scholarly journals Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 145564-145576 ◽  
Author(s):  
Amirhosein Mosavi ◽  
Farzaneh Sajedi Hosseini ◽  
Bahram Choubin ◽  
Massoud Goodarzi ◽  
Adrienn A. Dineva
Keyword(s):  
Machine Learning ◽  
Susceptibility Mapping ◽  
Classifier Ensemble ◽  
Groundwater Salinity ◽  
Learning Models ◽  
Bayesian Machine Learning ◽  
Machine Learning Models

Susceptibility mapping of groundwater salinity using machine learning models

2020 ◽  
Author(s):  
Amirhosein Mosavi ◽  
Farzaneh Sajedi Hosseini ◽  
Bahram Choubin ◽  
Fereshteh Taromideh ◽  
Marzieh Ghodsi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Susceptibility Mapping ◽  
Groundwater Salinity ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads

ACS Omega ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 2353-2361 ◽  
Author(s):  
Manu Anantpadma ◽  
Thomas Lane ◽  
Kimberley M. Zorn ◽  
Mary A. Lingerfelt ◽  
Alex M. Clark ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ebola Virus ◽  
Learning Models ◽  
Bayesian Machine Learning ◽  
Machine Learning Models

scholarly journals Machine Learning Models Identify Inhibitors of SARS-CoV-2

2020 ◽  
Author(s):  
Victor O. Gawriljuk ◽  
Phyo Phyo Kyaw Zin ◽  
Daniel H. Foil ◽  
Jean Bernatchez ◽  
Sungjun Beck ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Published Data ◽  
Learning Models ◽  
Machine Learning Model ◽  
Bayesian Machine Learning ◽  
Machine Learning Models

AbstractWith the ongoing SARS-CoV-2 pandemic there is an urgent need for the discovery of a treatment for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need and numerous compounds have been selected for in vitro testing by several groups already. These have led to a growing database of molecules with in vitro activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA approved compounds for in vitro testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, CPI1062 and CPI1155 showed antiviral activity in HeLa-ACE2 cell-based assays and represent potential repurposing opportunities for COVID-19. This approach can be greatly expanded to exhaustively virtually screen available molecules with predicted activity against this virus as well as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 is available at www.assaycentral.org.

scholarly journals Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping

2018 ◽  
Vol 10 (8) ◽  
pp. 1252 ◽  
Author(s):  
Prima Kadavi ◽  
Chang-Wook Lee ◽  
Saro Lee
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Learning Models ◽  
Landslide Occurrence ◽  
Susceptibility Maps ◽  
Multiclass Classifier ◽  
Landslide Susceptibility Maps ◽  
Machine Learning Models

The main purpose of this study was to produce landslide susceptibility maps using various ensemble-based machine learning models (i.e., the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models) for the Sacheon-myeon area of South Korea. A landslide inventory map including a total of 762 landslides was compiled based on reports and aerial photograph interpretations. The landslides were randomly separated into two datasets: 70% of landslides were selected for the model establishment and 30% were used for validation purposes. Additionally, 20 landslide condition factors divided into five categories (topographic factors, hydrological factors, soil map, geological map, and forest map) were considered in the landslide susceptibility mapping. The relationships among landslide occurrence and landslide conditioning factors were analyzed and the landslide susceptibility maps were calculated and drawn using the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models. Finally, the maps were validated using the area under the curve (AUC) method. The Multiclass Classifier method had higher prediction accuracy (85.9%) than the Bagging (AUC = 85.4%), LogitBoost (AUC = 84.8%), and AdaBoost (84.0%) methods.

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