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Shahram Golzari

Showing results (1-10 of 8) with videos related to

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Scientific Reports|September 7, 2022
Assessment of the uncertainty and interpretability of deep learning models for mapping soil salinity using DeepQuantreg and game theoryAliakbar Mohammadifar, Hamid Gholami, Shahram Golzari
Environmental Science and Pollution Research International|November 12, 2022
Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidenceAliakbar Mohammadifar, Hamid Gholami, Shahram Golzari
Journal of Environmental Management|August 18, 2023
Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood riskAliakbar Mohammadifar, Hamid Gholami, Shahram Golzari
Environmental Science and Pollution Research International|February 10, 2024
An interpretable deep learning model to map land subsidence hazardParia Rahmani, Hamid Gholami, Shahram Golzari
Environmental Science and Pollution Research International|March 24, 2021
Spatial modelling of soil salinity: deep or shallow learning models?Aliakbar Mohammadifar, Hamid Gholami, Shahram Golzari, et al.
The Science of the Total Environment|September 11, 2023
Interpretability of simple RNN and GRU deep learning models used to map land susceptibility to gully erosionHamid Gholami, Aliakbar Mohammadifar, Shahram Golzari, et al.
Iranian Red Crescent Medical Journal|May 30, 2015
Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition systemMahmoud Reza Saybani, Shahaboddin Shamshirband, Shahram Golzari Hormozi, et al.
Medical & Biological Engineering & Computing|June 18, 2015
RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition systemMahmoud Reza Saybani, Shahaboddin Shamshirband, Shahram Golzari, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Scientific Reports|September 7, 2022
Assessment of the uncertainty and interpretability of deep learning models for mapping soil salinity using DeepQuantreg and game theoryAliakbar Mohammadifar, Hamid Gholami, Shahram Golzari
Environmental Science and Pollution Research International|November 12, 2022
Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidenceAliakbar Mohammadifar, Hamid Gholami, Shahram Golzari
Journal of Environmental Management|August 18, 2023
Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood riskAliakbar Mohammadifar, Hamid Gholami, Shahram Golzari
Environmental Science and Pollution Research International|February 10, 2024
An interpretable deep learning model to map land subsidence hazardParia Rahmani, Hamid Gholami, Shahram Golzari
Environmental Science and Pollution Research International|March 24, 2021
Spatial modelling of soil salinity: deep or shallow learning models?Aliakbar Mohammadifar, Hamid Gholami, Shahram Golzari, et al.
The Science of the Total Environment|September 11, 2023
Interpretability of simple RNN and GRU deep learning models used to map land susceptibility to gully erosionHamid Gholami, Aliakbar Mohammadifar, Shahram Golzari, et al.
Iranian Red Crescent Medical Journal|May 30, 2015
Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition systemMahmoud Reza Saybani, Shahaboddin Shamshirband, Shahram Golzari Hormozi, et al.
Medical & Biological Engineering & Computing|June 18, 2015
RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition systemMahmoud Reza Saybani, Shahaboddin Shamshirband, Shahram Golzari, et al.
Pageof 1