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DEVELOPING MENU PLANNING SOFTWARE USING OPTIMIZATION AND ARTIFICIAL INTELLIGENCE ALGORITHM

Yıl 2024, Cilt: 49 Sayı: 5, 833 - 846, 10.10.2024
https://doi.org/10.15237/10.15237/gida.GD24063

Öz

In today's world, awareness of health and nutrition is growing, emphasizing the need for personalized nutrition recommendations and reducing food waste. This study collected demographic data and food preferences from users and analyzed them using artificial intelligence models. A model developed with the Random Forest algorithm was trained to predict users' future preferences and guide menu planning. Tests showed that combining AI with optimization techniques successfully creates user-focused menus, enhancing satisfaction and reducing food waste. The study also highlighted challenges related to the dataset's size, pointing to a need for more qualitative data. The developed model provides innovative solutions for catering companies and institutions offering mass dining, improving employee satisfaction while minimizing waste. Future research aims to refine the model for broader applications.

Kaynakça

  • Ahmed, F., Kim, K.Y. (2017). Data-driven weld nugget width prediction with decision tree algorithm. Procedia Manufacturing,10, 1009–1019, https://doi.org/10.1016/j.promfg.2017.07.092
  • Balcan, M.-F., Prasad, S., Sandholm, T., Vitercik, E. (2022). Structural analysis of branch-and-cut and the learnability of gomory mixed integer cuts. Advances in Neural Information Processing Systems, 35, 33890-33903, https://doi.org/ 10.48550/arXiv.2204.07312
  • Basu, A., Conforti, M., Di Summa, M., Jiang, H. (2023). Complexity of branch-and-bound and cutting planes in mixed-integer optimization. Mathematical Programming, 198, 787–810, https://doi.org/10.1007/s10107-022-01789-5
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/ 10.1023/A:1010933404324 Chandru, V., Rao, M. (1998). Linear programming. IIM Bangalore Research Paper, (109), https://doi.org/10.2139/ssrn.2170298
  • Kulhari, A. (2023). Significance of Linear Programming for Optimization. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 3(15), 179-183. https://doi.org/10.48175/IJARSCT-10899
  • Dantzig, G. B. (1982). Reminiscences about the origins of linear programming. Operations Research Letters, 1(2), 43-48. https://doi.org/10.1016/ 0167-6377(82)90043-8
  • Gong, X. (2022). Optimization algorithm of logistics warehousing and distribution path based on artificial intelligence technology. In 2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) (pp. 371-375).. https://doi.org/10.1109/ISAIEE57420.2022.00083
  • Hebbar, N. (2020). Freshness of food detection using IoT and machine learning. In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) (pp. 1-3). https://doi.org/10.1109/ic-ETITE47903.2020.80
  • Hou, S., Zhu, D., Xu, J. (2022). Artificial intelligence, financial canteen and internal control: A case study of Chinese catering industry. In 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI) (pp. 310-313). https://doi.org/10.1109/ IWECAI55315.2022.00066
  • Liaw, A., Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3), 18-22.
  • Li, Y., Yan, H., Zhang, Y. (2019). A deep learning method for material performance recognition in laser additive manufacturing. In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) (pp. 1735-1740). https://doi.org/ 10.1109/INDIN41052.2019.8972334
  • Miltenberger, M. (2023). Linear Programming in MILP Solving a Computational Perspective. https://nbn-resolving.org/urn:nbn:de:0297-zib-91873
  • Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., Martynenko, A. (2022). IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet of Things Journal, 9(9), 6305-6324. https://doi.org/10.1109/ JIOT.2020.2998584
  • Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons.B, 4, 51-62. https://doi.org/10.20544/ HORIZONS.B.04.1.17.P05
  • Precup, R.-E., Hedrea, E.-L., Roman, R.-C., Petriu, E. M., Szedlak-Stinean, A.-I., Bojan-Dragos, C.-A. (2021). Experiment-based approach to teach optimization techniques. IEEE Transactions on Education, 64(2), 88-94. https://doi.org/10.1109/TE.2020.3008878
  • Russell, S., Norvig, P. (2003). Artificial intelligence - a modern approach, 2nd Edition. Prentice Hall series in artificial intelligence.
  • Sharma, A., Jain, A., Gupta, P., Chowdary, V. (2021). Machine learning applications for precision agriculture: A comprehensive review. IEEE Access, 9, 4843-4873. https://doi.org/ 10.1109/ACCESS.2020.3048415
  • Tiwari, P., Agrawal, D. (2022). A study of linear programming technique. International Journal of Statistics and Applied Mathematics, 7(2), 54-56. https://doi.org/10.13140/RG.2.2.26343.52641

OPTİMİZASYON VE YAPAY ZEKÂ ALGORİTMALARI KULLANARAK MENÜ PLANLAMA YAZILIMI GELİŞTİRİLMESİ

Yıl 2024, Cilt: 49 Sayı: 5, 833 - 846, 10.10.2024
https://doi.org/10.15237/10.15237/gida.GD24063

Öz

Günümüzde sağlık ve beslenme bilinci giderek artmakta, bu da kişiye özel beslenme önerilerinin ve gıda israfının azaltılmasının önemini vurgulamaktadır. Bu çalışmada, kullanıcılardan demografik veriler ve gıda tercihleri toplanarak yapay zeka modelleri ile analiz edilmiştir. Random Forest algoritması kullanılarak geliştirilen bir model, kullanıcıların gelecekteki tercihlerini tahmin etmek ve menü planlamasına rehberlik etmek üzere eğitilmiştir. Yapılan testler, yapay zeka ve optimizasyon tekniklerinin birleştirilmesinin kullanıcı odaklı menüler oluşturduğunu, memnuniyeti artırdığını ve gıda israfını azalttığını göstermiştir. Çalışma ayrıca, veri setinin boyutuyla ilgili zorluklara dikkat çekerek, daha nitelikli verilere olan ihtiyacı ortaya koymuştur. Geliştirilen model, toplu yemek hizmeti sunan catering şirketleri ve diğer kurumlar için yenilikçi çözümler sunarak çalışan memnuniyetini artırırken israfı da minimize etmektedir. Gelecek araştırmalar, modelin daha geniş uygulamalar için geliştirilmesini hedeflemektedir.

Kaynakça

  • Ahmed, F., Kim, K.Y. (2017). Data-driven weld nugget width prediction with decision tree algorithm. Procedia Manufacturing,10, 1009–1019, https://doi.org/10.1016/j.promfg.2017.07.092
  • Balcan, M.-F., Prasad, S., Sandholm, T., Vitercik, E. (2022). Structural analysis of branch-and-cut and the learnability of gomory mixed integer cuts. Advances in Neural Information Processing Systems, 35, 33890-33903, https://doi.org/ 10.48550/arXiv.2204.07312
  • Basu, A., Conforti, M., Di Summa, M., Jiang, H. (2023). Complexity of branch-and-bound and cutting planes in mixed-integer optimization. Mathematical Programming, 198, 787–810, https://doi.org/10.1007/s10107-022-01789-5
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/ 10.1023/A:1010933404324 Chandru, V., Rao, M. (1998). Linear programming. IIM Bangalore Research Paper, (109), https://doi.org/10.2139/ssrn.2170298
  • Kulhari, A. (2023). Significance of Linear Programming for Optimization. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 3(15), 179-183. https://doi.org/10.48175/IJARSCT-10899
  • Dantzig, G. B. (1982). Reminiscences about the origins of linear programming. Operations Research Letters, 1(2), 43-48. https://doi.org/10.1016/ 0167-6377(82)90043-8
  • Gong, X. (2022). Optimization algorithm of logistics warehousing and distribution path based on artificial intelligence technology. In 2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) (pp. 371-375).. https://doi.org/10.1109/ISAIEE57420.2022.00083
  • Hebbar, N. (2020). Freshness of food detection using IoT and machine learning. In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) (pp. 1-3). https://doi.org/10.1109/ic-ETITE47903.2020.80
  • Hou, S., Zhu, D., Xu, J. (2022). Artificial intelligence, financial canteen and internal control: A case study of Chinese catering industry. In 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI) (pp. 310-313). https://doi.org/10.1109/ IWECAI55315.2022.00066
  • Liaw, A., Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3), 18-22.
  • Li, Y., Yan, H., Zhang, Y. (2019). A deep learning method for material performance recognition in laser additive manufacturing. In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) (pp. 1735-1740). https://doi.org/ 10.1109/INDIN41052.2019.8972334
  • Miltenberger, M. (2023). Linear Programming in MILP Solving a Computational Perspective. https://nbn-resolving.org/urn:nbn:de:0297-zib-91873
  • Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., Martynenko, A. (2022). IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet of Things Journal, 9(9), 6305-6324. https://doi.org/10.1109/ JIOT.2020.2998584
  • Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons.B, 4, 51-62. https://doi.org/10.20544/ HORIZONS.B.04.1.17.P05
  • Precup, R.-E., Hedrea, E.-L., Roman, R.-C., Petriu, E. M., Szedlak-Stinean, A.-I., Bojan-Dragos, C.-A. (2021). Experiment-based approach to teach optimization techniques. IEEE Transactions on Education, 64(2), 88-94. https://doi.org/10.1109/TE.2020.3008878
  • Russell, S., Norvig, P. (2003). Artificial intelligence - a modern approach, 2nd Edition. Prentice Hall series in artificial intelligence.
  • Sharma, A., Jain, A., Gupta, P., Chowdary, V. (2021). Machine learning applications for precision agriculture: A comprehensive review. IEEE Access, 9, 4843-4873. https://doi.org/ 10.1109/ACCESS.2020.3048415
  • Tiwari, P., Agrawal, D. (2022). A study of linear programming technique. International Journal of Statistics and Applied Mathematics, 7(2), 54-56. https://doi.org/10.13140/RG.2.2.26343.52641
Toplam 18 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Gıda ve Beslenme Dengesi
Bölüm Makaleler
Yazarlar

Fatih Tarlak 0000-0001-5351-1865

Yayımlanma Tarihi 10 Ekim 2024
Gönderilme Tarihi 12 Haziran 2024
Kabul Tarihi 17 Eylül 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 49 Sayı: 5

Kaynak Göster

APA Tarlak, F. (2024). DEVELOPING MENU PLANNING SOFTWARE USING OPTIMIZATION AND ARTIFICIAL INTELLIGENCE ALGORITHM. Gıda, 49(5), 833-846. https://doi.org/10.15237/10.15237/gida.GD24063
AMA Tarlak F. DEVELOPING MENU PLANNING SOFTWARE USING OPTIMIZATION AND ARTIFICIAL INTELLIGENCE ALGORITHM. GIDA. Ekim 2024;49(5):833-846. doi:10.15237/10.15237/gida.GD24063
Chicago Tarlak, Fatih. “DEVELOPING MENU PLANNING SOFTWARE USING OPTIMIZATION AND ARTIFICIAL INTELLIGENCE ALGORITHM”. Gıda 49, sy. 5 (Ekim 2024): 833-46. https://doi.org/10.15237/10.15237/gida.GD24063.
EndNote Tarlak F (01 Ekim 2024) DEVELOPING MENU PLANNING SOFTWARE USING OPTIMIZATION AND ARTIFICIAL INTELLIGENCE ALGORITHM. Gıda 49 5 833–846.
IEEE F. Tarlak, “DEVELOPING MENU PLANNING SOFTWARE USING OPTIMIZATION AND ARTIFICIAL INTELLIGENCE ALGORITHM”, GIDA, c. 49, sy. 5, ss. 833–846, 2024, doi: 10.15237/10.15237/gida.GD24063.
ISNAD Tarlak, Fatih. “DEVELOPING MENU PLANNING SOFTWARE USING OPTIMIZATION AND ARTIFICIAL INTELLIGENCE ALGORITHM”. Gıda 49/5 (Ekim 2024), 833-846. https://doi.org/10.15237/10.15237/gida.GD24063.
JAMA Tarlak F. DEVELOPING MENU PLANNING SOFTWARE USING OPTIMIZATION AND ARTIFICIAL INTELLIGENCE ALGORITHM. GIDA. 2024;49:833–846.
MLA Tarlak, Fatih. “DEVELOPING MENU PLANNING SOFTWARE USING OPTIMIZATION AND ARTIFICIAL INTELLIGENCE ALGORITHM”. Gıda, c. 49, sy. 5, 2024, ss. 833-46, doi:10.15237/10.15237/gida.GD24063.
Vancouver Tarlak F. DEVELOPING MENU PLANNING SOFTWARE USING OPTIMIZATION AND ARTIFICIAL INTELLIGENCE ALGORITHM. GIDA. 2024;49(5):833-46.

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