Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
Support vector machine (SVM) is one of the flagship methods in machine learning because SVM have good results in terms of classification and prediction. Classification Analysis is the process of finding the best model of the classifier to predict classes of an object or data whose class label is unknown. The study aims to select accurate classification methods in classification. The study used the Least Support Vector Machine (LS-SVM) method. LS-SVM is better compared to standard SVM in computation processes, rapid convergence, and high precision. On LS-SVM it has several parameters. Optimal parameter delivery on the LS-SVM method can affect increased classification accuracy. As for the various kinds of giving optimal parameter selection methods on LS-SMVS such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Gird Search, and Particle Swarm Optimization and Gravitational Seach Algorithm (PAO-GSA). The study results show that the selection of more optimal parameters can result in significantly increased classification and prediction accuracy and more even computational process times. PSO-GSA is a method of selecting optimization parameters that can be used to improve classification accuracy
In this study, the paper introduces the surface roughness results of heat-treatment, and unheated-treatment SKD61 steel was processed by the EDM method with the participation of tungsten carbide powder. The least-square method was used to model the relationship between surface roughness of heat-treatment and unheated-treatment SKD61 steels, and these models were used to evaluate the role of each technological parameter, comparing the role of each technological parameter in the two models