genetic algorithm (GA)

Unsupervised Learning for Optimal Personalized Dietary Menus to Prevent Diabetes and Cardiovascular Diseases

Healthy diets can slow disease progression, but their effectiveness may decrease.  Patients often give up these diets due to limited food choices, unappetizing meals, and reduced physical activity from cutting calories.  To address this, we developed an intelligent nutritional balance system to prevent cardio-diabetic diseases.  This system creates diets that optimize cholesterol and glycemic control through the following steps: (a) Characterizing Moroccan foods based on 19 nutrients and their glycemic load, (b) Classifying foods using a Gaussian mixture model, (c) Mode

Method and program model of microelectromechanical systems components synthesis based on genetic algorithm and ontology models

In this paper the general process and the concurrent synthesis realization model of microelectromechanical systems, which is based on developed genetic algorithm, are described. As the synthesis task in the sphere of complex microsystems is very comprehensive and timeconsuming, the actuality of performance and speed issues to generate the novel system and its components constructions is still up-to-date unsolved item. The developed model facilitates and accelerates the synthesis of the new and unique microelectromechanical systems structures.