AIMC Topic: Machine Learning

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Intelligent identification of picking periods of Lu'an Guapian tea by an indicator displacement colorimetric sensor array combined with machine learning.

Food research international (Ottawa, Ont.)
Lu'an Gua Pian (LAGP) tea is one of the most famous green teas in China. The quality of green tea is related to its picking periods, especially the green tea before Qingming Festival (usually April 6th) is highly praised as precious in the market. In...

Machine learning models to predict osteonecrosis in patients with femoral neck fractures undergoing internal fixation.

Injury
OBJECTIVE: This study aimed to use machine learning (ML) to establish risk factor and prediction models of osteonecrosis of the femoral head (ONFH) in patients with femoral neck fractures (FNFs) after internal fixation.

A machine learning system for artificial ligaments with desired mechanical properties in ACL reconstruction applications.

Journal of the mechanical behavior of biomedical materials
The anterior cruciate ligament is one of the important tissues to maintain the stability of the human knee joint, but it is difficult for this ligament to self-heal after injury. Consequently, transplantation of artificial ligaments (ALs) has gained ...

Predicting ammonia emissions and global warming potential in composting by machine learning.

Bioresource technology
The amounts of gases emitted from composting are key to evaluating global warming potential (GWP). However, few methods can accurately predict the quantities of relevant gas emissions. In this study, three developed machine-learning models were used ...

LC-SRM Combined With Machine Learning Enables Fast Identification and Quantification of Bacterial Pathogens in Urinary Tract Infections.

Molecular & cellular proteomics : MCP
Urinary tract infections (UTIs) are a worldwide health problem. Fast and accurate detection of bacterial infection is essential to provide appropriate antibiotherapy to patients and to avoid the emergence of drug-resistant pathogens. While the gold s...

Interpretable machine learning models based on shear-wave elastography radiomics for predicting cardiovascular disease in diabetic kidney disease patients.

Journal of diabetes investigation
BACKGROUND: The risk of cardiovascular complications is significantly elevated in patients with diabetic kidney disease (DKD). Recognizing the link between the progression of DKD and an increased risk of cardiovascular disease (CVD), it is crucial to...

Integrating automated machine learning and metabolic reprogramming for the identification of microplastic in soil: A case study on soybean.

Journal of hazardous materials
The accumulation of polyethylene microplastic (PE-MPs) in soil can significantly impact plant quality and yield, as well as affect human health and food chain cycles. Therefore, developing rapid and effective detection methods is crucial. In this stu...

Automated transtibial prosthesis alignment: A systematic review.

Artificial intelligence in medicine
This comprehensive systematic review critically analyzes the current progress and challenges in automating transtibial prosthesis alignment. The manual identification of alignment changes in prostheses has been found to lack reliability, necessitatin...

Osteoinductive biomaterials: Machine learning for prediction and interpretation.

Acta biomaterialia
Biomaterials with osteoinductivity are widely used for bone defect repair due to their unique structures and functions. Machine learning (ML) is pivotal in analyzing osteoinductivity and accelerating new material design. However, challenges include c...

StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features.

Methods (San Diego, Calif.)
Host defense or antimicrobial peptides (AMPs) are promising candidates for protecting host against microbial pathogens for example bacteria, virus, fungi, yeast. Defensins are the type of AMPs that act as potential therapeutic drug agent and perform ...