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Using the PRECEDE model, the purpose of this paper is to determine the predictors of mothers’ performance in daily consumption of fruit and vegetables (FV) in rural preschoolers.

This study was carried out on 350 mothers of preschool children who had health records in the rural health-care centers of Iran. To collect data, a researcher-made questionnaire based on the PRECEDE model was used. The data were analyzed using the SPSS 19 software.

The results showed that 11.42 percent of the mothers observed the FV intake for their children recommended by WHO. The independent t-test showed a significant difference between the mean scores of predisposing, enabling and reinforcing factors.

This study showed that the rate of FV intake by preschool children in rural areas was much lower than the recommended WHO rate. To promote behavior, attention to the predisposing, enabling and reinforcing factors seems to be necessary.

The purpose of this paper is to overcome the application limitations of other multi-variable regression based on polynomials due to the huge computation room and time cost.

In this paper, based on the idea of feature selection and cascaded regression, two strategies including Laguerre polynomials and manifolds optimization are proposed to enhance the accuracy of multi-variable regression. Laguerre polynomials were combined with the genetic algorithm to enhance the capacity of polynomials approximation and the manifolds optimization method was introduced to solve the co-related optimization problem.

Two multi-variable Laguerre polynomials regression methods are designed. Firstly, Laguerre polynomials are combined with feature selection method. Secondly, manifolds component analysis is adopted in cascaded Laguerre polynomials regression method. Two methods are brought to enhance the accuracy of multi-variable regression method.

With the increasing number of variables in regression problem, the stable accuracy performance might not be kept by using manifold-based optimization method. Moreover, the methods mentioned in this paper are not suitable for the classification problem.

Experiments are conducted on three types of datasets to evaluate the performance of the proposed regression methods. The best accuracy was achieved by the combination of cascade, manifold optimization and Chebyshev polynomials, which implies that the manifolds optimization has stronger contribution than the genetic algorithm and Laguerre polynomials.