首页|University of Malaya Researchers Have Provided New Study Findings on Support Vec tor Machines (Waste Prediction Approach Using Hybrid Long Short-Term Memory with Support Vector Machine)
University of Malaya Researchers Have Provided New Study Findings on Support Vec tor Machines (Waste Prediction Approach Using Hybrid Long Short-Term Memory with Support Vector Machine)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News - Investigators publish new report on . According to news originating from the University of Malaya by NewsRx correspond ents, research stated, “As climate change increases the risk of extreme rainfall events, concerns over flood management have also increased.” Our news correspondents obtained a quote from the research from University of Ma laya: “To recover quickly from flood damage and prevent further consequential da mage, flood waste prediction is of utmost importance. Therefore, developing a ra pid and accurate prediction of flood waste generation is important in order to r educe disaster. Several approaches of flood waste classification have been propo sed by various researchers, however only a few focus on prediction of flood wast e. In this study, a Long Short-Term Memory (LSTM) and Support Vector Machine (SV M) approach is adapted to address these challenges. Two different raw datasets w ere obtained from the ‘Advancing Sustainable Materials Management: Facts and Fig ures 2015’ source. The datasets were for 9 years (1960, 1970, 1980, 1990, 2000, 2005, 2010, 2014, 2015), and are labelled as the materials generated in the Muni cipal Waste Stream from 1960 to 2015 and the materials Recycled and Composted in Municipal Solid Waste from 1960 to 2015.”
University of MalayaEmerging Technolog iesMachine LearningSupport Vector MachinesVector Machines
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