Author
Contributions by role
Author 2
Atif Ur Rahman
Department of Computer Science, IQRA National University, Swat Campus Pakistan
Summary
Edited Journals
IECE Contributions

Free Access | Research Article | 25 March 2025
Comparative Analysis of Automated Knee Osteoarthritis Severity Classification from X-Ray Images Using CNNs and VGG16 Architecture
IECE Transactions on Sensing, Communication, and Control | Volume 2, Issue 1: 36-47, 2025 | DOI:10.62762/TSCC.2025.378503
Abstract
Osteoarthritis (OA) is a degenerative joint disease that primarily affects the knee, causing cartilage deterioration and discomfort. Early diagnosis is crucial for effective management, as it can slow disease progression and improve the quality of life. This study proposes a deep learning approach to automatically classify knee OA severity from X-ray images using Convolutional Neural Networks (CNNs) and the VGG16 model. The models were trained on a dataset of knee X-ray images, and performance was evaluated using accuracy, precision, recall, and F1-score. The proposed CNNs model achieved 99% training accuracy and 80% testing accuracy after 50 epochs, while the VGG16 model, after fine-tuning... More >

Graphical Abstract
Comparative Analysis of Automated Knee Osteoarthritis Severity Classification from X-Ray Images Using CNNs and VGG16 Architecture

Free Access | Research Article | 09 November 2024
In-depth Urdu Sentiment Analysis Through Multilingual BERT and Supervised Learning Approaches
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