Mobile Information System for Human Nutrition Control

2022;
: pp. 145 - 172
1
Lviv Polytechnic National University
2
Lviv Polytechnic National University, Osnabrück University, Institute of Computer Science

It is acknowledged that each person's life, group of people and nation is formed depending on geographical, economic, political, cultural and religious conditions. Lifestyle is formed as a result of daily repetition and consists of the following factors: nutrition, exercise, the presence of bad habits, moral and spiritual development, and so on. In recent decades, lifestyle has been considered an integral part of well-being, leading to increased research. According to the scientist's study, more than half of health problems are related to diet. Millions of people eat incorrectly and are not even aware of it. The actuality of the theme: there are many approaches to solving the problem of diet control, but it should be understood that different analogues offer different opportunities that are not always clear and convenient. It is because there are several ways to achieve the same goal. The need for research on healthy eating in modern conditions is one of the priority tasks to improve the physical condition of different age groups. The aim is to create a system that will be aimed at helping the end-user to follow a healthy diet by determining the composition and caloric content of the product and the formation of recommendations based on the appropriate rhythm of life. The system is designed to solve specific tasks: to recognize products, correlate the product and its caloric content, form a food diary, remind the user about missed meals and keep statistics.

  1. Kryvoruchko O. Usage of neural networks in image recognition / O. Kryvoruchko, K. Khorolska, V. Chubaievskyi // Зовнішня торгівля: економіка, фінанси, право. - 2019. - Vol. 3. - P. 83-92.
  2. Vapnik V. N. The nature of statistical learning theory / V. N. Vapnik. - Springer, 1999. - P. 314. https://doi.org/10.1007/978-1-4757-3264-1
  3. Adaptyvne navchannya neyronnoyi merezhi opornykh vektoriv naymenshykh kvadrativ / YE. V. Bodyansʹkyy, A. O. Deyneko, ZH. V. Deyneko, M. O. Shalamov // Informatsiyno-keruyuchi systemy na zaliznychnomu transporti. - 2015. - Vol. 2. - P. 71-74. https://doi.org/10.18664/ikszt.v0i2.52045
  4.  Alʹpert S. I. Suchasni kryteriyi otsinky tochnosti klasyfikatsiyi zobrazhenʹ / S. I. Alʹpert // Matematychni mashyny i systemy. - 2013. - Vol. 4. - P. 187-197.
  5. Bodyansʹkyy YE. V. Evolyutsiyna neyronna merezha z yadernymy funktsiyamy aktyvatsiyi y adaptyvnyy alhorytm yiyi navchannya / YE. V. Bodyansʹkyy, N. O. Teslenko, A. O. Deyneko // Naukovi pratsi [Chornomorsʹkoho derzhavnoho universytetu imeni Petra Mohyly]. Ser. : Kompʺyuterni tekhnolohiyi. - 2011. - Vol. 160(148). - P. 53-58.
  6. Shalkoff R. J. Digital image processing and computer vision / R. J. Shalkoff. - New York; Chichester; Brisbane; Toronto; Singapore: John Wiley & Sons, 1989. - 489 p.
  7. Gorokhovatskiy V. A. Klassifikatsiya izobrazheniy vizual'nykh ob"yektov po mnozhestvu deskriptorov osobennykh tochek na osnove neyronnoy seti Kokhonena / V. A. Gorokhovatskiy, D. V. Pupchenko // Sistemi upravlínnya, navígatsíí̈ ta zv'yazku. - 2018. - Vol. 2. - P. 68-72.
  8. Bilashenko S. V. Rozpiznavannya zobrazhenʹ za dopomohoyu z·hortkovykh neyronnykh merezh z vykorystannyam biblioteky Keras / S. V. Bilashenko, N. N. Shapovalova, O. H. Rybalʹchenko // Hirnychyy visnyk. - 2018. - Vol. 10. - P. 148-154. http://doi.org/10.31721/2306-5435-2018-1-103-148-154.
  9. Bortnyk K. YA. Mashynne navchannya, yak osnova dlya rozvytku tekhnolohiy maybutnʹoho / K. YA. Bortnyk, O. V. Olʹshevsʹkyy, A. L. Kyrylyuk // Kompʺyuterno-intehrovani tekhnolohiyi: osvita, nauka, vyrobnytstvo. - 2017. - Vol. 27. - P. 85-88.
  10. Brodkevych V. M. Alhorytmy mashynnoho navchannya ta hlybokoho navchannya (HN) i yikh vykorystannya v prykladnykh dodatkakh / V. M. Brodkevych, V. YA. Remeslo // Mizhnarodnyy naukovyy zhurnal «Internauka». - 2018. - Vol. 11(1). - P. 56-60.
  11. Butyrsʹka I. V. Tekhnolohiya QR-kodu yak instrument pidvyshchennya efektyvnosti funktsionuvannya servisnykh system / I. V. Butyrsʹka, A. V. Manhul // Visnyk Chernivetsʹkoho torhovelʹno-ekonomichnoho instytutu. Ekonomichni nauky. - 2015. - Vol. 1. - P. 165-171.
  12. Vitlinsʹkyy V. V. Shtuchnyy intelekt u systemi pryynyattya rishenʹ / V. V. Vitlinsʹkyy // Neyro-nechitki tekhnolohiyi modelyuvannya v ekonomitsi. - 2012. - Vol. 1. - P. 97-118.
  13. Voloshin G. YA. Metody raspoznavaniya obrazov / G. YA. Voloshin. - Vladivostok : VGUES, 2000. - 74 p.
  14. Hadetsʹka S. Metody strukturnoyi klasyfikatsiyi zobrazhenʹ na zasadakh bayesovsʹkoyi teoriyi pryynyattya rishenʹ / S. Hadetsʹka, V. Horokhovat·sʹkyy // Radioelektronika, informatyka, upravlinnya. - 2018. - Vol. 2. - P. 90-97. https://doi.org/10.15588/1607-3274-2018-2-10
  15. Hamanyuk I. Variant zastosuvannya bayyesivsʹkykh metodiv dlya mashynnoho navchannya shtuchnoho intelektu systemy pidtrymky pryynyattya rishenʹ u borotʹbi zi spamom / I. Hamanyuk // Zv'yazok. - 2018. - Vol. 6. - P. 14-18.
  16. Hlukhova N. V. Rozrobka metodu analizu kolʹorovykh zobrazhenʹ hazorozryadnoho vyprominyuvannya / N. V. Hlukhova, L. A. Pisotsʹka // Systemy upravlinnya, navihatsiyi ta zv'yazku. - 2018. - Vol. 2- P. 59-62. https://doi.org/10.26906/SUNZ.2018.2.059
  17. Hlukhova N. V. Rozrobka systemy ekspres-klasyfikatsiyi vody na osnovi bazy danykh zobrazhenʹ / N. V. Hlukhova, L. A. Pisotsʹka, N. H. Kuchuk // Zbirnyk naukovykh pratsʹ Kharkivsʹkoho universytetu Povitryanykh Syl. - 2015. - Vol. 3. - P. 112-118.
  18. Berehovyy V. K. Osnovy naukovoyi orhanizatsiyi zdorovoho kharchuvannya / V. K. Berehovyy. // Efektyvna ekonomika. - 2011. - Vol. 11. - Access mode: http://nbuv.gov.ua/UJRN/efek_2011_11_19.
  19. Klymenko D. O. Veb-dodatok dlya servisu skladannya ratsionu zdorovoho kharchuvannya ta dostavky produktiv / D. O. Klymenko, O. A. Rudenko // Systemy upravlinnya, navihatsiyi ta zv'yazku. - 2019. - Vol. 2. - P. 103-109.
  20. Hlushchenko L. Perspektyvy vykorystannya shtuchnoho intelektu dlya rozrobky funktsionalʹnomu kharchuvanni / L. Hlushchenko // Visnyk Lʹvivsʹkoho universytetu. Seriya biolohichna. - 2016. - Vol. 73. - P. 437. https://doi.org/10.26906/SUNZ.2019.2.103
  21. Leshchenko O. B. Veb-dodatok dlya vedennya shchodennyka kharchuvannya ta trenuvannya: vymohy, rozroblennya i vprovadzhennya / O. B. Leshchenko, A. S. Khlyupina, D. O. Bohdan // Radioelektronni i kompʺyuterni systemy. - 2018. - Vol. 3. - P. 49-62. https://doi.org/10.32620/reks.2018.3.06
  22. Makarova H. V. Stvorennya mobilʹnoho dodatku dlya optymizatsiyi vahy ta kharchuvannya lyudyny / H. V. Makarova // Systemy obrobky informatsiyi. - 2017. - Vol. 2. - P. 187-191.
  23. Mostensʹka T. L. Kharchuvannya yak skladova prodovolʹchoyi bezpeky / T. L. Mostensʹka, H. O. Kundyeyeva // Naukovi pratsi Natsionalʹnoho universytetu kharchovykh tekhnolohiy. - 2016. - Vol. 22(3). - P. 11- 3-122.
  24. Motuzka YU. Kharchova ta enerhetychna tsinnistʹ produktiv dlya spetsialʹnykh medychnykh tsiley / YU. Motuzka // Tovary i rynky. - 2017. - Vol. 2(1). - P. 59-66.
  25. Slastin V. V. Sbalansirovannyy ratsion pitaniya / V. V. Slastin, Ye. S. Samuseva, L. V. Moskal'chuk // Problemi kharchuvannya. - 2014. - Vol. 1. - P. 33-39.
  26. Trachuk T. V. Metod matematychnoho modelyuvannya yak zasib realizatsiyi shchodennoho ratsionu / T. V. Trachuk, T. P. Radishchuk // Pedahohichnyy poshuk. - 2014. - Vol. 1. - P. 34-36.
  27. Yaninovych Y. YE. Enerhetychna tsinnistʹ produktiv / Y. YE. Yaninovych, H. V. Kachay, T. M. Shvetsʹ // Rybohospodarsʹka nauka Ukrayiny. - 2011. - Vol. 2. - P. 122-126.
  28. Hryhorenko O. Naukovi pidkhody do formuvannya ratsioniv kharchuvannya / O. Hryhorenko // Prohresyvni tekhnika ta tekhnolohiyi kharchovykh vyrobnytstv restorannoho hospodarstva i torhivli. - 2009. - Vol. 2. - P. 210-218.
  29. Hrynʹov D. V. Metod rozpiznavannya zobrazhenʹ ob'yektiv zasobamy vydovoyi rozvidky / D. V. Hrynʹov // Systemy ozbroyennya i viysʹkova tekhnika. - 2007. - Vol. 4. - P. 72-75.
  30. Hrytsyk V. V. Metod obrobky skladnykh zobrazhenʹ ta yikh rozpiznavannya / V. V. Hrytsyk // Dopovidi Natsionalʹnoyi akademiyi nauk Ukrayiny. - 2011. - Vol. 1. - P. 28-32.
  31. Hrytsyk V. V. Obrobka skladnykh zobrazhenʹ ta yikh rozpiznavannya v informatsiyno-analitychnykh systemakh komp'yuternoho zoru / V. V. Hrytsyk // Dopovidi Natsionalʹnoyi akademiyi nauk Ukrayiny. - 2009. - Vol. 7. - P. 36-41.
  32. Dovbysh A. S Osnovy teoriyi rozpiznavannya obraziv : navch. posib. : u 2 ch. / A. S. Dovbysh, I. V. Shelekhov. - Sumy : Sumsʹkyy derzhavnyy universytet, 2015. - Vol. 1. - 109 p.
  33.  Domanetsʹka I. M. Neyromerezhevi tekhnolohyi opratsyuvannya zobrazhenʹ v adaptyvnykh systemakh navchannya / I. M. Domanetsʹka, O. V. Fedusenko, V. M. Khrolenko // Shtuchnyy intelekt. - 2017. - Vol. 3-4. - P. 24-31.
  34. Drofa V. O. Informatsiyno-ekstremalʹnyy alhorytm rozpiznavannya nestatsionarnykh za yaskravistyu zobrazhenʹ / V. O. Drofa, T. M. Yefimenko // Byonyka yntellekta. - 2015. - Vol. 2. - P. 100-104.
  35. Mestetskiy L. Matematicheskiye metody raspoznavaniya obrazov / L. Mestetskiy. - M. : MGU, 2004. - 85 p.
  36. Mokrintsev O. A. Poperednya obrobka zobrazhenʹ dlya avtomatychnoho rozpiznavannya odnovymirnykh shtrykh-kodiv / O. A. Mokrintsev // Systemy upravlinnya, navihatsiyi ta zv'yazku. - 2017. - Vol. 1. - P. 111-113.
  37. Simakov V. S. Adaptivnoye upravleniye slozhnymi sistemami na osnove teorii raspoznavaniya obrazov / V. S. Simakov, Ye. V. Lutsenko. - Krasnodar : KGTU, 1999. - 318 p.
  38. Tereykovsʹkyy I. Neyromerezheva metodolohiya rozpiznavannya Internet-oriyentovanoho shkidlyvoho prohramnoho zabezpechennya / I. Tereykovsʹkyy // Bezpeka informatsiyi. - 2013. - Vol. 19(1). - P. 24-28. https://doi.org/10.18372/2225-5036.19.4688
  39. Bolohova N. Image processing models and methods research and ways of improving marker recognition technologies in added reality systems / N. Bolohova, I. Ruban // Сучасний стан наукових досліджень та технологій в промисловості. - 2019. - Vol. 1. - P. 25-33. https://doi.org/10.30837/2522-9818.2019.7.025
  40. Dovbysh A. S. Informatsiyno-ekstremalʹnyy alhorytm navchannya systemy pidtrymky pryynyattya rishenʹ z hipertsylindroyidnym klasyfikatorom / A. S. Dovbysh, H. A. Stadnyk // Radioelektronni i kompʺyuterni systemy. - 2015. - Vol. 3. - P. 11-18.
  41. Krasylenko V. H. Modelyuvannya metodiv rozpiznavannya ta klasyfikatsiyi frahmentiv kolʹorovykh zobrazhenʹ zemelʹ silʹsʹkohospodarsʹkoho pryznachennya pry yikh dystantsiynomu monitorynhu / V. H. Krasylenko, R. O. Yatskovsʹka, V. I. Yatskovsʹkyy // Systemy obrobky informatsiyi. - 2017. - Vol. 5. - P. 55-61.
  42. Moroz O. YA. Shtuchnyy intelekt versus pryrodnyy intelekt? (maybutnye lyudyny v konteksti vyklykiv intelektualʹnykh supertekhnolohiy) / O. YA. Moroz // Politolohichnyy visnyk. - 2014. - Vol. 72. - P. 18-35.
  43. Oldenderfer M. Klasternyy analiz. Faktornyy, diskriminantnyy i klasternyy analiz: per. s angl.; pod. red. I. S. Yenyukova / M. Oldenderfer, R. K. Bleshfild. - M. : Finansy i statistika, 1989. - 215 p.
  44. Duda R. O. Pattern Classification, second ed. / R. O. Duda, P. E. Hart, D. G. Stork. - John Wiley & Sons, New York, 2001. - 738 p.
  45. Han J. Data mining: concepts and techniques. - 3rd ed. / J. Han, M. Kamber, J. Pei. - Morgan Kaufmann; Elsevier, 2012. - 744 p.
  46. Melnychuk S. Using Information Features in Computer Vision for 3d Pose Estimation in Space / S. Melnychuk, V. Gubarev, N. Salnikov // Кибернетика и вычислительная техника. - 2017. - Vol. 190. - P. 33-55. https://doi.org/10.15407/kvt190.04.03
  47. Motuzka I. Сlassification of products for enteral nutrition / I. Motuzka, D. Antiushko // Товари і ринки. - 2015. - Vol. 2. - P. 17-24.
  48. Pantelyat M. G. Application of the finite element nethod to computer simulation of electromagnetic and thermal processes in induction cookers and heated dishes / M. G. Pantelyat // Вісник Черкаського університету. Серія : Фізико-математичні науки. - 2017. - Vol. 1. - P. 79-85.
  49. Ziglio E. The WHO cross-national study of health behavior in school aged children from 35 countries: / Ziglio E. - New York; Chichester; Brisbane; Toronto; Singapore: J. School Health, 2001. - 206 p. https://doi.org/10.1111/j.1746-1561.2004.tb07933.x
  50. Danylyuk I. H. Aktualʹni problemy metodu hlybynnoho navchannya/ I. H. Danylyuk // Linhvistychni studiyi. - 2018. - Vol. 35. - P. 155-158.
  51. Lendyuk T. V. Modelyuvannya kompʺyuternoho adaptyvnoho navchannya i testuvannya / T. V. Lendyuk // Pratsi Odesʹkoho politekhnichnoho universytetu. - 2013. - Vol. 1. - P. 110-115.
  52. Malyaretsʹ L. M. Zastosuvannya QR-rozkladu pryamokutnykh matrytsʹ Khauskholderovymy vidobrazhennyamy v rehresiynomu analizi / L. M. Malyaretsʹ, I. YU. Ryzhykh // Ekonomika rozvytku. - 2009. - Vol. 1. - P. 16-20.
  53. Matvyeyeva N. O. Doslidzhennya zasobiv mashynnoho navchannya iz zaluchennyam suchasnykh mov prohramuvannya/ N. O. Matvyeyeva // Systemni tekhnolohiyi. - 2018. - Vol. 1. - P. 85-91.
  54. Mintser O. P. Obriyi rozvytku adaptyvnoho navchannya / O. P. Mintser // Medychna informatyka ta inzheneriya. - 2017. - Vol. 1. - P. 5-11. DOI: https://doi.org/10.11603/mie.1996-1960.2017.1.7665
  55. Pishvanova V. O. Pryntsypy adaptyvnoho navchannya / V. O. Pishvanova // Visnyk Zaporizʹkoho natsionalʹnoho universytetu. Pedahohichni nauky. - 2015. - Vol. 1. - P. 178-183.
  56. Tupalo YA. O. Vykorystannya metodiv mashynnoho navchannya na praktytsi / YA. O. Tupalo // Kompʺyuterni zasoby, merezhi ta systemy. - 2018. - Vol. 17. - P. 101-110.
  57. Fedusenko O. V. Kontseptualʹna modelʹ adaptyvnoyi informatsiynoyi systemy navchannya / O. V. Fedusenko, A. O. Fedusenko, I. M. Domanetsʹka // Upravlinnya rozvytkom skladnykh system. - 2017. - Vol. 32. - P. 86-90.
  58. An introduction to kernelbased learning algorithms / K. R. Muller, S. Mika, G. Ratsch, et al. // IEEE 108 Transactions on Neural Networks. - 2001.- Vol. 12(2). - Р. 181-202. https://doi.org/10.1109/72.914517
  59. Al-Janabi Aqeel Bahp Tarkhan Computer vision system for froth flotation based on centroid / Aqeel Bahp Tarkhan Al-Janabi // Системи обробки інформації. - 2014. - Vol. 9. - P. 3-5.
  60. Dovbysh A. S. Optimization of parameters of machine learning of the system of functional diagnostics of the electric drive of a shaft lifting machine / A. S. Dovbysh, D. V. Velykodnyi, O. B. Protsenko, V. I. Zimovets // Радіоелектроніка, інформатика, управління. - 2018. - Vol. 2. - P. 44-50. DOI: https://doi.org/10.15588/1607-3274-2018-2-5
  61. J. Long, E. Shelhamer and T. Darrell, Fully convolutional networks for semantic segmentation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). - 2015. - P. 3431-3440, doi: 10.1109/CVPR.2015.7298965. DOI: http://doi.org/10.1109/CVPR.2015.7298965. https://doi.org/10.1109/CVPR.2015.7298965