The world has entered the stage of rapid development of technology, especially the fourth industrial revolution with outstanding changes and developments in information technology. Artificial Intelligence (AI) is one of the most mentioned names in this period. AI is part of computer science, developing technology in the direction of automation, self-learning. As a result, it takes a solid knowledge to be able to operate any AI system. There have been many applications of artificial intelligence in the fields of science, technology and economics -finance. From previous decades, the application of algorithms to predict values and variables in economics has been implemented and improved over many different stages. This paper aims to predict VNINDEX value by the application of KNN algorithm machine learning to assess the changes of price indexes and stock variables in general and the VNIndex stock exchange in particular. Research result shows that the outcome of a buy -or -sell decision at the point of view. With a predict signal value of 1, investors should execute buy and sell orders that are advised when predict signal yields -1. Overall, the result indicates that 51% of the stock market price are correctly predicted by the KNN algorithm machine learning.

One main problem of Fuzzy c-Means (FCM) is deciding on an appropriate number of clusters. Although methods have been proposed to address this, they all require clustering algorithms to be executed several times before the right number is chosen. The aim of this study was to develop a method for determining cluster numbers without repeated execution. We propose a new method that combines FCM and singular value decomposition. Based on the percentage of variance, this method can calculate the appropriate number of clusters. The proposed method was applied to several well-known datasets to demonstrate its effectiveness.

No abstract

No abstract

The purpose of this study is to evaluate the factors affecting the management of social insurance collection in Tien Giang province in Vietnam by Analytic Hierarchy Process (AHP) method. This study surveyed the opinions of 12 experts in this field including managers and chief accountants. The research results show the differentiation in the importance of the criteria for evaluating the factors affecting the management of social insurance management. The AHP analytic results show that the criteria are arranged according to the weights of priority from high to low as follows: Qualification of staff in charge of social insurance collection management, Labour and employment policy, Salary policy, Inspection, and inspection work. The implication of this study could support the policy makers in social insurance collection management.