Forecasting Automobile Sales in Turkey with Artificial Neural Networks

This study aims to reveal significant factors which affect automobile sales and estimate the automobile sales in Turkey by using Artificial Neural Network (ANN), ARIMA, and time series decomposition techniques. The forecasting model includes automobile sales, automobile price, Euro and Dollar exchange rate, employment rate, consumer confidence index, oil prices and industrial production confidence index, the probability of buying an automobile, female employment rate, general economic situation, the expectation of general economic situation, financial status of households, expectation of financial status of households. According to the regression results, changes in Dollar exchange rate, the expectation of financial status of households, seasonally adjusted industrial production index, logarithmic form of automobile sales before-one-month which have a significant effect on automobile sales, are found to be the significant variables. The results show that ANN has a better estimation performance with MAPE=1.18% and RMSE=782 values than ARIMA and time series decomposition techniques.

Gender Differences in Child Malnutrition in Ethiopia: Evidence from Three Decomposition Techniques

Ethiopia has one of the highest under-five child mortality rate in the world, which is higher for boys than for girls. Malnutrition is a major contributing factor to child mortality and that is why we assess the differences in child malnutrition status of boys and girls. Specifically, we study the extent to which the gender differences in malnutrition are associated with observable factors and socio-economic characteristics and to what extent these differences are unexplained and attributable to factors such as latent parental preferences, societal biases, and other unobservable factors. We use data from the Ethiopia Demographic and Health Survey and evaluate three anthropometric status measures – wasting, stunting, and being underweight. We utilize a reduced-form demand for nutrition framework and several decomposition techniques: Oaxaca-Blinder decomposition for non-linear models, Machado-Mata quantile decomposition, and the recentered influence function. The results indicate that measurable socioeconomic and locational characteristics have significant and plausible associations with malnutrition by gender. We also find that 3% to 4% of the difference in the anthropometric status may be attributable to unobservable factors that may include implicit parental preferences. This approach is useful in evaluating gender differences in other human capital development outcomes such as health and education, as well as those in malnutrition.

CAPUTO TYPE FRACTIONAL OPERATOR APPLIED TO HEPATITIS B SYSTEM

In our research work, we develop the analysis of a noninteger-order model for hepatitis B (HBV) under singular type Caputo fractional-order derivative. We investigated our proposed system for an approximate or semi-analytical solution using Laplace transform along with decomposition techniques by Adomian polynomial of nonlinear terms and some perturbation techniques of Homotopy (HPM). The obtained solutions have been compared with each other against some real data by simulation via MATLAB. The graphical simulation in fractional form shows a better general result as compared to integer-order simulation.

Decomposition Strategies to Count Integer Solutions over Linear Constraints

Counting integer solutions of linear constraints has found interesting applications in various fields. It is equivalent to the problem of counting integer points inside a polytope. However, state-of-the-art algorithms for this problem become too slow for even a modest number of variables. In this paper, we propose new decomposition techniques which target both the elimination of variables as well as inequalities using structural properties of counting problems. Experiments on extensive benchmarks show that our algorithm improves the performance of state-of-the-art counting algorithms, while the overhead is usually negligible compared to the running time of integer counting.