AIMC Topic: Humans

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Development of a deep neural network model based on high throughput screening data for predicting synergistic estrogenic activity of binary mixtures for consumer products.

Journal of hazardous materials
A paradigm of chemical risk assessment is continuously extending from focusing on 'single substances' to more comprehensive approaches that examines the combined toxicity among different components in 'mixtures.' This change aims to account for the c...

TTGA U-Net: Two-stage two-stream graph attention U-Net for hepatic vessel connectivity enhancement.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate segmentation of hepatic vessels is pivotal for guiding preoperative planning in ablation surgery utilizing CT images. While non-contrast CT images often lack observable vessels, we focus on segmenting hepatic vessels within preoperative MR i...

Replica tree-based federated learning using limited data.

Neural networks : the official journal of the International Neural Network Society
Learning from limited data has been extensively studied in machine learning, considering that deep neural networks achieve optimal performance when trained using a large amount of samples. Although various strategies have been proposed for centralize...

Label as Equilibrium: A performance booster for Graph Neural Networks on node classification.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Network (GNN) is effective in graph mining and has become a dominant solution to the node classification task. Recently, a series of label reuse approaches emerged to boost the node classification performance of GNN. They repeatedly inpu...

GeM: Gaussian embeddings with Multi-hop graph transfer for next POI recommendation.

Neural networks : the official journal of the International Neural Network Society
Next Point-of-Interest (POI) recommendation is crucial in location-based applications, analyzing user behavior patterns from historical trajectories. Existing studies usually use graph structures and attention mechanisms for sequential prediction wit...

A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control.

Neural networks : the official journal of the International Neural Network Society
This paper proposes a strictly predefined-time convergent and anti-noise fractional-order zeroing neural network (SPTC-AN-FOZNN) model, meticulously designed for addressing time-variant quadratic programming (TVQP) problems. This model marks the firs...

ADFQ-ViT: Activation-Distribution-Friendly post-training Quantization for Vision Transformers.

Neural networks : the official journal of the International Neural Network Society
Vision Transformers (ViTs) have exhibited exceptional performance across diverse computer vision tasks, while their substantial parameter size incurs significantly increased memory and computational demands, impeding effective inference on resource-c...

Regional Cerebral Atrophy Contributes to Personalized Survival Prediction in Amyotrophic Lateral Sclerosis: A Multicentre, Machine Learning, Deformation-Based Morphometry Study.

Annals of neurology
OBJECTIVE: Accurate personalized survival prediction in amyotrophic lateral sclerosis is essential for effective patient care planning. This study investigates whether grey and white matter changes measured by magnetic resonance imaging can improve i...

MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging.

Medical image analysis
Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical practice. While conventional methods often focus on a specific sub-region, multi-view learning captures more information by analyzing multiple patches sim...

Enhancing readmission prediction model in older stroke patients by integrating insight from readiness for hospital discharge: Prospective cohort study.

International journal of medical informatics
BACKGROUND: The 30-day hospital readmission rate is a key indicator of healthcare quality and system efficiency. This study aimed to develop machine-learning (ML) models to predict unplanned 30-day readmissions in older patients with ischemic stroke ...