Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) is a monthly peer-reviewed scopus indexed journal originally founded in 1986. It is sponsored by the Ministry of Education, china and Northeastern University, china. Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) has always adhered to the correct purpose of running the journal, and has been committed to gathering and disseminating excellent academic achievements, inspiring technological innovation, and promoting the development of disciplines in my country.Aiming at major national needs and international frontiers, this journal has published a large number of original and high-level research result. The journal was selected into the "China Science and Technology Journal Excellence Action Plan Project" in December 2019.In the future, it will strive to build an open innovation, collaborative integration.
The study of non-Newtonian fluids has received a considerable and significant attention from engineers, scientists and medical mentors. In the current study, a mathematical framework has been developed to explore the physical properties of non-Newtonian Fluid Flow. Particularly, we aim to study the physical properties of a chemical reaction, and transfer of heat and mass features on magnetohydrodynamic flow of Casson fluid towards an extended permeable surface with non-Newtonian heating and viscous dissipation effect. The dimensionless regular system of ordinary differential equations (ODEs) h
Today, all universities utilize IT more and more in their daily operations. However, most of their personnel do not have enough knowledge and skills in solving problems related to IT usage problems, making the number of personnel responsible for problem-solving not enough to provide services thoroughly. It can be obvious that if there is a tool that helps university personnel able to learn how to solve problems related to IT usage by themselves, officers’ burden shall be decreased and they will be able to spend their time on developing other systems in the university increasingly. Therefore,
Detecting financial fraud is a critical concern for banks and businesses worldwide. Traditional rule-based systems and manual investigations have limitations in effectively identifying and preventing fraud in the ever-evolving landscape of financial crimes. Machine learning techniques have emerged as powerful tools for detecting financial fraud by leveraging patterns, anomalies, and behavioral data. These algorithms can analyze vast amounts of data and automatically learn patterns and relationships within it. By training on historical data labeled as fraudulent or legitimate, these algorithms
FinTech, an emerging realm of innovative financial technology utilizing information and communication technologies, has emerged as a major driver of change in the 4th industrial revolution, significantly impacting societies' living and working conditions. As advanced technologies converge and entrepreneurship takes on a more complex and computerized nature, the banking sector has found itself confronted with formidable challenges. Seizing the opportunity presented by these developments, new entrants such as FinTech and Big Tech companies have sought to gain market shares by promoting novel con
In the sphere of industrial applications, especially within the manufacturing sector, having a comprehensive understanding of a machine's status is crucial. While many companies boast advanced machinery, the neglect of monitoring machine conditions gives rise to numerous problems, such as reduced productivity and a high rejection rate. These issues translate into significant costs and environmental impact, highlighting the vital need for diligent machine status assessment. The purpose of this study is to determine the factors that cause a decrease in the effectiveness of equipment. This projec