AIMC Topic: Machine Learning

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Assessing the nonlinear impact of green space exposure on psychological stress perception using machine learning and street view images.

Frontiers in public health
INTRODUCTION: Urban green space (GS) exposure is recognized as a nature-based strategy for addressing urban challenges. However, the stress relieving effects and mechanisms of GS exposure are yet to be fully explored. The development of machine learn...

Prediction of antimicrobial resistance of Klebsiella pneumoniae from genomic data through machine learning.

PloS one
Antimicrobials, such as antibiotics or antivirals are medications employed to prevent and treat infectious diseases in humans, animals, and plants. Antimicrobial Resistance occurs when bacteria, viruses, and parasites no longer respond to these medic...

Improving Human Activity Recognition With Wearable Sensors Through BEE: Leveraging Early Exit and Gradient Boosting.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Early-exiting has recently provided an ideal solution for accelerating activity inference by attaching internal classifiers to deep neural networks. It allows easy activity samples to be predicted at shallower layers, without executing deeper layers,...

Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this ch...

Automatic Feature Selection for Sensorimotor Rhythms Brain-Computer Interface Fusing Expert and Data-Driven Knowledge.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Early brain-computer interface (BCI) systems were mainly based on prior neurophysiological knowledge coupled with feedback training, while state-of-the-art interfaces rely on data-driven, machine learning (ML)-oriented methods. Despite the advances i...

Evaluation of Sociomedical Factors on Corneal Donor Recovery Using Machine Learning.

Ophthalmic epidemiology
PURPOSE: To evaluate co-morbid sociomedical conditions affecting corneal donor endothelial cell density and transplant suitability.

Accurate Machine Learning-based Monitoring of Anesthesia Depth with EEG Recording.

Neuroscience bulletin
General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators...

A Machine Learning Approach to Concussion Risk Estimation Among Players Exhibiting Visible Signs in Professional Hockey.

Sports medicine (Auckland, N.Z.)
BACKGROUND: The identification of concussion risk factors, such as visible signs and mechanisms of injury, improves concussion identification. Exploring individual risk factors, such as concussion history, may help to improve existing concussion risk...

Machine Learning for the Prediction of Surgical Morbidity in Placenta Accreta Spectrum.

American journal of perinatology
OBJECTIVE:  We sought to create a machine learning (ML) model to identify variables that would aid in the prediction of surgical morbidity in cases of placenta accreta spectrum (PAS).

Analysis of sediment re-formation factors after ginseng beverage clarification based on XGBoost machine learning algorithm.

Food chemistry
The aim of this study was to explore the sediment re-formation factors of ginseng beverages subjected to four clarification ways (11 subgroups) including the ethanol precipitation, enzymatic treatment, clarifier clarification, and Hollow Fiber Column...