AIMC Topic: Neural Networks, Computer

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Semi-Supervised Semantic Image Segmentation by Deep Diffusion Models and Generative Adversarial Networks.

International journal of neural systems
Typically, deep learning models for image segmentation tasks are trained using large datasets of images annotated at the pixel level, which can be expensive and highly time-consuming. A way to reduce the amount of annotated images required for traini...

Characterizing feral swine movement across the contiguous United States using neural networks and genetic data.

Molecular ecology
Globalization has led to the frequent movement of species out of their native habitat. Some of these species become highly invasive and capable of profoundly altering invaded ecosystems. Feral swine (Sus scrofa × domesticus) are recognized as being a...

Kinematics-Based Predictions of External Loads during Handcycling.

Sensors (Basel, Switzerland)
The increased risk of cardiovascular disease in people with spinal cord injuries motivates work to identify exercise options that improve health outcomes without causing risk of musculoskeletal injury. Handcycling is an exercise mode that may be bene...

A supervised graph-based deep learning algorithm to detect and quantify clustered particles.

Nanoscale
Considerable efforts are currently being devoted to characterizing the topography of membrane-embedded proteins using combinations of biophysical and numerical analytical approaches. In this work, we present an end-to-end (, human intervention-indepe...

Differentiating hand gestures from forearm muscle activity using machine learning.

International journal of occupational safety and ergonomics : JOSE
This study explored the use of forearm electromyography data to distinguish eight hand gestures. The neural network (NN) and random forest (RF) algorithms were tested on data from 10 participants. As window sizes increase from 200 ms to 1000 ms, the ...

Generalized M-sparse algorithms for constructing fault tolerant RBF networks.

Neural networks : the official journal of the International Neural Network Society
In the construction process of radial basis function (RBF) networks, two common crucial issues arise: the selection of RBF centers and the effective utilization of the given source without encountering the overfitting problem. Another important issue...

Manifold-based Shapley explanations for high dimensional correlated features.

Neural networks : the official journal of the International Neural Network Society
Explainable artificial intelligence (XAI) holds significant importance in enhancing the reliability and transparency of network decision-making. SHapley Additive exPlanations (SHAP) is a game-theoretic approach for network interpretation, attributing...

Finding core labels for maximizing generalization of graph neural networks.

Neural networks : the official journal of the International Neural Network Society
Graph neural networks (GNNs) have become a popular approach for semi-supervised graph representation learning. GNNs research has generally focused on improving methodological details, whereas less attention has been paid to exploring the importance o...

Subject-independent auditory spatial attention detection based on brain topology modeling and feature distribution alignment.

Hearing research
Auditory spatial attention detection (ASAD) seeks to determine which speaker in a surround sound field a listener is focusing on based on the one's brain biosignals. Although existing studies have achieved ASAD from a single-trial electroencephalogra...

Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data.

Journal of neuroscience methods
BACKGROUND: Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wa...