AIMC Topic: Machine Learning

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Proximity Environmental Feature Based Tree Health Assessment Scheme Using Internet of Things and Machine Learning Algorithm.

Sensors (Basel, Switzerland)
Improperly grown trees may cause huge hazards to the environment and to humans, through e.g., climate change, soil erosion, etc. A proximity environmental feature-based tree health assessment (PTA) scheme is proposed to prevent these hazards by provi...

An Automatic Approach Using ELM Classifier for HFpEF Identification Based on Heart Sound Characteristics.

Journal of medical systems
Heart failure with preserved ejection fraction (HFpEF) is a complex and heterogeneous clinical syndrome. For the purpose of assisting HFpEF diagnosis, a non-invasive method using extreme learning machine and heart sound (HS) characteristics was provi...

Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences.

Nature communications
Biomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled, which creates barriers to their use in supervised machine learning. We develop a weakly supervised ...

The Model of Aging Acceleration Network Reveals the Correlation of Alzheimer's Disease and Aging at System Level.

BioMed research international
As the incidence of senile dementia continues to increase, researches on Alzheimer's disease (AD) have become more and more important. Several studies have reported that there is a close relationship between AD and aging. Some researchers even pointe...

Improving Operating Room Efficiency: Machine Learning Approach to Predict Case-Time Duration.

Journal of the American College of Surgeons
BACKGROUND: Accurate estimation of operative case-time duration is critical for optimizing operating room use. Current estimates are inaccurate and earlier models include data not available at the time of scheduling. Our objective was to develop stat...

A fast machine learning approach to facilitate the detection of interictal epileptiform discharges in the scalp electroencephalogram.

Journal of neuroscience methods
BACKGROUND: Finding interictal epileptiform discharges (IEDs) in the EEG is a part of diagnosing epilepsy. Automated software for annotating EEGs of patients with suspected epilepsy can therefore help with reaching a diagnosis. A large amount of data...

Disability in multiple sclerosis is associated with age and inflammatory, metabolic and oxidative/nitrosative stress biomarkers: results of multivariate and machine learning procedures.

Metabolic brain disease
The aim of this study was to evaluate the immune-inflammatory, metabolic, and nitro-oxidative stress (IM&NO) biomarkers as predictors of disability in multiple sclerosis (MS) patients. A total of 122 patients with MS were included; their disability w...

Solving visual pollution with deep learning: A new nexus in environmental management.

Journal of environmental management
Visual pollution is a relatively new concern amidst the existing plethora of mainstream environmental pollution, recommending the necessity for research to conceptualize, formalize, quantify and assess it from different dimensions. The purpose of thi...

Use of Machine Learning and Wearable Sensors to Predict Energetics and Kinematics of Cutting Maneuvers.

Sensors (Basel, Switzerland)
Changes of directions and cutting maneuvers, including 180-degree turns, are common locomotor actions in team sports, implying high mechanical load. While the mechanics and neurophysiology of turns have been extensively studied in laboratory conditio...