IEEE

Integrating Local and Global Features via CNN and Mamba for Semi-Supervised Change Detection

Abstract: Semi-supervised change detection (SSCD) has become increasingly important in remote sensing image (RSI) analysis due to the scarcity of labeled data. While state-of-the-art SSCD methods have ...
IEEE

A Novel Dense-Swish-CNN With Bi-LSTM Framework for Image Deepfake Detection

Abstract: Artificial intelligence (AI) developments have revolutionized technologies and methodologies, particularly for malicious uses, especially since the advent of generative adversarial networks ...
IEEE

Evaluating the Impact of Feature Engineering in Phishing URL Detection: A Comparative Study of URL, HTML, and Derived Features

Abstract: Phishing attacks have evolved into sophisticated threats, making effective cybersecurity detection strategies essential. While many studies focus on either URL or HTML features, limited work ...

What is CNN in Deep Learning? The AI Behind Computer Vision

Learn what CNN is in deep learning, how they work, and why they power modern image recognition AI and computer vision ...
IEEE

Polyp Segmentation with Transformer-CNN Integration and Multi-Scale Feature Fusion

Abstract: Polyp segmentation is, thus far, an essential task in the medical domain, especially in diagnosing and treating colorectal diseases. Polyps are abnormal tissue developments, which emerge on ...
IEEE

CNN-Based Robust Harmonic Feature Extraction for Open-Circuit Fault Detection and Localization in Modular Multilevel Converters

Abstract: This letter presents a novel convolutional neural network (CNN)-based methodology for robust and accurate open-circuit fault detection and submodule (SM) localization in modular multilevel ...
IEEE

Alcoholic EEG Signal Feature Extraction Based on Relative Wavelet Bispectrum and Bispectrum-Gaussian Using CNN and ANN Classifier

Abstract: A person who is addicted to alcohol is most likely to have problems related to health and brain function, such as in doing cognitive tasks. Thus, detecting the alcoholic condition is ...
IEEE

UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection

Abstract: Anomaly detection (AD) is a crucial visual task aimed at recognizing abnormal pattern within samples. However, most existing AD methods suffer from limited generalizability, as they are ...