基于多约束元启发式和背包优化算法的饮食推荐模型。

Leena K. Gautam, V. Gulhane
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引用次数: 1

摘要

各种营养物质是人类保持健康和活跃所必需的。现在,保持高质量的生活取决于保持每天的饮食习惯,以防止摄入过多的卡路里和不正确的营养。计算机化的应用程序可以帮助印度老年人保持和改善他们的整体健康,提供相关的信息,如卡路里和营养细节,并遵循严格的饮食计划,适合他们的疾病。为了创建考虑疾病患病率、食物可用性和用户偏好的优化饮食计划,本文提出了与背包方法相结合的多约束元启发式方法。解决方案的质量是通过应用一套动态的、个性化的食品项目来实现的。该算法的平均误差率为4.15%。
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Diet Recommendation Model Using Multi Constraint Metaheuristic and Knapsack Optimization Algorithm.
Various nutrients are necessary for humans to remain healthy and active. Maintaining a high quality of life now depends on keeping track of everyday eating habits to prevent consuming too many calories and incorrect nutrients. Computerized applications can help Indian elderly people maintain and improve their overall health by providing pertinent information such as calories and nutritional details and following a strict diet plan suited to their ailments. In order to create optimized diet plans that take disease prevalence, food availability, and user preferences into account, the paper offers the Multi Constraint Metaheuristic integrated with the Knapsack approach. The solution's quality is attained by applying a dynamic, personalized set of food items. The average error percentage obtained by the suggested algorithm is 4.15.
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来源期刊
International Journal of Next-Generation Computing
International Journal of Next-Generation Computing COMPUTER SCIENCE, THEORY & METHODS-
自引率
66.70%
发文量
60
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