AIMC Topic: Machine Learning

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Rapid estimation of soil water content based on hyperspectral reflectance combined with continuous wavelet transform, feature extraction, and extreme learning machine.

PeerJ
BACKGROUND: Soil water content is one of the critical indicators in agricultural systems. Visible/near-infrared hyperspectral remote sensing is an effective method for soil water estimation. However, noise removal from massive spectral datasets and e...

PredIL13: Stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides.

PloS one
Interleukin (IL)-13 has emerged as one of the recently identified cytokine. Since IL-13 causes the severity of COVID-19 and alters crucial biological processes, it is urgent to explore novel molecules or peptides capable of including IL-13. Computati...

Using machine learning to determine the nationalities of the fastest 100-mile ultra-marathoners and identify top racing events.

PloS one
The present study intended to determine the nationality of the fastest 100-mile ultra-marathoners and the country/events where the fastest 100-mile races are held. A machine learning model based on the XG Boost algorithm was built to predict the runn...

Examining worry and secondary stressors on grief severity using machine learning.

Anxiety, stress, and coping
BACKGROUND & OBJECTIVES: Worry and loss-related secondary stressors appear to be important correlates of problematic grief responses. However, the relative importance of these variables in the context of established correlates of grief responding, ra...

Machine learning identifies clusters of the normal adolescent spine based on sagittal balance.

Spine deformity
PURPOSE: This study applied a machine learning semi-supervised clustering approach to radiographs of adolescent sagittal spines from a single pediatric institution to identify patterns of sagittal alignment in the normal adolescent spine. We sought t...

Learning the feature distribution similarities for online time series anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Identifying anomalies in multi-dimensional sequential data is crucial for ensuring optimal performance across various domains and in large-scale systems. Traditional contrastive methods utilize feature similarity between different features extracted ...

Prediction of 90 day readmission in heart failure with preserved ejection fraction by interpretable machine learning.

ESC heart failure
AIMS: Certain critical risk factors of heart failure with preserved ejection fraction (HFpEF) patients were significantly different from those of heart failure with reduced ejection fraction (HFrEF) patients, resulting in the limitations of existing ...

Explore key genes of Crohn's disease based on glycerophospholipid metabolism: A comprehensive analysis Utilizing Mendelian Randomization, Multi-Omics integration, Machine Learning, and SHAP methodology.

International immunopharmacology
BACKGROUND AND AIMS: Crohn's disease (CD) is a chronic, complex inflammatory condition with increasing incidence and prevalence worldwide. However, the causes of CD remain incompletely understood. We identified CD-related metabolites, inflammatory fa...

Predicting rice phenology across China by integrating crop phenology model and machine learning.

The Science of the total environment
This study explores the integration of crop phenology models and machine learning approaches for predicting rice phenology across China, to gain a deeper understanding of rice phenology prediction. Multiple approaches were used to predict heading and...

Machine learning surveillance of foodborne infectious diseases using wastewater microbiome, crowdsourced, and environmental data.

Water research
Clostridium perfringens (CP) is a common cause of foodborne infection, leading to significant human health risks and a high economic burden. Thus, effective CP disease surveillance is essential for preventive and therapeutic interventions; however, c...