AIMC Topic: Iran
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Kernel machine SNP set analysis finds the association of BUD13, ZPR1, and APOA5 variants with metabolic syndrome in Tehran Cardio-metabolic Genetics Study.
Scientific reports
May 13, 2021
Metabolic syndrome (MetS) is one of the most important risk factors for cardiovascular disease. The 11p23.3 chromosomal region plays a potential role in the pathogenesis of MetS. The present study aimed to assess the association between 18 single nuc...
Mapping soil salinity using a combined spectral and topographical indices with artificial neural network.
PloS one
May 13, 2021
Monitoring the status of natural and ecological resources is necessary for conservation and protection. Soil is one of the most important environmental resources in agricultural lands and natural resources. In this research study, we used Landsat 8 a...
Prediction of Multiple sclerosis disease using machine learning classifiers: a comparative study.
Journal of preventive medicine and hygiene
Apr 29, 2021
INTRODUCTION: Hamedan Province is one of Iran's high-risk regions for Multiple Sclerosis (MS). Early diagnosis of MS based on an accurate system can control the disease. The aim of this study was to compare the performance of four machine learning te...
IrGO: Iranian traditional medicine General Ontology and knowledge base.
Journal of biomedical semantics
Apr 16, 2021
BACKGROUND: Iranian traditional medicine, also known as Persian Medicine, is a holistic school of medicine with a long prolific history. It describes numerous concepts and the relationships between them. However, no unified language system has been p...
Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study.
BMC pregnancy and childbirth
Mar 12, 2021
BACKGROUND: Stillbirth is defined as fetal loss in pregnancy beyond 28 weeks by WHO. In this study, a machine-learning based method is proposed to predict stillbirth from livebirth and discriminate stillbirth before and during delivery and rank the f...
Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran.
Biomedical journal
Feb 25, 2021
BACKGROUND: COVID-19 is an infectious disease that started spreading globally at the end of 2019. Due to differences in patient characteristics and symptoms in different regions, in this research, a comparative study was performed on COVID-19 patient...
COVID-19 in Iran: Forecasting Pandemic Using Deep Learning.
Computational and mathematical methods in medicine
Feb 25, 2021
COVID-19 has led to a pandemic, affecting almost all countries in a few months. In this work, we applied selected deep learning models including multilayer perceptron, random forest, and different versions of long short-term memory (LSTM), using thre...
Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation.
Journal of environmental management
Feb 5, 2021
Land subsidence (LS) in arid and semi-arid areas, such as Iran, is a significant threat to sustainable land management. The purpose of this study is to predict the LS distribution by generating land subsidence susceptibility models (LSSMs) for the Sh...
Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility.
Journal of environmental management
Jan 28, 2021
The objective of this study is to assess the gully head-cut erosion susceptibility and identify gully erosion prone areas in the Meimand watershed, Iran. In recent years, this study area has been greatly influenced by several head-cut gullies due to ...
Asthma-prone areas modeling using a machine learning model.
Scientific reports
Jan 21, 2021
Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran conside...
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