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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Journal of Health Management and Informatics
- نوع مقاله: Journal Article
- کلمات کلیدی:
- چکیده:
- چکیده انگلیسی: Introduction: Achievement of economic growth, as one of the most important macroeconomic variables, depends on the precise understanding of potential routes and the factors affecting on it. The aim of this study was to evaluate the health care sector’s effect on Iran Gross Domestic Product (GDP), as the status of economy.Method: Artificial Neural Network (ANN) and Dynamic Ordinary Least Squares (DOLS) were performed according to Iran GDP as the output variable and the input variables of life expectancy at birth, under five mortality rates, public health expenditures, the number of doctors and hospital beds during 1961-2012 in Iran. Data were collected from the Statistical Center of Iran, the Central Bank of the Islamic Republic of Iran, the World Health Organization and the World Bank databases. Data management and analysis were performed using Eviewes 7, stata 11 and also Mathlab. MSE, MAE and R2 were calculated to assess and compare the models.Results: One percent reduction in deaths of children under 5-years could improve Iran GDP as much as 1.9%. Additionally, one percent increment in the number of doctors, hospital beds or health expenditure would increase GDP by 0.37%, 0.27% and 0.29%, respectively. Mean Absolute Error (MAE) demonstrated the superiority of DOLS in the model estimation.Conclusion: The lack of sufficient considerations and excellent models in the health care sector is the main reason for underestimating the effect of this sector on economy. This limitation leads to neglecting the resource allocation to the health care sector, as the great potential motivation of the economic growth.Keywords: Neural Network, Health care sector, Life expectancy, Health expenditure, Econometric model
- انتشار مقاله: 05-07-1396
- نویسندگان: Marziyeh Sadat Safe,Mohsen Barouni,Seyed Mojtaba Saif
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Advances in Computer Engineering and Technology
- نوع مقاله: Journal Article
- کلمات کلیدی: Optimization algorithm,Imperialist competitive algorithm,Evolutionary algorithm,assimilation policy
- چکیده:
- چکیده انگلیسی: Meta-heuristic algorithms inspired by the natural processes are part of the optimization algorithms that they have been considered in recent years, such as genetic algorithm, particle swarm optimization, ant colony optimization, Firefly algorithm. Recently, a new kind of evolutionary algorithm has been proposed that it is inspired by the human sociopolitical evolution process. This new algorithm has been called Imperialist Competitive Algorithm (ICA). The ICA is a population-based algorithm where the populations are represented by countries that are classified as colonies or imperialists. This paper is going to present a modified ICA with considerable accuracy, referred to here as ICA2. The ICA2 is tested with six well-known benchmark functions. Results show high accuracy and avoidance of local optimum traps to reach the minimum global optimal.
Three important policies are in the ICA, and assimilation policy is the most important of them. This research focuses on an assimilation policy in the ICA to propose a meta-heuristic optimization algorithm for optimizing function with high accuracy and avoiding to trap in local optima rather than using original ICA by a new assimilation strategy.- انتشار مقاله: 14-05-1394
- نویسندگان: Seyed Mojtaba Saif,Seyed Mojtaba Saif,Seyed Mojtaba Saif
- مشاهده