Optimal fuzzy deep daily nutrients requirements representation: Application to optimal Morocco diet problem
| dc.citation.epage | 615 | |
| dc.citation.issue | 3 | |
| dc.citation.journalTitle | Математичне моделювання та комп'ютинг | |
| dc.citation.spage | 607 | |
| dc.contributor.affiliation | Інженерно-наукова лабораторія (LSI), USMBA | |
| dc.contributor.affiliation | Дослідницька лабораторія MorphoSciences, CAU | |
| dc.contributor.affiliation | Лабораторія біонаук і здоров’я, CAU | |
| dc.contributor.affiliation | Engineering Science Laboratory (LSI), USMBA | |
| dc.contributor.affiliation | MorphoSciences Research Laboratory, CAU | |
| dc.contributor.affiliation | Biosciences and Health laboratory, CAU | |
| dc.contributor.author | Ель Мутауакіл, К. | |
| dc.contributor.author | Саліха, С. | |
| dc.contributor.author | Хічам, Б. | |
| dc.contributor.author | El Moutaouakil, K. | |
| dc.contributor.author | Saliha, C. | |
| dc.contributor.author | Hicham, B. | |
| dc.coverage.placename | Львів | |
| dc.coverage.placename | Lviv | |
| dc.date.accessioned | 2025-03-04T11:32:58Z | |
| dc.date.created | 2022-02-28 | |
| dc.date.issued | 2022-02-28 | |
| dc.description.abstract | Вирішення проблеми оптимального харчування обов’язково передбачає оцінку добової потреби в позитивних і негативних поживних речовинах. Більшість підходів, запропонованих у літературі, засновані на стандартних номінальних оцінках, які можуть викликати дефіцит в одних і передозування в інших нутрієнтах. Підхід, запропонований у цій роботі, полягає в персоналізації цих потреб на основі інтелектуальної системи. На початку подаємо потреби, отримані з рекомендацій експертів у галузі харчування, трапецієподібними числами. На основі цієї моделі створюємо велику базу даних. Остання використовується для навчання глибокої нейронної мережі, архітектуру якої оптимізуємо за допомогою методу нечіткого генетичного алгоритму, приймаючи індивідуальну умову регулювання. Наша система оцінює потреби в поживних речовинах лише на основі статі та віку. Ці оцінки інтегровані в математичну модель, яку ми використовували в нашій попередній роботі. Потім ми знову використовуємо нечіткий генетичний алгоритм для складання персоналізованих дієт. Запропонована система показала дуже високу здатність прогнозування потреб різних за віком та статтю людей і дозволила складати дуже корисні раціони харчування. | |
| dc.description.abstract | Solving the optimal diet problem necessarily involves estimating the daily requirements in positive and negative nutrients. Most approaches proposed in the literature are based on standard nominal estimates, which may cause shortages in some nutrients and overdoses in others. The approach proposed in this paper consists in personalizing these needs based on an intelligent system. In the beginning, we present the needs derived from the recommendations of experts in the field of nutrition in trapezoidal numbers. Based on this model, we generate a vast database. The latter is used to educate a deep learning neural network, the architecture of which we optimize by the fuzzy genetic algorithm method in the way of adopting a customized regulation term. Our system estimates nutrient requirements based only on gender and age. These estimations are integrated into a mathematical model obtained in our previous work. Then we again use the fuzzy genetic algorithm to draw up personalized diets. The proposed system has demonstrated a very high capacity to predict the needs of different individuals and has allowed the drawing up of very high-quality diets. | |
| dc.format.extent | 607-615 | |
| dc.format.pages | 9 | |
| dc.identifier.citation | El Moutaouakil K. Optimal fuzzy deep daily nutrients requirements representation: Application to optimal Morocco diet problem / K. El Moutaouakil, C. Saliha, B. Hicham // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 9. — No 3. — P. 607–615. | |
| dc.identifier.citationen | El Moutaouakil K. Optimal fuzzy deep daily nutrients requirements representation: Application to optimal Morocco diet problem / K. El Moutaouakil, C. Saliha, B. Hicham // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 9. — No 3. — P. 607–615. | |
| dc.identifier.doi | doi.org/10.23939/mmc2022.03.607 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/63459 | |
| dc.language.iso | en | |
| dc.publisher | Видавництво Львівської політехніки | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Математичне моделювання та комп'ютинг, 3 (9), 2022 | |
| dc.relation.ispartof | Mathematical Modeling and Computing, 3 (9), 2022 | |
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| dc.relation.references | [2] El Moutaouakil K., Cheggour M. Chellak S., Baizri H. Metaheuristics Optimiza-tion Algorithm to an Optimal Moroccan Diet. 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC). 364–368 (2021). | |
| dc.relation.references | [3] Bas E. A three-step methodology for GI classification, GL estimation of foods by fuzzy c-means classification and fuzzy pattern recognition, and an LP-based diet model for glycaemic control. Food Research International. 83, 1–13 (2016). | |
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| dc.relation.references | [7] National Academies of Sciences, Engineering, and Medicine. Dietary reference intakes for sodium and potassium (2019). | |
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| dc.relation.references | [18] El Moutaouakil K., Touhafi A. A New Recurrent Neural Network Fuzzy Mean Square Clustering Method. 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech). 1–5 (2020). | |
| dc.relation.references | [19] Haddouch K., El Moutaouakil K. New Starting Point of the Continuous Hopfield Network. International Conference on Big Data, Cloud and Applications. 379–389 (2018). | |
| dc.relation.references | [20] El Ouissari A., El Moutaouakil K. Density based fuzzy support vector machine: application to diabetes dataset. Mathematical Modeling and Computing. 8 (4), 747–760 (2021). | |
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| dc.relation.referencesen | [1] Bas E. A robust optimization approach to diet problem with overall glycemic load as objective function. Applied Mathematical Modelling. 38 (19–20), 4926–4940 (2014). | |
| dc.relation.referencesen | [2] El Moutaouakil K., Cheggour M. Chellak S., Baizri H. Metaheuristics Optimiza-tion Algorithm to an Optimal Moroccan Diet. 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC). 364–368 (2021). | |
| dc.relation.referencesen | [3] Bas E. A three-step methodology for GI classification, GL estimation of foods by fuzzy c-means classification and fuzzy pattern recognition, and an LP-based diet model for glycaemic control. Food Research International. 83, 1–13 (2016). | |
| dc.relation.referencesen | [4] Amin S. H., Mulligan-Gow S., Zhang G. Food selection for a feeding problem using a multi-objective approach under uncertainty. Application of decision science to business and management. 181 (2019). | |
| dc.relation.referencesen | [5] Khan M. A., Haq A. L., Ahmed A. Modele multi-objectifs pour la planification de l’alimentation quotidienne. Fiabilite: theorie et applications. 16 (1), 61 (2021). | |
| dc.relation.referencesen | [6] You A. Dietary guidelines for Americans. US Department of Health and Human Services and US Department of Agriculture (2015). | |
| dc.relation.referencesen | [7] National Academies of Sciences, Engineering, and Medicine. Dietary reference intakes for sodium and potassium (2019). | |
| dc.relation.referencesen | [8] Morris R. C. (Jr), Sebastian A., Forman A., Tanaka M., Schmidlin O. Normotensive salt sensitivity: effects of race and dietary potassium. Hypertension. 33 (1), 18–23 (1999). | |
| dc.relation.referencesen | [9] Lind T., Lonnerdal B., Stenlund H., Ismail D., Seswandhana R., Ekstrom E.-C., Persson L.-A. A community-based, randomized controlled trial of iron and/or zinc supplementation of Indonesian infants - interactions between iron and zinc. The American Journal of Clinical Nutrition. 77 (4), 883–890 (2004). | |
| dc.relation.referencesen | [10] O’Brien K. O., Zavaleta N., Caulfield L. E., Wen J., Abrams S. A. Prenatal Iron Supplements Impair Zinc Absorption in Pregnant Peruvian Women. The Journal of Nutrition. 130 (9), 2251–2255 (2000). | |
| dc.relation.referencesen | [11] Weaver C. M., Gordon C. M., Janz K. F., Kalkwarf H. J., Lappe J. M., Lewis R., Zemel B. S. The National Osteoporosis Foundation’s position statement on peak bone mass development and lifestyle factors: a systematic review and implementation recommendations. Osteoporosis international. 27 (4), 1281–1386 (2016). | |
| dc.relation.referencesen | [12] D’Odorico P., Davis K. F., Rosa L., Carr J. A., Chiarelli D., Dell’Angelo J., Gephart J., MacDonald G. K., Seekell D. A., Suweis S., Rulli M. C. The global food–energy–water nexus. Reviews of Geophysics. 56 (3), 456–531 (2018). | |
| dc.relation.referencesen | [13] Zimmermann M. B., Chassard C., Rohner F., N’goran E. K., Nindjin C., Dostal A., Hurrell R. F. The effects of iron fortification on the gut microbiota in African children: a randomized controlled trial in Cote d’Ivoire. The American Journal of Clinical Nutrition. 92 (6), 1406–1415 (2010). | |
| dc.relation.referencesen | [14] Meschia J. F., Bushnell C., Boden-Albala B., Braun L. T., Bravata D. M., Chaturvedi S., Wilson J. A. Guidelines for the primary prevention of stroke: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 45 (12), 3754–3832 (2014). | |
| dc.relation.referencesen | [15] Donati M., Menozzi D., Zighetti C., Rosi A., Zinetti A., Scazzina F. Towards a sustainable diet combining economic, environmental and nutritional objectives. Appetite. 106, 48-57 (2016). | |
| dc.relation.referencesen | [16] Møller M. F. A scaled conjugate gradient algorithm for fast supervised learning. Neural networks. 6 (4), 525–533 (1993). | |
| dc.relation.referencesen | [17] Olshausen B. A., Field D. J. Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research. 37 (23), 3311–3325 (1997). | |
| dc.relation.referencesen | [18] El Moutaouakil K., Touhafi A. A New Recurrent Neural Network Fuzzy Mean Square Clustering Method. 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech). 1–5 (2020). | |
| dc.relation.referencesen | [19] Haddouch K., El Moutaouakil K. New Starting Point of the Continuous Hopfield Network. International Conference on Big Data, Cloud and Applications. 379–389 (2018). | |
| dc.relation.referencesen | [20] El Ouissari A., El Moutaouakil K. Density based fuzzy support vector machine: application to diabetes dataset. Mathematical Modeling and Computing. 8 (4), 747–760 (2021). | |
| dc.relation.referencesen | [21] Yang X. S. Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008). | |
| dc.relation.referencesen | [22] Jang J. S. R., Sun C. T., Mizutani E. Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Transactions on Automatic Control. 42 (10), 1482–1484 (1997). | |
| dc.relation.referencesen | [23] Civicioglu P. Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers and Geosciences. 46, 229–247 (2012). | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2022 | |
| dc.subject | глибока нейронна мережа | |
| dc.subject | потреби в поживних речовинах | |
| dc.subject | оптимальна дієта | |
| dc.subject | генетичний алгоритм | |
| dc.subject | алгоритм світлячка | |
| dc.subject | нечітке квадратичне програмування | |
| dc.subject | трикутні нечіткі числа | |
| dc.subject | deep neural network | |
| dc.subject | nutrients requirements | |
| dc.subject | optimal diet | |
| dc.subject | genetic algorithm | |
| dc.subject | firefly algorithm | |
| dc.subject | fuzzy quadratic programming | |
| dc.subject | triangular fuzzy numbers | |
| dc.title | Optimal fuzzy deep daily nutrients requirements representation: Application to optimal Morocco diet problem | |
| dc.title.alternative | Оптимальне нечітке глибоке представлення добових потреб у поживних речовинах: застосування до оптимальної проблеми дієти Марокко | |
| dc.type | Article |
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