Optimal fuzzy deep daily nutrients requirements representation: Application to optimal Morocco diet problem

: pp. 607–615
Received: February 14, 2022
Accepted: May 15, 2022
Engineering Science Laboratory (LSI), Faculty Polydisciplinary of Taza, USMBA, Morocco
MorphoSciences Research Laboratory, Faculty of Medicine and Pharmacy, CAU, Morocco
Biosciences and Health laboratory, Faculty of Medicine and Pharmacy, CAU, Morocco

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.

  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).
  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).
  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).
  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).
  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).
  6. You A.  Dietary guidelines for Americans.  US Department of Health and Human Services and US Department of Agriculture (2015).
  7. National Academies of Sciences, Engineering, and Medicine.  Dietary reference intakes for sodium and potassium  (2019).
  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).
  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).
  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).
  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).
  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).
  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).
  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).
  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).
  16. Møller M. F.  A scaled conjugate gradient algorithm for fast supervised learning.  Neural networks. 6 (4), 525–533 (1993).
  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).
  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).
  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).
  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).
  21. Yang X. S.  Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008).
  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).
  23. Civicioglu P.  Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm.  Computers and Geosciences. 46, 229–247 (2012).
Mathematical Modeling and Computing, Vol. 9, No. 3, pp. 607–615 (2022)