• 무역학회지
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• 제46권1호
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• pp.169-187
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• 2021

This paper analyzes the feasibility of using machine learning and deep learning methods to forecast the income and employment using the strategic industries as well as investment, export, and exchange rates. The decision tree, artificial neural network, support vector machine, and deep learning models were used to forecast the income and employment in Busan. The following were the main findings of the comparison of their predictive abilities. First, the decision tree models predict the income and employment well. The forecasting values for the income and employment appeared somewhat differently according to the depth of decision trees and several conditions of strategic industries as well as investment, export, and exchange rates. Second, since the artificial neural network models show that the coefficients are somewhat low and RMSE are somewhat high, these models are not good forecasting the income and employment. Third, the support vector machine models show the high predictive power with the high coefficients of determination and low RMSE. Fourth, the deep neural network models show the higher predictive power with appropriate epochs and batch sizes. Thus, since the machine learning and deep learning models can predict the employment well, we need to adopt the machine learning and deep learning models to forecast the income and employment.

• 무역학회지
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• 제46권2호
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• pp.281-299
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• 2021

This paper analyzes the feasibility of using machine learning methods to forecast the employment. The machine learning methods, such as decision tree, artificial neural network, and ensemble models such as random forest and gradient boosting regression tree were used to forecast the employment in Busan regional economy. The following were the main findings of the comparison of their predictive abilities. First, the forecasting power of machine learning methods can predict the employment well. Second, the forecasting values for the employment by decision tree models appeared somewhat differently according to the depth of decision trees. Third, the predictive power of artificial neural network model, however, does not show the high predictive power. Fourth, the ensemble models such as random forest and gradient boosting regression tree model show the higher predictive power. Thus, since the machine learning method can accurately predict the employment, we need to improve the accuracy of forecasting employment with the use of machine learning methods.

• 디지털융복합연구
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• 제16권3호
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• pp.181-187
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• 2018

본 연구는 한국 중소기업의 수출 증대와 고용 증대에 양(+)의 영향을 미치는 요인들에 대한 연구 흐름의 깊이를 심화시키기 위해 외부 기술 연구개발 협력 네트워크의 다양성이 중소기업의 수출 증대와 고용 증대에 미치는 영향을 연구 모형화 하였으며, 대한민국의 2,200개의 중소기업으로부터 수집된 데이터를 사용하여 연구 모형을 실증적으로 검증 하였다. 본 연구는 이러한 실증 검증 결과를 통해 다음과 같은 2개의 연구 결과를 조명한다. 첫째, 중소기업의 외부 기술 연구개발 협력 네트워크의 다양성은 기술 개발로 인한 고용 증대에 유의한 양(+)의 영향을 미친다. 둘째, 중소기업의 기술 개발로 인한 수출 증대는 기술 개발로 인한 고용 증대가 외부 기술 연구개발 협력 네트워크의 다양성에 의해 받는 양(+)의 영향을 부분 매개한다.

• 대한산업공학회지
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• 제39권4호
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• pp.278-289
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• 2013

Up-to-date market dynamics and intense competition have forced a production system to be more widely distributed and decentralized than ever, and the production system itself can be regarded as a collaborative network of autonomous production resources in which the responsibility of decision making is also decentralized into individual autonomous entities. The conventional resource management models, however, are not suitable for the distributed and decentralized environment because of their centralized nature. In this paper, an agent-based resource management model is proposed. The proposed model applies employment relation-driven fractal organization (FrOrg) into organizational model for distributed production resources and presents a resource management framework based on employment contracts. The fractal organization is a structured association in which a self-similar pattern recursively appears, and employment relations between production resources are recursively constructed throughout the entire production system.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.303-307
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• 2022

The purpose of the study is to determine the key aspects of the problems of employment of the population and the directions of their state regulation in war conditions. Employment policy involves a system of measures to create conditions for a more complete use of the potential of labor and business activity of the able-bodied population, linking this activity through taking into account the specifics of group interests with the tasks and guidelines for socio-economic development. But in the conditions of war, this problem acquires a new relevance. For the study, a number of theoretical methods of analysis were used. Based on the results of the study, the key aspects of the problems of employment of the population and the directions of their state regulation in war conditions were identified.

• 한국사회복지학
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• 제63권2호
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• pp.261-289
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• 2011

본 연구는 노인일자리사업이 노인의 사회적 자본 변화에 어떠한 영향력을 미치는지 규명하여 노인일자리사업의 효과성을 사회적 자본 변화를 통해 평가하고자 하였다. 노인일자리사업에 참여한 노인 중 사회적 자본이 변화하였을 때, 사회적 자본의 변화를 유형별로 나누어 참여노인의 인구사회학적 특성과 노인일자리사업 특성을 변화유형별로 비교해 보았다. 이는 노인일자리사업 참여가 노인 삶을 변화시키는데 직 간접적인 역할을 할 것이라는 실증적 자료를 제공할 수 있다. 노인일자리사업에 참여한 노인 중 사회적 자본이 변화하였을 때, 생활상의 어려움에 있는 노인층에서 사회적 자본 중 신뢰, 관계망, 사회참여가 모두 증가하였고, 노인일자리사업 참여가 생활상의 어려움을 가진 노인층에게 사회적 자본 증가의 효과를 주는 것으로 볼 수 있다. 이들 사회적 자본 중 노인 일자리사업에 참여한 노인은 신뢰가 가장 많이 증가하였다. 특히 생활상의 어려움에 있는 노인층과 생계비를 벌기 위해 참여한 노인층에서 사회적 자본 중 신뢰, 관계망, 사회참여가 모두 증가한 것을 볼 수 있으며, 노인일자리사업이 생활상의 어려움을 가진 노인층에게 사회적 자본 변화의 효과가 있는 것으로 나타났다. 모든 일자리유형에서 사회참여는 상승하였고, 생계를 위해서 참여한 노인층이나 생활상의 어려움이 있는 노인층에서 특히 사회참여가 증가하였다. 짧은 참여기간 동안의 조사로 나타난 결과이기도 하지만, 궁극적으로 노인의 노동참여를 통한 사회참여는 보호적 시장에서 이루어지는 것이 효과적이었다고 볼 수 있다.

• Communications for Statistical Applications and Methods
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• 제26권5호
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• pp.497-506
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• 2019

Forecasting the U.S. employment level is made using machine learning methods of the artificial neural network: deep neural network, long short term memory (LSTM), gated recurrent unit (GRU). We consider the big data of the federal reserve economic data among which 105 important macroeconomic variables chosen by McCracken and Ng (Journal of Business and Economic Statistics, 34, 574-589, 2016) are considered as predictors. We investigate the influence of the two statistical issues of the dimension reduction and time series differencing on the machine learning forecast. An out-of-sample forecast comparison shows that (LSTM, GRU) with differencing performs better than the autoregressive model and the dimension reduction improves long-term forecasts and some short-term forecasts.

• International Journal of Computer Science & Network Security
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• 제21권5호
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• pp.160-167
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• 2021

The authors of the article investigate the impact of digitalization on the forms change of employment and the labor market in the modern conditions. It is substantiated that digitalization processes directly affect the change of forms of employment and the range of occupations in the labor market, as well as the change of jobs. The positive and negative consequences of changing forms of employment for the economy in the process of digital transformation are analyzed, which include: reduction of personnel costs by employers; dissemination of non-standard, informal employment (electronic freelance, electronic outsourcing, start-employment); enabling employees to manage their working hours; increasing unemployment and imbalance of supply and demand in the labor market; reduction of productivity of enterprises, as a result of reduced productivity of workers with non-standard forms of employment, or with a negative indirect impact on productivity. A model for managing the process of digitalization in the labor market of Ukraine under different scenarios of their implementation, namely evolutionary and forced is devoted. To recognize the possible change in the level of employment in connection with the implementation of the forced scenario of digitalization in Ukraine, a model for estimating probable changes in the labor market is proposed. The directions of development of the state strategy of prevention of risks of reduction of number of workplaces in the course of digitalization are outlined.

• 한국사회복지학
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• 제46권
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• pp.427-458
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• 2001

The present study is designed to trace the successful reform steps taken within the employment support system in the UK and to extract a set of instructive implications for Korea's future reform path towards an efficient employment support system conductive to youth employment by setting out an analytical framework that links the British experience to the Korean context. To this end, this study focuses on three major aspects of employment support system: delivery mechanism, method of delivery, and programs. Implications drawn from the comparative analysis are as follows. First, the current employment support services in Korea-including job placement service, vocational training, and public works-should be more tightly integrated and delivered in a more consistent fashion in order to become highly accessible to the unemployed. Second, the cooperative network between employment service providers and the corporate sector as demander of labor should be institutionalized in order to make the employment support system more responsive to labor demand. Third, to create an enhanced employment support system that counts in individual differences of the unemployed would require continued, in-depth consultation and case management services along with fully specialized employment caseworkers who would provide them. Fourth, the government should take the initiative and be unflaggingly supportive both institutionally and financially in making future efforts to continuously improve the employment support system on the basis of the implications mentioned above.

• 한국생활과학회지
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• 제23권6호
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• pp.1105-1126
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• 2014

The purpose of this study was to explore the factors related to depression of married baby-boomer female by employment status. Data for this study was based on a convenience sample of 499 married female baby-boomer who have residence in Busan and Gyeongnam province. The prevalence rate of depression symptoms was 21.4%. The employment status made differences on the factors related to depression of married female baby boomers. Depressive symptoms were significantly associated with attitudes toward menopause, menopausal symptoms and marital satisfaction for both baby-boomer female with and without a job. While monthly household income, participation in voluntary associations and satisfaction with children were significantly related to depression symptoms of baby-boomer female without employment, perimenopausal stage, perceived size of social network was important factors for the depression of married female baby boomers with a job.