AIMC Topic: Neural Networks, Computer

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Dual-stream multi-dependency graph neural network enables precise cancer survival analysis.

Medical image analysis
Histopathology image-based survival prediction aims to provide a precise assessment of cancer prognosis and can inform personalized treatment decision-making in order to improve patient outcomes. However, existing methods cannot automatically model t...

STaRNet: A spatio-temporal and Riemannian network for high-performance motor imagery decoding.

Neural networks : the official journal of the International Neural Network Society
Brain-computer interfaces (BCIs), representing a transformative form of human-computer interaction, empower users to interact directly with external environments through brain signals. In response to the demands for high accuracy, robustness, and end...

A study of forecasting the Nephila clavipes silk fiber's ultimate tensile strength using machine learning strategies.

Journal of the mechanical behavior of biomedical materials
Recent advancements in biomaterial research conduct artificial intelligence for predicting diverse material properties. However, research predicting the mechanical properties of biomaterial based on amino acid sequences have been notably absent. This...

Clinical knowledge-based ECG abnormalities detection using dual-view CNN-Transformer and external attention mechanism.

Computers in biology and medicine
BACKGROUND: Automatic abnormalities detection based on Electrocardiogram (ECG) contributes greatly to early prevention, computer aided diagnosis, and dynamic analysis of cardiovascular diseases. In order to achieve cardiologist-level performance, dee...

PT-KGNN: A framework for pre-training biomedical knowledge graphs with graph neural networks.

Computers in biology and medicine
Biomedical knowledge graphs (KGs) serve as comprehensive data repositories that contain rich information about nodes and edges, providing modeling capabilities for complex relationships among biological entities. Many approaches either learn node fea...

Enhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines.

Marine pollution bulletin
Accurately classifying microalgae species is vital for monitoring marine ecosystems and managing the emergence of marine mucilage, which is crucial for monitoring mucilage phenomena in marine environments. Traditional methods have been inadequate due...

Infusion of active compound into sliced button mushrooms through vacuum impregnation to improve functionality: Comparing response surface methodology and artificial neural network.

Journal of food science
The present study explores the infusion of active compounds (ascorbic acid and calcium lactate) into sliced button mushrooms (Agaricus bisporus) to increase the nutritional value and reduce the browning effect of sliced mushrooms using the vacuum imp...

NPI-DCGNN: An Accurate Tool for Identifying ncRNA-Protein Interactions Using a Dual-Channel Graph Neural Network.

Journal of computational biology : a journal of computational molecular cell biology
Noncoding RNA (NcRNA)-protein interactions (NPIs) play fundamentally important roles in carrying out cellular activities. Although various predictors based on molecular features and graphs have been published to boost the identification of NPIs, most...

Enhanced Hybrid Vision Transformer with Multi-Scale Feature Integration and Patch Dropping for Facial Expression Recognition.

Sensors (Basel, Switzerland)
Convolutional neural networks (CNNs) have made significant progress in the field of facial expression recognition (FER). However, due to challenges such as occlusion, lighting variations, and changes in head pose, facial expression recognition in rea...

Securing China's rice harvest: unveiling dominant factors in production using multi-source data and hybrid machine learning models.

Scientific reports
Ensuring the security of China's rice harvest is imperative for sustainable food production. The existing study addresses a critical need by employing a comprehensive approach that integrates multi-source data, including climate, remote sensing, soil...