AIMC Topic: Machine Learning

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A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra.

Journal of dairy science
Lactoferrin (LF) is a glycoprotein naturally present in milk. Its content varies throughout lactation, but also with mastitis; therefore it is a potential additional indicator of udder health beyond somatic cell count. Condequently, there is an inter...

Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy.

Proceedings of the National Academy of Sciences of the United States of America
Taxonomic resolution is a major challenge in palynology, largely limiting the ecological and evolutionary interpretations possible with deep-time fossil pollen data. We present an approach for fossil pollen analysis that uses optical superresolution ...

Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia.

Sensors (Basel, Switzerland)
Identifying the symptoms of the early stages of dementia is a difficult task, particularly for older adults living in residential care. Internet of Things (IoT) and smart environments can assist with the early detection of dementia, by nonintrusive m...

Pattern recognition based on machine learning identifies oil adulteration and edible oil mixtures.

Nature communications
Previous studies have shown that each edible oil type has its own characteristic fatty acid profile; however, no method has yet been described allowing the identification of oil types simply based on this characteristic. Moreover, the fatty acid prof...

Application of ensemble learning to genomic selection in chinese simmental beef cattle.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
Genomic selection (GS) using the whole-genome molecular makers to predict genomic estimated breeding values (GEBVs) is revolutionizing the livestock and plant breeding. Seeking out novel strategies with higher prediction accuracy for GS has been the ...

Machine learning for precision medicine.

Genome
Precision medicine is an emerging approach to clinical research and patient care that focuses on understanding and treating disease by integrating multi-modal or multi-omics data from an individual to make patient-tailored decisions. With the large a...

Prediction of obstetrical and fetal complications using automated electronic health record data.

American journal of obstetrics and gynecology
An increasing number of delivering women experience major morbidity and mortality. Limited work has been done on automated predictive models that could be used for prevention. Using only routinely collected obstetrical data, this study aimed to devel...

Combining wearable sensor signals, machine learning and biomechanics to estimate tibial bone force and damage during running.

Human movement science
There are tremendous opportunities to advance science, clinical care, sports performance, and societal health if we are able to develop tools for monitoring musculoskeletal loading (e.g., forces on bones or muscles) outside the lab. While wearable se...

Using Machine Learning to Predict Suicide Attempts in Military Personnel.

Psychiatry research
Identifying predictors of suicide attempts is critical in intervention and prevention efforts, yet finding predictors has proven difficult due to the low base rate and underpowered statistical approaches. The objective of the current study was to use...

Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification.

Sensors (Basel, Switzerland)
Observation of neuromotor development at an early stage of an infant's life allows for early diagnosis of deficits and the beginning of the therapeutic process. General movement assessment is a method of spontaneous movement observation, which is the...