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Volume 2, Issue 1 (Online First) - Table of Contents

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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 | 20 March 2025
Visual Intelligence in Neuro-Oncology: Effective Brain Tumor Detection through Optimized Convolutional Neural Networks
IECE Transactions on Sensing, Communication, and Control | Volume 2, Issue 1: 25-35, 2025 | DOI:10.62762/TSCC.2024.964451
Abstract
Brain tumor detection (BTD) is a crucial task, as early detection can save lives. Medical professionals require visual intelligence assistance to efficiently and accurately identify brain tumors. Conventional methods often result in misrecognition, highlighting a critical research gap. To address this, a novel BTD system is proposed to predict the presence of a tumor in a given MRI image. The system leverages a convolutional neural network (CNN) architecture, combined with a multi-layer perceptron (MLP) for feature extraction and understanding complex pixel patterns. An extensive ablation study was conducted to empirically analyze and identify the optimal model for the task. The findings dem... More >

Graphical Abstract
Visual Intelligence in Neuro-Oncology: Effective Brain Tumor Detection through Optimized Convolutional Neural Networks

Free Access | Research Article | 05 March 2025
Attention-Guided Wheat Disease Recognition Network through Multi-Scale Feature Optimization
IECE Transactions on Sensing, Communication, and Control | Volume 2, Issue 1: 11-24, 2025 | DOI:10.62762/TSCC.2025.435806
Abstract
Accurate and timely detection of wheat diseases remains crucial for sustainable agriculture, particularly in major wheat-producing regions. Wheat diseases pose a significant threat to global food security, need precise and timely detection to promote sustainable agriculture. Existing approaches consistently employ single-scale features with shallow-layered convolutional neural networks (CNNs). To bridge the research gaps, we introduce a novel Multi-Scale Wheat Disease Network (MSWDNet) with feature collaboration for wheat disease recognition supported by a comprehensive dataset collected from wheat fields. This study fills research gaps by introducing a novel technique to improve detection a... More >

Graphical Abstract
Attention-Guided Wheat Disease Recognition Network through Multi-Scale Feature Optimization

Free Access | Research Article | 10 February 2025
High-Voltage Power Supply: Design Considerations and Optimization Techniques
IECE Transactions on Sensing, Communication, and Control | Volume 2, Issue 1: 1-10, 2025 | DOI:10.62762/TSCC.2024.741277
Abstract
The main goal of this study is to design and develop a half-bridge inverter architecture specifically for high-voltage power supply applications. An effective, small, and affordable system that converts direct current (DC) to alternating current(AC) can be built, thanks to the IR2151 chip’s dependable characteristics and performance. To get the desired output voltage, the transformer first increases the voltage and then the voltage is increased with a voltage-doubling rectifier (VDR) circuit. The study emphasizes how crucial it is to choose components carefully and simulate the circuit design and implementation process to guarantee dependable performance. The experimental results validate... More >

Graphical Abstract
High-Voltage Power Supply: Design Considerations and Optimization Techniques