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Australia

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Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models.

Environmental pollution (Barking, Essex : 1987)
Hybrid artificial intelligence (AI) models are developed for sediment lead (Pb) prediction in two Bays (i.e., Bramble (BB) and Deception (DB)) stations, Australia. A feature selection (FS) algorithm called extreme gradient boosting (XGBoost) is propo...

A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis.

Scientific reports
Visual analysis of complex fish habitats is an important step towards sustainable fisheries for human consumption and environmental protection. Deep Learning methods have shown great promise for scene analysis when trained on large-scale datasets. Ho...

Predicting Absenteeism and Temporary Disability Using Machine Learning: a Systematic Review and Analysis.

Journal of medical systems
The main objective of this paper is to present a systematic analysis and review of the state of the art regarding the prediction of absenteeism and temporary incapacity using machine learning techniques. Moreover, the main contribution of this resear...

A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication.

Journal of exposure science & environmental epidemiology
BACKGROUND: Wildland fire (wildfire; bushfire) pollution contributes to poor air quality, a risk factor for premature death. The frequency and intensity of wildfires are expected to increase; improved tools for estimating exposure to fire smoke are v...

A Physical Activity and Diet Program Delivered by Artificially Intelligent Virtual Health Coach: Proof-of-Concept Study.

JMIR mHealth and uHealth
BACKGROUND: Poor diet and physical inactivity are leading modifiable causes of death and disease. Advances in artificial intelligence technology present tantalizing opportunities for creating virtual health coaches capable of providing personalized s...

An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans.

The Journal of laryngology and otology
OBJECTIVE: Convolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algori...

Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus 'trained' machine learning models.

Addiction (Abingdon, England)
BACKGROUND AND AIMS: Clinical staff are typically poor at predicting alcohol dependence treatment outcomes. Machine learning (ML) offers the potential to model complex clinical data more effectively. This study tested the predictive accuracy of ML al...

The Effect of Using PARO for People Living With Dementia and Chronic Pain: A Pilot Randomized Controlled Trial.

Journal of the American Medical Directors Association
OBJECTIVES: To evaluate the effect of interaction with a robotic seal (PARO) on pain and behavioral and psychological symptoms of people with dementia and chronic pain.

The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set.

International journal of medical informatics
INTRODUCTION: Research has shown that frailty, a geriatric syndrome associated with an increased risk of negative outcomes for older people, is highly prevalent among residents of residential aged care facilities (also called long term care facilitie...