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There are several tables with important data used in the calculus of different processes like machining tables, friction tables and thermodynamics processes tables, or as it is explored in this paper, the description of saturated water and steam table. We propose the generation of equations for describing the entire behavior of numerical values in a table using Genetic Programming (GP), when table data describes the variable behavior of a dependent function. This obtained equations simplify the calculus process without requiring several tables and allowing to work when tables are not available for a desired value of an independent variable, a common situation in thermodynamics. In this case it is tested the proposed algorithm for synthesizing the saturated water and steam table.

Recoloring it is a technique for changing the color an image resulting in a different new one. Recoloration is a common photo edition operation since digital images are around every media resource and several algorithms are used for editing these pictures, nevertheless, recent digital cameras have increased enormously the quantity of pixels for producing them. This increase in the size of digital images makes difficult the recoloring operation. In order to solve the recoloring problem, there had been applied several algorithms, some algorithms directly detect the color by performing transformations on color representations to different spaces where color is easily separated but this transformation require several no linear operations. On the other hand, numerical parameters on CNNs make than this approach cannot be trained or implemented on a mobile device, more over the time required for computing an input image will made that the processed pictures be delayed continually. Considering this limitation is proposed a specific short architecture for detecting a specific color in general objects using a feedforward neural network trained with gradient descent backpropagation with variable learning rate.

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