AIMC Topic: Machine Learning

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Identifying bee species origins of Philippine honey using X-ray fluorescence elemental analysis coupled with machine learning.

Food chemistry
Stingless bee honey is emerging as a superfood, given its enhanced health and therapeutic benefits. In this paper, we used handheld X-ray fluorescence spectroscopy (hXRF) with machine learning techniques to classify Philippine honey based on its ento...

FxTS-Net: Fixed-time stable learning framework for Neural ODEs.

Neural networks : the official journal of the International Neural Network Society
Neural Ordinary Differential Equations (Neural ODEs), as a novel category of modeling big data methods, cleverly link traditional neural networks and dynamical systems. However, it is challenging to ensure the dynamics system reaches a correctly pred...

Conventional and machine learning-based analysis of age, body weight and body height significance in knot position-related thyrohyoid and cervical spine fractures in suicidal hangings.

International journal of legal medicine
The thyrohyoid complex and cervical spine fracture distribution patterns may reflect the knot position as the force distribution by the noose to different neck regions may vary depending on it. Recently, machine learning models (MLm) were used to cla...

Improved workflow for constructing machine learning models: Predicting retention times and peak widths in oligonucleotide separation.

Journal of chromatography. A
This study presents an improved workflow to support the development of machine learning models to predict oligonucleotide retention times, peak widths and thus peak resolutions, from larger datasets where manual processing is not feasible. We explore...

A comparative analysis of trauma-related mortality in South Korea using classification models.

International journal of medical informatics
BACKGROUND: Reducing mortality among severe trauma patients requires the establishment of an effective emergency transportation system and the rapid transfer of patients to appropriate medical facilities. Machine learning offers significant potential...

Prediction of insulin resistance using multiple adaptive regression spline in Chinese women.

Endocrine journal
Insulin resistance (IR) is the core for type 2 diabetes and metabolic syndrome. The homeostasis assessment model is a straightforward and practical tool for quantifying insulin resistance (HOMA-IR). Multiple adaptive regression spline (MARS) is a mac...

A holistic strategy for the in-depth discrimination and authentication of 16 citrus herbs and associated commercial products based on machine learning techniques and non-targeted metabolomics.

Journal of chromatography. A
Citrus-derived raw medicinal materials are frequently used for health care, flavoring, and therapeutic purposes. However, Due to similarities in origin or appearance, citrus herbs are often misused in the market, necessitating effective differentiati...

Practical X-ray gastric cancer diagnostic support using refined stochastic data augmentation and hard boundary box training.

Artificial intelligence in medicine
Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it must be performed by a physician, which limits the number of people who can be diagnosed. In contrast, gastric X-rays can be taken by radiographers, thu...

Automated and explainable machine learning for monitoring lipid and protein oxidative damage in mutton using hyperspectral imaging.

Food research international (Ottawa, Ont.)
Current detection methods for lipid and protein oxidation using hyperspectral imaging (HSI) in conjunction with machine learning (ML) necessitate the involvement of data scientists and domain experts to adjust the model architecture and tune hyperpar...