ALMA: Machine learning breastfeeding chatbot

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dc.citation.epage497
dc.citation.issue2
dc.citation.journalTitleМатематичне моделювання та комп'ютинг
dc.citation.spage487
dc.contributor.affiliationУніверситет Хасана ІІ Касабланки
dc.contributor.affiliationУніверситет Сіді Беннур Чуайб Дуккалі Ель-Джадіда
dc.contributor.affiliationHassan II of Casablanca University
dc.contributor.affiliationSidi Bennour Chouaib Doukkali University El Jadida
dc.contributor.authorАхтаїх, К.
dc.contributor.authorАхтаїх, Н.
dc.contributor.authorФагруд, Ф. З.
dc.contributor.authorТоумі, Х.
dc.contributor.authorAchtaich, K.
dc.contributor.authorAchtaich, N.
dc.contributor.authorFagroud, F. Z.
dc.contributor.authorToumi, H.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-03-04T10:28:10Z
dc.date.created2023-02-28
dc.date.issued2023-02-28
dc.description.abstractЗ часу появи першого комп’ютера дослідники завжди намагаються імітувати поведінку людини. Для чат-ботів однією з першочергових цілей є взаємодія з користувачем як з людиною за допомогою природної мови. Для чат-ботів здоров’я інша мета є не менш важливою: вміти надати правильну відповідь на запит користувача. Протягом багатьох років було розроблено багато чат-ботів для охорони здоров’я для багатьох сфер, таких як рак, орієнтація на діагностику, психіатрія тощо. Однак чат-боти для грудного вигодовування зустрічаються рідко (лише два чат-боти для грудного вигодовування). У цій статті розроблено ALMA, чат-бот для грудного вигодовування (BC), який може спілкуватися з мамою, яка годує грудьми, про розуміння природної мови (NLU) і створення природної мови (NLG), і надавати їй – мамі, яка годує грудьми, – відповідну інформацію, використовуючи базу знань AIML і попередньо навчену модель CNN. Зроблено ALMA доступною для звичайної розмови WhatsApp через Twilio API. ALMA було протестовано матерями–добровольцями, які годують грудьми, і результати підтверджено консультацією з грудного вигодовування.
dc.description.abstractSince the first computer, researchers always try to simulate human behave. For Chatbots, one of the first goals is to interact with the user like a human using Natural Language. For Health chatbots, another goal is as much important: be able to provide the correct answer to the user request. Over Years, many health chatbots have been developed for many fields such as cancer, diagnosis orientation, psychiatrics, etc. breastfeeding companion are, however, rare (only two breastfeeding chatbots). In this paper, we have developed ALMA, a Breastfeeding Chatbot (BC) that can converse with a breastfeeding mom throw natural language understanding (NLU) and natural language generation (NLG), and provide her – breastfeeding mom – with the relevant information using AIML knowledge base and CNN pre-trained model. We made ALMA available for a normal WhatsApp conversation throw Twilio API. ALMA was tested by volunteering breastfeeding moms and the results validated by breastfeeding consult.
dc.format.extent487-497
dc.format.pages11
dc.identifier.citationALMA: Machine learning breastfeeding chatbot / K. Achtaich, N. Achtaich, F. Z. Fagroud, H. Toumi // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 10. — No 2. — P. 487–497.
dc.identifier.citationenALMA: Machine learning breastfeeding chatbot / K. Achtaich, N. Achtaich, F. Z. Fagroud, H. Toumi // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 10. — No 2. — P. 487–497.
dc.identifier.doidoi.org/10.23939/mmc2023.02.487
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/63409
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofМатематичне моделювання та комп'ютинг, 2 (10), 2023
dc.relation.ispartofMathematical Modeling and Computing, 2 (10), 2023
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dc.relation.referencesen[1] Turing A. M. I, Computing machinery and intelligence. Mind. LIX (236), 433–460 (1950).
dc.relation.referencesen[2] Weizenbaum J. ELIZA – a computer program for the study of natural language communication between man and machine. Communications of the ACM. 9 (1), 36–45 (1966).
dc.relation.referencesen[3] Carpenter R. Jabberwacky (2007).
dc.relation.referencesen[4] Ask J. A., Facemire M., Hogan A. The state of chatbots. Forrester.com Report. 20 (2016).
dc.relation.referencesen[5] Nimavat K., Champaneria T. Chatbots: an overview types, architecture, tools and future possibilities. International Journal for Scientific Research and Development. 5 (7), 1019–1024 (2017).
dc.relation.referencesen[6] Hien H. T., Cuong P.-N., Nam L. N. H., Nhung H. L. T. K., Thang L. D. Intelligent assistants in highereducation environments: the FIT-EBot, a chatbot for administrative and learning support. SoICT ’18: Proceedings of the 9th International Symposium on Information and Communication Technology. 69–76 (2018).
dc.relation.referencesen[7] Shum H.-y., He X.-d., Li D. From Eliza to XiaoIce: challenges and opportunities with social chatbots. Frontiers of Information Technology & Electronic Engineering. 19 (1), 10–26 (2018).
dc.relation.referencesen[8] Tran D. C., Nguyen D. L., Hassan M. F. Development and testing of an FPT.AI-based voicebot. Bulletin of Electrical Engineering and Informatics. 9 (6), 2388–2395 (2020).
dc.relation.referencesen[9] Kucherbaev P., Bozzon A., Houben G.-J. Human-aided bots. IEEE Internet Computing. 22 (6), 36–43 (2018).
dc.relation.referencesen[10] Liu B., Mei C., Hassan M. F. Lifelong knowledge learning in rule-based dialogue systems. arXiv preprint arXiv:2011.09811 (2020).
dc.relation.referencesen[11] Wallace R. The elements of AIML style. ALICE A. I. Foundation, Inc. (2003).
dc.relation.referencesen[12] Shakhovska N., Basystiuk O., Shakhovska K. Development of the Speech-to-Text Chatbot Interface Based on Google API. Modern Machine Learning Technologies. 212–221 (2019).
dc.relation.referencesen[13] Mi F., Huang M., Zhang J., Faltings B. Meta-learning for low-resource natural language generation in taskoriented dialogue systems. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence IJCAI–19. 3151–3157 (2019).
dc.relation.referencesen[14] Khadija A., Zahra F. F., Naceur A. AI-Powered Health Chatbots: Toward a general architecture. Procedia Computer Science. 191, 355–360 (2021).
dc.relation.referencesen[15] Babylone Health, The application. https://www.babylonhealth.com/en-gb/download-app.
dc.relation.referencesen[16] Sensely Molly, The application. https://sensely.com/.
dc.relation.referencesen[17] Gupta J., Singh V., Kumar I. Florence-a health care chatbot. 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). 1, 504–508 (2021).
dc.relation.referencesen[18] Wang W., Siau K. Trust in health chatbots. Thirty ninth International Conference on Information Systems. San Francisco (2018).
dc.relation.referencesen[19] Divyarani D. C., Goudappa R. P. Knowledge, attitude and practices of breast feeding among post natal mothers. International Journal of Contemporary Pediatrics. 2 (4), 445–449 (2015).
dc.relation.referencesen[20] Patel A., Kuhite P., Puranik A., Khan S. S., Borkar J., Dhande L. Effectiveness of weekly cell phone counselling calls and daily text messages to improve breastfeeding indicators. BMC Pediatrics. 18, 337 (2018).
dc.relation.referencesen[21] Gupta V., Arora N., Jain Y., Mokashi S., Panda C. Assessment on adoption behavior of firsttime mothers on the usage of chatbots for breastfeeding consultation. Journal of Mahatma Gandhi University of Medical Sciences & Technology. 6 (2), 65–68 (2021).
dc.relation.referencesen[22] La Leche League France. https://www.lllfrance.org/.
dc.relation.referencesen[23] Social Media: 84% of Moroccans Use WhatsApp in 2021 (Survey). Accessed: Nov. 29, 2022. https://www.mapnews.ma/en/actualites/social/social-media-84-moroccans-usewhatsapp-2021-survey.
dc.relation.referencesen[24] Simonyan K., Zisserman A. Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations (ICLR 2015). 1–14 (2015).
dc.relation.referencesen[25] Al-Ghadhban D., Al-Twairesh N. Nabiha: an Arabic dialect chatbot. International Journal of Advanced Computer Science and Applications(IJACSA). 11 (3), 452–459 (2020).
dc.relation.referencesen[26] Manik L. Out-of-scope intent detection on a knowledge-based chatbot. International Journal of Intelligent Engineering and Systems. 14 (5), 446–457 (2021).
dc.relation.referencesen[27] Boser B. E., Guyon I. M., Vapnik V. N. A training algorithm for optimal margin classifiers. COLT ’92: Proceedings of the fifth annual workshop on Computational learning theory. 144–152 (1992).
dc.relation.referencesen[28] Maroengsit W., Piyakulpinyo T., Phonyiam K., Pongnumkul S., Chaovalit P., Theeramunkong T. A survey on evaluation methods for chatbots. ICIET 2019: Proceedings of the 2019 7th International Conference on Information and Education Technology. 111–119 (2019).
dc.relation.referencesen[29] Abran A., Khelifi A., Suryn W., Seffah A. Usability meanings and interpretations in ISO standards. Software Quality Journal. 11 (4), 325–338 (2003).
dc.relation.urihttps://www.babylonhealth.com/en-gb/download-app
dc.relation.urihttps://sensely.com/
dc.relation.urihttps://www.lllfrance.org/
dc.relation.urihttps://www.mapnews.ma/en/actualites/social/social-media-84-moroccans-usewhatsapp-2021-survey
dc.rights.holder© Національний університет “Львівська політехніка”, 2023
dc.subjectчат-бот грудного вигодовування
dc.subjectмашинне навчання
dc.subjectобробка природної мови
dc.subjectштучний інтелект
dc.subjectмова розмітки штучного інтелекту
dc.subjectbreastfeeding chatbot
dc.subjectmachine learning
dc.subjectnatural language processing
dc.subjectartificial intelligence
dc.subjectartificial intelligence markup language
dc.titleALMA: Machine learning breastfeeding chatbot
dc.title.alternativeALMA: чат-бот для машинного навчання грудного вигодовування
dc.typeArticle

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Mathematical Modeling And Computing. – 2023. – Vol. 10, No. 2