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:
Kongzhi yu Juece/Control and Decision
Azerbaijan Medical Journal
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery
Zhenkong Kexue yu Jishu Xuebao/Journal of Vacuum Science and Technology
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)
Zhonghua yi shi za zhi (Beijing, China : 1980)
Changjiang Liuyu Ziyuan Yu Huanjing/Resources and Environment in the Yangtze Valley
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.
Every human being has cholesterol in their body system; it depends on managing the cholesterol. Some people have more LDL (low-density lipoprotein) over the HDL (High-Density Lipoprotein); LDL is also called bad cholesterol. When a person has more LDL over the HDL, it can cause many health problems, so we designed a system that can detect cholesterol itself to prevent the bad cholesterol can do to the human body system. There's a condition when a person has more LDL over the HDL, and he doesn't have good blood circulation. Somehow the fat in his body showed up in the eyelid, the fat forced to push through the surface of the skin. Not every person can have xanthelasma. It's a kind of abnormalities in a person's body, but when a person has a xanthelasma in their eyelid, we recommend him to meet a doctor because that thing in his eyes is removable. Usually, the person who has xanthelasma in his eyelids have move LDL over the HDL, but they don't realize it. o that a program is made to detect cholesterol levels with an expert system using the certainty factor method and image processing of the eyelids with the SURF algorithm. The accuracy of the Certainty Factor algorithm is 100%. The accuracy of the SURF and clustering methods is 93.33%.