IJLTEMASInternational Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS)IJLTEMAS2278-2540IJLTEMAS53010.51583/IJLTEMAS.2026.151000011Cloud ComputingDesign, Mathematical Basis and Evaluation of a Cloud-Hosted Calorie Tracking and Nutrition Management System on AWS Elastic BeanstalkKumar RDeepak
PG Student, Department of Computer Applications (PG), Department of MCA, PSG College of Arts & Science, Coimbatore, Tamil Nadu, India IndiaKumuthaveni RDr
Associate Professor, Department of Computer Applications (PG), Department of MCA, PSG College of Arts & Science, Coimbatore, Tamil Nadu, India IndiaThara LDr.
Associate Professor and Head of Department of Computer Applications (PG), Department of MCA, PSG College of Arts & Science, Coimbatore, Tamil Nadu, India India1510122130081020261310202629102026Nutrition InformaticsCalorie TrackingTotal Daily Energy Expenditure (TDEE)Mifflin–St Jeor EquationDjangoAWS Elastic BeanstalkNutrient AnalysisHealth InformaticsAbstract
Self monitoring of eating habits is commonly done by people to track their diet. Weight management and health care but paper diary spreads. The sheets are responsible for performing lookups and calculations on their own. This paper This is an online application for tracking meals on a server. Automated Nutrient Extraction and TDEE (Total Daily Energy Expenditure). TDEE calculation and aggregate reports. The system is Django-based application built on SQLite for development purposes and PostgreSQL database management system. In production, it runs on AWS Elastic Beanstalk. The quantitative component is defined by me as "Mifflin-St Jeor". Resting energy equation, Activity Factor Model of Total Daily Energy Expenditure. Nutrient per 100g scale and daily/weekly aggregate. The sensitivity of TDEE for each variable is calculated here. Error due to treating milliliters as grams. Using the Records are kept by us at work; therefore, We have an Atwater factor. Nutrient consistency check for retrieved data Report Manual. Functional tests for 14 items out of which 12 are exercised and 2. were not. There are no data on loads and usability of this product. because none were taken. Terms calorie tracker ndex Calories per day (TDEE) Mifflin St Jeor Django. Elastic Beanstalk AWS Nutrition Informatics
References“A systematic review of the use of dietary self-monitoring in behavioural weight loss interventions: delivery, intensity and effectiveness,” Public Health Nutrition. [authors, year, volume, pages: complete from the journal page]“Patterns and predictors of engagement with digital self-monitoring during the maintenance phase of a behavioral weight loss program: quantitative study,” JMIR mHealth and uHealth, vol. 11, p. e45057, 2023. [authors: complete from the journal page]“Psychosocial determinants of mHealth application adherence for healthy eating: a systematic literature review,” F1000Research, doi: 10.12688/f1000research.184384.1. [authors, year, volume: complete from the DOI page]D. K. N. Ho, W.-C. Chiu, J.-W. Kao, H.-T. Tseng, C.-Y. Lin, P.-H.Huang, Y.-R. Fang, K.-H. Chen, T.-Y. Su, C.-H. Yang, C.-Y. Yao, H.-Y. Su, P.-H. Wei and J.-S.Chang, “Reliability issues of mobile nutrition apps for cardiovascular disease prevention: comparative study,” JMIR mHealth and uHealth, vol. 12, p. e54509, 2024.E. Pavlidou, S. K. Papadopoulou, K.Seroglou and C. Giaginis, “RevisedHarris-Benedict equation: new human resting metabolic rate equation,” Metabolites, vol. 13, no. 2,p. 189, 2023, doi:10.3390/metabo13020189.Y. Jalilpiran, N. Azizi, K. Alipoor, S. Mehranfar, M. Ebaditabar, S.Shab-Bidar, A. Ostadrahimi and K. Djafarian, “Development of a new equation and validation of earlier resting energy expenditure predicting equations in adults living in Tehran,” Health Promotion Perspectives, vol. 14, no. 2, pp. 185–192, 2024, doi: 10.34172/hpp.42570.“Validation of predictive equations for resting energy expenditure inchildren and adolescents with different body mass indexes,” Nutrition Journal, vol. 22, no. 1, art.39, 2023, doi: 10.1186/s12937-023-00868-3. [authors: complete from PubMed]J. Sultana, B. M. Ahmed and M. M. Masud, “A study on food value estimation from images: taxonomies, datasets, and techniques,” published 8 May 2023. [journal, volume, pages: complete from the publication record]D. Liu, E. Zuo, D. Wang, L. He, L. Dong and X. Lu, “Deep learning in food image recognition: a comprehensive review,” Applied Sciences, vol. 15, no. 14, p. 7626, 2025, doi: 10.3390/app15147626.G. Parasa, G. S. Chauhan, M. R. Krishna, J. Lande, M. Nisha and V. Nair, “A comprehensive survey on food image recognition and calorie estimation systems: techniques, datasets, challenges, and future direc- tions,” in Proc. 2026 1st Int. Conf. AI, Data Science, Cyber Security and Smart Manufacturing for Sustainable Development (ICADCS), IEEE, 2026, doi: 10.1109/icadcs70036.2026.11583455.“Deep learning food detection and calories counter,” in Proc. 2023 IEEE Int. Conf. Computing (ICOCO), 2023, doi: 10.1109/IC- OCO59262.2023.10397620. [authors: complete from IEEE Xplore]M. D. Mifflin, S. T. St Jeor, L. A. Hill, B. J. Scott, S. A. Daugherty and Y. O. Koh, “A new predictive equation for resting energy expenditure in healthy individuals,” Am. J. Clin. Nutr., vol. 51, no. 2, pp. 241–247, 1990.