AIMC Topic: Algorithms

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High-precision deformation monitoring and intelligent early warning for wellbore based on BDS/GNSS.

PloS one
To address the complex deformation of wellbores influenced by surrounding coal mining operations, this study employed an improved modified least-squares ambiguity decorrelation (MLAMBDA) algorithm based on the double-difference model for high-frequen...

Fine-grained image generation with EEG multi-level semantics.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Decoding visual information from electroencephalography (EEG) signals is crucial in neuroscience and artificial intelligence. While existing methods have been able to extract high-level features such as object categories, th...

Comprehensive disentanglement with fine-grained feature mitigation for domain generalization.

Neural networks : the official journal of the International Neural Network Society
Domain generalization is proposed as an approach capable of solving the domain shift challenge, which aims at generalizing knowledge learned from multiple source domains with different distributions to the target domain that is invisible during the t...

Path-aware multi-scale learning for heterogeneous graph neural network.

Neural networks : the official journal of the International Neural Network Society
Heterogeneous Graph Neural Networks (HGNNs) are a powerful tool for modeling data with diverse node and edge types, found in applications like social networks, recommendation systems, and knowledge graphs, including tasks such as node classification,...

The architecture design and training optimization of spiking neural network with low-latency and high-performance for classification and segmentation.

Neural networks : the official journal of the International Neural Network Society
Spiking Neural Networks (SNNs) are the new third generation of bio-mimetic neural networks suitable for large-scale parallel computation due to its advantages of low power consumption and low latency. However, most of the training algorithms and netw...

RIVA: Efficient relational inference with variate attention.

Neural networks : the official journal of the International Neural Network Society
Interactive systems are omnipresent across various domains, ranging from dynamic systems in physics to intricate societal dynamics. Relational inference aims to uncover implicit interactions between components based on observed system trajectories. E...

Learning multi-regularized mutation-aware correlation filter for object tracking via an adaptive hybrid model.

Neural networks : the official journal of the International Neural Network Society
Discriminative Correlation Filters (DCF) have emerged as a popular and effective approach in object tracking. With promising performance and efficiency, DCF-based trackers achieved impressive attention and reliable tracking results in several challen...

A two-stage machine learning-based risk assessment model for intravenous thrombolysis in acute ischemic stroke (AIS): A multi-center modeling study of pooled datasets.

International journal of medical informatics
OBJECTIVE: Develop a two-stage, machine learning-based thrombolysis risk stratification model from existing medical datasets and electronic health records to predict the risk of early hemorrhagic transformation(HT) and in-hospital mortality(IM) follo...

Enhancing rare disease detection with deep phenotyping from EHR narratives: evaluation on Jeune syndrome.

International journal of medical informatics
BACKGROUND: Patients with rare diseases frequently experience misdiagnoses and long diagnostic delays. Accelerating their diagnosis is essential to ensure timely access to appropriate care. Given the increasing availability of EHRs, combining artific...

Stabilizing machine learning for reproducible and explainable results: A novel validation approach to subject-specific insights.

Computer methods and programs in biomedicine
INTRODUCTION: Machine Learning (ML) is transforming medical research by enhancing diagnostic accuracy, predicting disease progression, and personalizing treatments. While general models trained on large datasets identify broad patterns across populat...