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 research significance arose from the strong impact of creative accounting (CA) concerns as it depicts a particularly significant issue in light of companies' use techniques. It stands to illustrate the outcome of the acts, in addition to the financial state, of which through the use of creative accounting achieves the short-term and long-term objectives, albeit at the expense of other entities like the persons who invest, persons who analyze, persons who audit and other community groups. In the mentioned instances, creative accounting emerged as problematic in the face of an economic recession. This research aims to investigate creative accounting in previous studies in order to highlight the gaps in this direction by relying on a comprehensive literature review. The current study investigates and analyzes the impact of austerity, culture, and authority differences in the financial sector.
The Internet today, has proven and since become an effective means to share information. There is also the consequent proliferation of adversaries who aim at unauthorized access to information being shared over the Internet medium. Most of these adversaries employ various methods, tools and techniques that are well-crafted to coordinate such attacks – which aim to deny services to authorized users as well as degrade system performance and service quality. The Distributed denial of service attacks have become a major threat to the information society and age in that they are carefully crafted attacks of large magnitude that possess the capability to wreak havoc at very high levels and national infrastructures. This study posits intelligent systems via the use of machine learning frameworks to detect such. We employ reinforcement deep learning method to distinguish between benign exchange of data and malicious attacks from data traffic. Results shows consequent success in the employment of deep learning neural network to effectively differentiate between acceptable and non-acceptable data packets (intrusion) on a network data traffic. (9 pt).