AIMC Topic: Humans

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Machine learning to predict the decision to perform surgery in hepatic echinococcosis.

HPB : the official journal of the International Hepato Pancreato Biliary Association
BACKGROUND: Cystic echinococcosis (CE) is a significant public health issue, primarily affecting the liver. While several management strategies exist, there is a lack of predictive tools to guide surgical decisions for hepatic CE. This study aimed to...

Intrinsic plasticity coding improved spiking actor network for reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
Deep reinforcement learning (DRL) exploits the powerful representational capabilities of deep neural networks (DNNs) and has achieved significant success. However, compared to DNNs, spiking neural networks (SNNs), which operate on binary signals, mor...

RAIN: Reconstructed-aware in-context enhancement with graph denoising for session-based recommendation.

Neural networks : the official journal of the International Neural Network Society
Session-based recommendation aims to recommend the next item based on short-term interactions. Traditional session-based recommendation methods assume that all interacted items are closely related to the user's interests. However, noise (e.g., accide...

ST-GMLP: A concise spatial-temporal framework based on gated multi-layer perceptron for traffic flow forecasting.

Neural networks : the official journal of the International Neural Network Society
The field of traffic forecasting has been the subject of considerable attention as a critical component in alleviating traffic congestion and improving urban services. Given the regular patterns of human activities, it is evident that traffic flow is...

Local interpretable spammer detection model with multi-head graph channel attention network.

Neural networks : the official journal of the International Neural Network Society
Fraudulent reviews posted by spammers on the online shopping websites mislead consumers' purchasing decisions. To curb fraudulent reviews, many methods have been proposed for detecting spammers. However, the existing spammer detection methods operate...

A sparse and wide neural network model for DNA sequences.

Neural networks : the official journal of the International Neural Network Society
Accurate modeling of DNA sequences requires capturing distant semantic relationships between the nucleotide acid bases. Most existing deep neural network models face two challenges: (1) they are limited to short DNA fragments and cannot capture long-...

Reliability of post-contrast deep learning-based highly accelerated cardiac cine MRI for the assessment of ventricular function.

Magnetic resonance imaging
OBJECTIVE: The total examination time can be reduced if high-quality two-dimensional (2D) cine images can be collected post-contrast to minimize non-scanning time prior to late gadolinium-enhanced imaging. This study aimed to assess the equivalency o...

Personalized deep learning auto-segmentation models for adaptive fractionated magnetic resonance-guided radiation therapy of the abdomen.

Medical physics
BACKGROUND: Manual contour corrections during fractionated magnetic resonance (MR)-guided radiotherapy (MRgRT) are time-consuming. Conventional population models for deep learning auto-segmentation might be suboptimal for MRgRT at MR-Linacs since the...

Augmented Effect of Combined Robotic Assisted Gait Training and Proprioceptive Neuromuscular Facilitation-irradiation Technique on Muscle Activation and Ankle Kinematics in Hemiparetic Gait: A Preliminary Study.

NeuroRehabilitation
BackgroundProprioceptive neuromuscular facilitation (PNF) alone has limited effectiveness in restoring gait, while robotic-assisted gait training (RAGT) improves motor relearning through repetitive, task-specific movements. Combining PNF with robotic...