AIMC Topic: Machine Learning

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Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection.

Frontiers in public health
Many works have employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that affects the human eye. However, the accuracy of most DR detection methods still need improvement. Gray Wolf Optimization-Extreme L...

Machine Learning-Driven Enterprise Human Resource Management Optimization and Its Application.

Computational intelligence and neuroscience
With the advent of the Internet era, the frequency and proportion of candidates obtaining recruitment information through the Internet is getting higher and higher, and the amount of human resource information, such as talent information and job info...

Using Healthcare Resources Wisely: A Predictive Support System Regarding the Severity of Patient Falls.

Journal of healthcare engineering
BACKGROUND: An injurious fall is one of the main indicators of care quality in healthcare facilities. Despite several fall screen tools being widely used to evaluate a patient's fall risk, they are frequently unable to reveal the severity level of pa...

Applications of artificial intelligence for patients with peripheral artery disease.

Journal of vascular surgery
OBJECTIVE: Applications of artificial intelligence (AI) have been reported in several cardiovascular diseases but its interest in patients with peripheral artery disease (PAD) has been so far less reported. The aim of this review was to summarize cur...

Machine Learning-Based Prediction Models for Delirium: A Systematic Review and Meta-Analysis.

Journal of the American Medical Directors Association
OBJECTIVE: To critically appraise and quantify the performance studies by employing machine learning (ML) to predict delirium.

ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches.

Computational intelligence and neuroscience
According to the World Health Organization (WHO) report, heart disease is spreading throughout the world very rapidly and the situation is becoming alarming in people aged 40 or above (Xu, 2020). Different methods and procedures are adopted to detect...

Use of Machine Learning to Develop Prediction Models for Mortality and Stroke in Patients Undergoing Balloon Aortic Valvuloplasty.

Cardiovascular revascularization medicine : including molecular interventions
OBJECTIVE: To develop an artificial intelligence, machine learning prediction model for estimating in-hospital mortality and stroke in patients undergoing balloon aortic valvuloplasty (BAV).

Machine learning and clinical neurophysiology.

Journal of neurology
Clinical neurophysiology constructs a wealth of dynamic information pertaining to the integrity and function of both central and peripheral nervous systems. As with many technological fields, there has been an explosion of data in neurophysiology ove...

Intermittent fasting-induced biomolecular modifications in rat tissues detected by ATR-FTIR spectroscopy and machine learning algorithms.

Analytical biochemistry
This study aimed to reveal the intermittent fasting-induced alterations in biomolecules of the liver, ileum, and colon tissues of rats using Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) algorithms developed on infrared spectroc...

Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms.

Scientific reports
Evaporation is the primary aspect causing water loss in the hydrological cycle; therefore, water loss must be precisely measured. Evaporation is an intricate nonlinear process occurring as a result of several climatic aspects. The purpose of this res...