AIMC Topic: Machine Learning

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How do neighborhood environments impact adolescent health: a comprehensive study from subjective and objective perspectives using machine learning method.

Frontiers in public health
Existing studies have established a linear relationship between urban environments and adolescent health, but the combined impacts of subjective and objective environments on multi-dimensional health status (including physical and mental health) have...

STI/HIV risk prediction model development-A novel use of public data to forecast STIs/HIV risk for men who have sex with men.

Frontiers in public health
A novel automatic framework is proposed for global sexually transmissible infections (STIs) and HIV risk prediction. Four machine learning methods, namely, Gradient Boosting Machine (GBM), Random Forest (RF), XG Boost, and Ensemble learning GBM-RF-XG...

Quantifying the contributions of factors to bioaccessible Cd and Pb in soil using machine learning.

Journal of hazardous materials
The bioaccessibility of cadmium (Cd) and lead (Pb) in the gastrointestinal tract is crucial for health risk assessments of contaminated soils. However, variability in In vitro analytical conditions and soil properties introduces bias and uncertainty ...

Biological contaminants analysis in microalgae culture by UV-vis spectroscopy and machine learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study elucidates the utility and efficacy of UV-visible spectroscopy for the detection and characterization of biological contaminants within microalgae cultures, augmented by machine learning algorithms. Biological contamination, exemplified by...

Success History Adaptive Competitive Swarm Optimizer with Linear Population Reduction: Performance benchmarking and application in eye disease detection.

Computers in biology and medicine
Eye disease detection has achieved significant advancements thanks to artificial intelligence (AI) techniques. However, the construction of high-accuracy predictive models still faces challenges, and one reason is the deficiency of the optimizer. Thi...

A novel fuzzy three-valued logic computational framework in machine learning for medicine dataset.

Computers in biology and medicine
For consideration of uncertainties of a medicine dataset, a new conceptual architecture fuzzy three-valued logic is introduced in this research work. The proposed concept is applied to the heart disease dataset for the assessment of heart disease ris...

Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets.

Cancer cell
Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suita...

Characterizing Chinese saffron Origin, Age and grade using VNlR hyperspectral imaging and Machine learning.

Food research international (Ottawa, Ont.)
Saffron (Crocus sativus L.), the dried stigma, is an extremely valuable spice and medicinal herb, whose economic value is affected by geographical origin, age and grade. In this study, we proposed a method to identify saffron from different Chinese o...

Machine learning driven benchtop Vis/NIR spectroscopy for online detection of hybrid citrus quality.

Food research international (Ottawa, Ont.)
The aim of this study was to explore application of visible and near-infrared (Vis/NIR) spectroscopy combined with machine learning models for SSC and TA prediction of hybrid citrus. The Vis/NIR spectra of samples including navel-region, equator-regi...