AIMC Topic: Algorithms

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Multi-hop interpretable meta learning for few-shot temporal knowledge graph completion.

Neural networks : the official journal of the International Neural Network Society
Multi-hop path completion is a key part of temporal knowledge graph completion, which aims to infer complex relationships and obtain interpretable completion results. However, the traditional multi-hop path completion models mainly focus on the stati...

Lie group convolution neural networks with scale-rotation equivariance.

Neural networks : the official journal of the International Neural Network Society
The weight-sharing mechanism of convolutional kernels ensures the translation equivariance of convolutional neural networks (CNNs) but not scale and rotation equivariance. This study proposes a SIM(2) Lie group-CNN, which can simultaneously keep scal...

Event-based adaptive fixed-time optimal control for saturated fault-tolerant nonlinear multiagent systems via reinforcement learning algorithm.

Neural networks : the official journal of the International Neural Network Society
This article investigates the problem of adaptive fixed-time optimal consensus tracking control for nonlinear multiagent systems (MASs) affected by actuator faults and input saturation. To achieve optimal control, reinforcement learning (RL) algorith...

Estimating global phase synchronization by quantifying multivariate mutual information and detecting network structure.

Neural networks : the official journal of the International Neural Network Society
In neuroscience, phase synchronization (PS) is a crucial mechanism that facilitates information processing and transmission between different brain regions. Specifically, global phase synchronization (GPS) characterizes the degree of PS among multiva...

Using supervised machine learning algorithms to predict bovine leukemia virus seropositivity in dairy cattle in Florida: A 10-year retrospective study.

Preventive veterinary medicine
Supervised machine-learning (SML) algorithms are potentially powerful tools that may be used for screening cows for infectious diseases such as bovine leukemia virus (BLV) infection. Here, we compared six different SML algorithms to identify the most...

AI-driven predictive models for sustainability.

Journal of environmental management
This research presents an AI-driven, explainable energy management model that aligns with Net Zero sustainability objectives by optimizing energy consumption, enhancing predictive accuracy, and ensuring transparency. The model integrates machine lear...

Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm.

Photodiagnosis and photodynamic therapy
The existing clinical diagnostic methods of pulmonary tuberculosis (TB) usually have some of the following limitations, such as time-consuming, invasive, radioactive, insufficiently sensitive and accurate. This study demonstrates the possibility of u...

A hybrid model for monthly runoff forecasting based on mixed signal processing and machine learning.

Environmental science and pollution research international
Monthly runoff forecasting plays a critically supportive role in water resources planning and management. Various signal decomposition techniques have been widely applied to enhance the accuracy of monthly runoff forecasting. However, the forecasting...

MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification.

Sensors (Basel, Switzerland)
Decoding lower-limb motor imagery (MI) is highly important in brain-computer interfaces (BCIs) and rehabilitation engineering. However, it is challenging to classify lower-limb MI from electroencephalogram (EEG) signals, because lower-limb motions (L...

Research on Multi-Scale Spatio-Temporal Graph Convolutional Human Behavior Recognition Method Incorporating Multi-Granularity Features.

Sensors (Basel, Switzerland)
Aiming at the problem that the existing human skeleton behavior recognition methods are insensitive to human local movements and show inaccurate recognition in distinguishing similar behaviors, a multi-scale spatio-temporal graph convolution method i...