AIMC Topic: Machine Learning

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Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms.

Journal of health, population, and nutrition
BACKGROUND: Stress-related mental health problems are one of the most common causes of the burden in university students worldwide. Many studies have been conducted to predict the prevalence of stress among university students, however most of these ...

A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm.

BMC medical informatics and decision making
BACKGROUND: Gene expression data play an important role in bioinformatics applications. Although there may be a large number of features in such data, they mainly tend to contain only a few samples. This can negatively impact the performance of data ...

Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models.

Computational intelligence and neuroscience
Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the...

A Comparison among Different Machine Learning Pretest Approaches to Predict Stress-Induced Ischemia at PET/CT Myocardial Perfusion Imaging.

Computational and mathematical methods in medicine
Traditional approach for predicting coronary artery disease (CAD) is based on demographic data, symptoms such as chest pain and dyspnea, and comorbidity related to cardiovascular diseases. Usually, these variables are analyzed by logistic regression ...

Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients.

Journal of voice : official journal of the Voice Foundation
Many virological tests have been implemented during the Coronavirus Disease 2019 (COVID-19) pandemic for diagnostic purposes, but they appear unsuitable for screening purposes. Furthermore, current screening strategies are not accurate enough to effe...

Navigating the pitfalls of applying machine learning in genomics.

Nature reviews. Genetics
The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the application of supervised learning in genomics research. However, the assu...

Embracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation.

Medical image analysis
Domain shift, the mismatch between training and testing data characteristics, causes significant degradation in the predictive performance in multi-source imaging scenarios. In medical imaging, the heterogeneity of population, scanners and acquisitio...

Charge-Transfer Landscape Manifesting the Structure-Rate Relationship in the Condensed Phase Machine Learning.

The journal of physical chemistry. B
In this work, we develop a machine learning (ML) strategy to map the molecular structure to condensed phase charge-transfer (CT) properties including CT rate constants, energy levels, electronic couplings, energy gaps, reorganization energies, and re...

Multipath Lightweight Deep Network Using Randomly Selected Dilated Convolution.

Sensors (Basel, Switzerland)
Robot vision is an essential research field that enables machines to perform various tasks by classifying/detecting/segmenting objects as humans do. The classification accuracy of machine learning algorithms already exceeds that of a well-trained hum...