In recent years, convolutional neural networks (CNNs) have become effective tools for signal processing tasks. In this study, we propose a decision-level fusion model that integrates time-domain ...

However, as a kind of neural electrophysiological signal, EEG contains different types of temporal and spatial information. Therefore, we propose a lightweight spatio-temporal attention-based ...

However, such methods are frequently constrained by inherent limitations, including overfitting and suboptimal parameter efficiency. This study examines the potential of Kolmogorov-Arnold networks ...

This study utilized 1D and 2D convolutional neural networks (CNN) to assess OA of the knee using vibroarthrographic (VAG) signals recorded by an inertial measurement unit sensor. VAG signals were ...

A sample size of 15 individuals is insufficient for building robust neural networks. Consequently, the sample of Parkinson's disease patients was too small and not representative.

1D Convolutional Neural Networks (CNNs) have recently become the state-of-the-art technique for crucial signal processing applications such as patient-specific ECG classification, structural health ...

Shock polling showing Democrats still languishing behind Republicans on economic issues stunned CNN analyst Harry Enten on Monday.