AIMC Topic: Neural Networks, Computer

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Enhancing multiview synergy: Robust learning by exploiting the wave loss function with consensus and complementarity principles.

Neural networks : the official journal of the International Neural Network Society
Multiview learning (MvL) is an advancing domain in machine learning, leveraging multiple data perspectives to enhance model performance through view-consistency and view-discrepancy. Despite numerous successful multiview-based support vector machine ...

A new approach to bioceramics based on tissue reaction of tricalcium phosphate for biomedical and sport applications using machine learning modeling.

Tissue & cell
Tricalcium phosphate (TCP) bioceramics have emerged as a promising option to meet the specific requirements associated with athlete bone fractures. Advanced computational modeling techniques, such as artificial neural networks (ANNs), have been lever...

Source-free time series domain adaptation with wavelet-based multi-scale temporal imputation.

Neural networks : the official journal of the International Neural Network Society
Recent works on source-free domain adaptation (SFDA) for time series reveal the effectiveness of learning domain-invariant temporal dynamics on improving the cross-domain performance of the model. However, existing SFDA methods for time series mainly...

Neural networks can accurately identify individual runners from their foot kinematics, but fail to predict their running performance.

Journal of biomechanics
Athletes and coaches may seek to improve running performance through adjustments to running form. Running form refers to the biomechanical characteristics of a runner's movement, and can distinguish individual runners as well as groups of runners, su...

Automated segmentation of the dorsal root ganglia in MRI.

NeuroImage
The dorsal root ganglion (DRG) contains all primary sensory neurons, but its functional role in somatosensory and pain processing remains unclear. Recently, MR imaging techniques have been developed for objective in vivo observation of the DRG. In pa...

Forecasting motion trajectories of elbow and knee joints during infant crawling based on long-short-term memory (LSTM) networks.

Biomedical engineering online
BACKGROUND: Hands-and-knees crawling is a promising rehabilitation intervention for infants with motor impairments, while research on assistive crawling devices for rehabilitation training was still in its early stages. In particular, precisely gener...

Hybrid deep learning model for density and growth rate estimation on weed image dataset.

Scientific reports
Agriculture research is particularly essential since crop production is a challenge for farmers in India and around the world. 37% of the crop is impacted by invasive plants (weeds). Those unwelcome plants that interbreed with cultivated crops and de...

Addressing model discrepancy in a clinical model of the oxygen dissociation curve.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Many mathematical models suffer from model discrepancy, posing a significant challenge to their use in clinical decision-making. In this article, we consider methods for addressing this issue. In the first approach, a mathematical model is treated as...

InVAErt networks for amortized inference and identifiability analysis of lumped-parameter haemodynamic models.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Estimation of cardiovascular model parameters from electronic health records (EHRs) poses a significant challenge primarily due to lack of identifiability. Structural non-identifiability arises when a manifold in the space of parameters is mapped to ...

Comprehensive Segmentation of Gray Matter Structures on T1-Weighted Brain MRI: A Comparative Study of Convolutional Neural Network, Convolutional Neural Network Hybrid-Transformer or -Mamba Architectures.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Recent advances in deep learning have shown promising results in medical image analysis and segmentation. However, most brain MRI segmentation models are limited by the size of their data sets and/or the number of structures t...