首页|University Teknologi Malaysia Reports Findings in Machine Learning [Traffic noise prediction model using GIS and ensemble machine learning: a case study at Universiti Teknologi Malaysia (UTM) Campus]
University Teknologi Malaysia Reports Findings in Machine Learning [Traffic noise prediction model using GIS and ensemble machine learning: a case study at Universiti Teknologi Malaysia (UTM) Campus]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news reporting originating from Johor, Malay sia, by NewsRx correspondents, research stated, “This study represents a pioneer ing effort to integrate geographic information systems (GIS) and ensemble machin e learning methods to predict noise levels on a university campus. Three ensembl e models including random forest (RF), gradient boosting (GB), and extreme gradi ent boosting (XGB) were developed to predict traffic noise based on data collect ed over a 4-week period at the Universiti Teknologi Malaysia (UTM) campus.”
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