Publications by authors named "Majid Niazkar"

Article Synopsis
  • Climate data is essential for managing water resources and is utilized in various hydrological analyses beyond just climate change studies.
  • Though climate models are becoming more accurate over time, their outputs often require adjustments to be applicable for local contexts.
  • This study evaluates various statistical and machine learning methods for bias correction of climate data at different time scales, revealing distinct performance trends for correcting precipitation and temperature data.
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Identifying patients who may develop severe COVID-19 has been of interest to clinical physicians since it facilitates personalized treatment and optimizes the allocation of medical resources. In this study, multi-gene genetic programming (MGGP), as an advanced artificial intelligence (AI) tool, was used to determine the importance of laboratory predictors in the prognosis of COVID-19 patients. The present retrospective study was conducted on 1455 patients with COVID-19 (727 males and 728 females), who were admitted to Allameh Behlool Gonabadi Hospital, Gonabad, Iran in 2020-2021.

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COVID-19 pandemic has become a concern of every nation, and it is crucial to apply an estimation model with a favorably-high accuracy to provide an accurate perspective of the situation. In this study, three explicit mathematical prediction models were applied to forecast the COVID-19 outbreak in Iran and Turkey. These models include a recursive-based method, Boltzmann Function-based model and Beesham's prediction model.

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Background: Millions of people have been infected worldwide in the COVID-19 pandemic. In this study, we aim to propose fourteen prediction models based on artificial neural networks (ANN) to predict the COVID-19 outbreak for policy makers.

Methods: The ANN-based models were utilized to estimate the confirmed cases of COVID-19 in China, Japan, Singapore, Iran, Italy, South Africa and United States of America.

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