AIMC Topic: Algorithms

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Machine learning prediction of groundwater arsenic contamination using water quality parameters in the coastal region of Bangladesh.

Environmental geochemistry and health
Groundwater arsenic contamination poses a significant health risk in coastal region of Bangladesh. However, existing studies have rarely applied advanced machine learning (ML) algorithms to predict arsenic concentrations using comprehensive water qua...

SSMCE: A semi-supervised learning framework for myocardial segmentation in myocardial contrast echocardiography.

Biomedical physics & engineering express
Accurate myocardial segmentation in myocardial contrast echocardiography (MCE) images remains challenging due to the scarcity of publicly available labeled datasets and the pervasive presence of speckle noise.Currently, echocardiographers must manual...

Enhancing network traffic detection via interpolation augmentation and contrastive learning.

PloS one
With the rapid advancement of information technology, the Internet, as the core infrastructure for global information exchange, faces increasingly severe security challenges. However, traditional network traffic detection methods typically focus sole...

Indoor location perception model based on Resnet50 and Elman network.

PloS one
The visible light indoor position perception method not only solves the limitations of traditional positioning technology indoors, but also promotes innovation in fields such as smart retail and healthcare with its advantages of high accuracy and low...

Towards AI-based precision rehabilitation via contextual model-based reinforcement learning.

Journal of neuroengineering and rehabilitation
BACKGROUND: Stroke is a condition marked by considerable variability in lesions, recovery trajectories, and responses to therapy. Consequently, precision medicine in rehabilitation post-stroke, which aims to deliver the "right intervention, at the ri...

SALT: Introducing a framework for hierarchical segmentations in medical imaging using label trees.

Scientific reports
Traditional segmentation networks treat anatomical structures as isolated elements, often neglecting their hierarchical relationships. This study introduces Softmax for Arbitrary Label Trees (SALT), a novel method that leverages these hierarchical co...

Non-invasive anemia detection from conjunctiva and sclera images using vision transformer with attention map explainability.

Scientific reports
Iron-deficiency anemia, a prevalent global health issue, traditionally requires invasive procedures for accurate diagnosis, such as a blood sample for measuring hemoglobin (Hgb) concentration. Nevertheless, this marker can be visually assessed by obs...

A teacherless lightweight classification framework for benign and malignant pulmonary nodules based on GAS.

Biomedical physics & engineering express
Deep learning methods have been widely adopted for classifying benign and malignant pulmonary nodules. However, existing models often suffer from high memory usage, computational cost, and large parameter counts. As a result, the development of light...

Machine learning-based risk prediction model for cognitive dysfunction in elderly individuals.

PloS one
BACKGROUND: With the advancement of globalization, the prevalence of cognitive dysfunction in the elderly population has risen significantly. Early intervention may dramatically alleviate the disease burden and reduce economic costs associated with c...

Predicting the influence of homologous recombination repair deficiency genes on glioma heterogeneity and patient prognosis using multi-omics analysis and machine learning.

PloS one
BACKGROUND: Glioma is the most common malignant tumor of the central nervous system, and homologous recombination deficiency (HRD) may play a crucial role in its progression. Our study aimed to predict the impact of HRD on glioma heterogeneity and pa...