AIMC Topic: Algorithms

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Decision model of public opinion risk in campus social network based on hybrid dynamic deletion and shortest path algorithm.

PloS one
Aiming at the problem that traditional network public opinion monitoring and searching are inefficient and can easily cause resource waste, the study firstly, through the dynamic deletion-shortest path algorithm to classify network text, and on this ...

Discrimination of wheat gluten quality utilizing terahertz time-domain spectroscopy (THz-TDS).

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Wheat is an important food crop in the world, and wheat gluten quality is one of the important standards for judging the use of wheat. In this study, a combination of chemometric and machine learning methods based on THz-TDS were used to identify thr...

Stress recognition identifying relevant facial action units through explainable artificial intelligence and machine learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Facial cues and expressions constitute a component of bodily responses that provide useful information about one's stress levels. According to the Facial Action Coding System, they can be modelled consistently in terms of fu...

Towards generalizable face forgery detection via mitigating spurious correlation.

Neural networks : the official journal of the International Neural Network Society
The continuous advancement of face forgery techniques has caused a series of trust crises, posing a significant menace to information security and personal privacy. In response, deep learning is being employed to develop effective detection methods t...

Signed graph embedding via multi-order neighborhood feature fusion and contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Signed graphs have been widely applied to model real-world complex networks with positive and negative links, and signed graph embedding has become a popular topic in the field of signed graph analysis. Although various signed graph embedding methods...

Multiscroll hopfield neural network with extreme multistability and its application in video encryption for IIoT.

Neural networks : the official journal of the International Neural Network Society
In Industrial Internet of Things (IIoT) production and operation processes, a substantial amount of video data is generated, often containing sensitive personal and commercial information. This paper proposed three new multiscroll Hopfield neural net...

Multi-compartment neuron and population encoding powered spiking neural network for deep distributional reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
Inspired by the brain's information processing using binary spikes, spiking neural networks (SNNs) offer significant reductions in energy consumption and are more adept at incorporating multi-scale biological characteristics. In SNNs, spiking neurons...

GraFMRI: A graph-based fusion framework for robust multi-modal MRI reconstruction.

Magnetic resonance imaging
PURPOSE: This study introduces GraFMRI, a novel framework designed to address the challenges of reconstructing high-quality MRI images from undersampled k-space data. Traditional methods often suffer from noise amplification and loss of structural de...

Multi-Institutional Evaluation and Training of Breast Density Classification AI Algorithm Using ACR Connect and AI-LAB.

Journal of the American College of Radiology : JACR
OBJECTIVE: To demonstrate and test the capabilities of the ACR Connect and AI-LAB software platform by implementing multi-institutional artificial intelligence (AI) training and validation for breast density classification.

DMHGNN: Double multi-view heterogeneous graph neural network framework for drug-target interaction prediction.

Artificial intelligence in medicine
Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods for drug-target interactions predicti...