首页|Reports Summarize Machine Learning Research from University of Tehran (Evaluatin g the Impact of Recursive Feature Elimination on Machine Learning Models for Pre dicting Forest Fire-Prone Zones)
Reports Summarize Machine Learning Research from University of Tehran (Evaluatin g the Impact of Recursive Feature Elimination on Machine Learning Models for Pre dicting Forest Fire-Prone Zones)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New study results on artificial intell igence have been published. According to newsreporting originating from Tehran, Iran, by NewsRx correspondents, research stated, “This study aimedto enhance t he accuracy of forest fire susceptibility mapping (FSM) by innovatively applying recursivefeature elimination (RFE) with an ensemble of machine learning models , specifically Support VectorMachine (SVM) and Random Forest (RF), to identify key fire factors. The fire zones were derived fromMODIS satellite imagery from 2012 to 2017.”
University of TehranTehranIranAsiaCyborgsEmerging TechnologiesMachine LearningSupport Vector Machines
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