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:
Iraqi cities, especially the holy city of Karbala, suffer from visual pollution caused by various distortions and violations resulting from individuals’ and organizations’ unawareness of beauty standards, which creates an imbalance between various city elements and the overall urban context. This chaos supports the establishment and imposition of scientific standards that can be used to develop a reintegration process for these distorted city elements to restore city aesthetics. In this research, advertisements and commercial signs were studied as one of the urban visual elements that contribute to the distortion of the city’s visual landscape. This research thus examines the possibility of creating an evaluation tool to quantify the distortion of the current visual scene of the city, in order to use these results to achieve a more balanced urban display. Furthermore, the research aimed to identify which regulations could control the operation and the physical proportions of advertisement signs on buildings, a measure predicted to help improve users’ awareness of the effects of using these signs and to thus restore the aesthetic visual image of these urban areas. Finally, this research reviews the aesthetic standards used in similar successful international experiences to help with the creation and adoption of new organisational standards to help restore balance to these distorted cities.
Density-based methods have appeared as a practical approach for the clustering of data streams. While various density-based algorithms have recently been developed for data stream clustering, these algorithms are not without problems. The efficiency of the clustering is considerably reduced when an insufficient distance function is used. In addition, the majority of the approaches is their non-autonomous nature, which means they need manual tuning of their internal parameters. Unfortunately, the tuning process requires considerable effort and parameter results might become invalid after a certain time due to statistical changes in data or concept drift characteristics. In this study, we propose a new Density-based method for Clustering Data stream using Genetic Algorithm (DCDGA). This method using a Genetic Algorithm (GA) to adjust suitable parameters for the cluster radius and minimum density threshold to cover the density clusters more accurately. A Chebychev distance function is also introduced to calculate the distance between the Core Micro-Clusters (CMCs) center and the arriving data point. The proposed method was evaluated using an artificial and real dataset with different evaluation metrics. The experimental results were compared with another online density-based clustering. The recommended method provides an effective solution for improving the quality of clusters.