IEEE

PCB Defect Detection Using Machine Learning: YOLOv5 and Inception v3 CNN-Based Approach

Abstract: Identification of PCB board defects early in the manufacturing process is crucial, as PCB quality control plays an important role in the electronics manufacturing industry. Defective PCBs ...
IEEE

A Comprehensive Framework for Accident Detection and Risk Assessment Using CNN and Graph-Based Analytics

Abstract: This proposed work introduces a comprehensive accident detection and risk assessment system that coordinate the strengths of Convolutional Neural Networks (CNNs) and a graph-based analytical ...
IEEE

A Parallel Fusion of CNN and Transformer for CT Lung Nodule Detection

Abstract: To improve the precision of CT lung nodule detection, this paper presents a parallel fusion model based on CNN and Transformer network, which integrates features of the two networks to fully ...
IEEE

Deep Learning Models for Precision Agriculture: Evaluating CNN Architectures for Accurate Plant Disease Detection

Abstract: Early and precise detection of plant diseases is crucial for enhancing crop yield and minimizing agricultural losses. This paper evaluates the performance of deep learning-based ...
IEEE

AI-Powered Lung Cancer Detection: CNN-SVM Hybrid Model with Statistical Insights

Abstract: Liability of lung cancer as a primary cancer mortality agent around the world makes it vital to create diagnostic systems that guarantee both accuracy and operational efficiency. This ...
IEEE

HTC-HAD: A Hybrid Transformer-CNN Approach for Hyperspectral Anomaly Detection

Abstract: Hyperspectral anomaly detection (HAD) identifies anomalies by analyzing differences between anomalies and background pixels without prior information, presenting a significant challenge.
IEEE

Boosting Image-Text Detection Performance with Python Tesseract and the Tesseract OCR Engine

Abstract: There is a sudden increase in digital data as well as a rising demand for extracting text efficiently from images. These two led to full optical character recognition systems are introduced ...
IEEE

Temporal-Spatial Feature Extraction in IoT-Based SCADA System Security: Hybrid CNN-LSTM and Attention-Based Architectures for Malware Classification and Attack Detection

Abstract: This research presents a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model developed for malware classification from IoT devices in the SCADA system and for ...
IEEE

Rice Disease Detection Using Faster R-CNN Approach

Abstract: It has never been easy to identify plant diseases accurately and quickly. A significant amount of food grains is lost by farmers each year as a result of the absence of automated tools that ...
IEEE

Wavelet-Enhanced CNN-Transformer Hybrid Network for Semantic Segmentation of Multi-Scale Geospatial Objects

Abstract: Semantic segmentation of remote sensing imagery has achieved pixel-level precision in land cover classification through deep learning and computer vision technologies, providing automated ...
IEEE

Shadow-Aware Moving Target Detection in ViSAR: A Multiscale CNN–Transformer Hybrid Detection Framework

Abstract: Based on the all-weather imaging capability of synthetic aperture radar (SAR), video synthetic aperture radar (ViSAR) enables dynamic ground target monitoring through high-frame-rate ...
IEEE

Hybrid 1D and 2D CNN-Transformer Architectures for Multilabel 12-Lead EKG Anomaly Detection

Abstract: This study introduces two novel hybrid machine-learning architectures for multilabel anomaly detection in electrocardiograms (EKGs): a 1D modified ResNet combined with a transformer encoder ...