98%
921
2 minutes
20
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.33963/v.phj.101280 | DOI Listing |
Front Artif Intell
August 2025
School of Electronics Engineering (SENSE), Vellore Institute of Technology, Chennai, India.
Introduction: In recent years, Deep Learning (DL) architectures such as Convolutional Neural Network (CNN) and its variants have been shown to be effective in the diagnosis of cardiovascular disease from ElectroCardioGram (ECG) signals. In the case of ECG as a one-dimensional signal, 1-D CNNs are deployed, whereas in the case of a 2D-represented ECG signal, i.e.
View Article and Find Full Text PDFJ Neurosci Methods
September 2025
Department of CSE, Indian Institute of Information Technology Vadodara- International Campus Diu (IIITV-ICD), 362520, Diu, India. Electronic address:
The Electroencephalogram (EEG) is a vital physiological signal for monitoring brain activity and understanding neurological capacities, disabilities, and cognitive processes. Analyzing and classifying EEG signals are key to assessing an individual's reactions to various stimuli. Manual EEG analysis is time-consuming and labor-intensive, necessitating automated tools for efficiency.
View Article and Find Full Text PDFThis paper presents a novel multiscale signal processing framework for power quality disturbance (PQD) and cyber intrusion detection in smart grids, combining Non-Subsampled Contourlet Transform (NSCT), Split Augmented Lagrangian Shrinkage Algorithm (SALSA), and Morphological Component Analysis (MCA). A key innovation lies in an adaptive weighting mechanism within NSCT's directional sub bands, enabling dynamic energy redistribution and enhanced representation of both low-frequency anomalies (e.g.
View Article and Find Full Text PDFPLoS One
September 2025
School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, China.
Remote sensing object detection (RSOD) is highly challenging due to large variations in object scales. Existing deep learning-based methods still face limitations in addressing this challenge. Specifically, reliance on stride convolutions during downsampling leads to the loss of object information, and insufficient context-aware modeling capability hampers full utilization of object information at different scales.
View Article and Find Full Text PDFPLoS One
September 2025
Universiti Putra Malaysia, Serdang, Malaysia.
In this research, we have proposed a novel anomaly detection algorithm for processing hyperspectral images (HSIs), called the Graph Attention Network-Beta Wavelet Graph Neural Network-based Hyperspectral Anomaly Detection (GAN-BWGNN HAD). This algorithm treats each pixel as a node in a graph, where edges represent pixel correlations and node attributes correspond to spectral features. The algorithm integrates spatial and spectral information, utilizing graph neural networks to identify nonlinear relationships within the image, thereby enhancing anomaly detection precision.
View Article and Find Full Text PDF