AIMC Topic: Algorithms

Clear Filters Showing 4661 to 4670 of 28713 articles

Human motion recognition based on feature fusion and residual networks.

Scientific reports
Addressing the issue of low recognition accuracy in human motion detection when relying on a single feature, a novel approach integrating Frequency Modulated Continuous Wave (FMCW) radar technology with a Residual Network (ResNet) architecture has be...

Optimization control for mean square synchronization of stochastic semi-Markov jump neural networks with non-fragile hidden information and actuator saturation.

Neural networks : the official journal of the International Neural Network Society
This paper studies the asynchronous output feedback control and H synchronization problems for a class of continuous-time stochastic hidden semi-Markov jump neural networks (SMJNNs) affected by actuator saturation. Initially, a novel neural networks ...

Binarized Simplicial Convolutional Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks have the limitation of processing features solely on graph nodes, neglecting data on high-dimensional structures such as edges and triangles. Simplicial Convolutional Neural Networks (SCNN) represent high-order structures using ...

Separable integral neural networks.

Neural networks : the official journal of the International Neural Network Society
Integral neural networks adopt continuous integral operators instead of conventional discrete convolutional operations to perform deep learning tasks. As this integral operator is the continuous representation of the regular convolutional operation, ...

BalancerGNN: Balancer Graph Neural Networks for imbalanced datasets: A case study on fraud detection.

Neural networks : the official journal of the International Neural Network Society
Fraud detection for imbalanced datasets is challenging due to machine learning models inclination to learn the majority class. Imbalance in fraud detection datasets affects how graphs are built, an important step in many Graph Neural Networks (GNNs)....

Multi-scale graph harmonies: Unleashing U-Net's potential for medical image segmentation through contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Medical image segmentation is essential for accurately representing tissues and organs in scans, improving diagnosis, guiding treatment, enabling quantitative analysis, and advancing AI-assisted healthcare. Organs and lesion areas in medical images h...

Iterative neural networks for improving memory capacity.

Neural networks : the official journal of the International Neural Network Society
In recent years, the problem of the multistability of neural networks has been studied extensively. From the research results obtained, the number of stable equilibrium points depends only on a power form of the network dimension. However, in practic...

Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model.

Journal of neuroscience methods
Signal decomposition techniques utilizing multi-channel spatial features are critical for analyzing, denoising, and classifying electroencephalography (EEG) signals. To facilitate the decomposition of signals recorded with limited channels, this pape...

Artificial intelligence-powered image analysis: A paradigm shift in infectious disease detection.

Artificial intelligence in medicine
The global burden of infectious diseases significantly affects mortality rates, with their varying symptoms making it challenging to assess and determine the severity of infections. Different countries face unique challenges related to these diseases...