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:
The aim of this project is to detect and calculate the coins in a coin machine in an automatic way and other than that, the sum of the coin can be monitored remotely so that the number of visits to the laundromats could be reduced accordingly. Besides that, this automated coin – counting system will be integrated with real time information system (IoT). This project has a potential to be approach in for all type of self-operated coin laundry machine in order to reduce the number of visit and counting errors during coin collection also reduce the time operation and maintenance. Furthermore, this project is able embed a network platform that can send communicate with remote server such as information of: number of customer visit, frequency of use of the machine, estimates of the number of machines that need to be added on the future and frequency of maintenance need to be done
Large-volume data is very difficult to find hidden patterns in the data. The complexity and computational time for analyzing large volumes of data to obtain important information are very dependent on the number of data and variables in a dataset. Big data intersects with incomplete data. This study aims to develop a method of data clustering that is sensitive to missing values in big data that is fast and efficient. This research develops data clustering using fuzzy c-means clustering methods. This method can accommodate the incompleteness of data by calculating the datum expertise in the dataset. Dimension reduction is applied to reduce dimensions in a data set while maintaining important information in the dataset. Core and Reduct which is one of the concepts in the rough set theory was chosen to reduce and leave only the core of a dataset. Core and Reduct are applied to look for core data patterns and select important variables in the data. The results showed that the application of Core and Reduct in the Fuzzy C-Means clustering could shorten the computational time and reduce the value of objective functions until the remaining 43.49%. At the same time, the quality of the clusters produced can be better with relatively unchanged purity and far better accuracy. The combined advantage of this method is that it has a better performance compared to the standard fuzzy c-means clustering
