AIMC Topic: Neural Networks, Computer

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An Active Control Method for a Lower Limb Rehabilitation Robot with Human Motion Intention Recognition.

Sensors (Basel, Switzerland)
This study presents a method for the active control of a follow-up lower extremity exoskeleton rehabilitation robot (LEERR) based on human motion intention recognition. Initially, to effectively support body weight and compensate for the vertical mov...

Knowledge-point classification using simple LSTM-based and siamese-based networks for virtual patient simulation.

BMC medical informatics and decision making
BACKGROUND: In medical education, enhancing thinking skills is vital. The Virtual Diagnosis and Treatment Platform (VP) refines medical students' diagnostic abilities through interactive patient interviews (simulated patient interactions). By analyzi...

Optimization of dried garlic physicochemical properties using a self-organizing map and the development of an artificial intelligence prediction model.

Scientific reports
The experiments were conducted at different levels of infrared power, airflow, and temperature. The relationships between the input process factors and response factors' physicochemical properties of dried garlic were optimized by a self-organizing m...

Optimising deep learning models for ophthalmological disorder classification.

Scientific reports
Fundus imaging, a technique for recording retinal structural components and anomalies, is essential for observing and identifying ophthalmological diseases. Disorders such as hypertension, glaucoma, and diabetic retinopathy are indicated by structura...

Generative adversarial local density-based unsupervised anomaly detection.

PloS one
Anomaly detection is crucial in areas such as financial fraud identification, cybersecurity defense, and health monitoring, as it directly affects the accuracy and security of decision-making. Existing generative adversarial nets (GANs)-based anomaly...

A spatial interpolation method based on 3D-CNN for soil petroleum hydrocarbon pollution.

PloS one
Petroleum hydrocarbon pollution causes significant damage to soil, so accurate prediction and early intervention are crucial for sustainable soil management. However, traditional soil analysis methods often rely on statistical methods, which means th...

Knowledge-Guided Semantically Consistent Contrastive Learning for sequential recommendation.

Neural networks : the official journal of the International Neural Network Society
Contrastive learning has gained dominance in sequential recommendation due to its ability to derive self-supervised signals for addressing data sparsity problems. However, caused by random augmentations (e.g., crop, mask, and reorder), existing metho...

UNAGI: Unified neighbor-aware graph neural network for multi-view clustering.

Neural networks : the official journal of the International Neural Network Society
Multi-view graph refining-based clustering (MGRC) methods aim to facilitate the clustering of data via Graph Neural Networks (GNNs) by learning optimal graphs that reflect the underlying topology of the data. However, current MGRC approaches are limi...

Attention-based deep learning models for predicting anomalous shock of wastewater treatment plants.

Water research
Quickly grasping the time-consuming water quality indicators (WQIs) such as total nitrogen (TN) and total phosphorus (TP) of influent is an essential prerequisite for wastewater treatment plants (WWTPs) to prompt respond to sudden shock loads. Soft d...