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:
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery
Interventional Pulmonology
Improving occupational safety and health (OSH) performance in the construction industry is crucial because it represents the excellence of the executed projects, and more importantly the protection of life for people who work in the field. One initiative for improving the performance of OSH in construction is through the application of risk management concepts. Effective risk management depends on the consensus and collaboration of all stakeholders, but such integration is hard to attain. This study seeks to investigate the level of shared understandings of perceived risk among construction stakeholders. Utilizing two case study scenario survey that represents the risk exposure or consequence due to crush of panel and vibration activity, data were collected from a sample of 12 representing construction professionals in Malaysia. Data analysis describes the variety of risk perceptions among stakeholders in construction. The immediate-type of risk was rated higher than those from expected scores, whereas delayed-effect risks such as ‘vibration’ in this study, were rated lower than those from expected scores. Findings from this study suggest that there is a need to have a strategic measure to engage stakeholders in the risk management process and should take account of people’s perceptions of risk
Data mining techniques had used to detect DDoS attacks by analyzing network traffic patterns. The random forest (RF) algorithm is used as a detection model after dividing the dataset into (training data) and (test data). Encoding was utilized to initialization the data set used, which is an important step in the preprocessing process to obtain the best results the log2 algorithm is used to standardize data. In addition, the principal component analysis (PCA) technique is applied several times to reduce data dimensions. Emphasis was placed on strengthening the preprocessing step to obtain high accuracy and efficiency in the classification and detection of attacks. In this paper, the proposed model was applied to datasets (CICDDOS2019) it is extracted from CICIDS2018, the dataset has two versions, the first version is CSV files, which contain 13 different DDoS attacks, and another version is raw PCAP files. The results showed an increase in the level of accuracy when using the PCA technique to 99.9%
