首页|Reports Outline Support Vector Machines Findings from Department of Computer Sci ences and Engineering (Improved Prediction Analysis With Hybrid Models for Thund erstorm Classification Over the Ranchi Region)
Reports Outline Support Vector Machines Findings from Department of Computer Sci ences and Engineering (Improved Prediction Analysis With Hybrid Models for Thund erstorm Classification Over the Ranchi Region)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning - Suppo rt Vector Machines are presented in a new report. According to news originating from Hyderabad, India, by NewsRx correspondents, research stated, “Thunderstorms are natural disasters that impact people, animals, and the economy. Thunderstor ms’ detrimental repercussions can be avoided by identifying their occurrence in advance.” Our news journalists obtained a quote from the research from the Department of C omputer Sciences and Engineering, “The current work, in this respect, uses soft computing techniques such as K-Nearest Neighbour (KNN), Decision Tree (DT), Logi stic Regression (LR), and Support Vector Machine (SVM) with various kernel funct ions to categorize the occurrence of thunderstorms over Ranchi, India. These tec hniques were trained and tested using two data sets: daily average and hourly me teorological datasets. The primary purpose of this study is to find which datase t-classifier combination is optimal for categorizing thunderstorm occurrence in Ranchi. No classifier was found to adequately classify either the Day Average Da taset or the Modified Day Average Dataset. On the other hand, the Hourly Dataset was found to be more balanced in terms of the number of thunderstorms that occu rred than the Day Average and Modified Average datasets. The F-Score value of th e incidence of thunderstorm incidents after using different classifiers was used to compare the outcomes of these datasets. The results reveal that using SVM wi th radial basis function. The Hourly Dataset is the best for thunderstorm day cl assification. For the overall and only incidence of thunderstorms classes, SVM-R BF gets 0.81 and 0.74 F-Scores, respectively. Other approaches, like grid search and Bagging, have been used to increase SVM-RBF performance.”
HyderabadIndiaAsiaMachine LearningSupport Vector MachinesDepartment of Computer Sciences and Engineering
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