstract
    This systematic review and meta-analysis examine the transformative impact of artificial intelligence (AI) applications on forensic odontology, specifically focusing on the enhancement of identification accuracy and operational efficiency. Traditionally, forensic odontology depends on detailed dental records for human identification purposes. However, with the integration of AI-driven advancements, including machine learning algorithms and image recognition systems, the field is undergoing significant evolution. These AI technologies offer notable improvements in the precision of complex tasks such as bite mark analysis, dental age estimation, and dental record matching, while simultaneously... More >
    Graphical Abstract
    Comprehensive Evaluation of Artificial Intelligence Applications in Forensic Odontology: A Systematic Review and Meta-Analysis
    Free Access | Research Article | 09 November 2024
    In-depth Urdu Sentiment Analysis Through Multilingual BERT and Supervised Learning Approaches
    by
    Muhammad Saeed
    ,
    Naeem Ahmed
    ,
    Danish Ali
    ,
    Muhammad Ramzan
    ,
    Muzamil Mohib
    ,
    Kajol Bagga
    ,
    Atif Ur Rahman
    ,
    Ikram Majeed Khan
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 161-175, 2024 | DOI:10.62762/TIS.2024.585616
    Abstract
    Sentiment analysis is the process of identifying and categorizing opinions expressed in a piece of text. It has been extensively studied for languages like English and Chinese but still needs to be explored for languages such as Urdu and Hindi. This paper presents an in-depth analysis of Urdu text using state-of-the-art supervised learning techniques and a transformer-based technique. We manually annotated and preprocessed the dataset from various Urdu blog websites to categorize the sentiments into positive, neutral, and negative classes. We utilize five machine learning classifiers: Support Vector Machine (SVM), K-nearest neighbor (KNN), Naive Bayes, Multinomial Logistic Regression (MLR),... More >
    Graphical Abstract
    In-depth Urdu Sentiment Analysis Through Multilingual BERT and Supervised Learning Approaches
    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
    Free Access | Review Article | 21 October 2024
    Transforming Industry 4.0 Security: Analysis of ABE and ABA Technologies
    by
    Jibran Saleem
    ,
    Umar Raza
    ,
    William Holderbaum
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 127-144, 2024 | DOI:10.62762/TIS.2024.993235
    Abstract
    The sharing of data and private information has been greatly improved by Industry 4.0's broad usage of cloud technologies. In their quest to improve their services, many firms have made automation and effective authentication a priority. As a result, in Industry 4.0, Attribute-Based Encryption (ABE) and Attribute-Based Authentication (ABA) have established themselves as dependable models for data sharing across cloud environments. For difficult situations like fine-grained access control and secure authentication, these models offer practical answers. Organizations can utilize ABA to specifically authenticate people based on their attributes, ensuring appropriate and safe access to critical... More >
    Graphical Abstract
    Transforming Industry 4.0 Security: Analysis of ABE and ABA Technologies
    Free Access | Research Article | 20 October 2024
    Comparison of Deep Learning Algorithms for Retail Sales Forecasting
    by
    Rana Shakeel Ahmed
    ,
    Muhammad Hasnain
    ,
    Muhammad Hamza Mahmood
    ,
    Muhammad Abid Mehmood
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 112-126, 2024 | DOI:10.62762/TIS.2024.300700
    Abstract
    We investigate the use of deep learning models for retail sales predictions in this research. Having a proper sales forecasting can lead to optimization in inventory management, marketing strategies, and other core business operations. This research proposed to assess deep learning models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Multilayer Perceptron and hybrid CNN-LSTM model. The models are further improved by using some dense layers to embed daily sales data from the biggest pharmaceutical firm in the study. Models are then trained on 80% of the dataset and tested on remaining 20%. The accuracy of the proposed research is compared using evaluation metrics... More >
    Graphical Abstract
    Comparison of Deep Learning Algorithms for Retail Sales Forecasting