Allergy Bot the Intelligent Way to Predict Food Allergy
DOI:
https://doi.org/10.61841/k6qcsb73Keywords:
Food Allergy, Allergy Bot, Prediction, Intelligent, Artificial IntelligentAbstract
Allergy Bot is also known as Smart Allergy is a mobile application that provide smart-messaging application to let the user capture and collect crucial information about their allergies and health. By using artificial intelligent and highly developed algorithms, Allergy Bot detects which food is causing the reactions and allergic symptoms. In fact, user’s interaction with the application will help the application learn the user’s behaviours and become more personalized to the user as it also gradually improves the accuracy of the allergy prediction. As a result, Allergy Bot could help user allergies without compromising nutrition and their quality of life, thus contributing to improving user health. This paper is reviewing several existing system such as Alli App, Food Intolerance and Ada system to identify any lacking or improvement of the systems that needed to apply to the Allergy Bot framework. On top of that, users’ requirements are also gathered and analyzed to identify the features that need to be included to the framework. Allergy Bot framework is proposed based on the research on similar system and users’ requirements. Detail components of the framework are being explained.
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References
[1] Food Allergy Research and Education, 2010, “Life with Food Allergies,” Retrieved May 31, 2019 from
https://www.foodallergy.org/life-with-food-allergies
[2] Ben, M. ,2015.”Child Food Allergy Test Could Be Unreliable.” Retrieved May 31, 2019
fromhttp://www.scotsman.com/news/child-food-allergy-tests-could-be-unreliable-1-3830715
[3] Alliapp.com. 2018. Allergy App. Retrieved May 30, 2019 from http://alliapp.com
[4] Baliza GmbH, 2019. “Ideas Mobilized”. Retrieved 14 June 2019 from
http://www.baliza.de/en/apps/histamine.html
[5] Your Bibliography: Ada.com. 2018. Ada: Your Personal Health Guide. Retrieved May 30, 2019
fromhttps://ada.com
[6] Amazon Cognito. Retrieved May 30, 2019 from https://searchaws.techtarget.com/definition/Amazon-Cognito.
[7] Amazon Lex. Retrieved May 30 2019 from https://aws.amazon.com/lex/
[8] Amazon Lambda. Amazon Machine Learning.
[9] Amazon DynamoDB Available. Retrieved May 30, 2019 from
https://www.tutorialspoint.com/amazon_web_services/amazon_web_services_dynamodb.htm
[10] Amazon Data Pipeline. Retrieved May 30, 2019 fromhttps://medium.com/@frantzdyromain/how-to-set-upaws-data-pipeline-for-the-first-time-a-visual-guide-4b3d16310b90
[11] Susan Waserman, Philippe Begin and Wade Watson, (2018). IgE-mediated food allergy. Allergy, Asthma &
Clinical Immunology, 2018, Vol 14 (Suppl 2):55
[12] Irena Manea, Elene Ailenei and Diana Deleanu, (2016). Overview of Food Allergy. Clujul Medical, 2016;
89(1): 5-10
[13] Hugh A.Sampson, 1999. Food Allergy. Part 2:Diagnosis and Management. The Journal of Allergy and Clinical
Immunology, volume 103, Issue 6, pp 981-989
[14] P. Mary Jeyanthi, Santosh Shrivastava Kumar “The Determinant Parameters of Knowledge Transfer among
Academicians in Colleges of Chennai Region”, Theoretical Economics Letters, 2019, 9, 752-760.
[15] P. Mary Jeyanthi, “An Empirical Study of Fraudulent and Bankruptcy in Indian Banking Sectors”, The
Empirical Economics Letters, Vol.18; No. 3, March 2019.
[16] Mary Jeyanthi, S and Karnan, M.: “Business Intelligence: Hybrid Metaheuristic techniques”, International
Journal of Business Intelligence Research, - Volume 5, Issue 1, April-2014.
[17] P. Mary Jeyanthi, “INDUSTRY 4.O: The combination of the Internet of Things (IoT)and the Internet of People
(IoP)”, Journal of Contemporary Research in Management, Vol.13; No. 4 Oct-Dec, 2018, ISSN: 0973-9785.
[18] P. Mary Jeyanthi, "The transformation of Social media information systems leads to Global business: An
Empirical Survey", International Journal of Technology and Science (IJTS), issue 3, volume 5.
[19] P. Mary Jeyanthi,” An Empirical Study of Fraud Control Techniques using Business Intelligence in Financial
Institutions”, Vivekananda Journal of Research Vol. 7, Special Issue 1, May 2018, ISSN 2319-8702(Print),
ISSN 2456-7574(Online). URL: http://vips.edu/wp-content/uploads/2016/09/Special-Issue-VJR-conference-
2018.pdf Page no: 159-164.
[20] Mary Jeyanthi, S and Karnan, M.: “Business Intelligence: Artificial bear Optimization Approach”,
International Journal of Scientific & Engineering Research, Volume 4, Issue 8, August-2013.
[21] Mary Jeyanthi, S and Karnan, M.: “Business Intelligence: Optimization techniques for Decision Making”,
International Journal of Engineering Research and Technology, Volume 2, Issue 8, August-2013.
[22] Mary Jeyanthi, S and Karnan, M.: “A New Implementation of Mathematical Models with metaheuristic
Algorithms for Business Intelligence”, International Journal of Advanced Research in Computer and
Communication Engineering, Volume 3, Issue 3, March-2014.
[23] Dr. Mary Jeyanthi: “Partial Image Retrieval Systems in Luminance and Color Invariants: An Empirical Study”,
International Journal of Web Technology (ISSN: 2278-2389) – Volume-4, Issue-2.
[24] Dr. Mary Jeyanthi: “CipherText Policy attribute-based Encryption for Patients Health Information in Cloud
Platform”, Journal of Information Science and Engineering (ISSN: 1016-2364)
[25] Mary Jeyanthi, P, Adarsh Sharma, Purva Verma: “Sustainability of the business and employment generation in
the field of UPVC widows” (ICSMS2019).
[26] Mary Jeyanthi, P: “An Empirical Survey of Sustainability in Social Media and Information Systems across
emerging countries”, International Conference on Sustainability Management and Strategy” (ICSMS2018).
[27] Mary Jeyanthi, P: “Agile Analytics in Business Decision Making: An Empirical Study”, International
Conference on Business Management and Information Systems” (ICBMIS2015).
[28] Mary Jeyanthi, S and Karnan, M.: “Business Intelligence – soft computing Techniques”, International
Conference on Mathematics in Engineering & Business Management (ICMEB 2012).
[29] Mary Jeyanthi, S and Karnan, M.: “A Comparative Study of Genetic algorithm and Artificial Bear
Optimization algorithm in Business Intelligence”, International Conference on Mathematics in Engineering &
Business Management (ICMEB 2012).
[30] Mary Jeyanthi, S and Karnan, M.: “Business Intelligence: Data Mining and Optimization for Decision Making
”, 2011 IEEE International Conference on Computational Intelligence and Computing Research (2011 IEEE
ICCIC).
[31] Mary Jeyanthi, S and Karnan, M.: “Business Intelligence: Data Mining and Decision making to overcome the
Financial Risk”, 2011 IEEE International Conference on Computational Intelligence and Computing Research
(2011 IEEE ICCIC).
[32] Dr. Mary Jeyanthi, S: “Pervasive Computing in Business Intelligence”, State level seminar on Computing and
Communication Technologies. (SCCT-2015)
[33] Dr.P.Mary Jeyanthi, “Artificial Bear Optimization (ABO) – A new approach of Metaheuristic algorithm for
Business Intelligence”, ISBN no: 978-93-87862-65-4, Bonfring Publication. Issue Date: 01-Apr-2019
[34] Dr.P.Mary Jeyanthi , “Customer Value Management (CVM) – Thinking Inside the box” – ISBN : 978-93-
87862-94-4, Bonfring Publication, Issue Date: 16-Oct-2019.
[35] Jeyanthi, P. M., & Shrivastava, S. K. (2019). The Determinant Parameters of Knowledge Transfer among
Academicians in Colleges of Chennai Region. Theoretical Economics Letters, 9(4), 752-760.
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