IECE - Institute of Emerging and Computer Engineers

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    Academic Editor
    Contributions by role
    Editor 3
    ORCID
    0000-0003-2722-7574
    Habib Khan
    Gachon University, Republic of Korea
    Summary
    Edited Journals
    IECE Transactions on Intelligent Systematics
    IECE Contributions
    Articles
    0
    Edited
    3
    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