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Journal of Artificial Intelligence Research & Advances

Solitary Identification and Analysis of Considerate Nutritional Consumption and Identifying Malnutrition

  1. Aryan Siddhabathula
  2. V. Satyanarayana
  3. M. Shiva Ganesh
  4. Narala Tejaswe
  5. Uma Sankararao Varri

Abstract

The food supply chain is in danger from various factors, including climate threats, such as heat waves, floods, droughts, and other extreme occurrences such as intensive livestock farming, demand for animal protein, practices for antimicrobial resistance and various other factors leading to serious food shortages and an increase in food prices. Malnutrition is a major issue, with an estimated 663 million individuals affected. Poor nutrition is a serious problem that affects a vast number of people globally. Recurring infections, which make the issue worse, add to this dilemma. The fact that 22% of children under the age of five are considered “stunted,” meaning they are much shorter than the usual child of their age, is one of the most alarming effects of this problem. Additionally, a sizeable portion of people—over 697 million—who account for 9% of the global population—experience extreme food insecurity. The majority of fatalities occur in mostly high and medium poverty nations. One of major factors that helps us identify the quality of nutritional consumption is the body's main defense against external aggressions is its skin, which is affected by chronological ageing and photo-ageing. Poor eating habits and nutritional imbalances are major contributors to undernutrition and overnutrition among individuals, and also obesity, so it is important to build a machine learning model to regulate the flow of nutritional consumption and depict malnutrition among individuals. This model can be used to anticipate malnutrition on an individual basis, making it a more precise and effective method of recognizing the need to avoid incorrect food intake.

Keywords

References (16)

  1. World Health Organization. Nutrition. [Online]. WHO. 2019. Available at https://www.who.int/ india/health-topics/nutrition Journal of Artificial Intelligence Research & Advances Volume 10, Issue 2 ISSN: 2395-6720 © STM Journals 2023. All Rights Reserved 62
  2. Food and Nutritional Security in India: Charting the Way to a Robust Agri-Food System. [Online]. NABARD. November 2022. Available at https://www.nabard.org/auth/writereaddata/tender/ 2501230131nrs-35-food-and-nutritional-security-in-india.pdf
  3. Food Safety and Standards Authority of India (FSSAI) National Food Survey 2022. [Online]. 2022. Available at https://www.fssai.gov.in/cms/national-surveys.php
  4. Mohanty A. Impacts of climate change on human health and agriculture in recent years. In2021 IEEE Region 10 Symposium (TENSYMP), Jeju, Republic of Korea, August 23–25, 2021. pp. 1–4.
  5. Haritosh A, Gupta A, Chahal ES, Misra A, Chandra S. A novel method to estimate height, weight and body mass index from face image s. In : 2019 Twelfth International Conference on Contemporary Computing (IC3), Noida, India, August 8–10, 2019. pp. 1–6.
  6. Kanimozhi E, Akila D. Analysing the impact of climate change on the crop yields of irrigated crops and their water requirements in India using neuro evolutionary algorithm. In: 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, September 3–4, 2021. pp. 1–4.
  7. Aasim O. Machine learning project 9 — Predict weight based on height and gender. Medium. [Online]. 2019. Towards Data Science . Available at https://towardsdatascience.com/machine- learning-project-9-predict-weight-based-on-height-and-gender-9ed47285bcbb
  8. Jägermeyr J, Müller C, Ruane AC, Elliott J, Balkovic J, Castillo O, Faye B, Foster I, Folberth C, Franke JA, Fuchs K. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat Food. 2021; 2 (11): 873–885.
  9. Hasegawa T, Wakatsuki H, Ju H, Vyas S, Nelson GC, Farrell A, Deryng D, Meza F, Makowski D. A global dataset for the projected impacts of climate change on four major crops. Sci Data. 2022; 9 (1): 58.
  10. Kar S, Pratihar S, Nayak S, Bal S, Gururai HL, Ravikumar V. Prediction of child malnutrition using machine learning. In: 2021 10th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON) , Jaipur, India, December 1 –2, 2021. pp. 1–4.
  11. Momand Z, Mongkolnam P, Kositpanthavong P, Chan JH. Data mining based prediction of malnutrition in Afghan children. In: 2020 12th International Conference on Knowledge and Smart Technology (KST), Pattaya, Thailand, January 29–February 1, 2020. pp. 12–17.
  12. Jain S, Khanam T, Abedi AJ, Khan AA. Efficient machine learning for malnutrition prediction among under-five children in India. In : 2022 IEEE Delhi Section Conference (DELCON) , New Delhi, India, February 11–13, 2022. pp. 1–10.
  13. Di Martino F, Delmastro F, Dolciotti C. Malnutrition risk assessment in frail older adults using m- health and machine learning. In: ICC 2021 – IEEE International Conference on Communications, Montreal, Quebec, Canada, June 14–23, 2021. pp. 1–6.
  14. Cao C, Xiao Z, Wu Y, Ge C. Diet and skin aging—from the perspective of food nutrition. Nutrients. 2020; 12 (3): 870.
  15. Wikipedia. Body mass index. [Online]. 2023. Available at https://en.wikipedia.org/wiki/Body_mass_index
  16. Eidul TS. Height and Weight Prediction by Regression Analysis . Project Submission CSE 225. Dhaka, Bangladesh: East West University; 2016.

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