masuleh

Background Mortality is one of the indicators of community health and reflects the social, economic and environmental status of the residence of people. In this regard, countries in the Eastern Mediterranean Region (EMR) have many problems. Therefore, this study was conducted to investigate the factors affecting on mortality in the region. Materials and Methods This study was conducted in the 22 EMR countries. Required data on mortality were collected from WHO online database and Weather, Geneva, Switzerland. The data were analyzed by ArcGIS 10.6.1 software, graphic statistical methods, SPSS software version 23.0, descriptive statistical tests, ANOVA, and regression correlation coefficient. Results The results showed that in the 22 EMR countries, mortality in children under five, neonatal mortality rate, mortality rate attributed to household and ambient air pollution, mortality rate attributed to exposure to unsafe WASH services and mortality rate attributed to unintentional poisoning were 52 per 1000 live births, 26.6 per 1000 live births, 58.8 per 100,000 population, 13.1 per 100,000 population and 1.4 per 100,000 population, respectively. The results showed that the countries of Somalia, Yemen, Iraq, Afghanistan, Pakistan, Sudan, and Djibouti were in a very poor situation and there was an inequality in health in the countries of the region. Conclusion Based on the results, the main factors affecting mortality rate included: 1) Average precipitation, 2) Latitude, 3) Above sea level, 4) Food safety, and 5) Births attended by skilled health personnel.

In the present time, evaluating the performance of banks is one of the important subjects for societies and the bank managers who want to expand the scope of their operation. One of the non-parametric approaches for evaluating efficiency is data envelopment analysis(DEA). By a mathematical programming model, DEA provides an estimation of efficiency surfaces. A major problem faced by DEA is that the frontier calculated by DEA may be slightly distorted if the data is affected by statistical noises. In recent years, using the neural networks is a powerful non-parametric approach for modeling the nonlinear relations in a wide variety of decision making applications. The radial basis function neural networks (RBFNN) have proved significantly beneficial in the evaluation and assessment of complex systems. Clustering is a method by which a large set of data is grouped into clusters of smaller sets of similar data. In this paper, we proposed RBFNN with the K-means clustering method for the efficiency evaluation of a large set of branches for an Iranian bank. This approach leads to an appropriate classification of branches. The results are compared with the conventional DEA results. It is shown that, using the hybrid learning method, the weights of the neural network are convergent.