Towards Predictive Vietnamese Human Resource Migration by Machine Learning: A Case Study in Northeast Asian Countries

Abstract

Labor exports are currently considered among the most important foreign economic sectors, implying that they contribute to a country’s economic development and serve as a strategic solution for employment creation. Therefore, with the support of data collected between 1992 and 2020, this paper proposes that labor exports contribute significantly to Vietnam’s socio-economic development. This study also aims to employ the Backpropagation Neural Network (BPNN), k-Nearest Neighbor (kNN), and Random Forest Regression (RFR) models to analyze labor migration forecasting in Taiwan, Korea, and Japan. The study results indicate that the BPNN model was able to achieve the highest accuracy regarding the actual labor exports. In terms of these accuracy metrics, this study will aid the Vietnamese government in establishing new legislation for Vietnamese migrant workers in order to improve the nation’s economic development.

Transcending Time and Space: A Survey Methods, Uncertainty, and Development in Human Migration Prediction

“…internal migration, long-term, [68]; international migration, short-term, [69]; international migration, long-term, [70]; internal migration, --, [71]; international migration, short-term, [72]. internal migration, short-term, [73]; internal migration, short-term, [74]; internal migration, short-term, [75]; international migration, long-term, [76].…”

“…According to different learning methods, machine learning can be divided into classical machine learning and deep learning. As shown in Figure 3, a range of machine learning methods have been applied in population migration prediction research, including illegal migration prediction, conventional migration prediction, labour migration prediction, migration flow data generation, migration trend prediction, international migration drivers, and asylum seeker prediction [68][69][70][71][72][73][74][75][76]. Robinson and Dilkina were probably the first to use machine learning models to predict population migration; addressing the inability of traditional linear models to model the non-linear relationship between population migration and its characteristics, while proposing a comprehensive solution to the problems of data imbalance, hyperparameter tuning and performance evaluation in model training, providing a new tool and instrument for population migration prediction [98].…”

“…With the development of information technology and statistics, many forecasting methods have emerged, and these mainly focus on econometrics, time series, Bayesian statistics, etc. In recent years, with the rapid development of artificial intelligence (AI) technology with big data and machine learning (ML) as the core, some scholars have tried to use ML technology for HMP [67][68][69][70][71][72][73]; however, the limitations include the limited amount of HM data, different standards, and difficulties in access. In addition, the uncertainty of HM drivers and the difficulty of their quantification have led to the slow development of HMP research [4].…”

Solution of the Ill-Posed Cauchy Problem for Systems of Elliptic Type of the First Order

“…Based on [6,7,[12][13][14] we have constructed the Carleman matrix and based on it the approximate solution of the Cauchy problem for the matrix factorization of the Helmholtz equation. Boundary value problems, as well as numerical solutions of some problems, are considered in [30][31][32][33][34][35][36][37][38][39]. When solving correct problems, sometimes, it is not possible to find the value of the vector function on the entire boundary.…”

On an Approximate Solution of the Cauchy Problem for Systems of Equations of Elliptic Type of the First Order

“…[ 10 ]. Boundary problems, as well as numerical solutions of some problems, are considered in works [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ].…”