Relevant bibliographies by topics / Diet Recommendation System

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Academic literature on the topic 'Diet Recommendation System'

Author: Grafiati
Published: 7 June 2025
Last updated: 26 June 2025

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Journal articles on the topic "Diet Recommendation System"

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Shete, Dipanshu. "Diet Recommendation System Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41831.

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In today’s modern world people all around the globe are becoming more interested in their health and lifestyle. But just avoiding junk food and doing an exercise is not enough, we require a balanced diet. A balanced diet based on our height, weight and age can lead a healthy life. Combined with physical activity, your diet can help you to reach and maintain a healthy weight, reduce your risk of chronic diseases (like heart disease and cancer), and promote your overall health. A balanced diet is one that gives your body the nutrients it needs to function correctly. Calories in the food is the measure of amount of energy store in that food. Our body use calories for basically everything like breathing, walking, running etc. On average a person needs 2000 calories per day but specifically intake of calories depends upon persons physical aspects like weight, height, age and gender. The fast-food consumption rate is alarmingly high and this consequently has led to the intake of unhealthy food. This leads to various health issues such as obesity, diabetes, an increase in blood pressure etc. Hence it has become very essential for people to have a good balanced nutritional healthy diet. Keywords — Machine Learning, Diet Recommendation, Personalized Nutrition, Health, BMI Calculation, Calorie Calculation, Nutritional Content Analysis.
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Kansagara, Harsh. "Diet and Workout Recommendation System Using KNN." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45702.

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Abstract— The “Diet and Workout Recommendation System” is a personalized platform that provides real-time dietary and fitness guidance based on individual user profiles. Through an intuitive web interface, the system collects data, including age, weight, height, fitness goals, and dietary preferences. The backend processes this data with API integrations and machine learning techniques, including a K-Nearest Neighbors (KNN) model, to generate dynamic meal and workout plans that adapt to user feedback and progress. Using OpenCV for exercise tracking and Flask for efficient data handling, the system delivers instant workout feedback and personalized dietary suggestions. Additionally, location-based APIs recommend local dining options, connecting digital health guidance with real-world choices. Unlike one-size-fits-all platforms, this system emphasizes personalization and adaptability to address the limitations of traditional health tools, offering scalable, user-centered recommendations. The architecture supports future integrations with wearable devices and social features, promoting sustainable lifestyle changes and long-term engagement. This project demonstrates a comprehensive approach to personalized health management, focusing on usability, real-world application, and an evolving user experience. Keywords— Health Recommendation System, Diet Planning, Workout Recommendation, Personalization, Nutritional Analysis, Strength Assessment, User Profiling, Wellness and Fitness.
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Chen, Rung-Ching, Chung-Yi Huang, and Yu-Hsien Ting. "A Chronic Disease Diet Recommendation System Based on Domain Ontology and Decision Tree." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 3 (2017): 474–82. http://dx.doi.org/10.20965/jaciii.2017.p0474.

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As society develops and science and technology improve, people have come to care more about a healthy diet. Diet types have gradually changed and focused more on health management. Taiwan is becoming an aging society in which individuals have irregular lifestyles, long-term unhealthy diets, stressful work, and chronic diseases such as diabetes, hypertension, and high cholesterol. However, most dietary recommendation systems cannot give dietary recommendations for patients with chronic diseases. Though healthy foods are recommended, the systems contain little information on whether nutrients are in balance. Therefore, this study constructed a diet recommendation system for chronic diseases using expert knowledge, which enables more convenient and precise dietary recommendations for chronic diseases. In this study, we use an ontology, decision trees, and Jena to construct the recommendation system. The dietary recommendations results are evaluated by dietitians, and the verification accuracy is 100%. Therefore, this system of dietary recommendations can provide convenient, healthy, dietary recommendations for nutrients for patients with chronic diseases.
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Rakhman, Raihan Romzi, and Dana Sulistyo Kusumo. "User-Centric Diet Recommender Systems with Human-Recommender System Interaction (HRI) based Serendipity Aspect." Building of Informatics, Technology and Science (BITS) 6, no. 2 (2024): 1020–33. https://doi.org/10.47065/bits.v6i2.5754.

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Currently, obesity is on the rise globally with predictions to continue rising until 2030. Adopting a healthy diet and increasing physical activity are key strategies to reduce the risk of obesity. However, there are significant challenges in adhering to a diet, including the monotony of food choices and difficulty in maintaining motivation. This research aims to develop a user-centered dietary recommendation system that addresses these challenges by introducing serendipity into the diet planning process. Serendipity in this context refers to generating unexpected yet relevant food recommendations, thereby enhancing user engagement and satisfaction. The system uses content-based recommendation techniques, including TF-IDF, Cosine Similarity, and K-Means clustering, to provide personalized dietary suggestions based on individual health profiles, calorie needs, and food preferences. The evaluation of the system demonstrated that incorporating serendipity into recommendations significantly improves user experience and adherence to dietary plans. The findings highlight the potential of serendipity to transform dietary adherence, making the dieting process more enjoyable and sustainable.
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Reddy, K. Sneha. "Diet Recommendation System Based On Vitamin Intake." Advances in Computational Sciences and Technology 16, no. 1 (2023): 35–43. http://dx.doi.org/10.37622/acst/16.1.2023.35-43.

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Uma, Pyla. "DIET PLANNING AND RECOMMENDATION SYSTEM USING ML AND MERN STACK." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32364.

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The "Diet Planning and Recommendation System Using ML and MERN Stack" project aims to develop an innovative solution to address the challenge of personalized diet planning and recommendation. With the rising awareness of the importance of nutrition in maintaining overall health and wellness, there is a growing demand for tools that can offer tailored dietary guidance to individuals based on their unique needs and preferences. This project leverages the power of Machine Learning (ML) algorithms and the MERN (MongoDB, Express.js, React.js, Node.js) stack to create a comprehensive and user-friendly platform. The system collects user data encompassing demographic information, health metrics, dietary habits, and goals. Using ML techniques such as regression, classification, and clustering, the system analyzes this data to generate personalized diet plans and recommendations. The backend of the system, built on Node.js and Express.js, manages data storage and processing, while the frontend, developed with React.js, provides an intuitive interface for users to interact with the system. MongoDB serves as the database, ensuring scalability and flexibility in data management. The ML models continuously learn and adapt based on user feedback and outcomes, enhancing the accuracy and effectiveness of the recommendations over time. Reinforcement learning techniques are employed to optimize diet plans based on real-world outcomes and user satisfaction. By integrating ML with the MERN stack, this project offers a novel approach to diet planning and recommendation, empowering individuals to make informed dietary choices and improve their overall health and well-being. KeyWords:Diet Planning ,Recommendation System,Machine Learning,(ML),MERNStack,Personalized,Nutrition,Health,,,Metrics,Dietary,Habits,Regression,Classification,Node.js, ,React.js,,MongoDB,User,Feedback,ReinforcementLearni,Real-world Outcomes,User Satisfaction,Wellness,Informed Dietary Choices
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Khopkar, Prof P. V. "Fit Fusion : Diet & Workout Recommendation." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47062.

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Abstract: In today’s fast-paced world, maintaining a healthy lifestyle through balanced diet and regular exercise is challenging for many individuals. Generic fitness plans often lack the personalization needed to support diverse body types and lifestyles. This paper presents FitFusion, a web-based system designed to offer personalized diet and workout recommendations based on individual characteristics such as age, weight, height, and dietary preferences. The system integrates machine learning, specifically the Random Forest algorithm, to predict and recommend diet plans based on user data and nutritional requirements. Developed using ReactJS, Express.js, and MongoDB, the platform includes a BMI calculator, intelligent recommendation engines, and a progress tracker. FitFusion aims to enhance user health outcomes and long-term adherence to healthy habits through real-time, personalized guidance.
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Archana, S., Kumar N. Harish, M. K. KavinNandha, K. KeerthiRaghavan, R. Keren, and Rishawanth L. Liyander. "Smart Health Monitoring and Recommendation System." Recent Trends in Androids and IOS Applications 7 (May 30, 2025): 38–51. https://doi.org/10.5281/zenodo.15550182.

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<em>The Smart Health Monitoring and Recommendation System is an intuitive and comprehensive mobile application designed to enhance healthcare through continuous monitoring, personalized recommendations, and doctor-patient interaction. Tailored for both patients and medical professionals, the app leverages user input and sensor data to monitor health metrics like glucose levels, medication intake, physical activity, diet, and more. With specialized dashboards for patients and doctors, it fosters timely communication, improved decision-making, and better overall health outcomes..</em>
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Lakshmi, N. Naga, M. Jagadeesh Reddy, K. Hari Krishna, and S. Sindhuja Reddy. "Vitamin Deficiency and Food Recommendation System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3823–30. http://dx.doi.org/10.22214/ijraset.2022.43236.

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Abstract: People are not paying attention to the quality of food they eat in our fast-paced and hectic world. They frequently ignore their eating routines and behaviours. Fast-food consumption is frighteningly increasing, which has resulted in the consumption of harmful foods. This causes a variety of health problems, including obesity, diabetes, and an increase in blood pressure, and so forth. As a result, it has become critical for people to have a well-balanced nutritionally sound diet. There are several applications that are thriving to assist folks in gaining control of their food and therefore can help individuals lose weight or maintain their fitness and health. The study article proposes healthy eating habits and patterns so that anybody may know the number of calories expended, macronutrient intake, and so on using data mining technologies. This technology is designed to uncover hidden patterns and client eating habits from various data sources. This approach will aid in tracking and improving an individual's health as well as the types of food that they should avoid in order to reduce their chance of disease. A balanced diet is one in which the intake of each basic nutrient meets its sufficient demand and real caloric intake equals calories burnt. Additionally, making a variety of dietary choices is vital for lowering the chance of acquiring chronic illnesses. This diet recommendation system tailors its recommendations to each individual depending on their eating patterns and body data. This study aids in the prediction of a healthy diet for any individual, as well as the construction of a diet plan based on the needs of the patient. Keywords : BMR, Healthy Diet, Recommender System, Harris Benedict equation, Nutrition, Calories, Data mining
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Thakar, Ajay. "Virtual Dietician for Diet Plan Recommendation." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 111–13. http://dx.doi.org/10.22214/ijraset.2021.34864.

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In this fast and busy schedule life, people are not giving importance to the quality of food they are eating. They tend to neglect their eating patterns and habits. The fast-food consumption rate is alarmingly high and this consequently has led to the intake of unhealthy food. This leads to various health issues such as obesity, diabetes, an increase in blood pressure etc. Hence it has become very essential for people to have a good balanced nutritional healthy diet. There are many applications which are booming to help people so that they can have control over their diet and hence can reduce weight or they can help them to keep them fit and healthy. The project is proposing healthy food habits and dieting patterns so that anyone can know the number of calories burned, the intake of macro nutrients and so on using on data mining tools. This tool is used for discovering hidden patterns and customer eating habits from different types of data sources. This system will help in tracking and improving the individual’s health and the type of food which they can avoid leading towards the risk of illness. A balanced diet means that the intake of each necessary nutrient meets its adequate demand and actual caloric intake balances with calories burned. Additionally, making a diversity of choices from various types of food is also essential to reduce the risk of developing chronic diseases. This diet recommended system focuses on every individual based on their eating habits and body statistics. This research helps in the prediction of a healthy diet for any individual and nutrition is to doctor to design a diet plan as per patient’s need.
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Dissertations / Theses on the topic "Diet Recommendation System"

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LO, YU-WEN, and 羅育文. "The Implementation of Chronic Diet Recommendation System Based on Dietary Ontology and Decision Tree." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/15777217076469629010.

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碩士<br>朝陽科技大學<br>資訊管理系<br>104<br>Due to the improvement level of medical technology, the person's average age has increased in Taiwan. Lifestyle changes such as unhealthy diet, work pressure and other factor will result chronic disease like diabetic, hypertension and so on. These chronic diseases usually need to spend a lot of time for examinations in hospital; the daily diet management also can help reducing the incidence of the disease and complications. Therefore, the dietary management has become very popular and important topic in recent years. The healthy diet not only to maintain good health, but also assist to reduce this chronic disease and the complication happening. Based on these reasoning, we design a new diet recommendation system based on food Ontology, Jena reasoning and decision tree for patients with diabetes or hypertension and other chronic diseases. Using Ontology and Jena reasoning to recommends suitable food according to user diet preferences, and combine with decision tree to calculate user’s daily nutrients to reach a balanced diet management.
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Books on the topic "Diet Recommendation System"

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Nutrient Requirements of Domesticated Ruminants. CSIRO Publishing, 2007. http://dx.doi.org/10.1071/9780643095106.

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Nutrient Requirements of Domesticated Ruminants draws on the most up-to-date research on the energy, protein, mineral, vitamin and water requirements of beef and dairy cattle, sheep and goats. It defines the responses of animals, in weight change, milk production and wool growth, to quantitative and qualitative changes in their feed supply. It has particular application to grazing animals.&#x0D; Factors affecting the intake of feed are taken into account and recommendations are given according to the production systems being used; for instance, the feed intake of a grazing animal is affected by a larger number of variables than a housed animal. Examples of the estimation of the energy and nutrients required for the different production systems are given, as well as the production expected from predicted feed intakes. The interactions between the grazing animal, the pasture and any supplementary feeds are complex, involving herbage availability, diet selection and substitution. To facilitate the application of these recommendations to particular grazing situations, readers are directed to decision support tools and spreadsheet programs. &#x0D; Nutrient Requirements of Domesticated Ruminants is based on the benchmark publication, Feeding Standards for Australian Livestock: Ruminants, published in 1990 by CSIRO Publishing on behalf of the Standing Committee on Agriculture.&#x0D; It provides comprehensive and useful information for graziers, livestock advisors, veterinarians, feed manufacturers and animal nutrition researchers. The recommendations described are equally applicable to animals in feedlots or drought yards.
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Nutrition and feeding of organic cattle. 2nd ed. CABI, 2021. http://dx.doi.org/10.1079/9781789245554.0000.

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Abstract The second edition of this book contains 7 chapters that describe the introductory concepts of cattle nutrition, the aims and principles of organic cattle production, the elements of cattle nutrition, ingredients for organic diets, cattle breeds for organic production, integration of feeding programmes into organic production systems and conclusions and recommendations for the future of nutrition of cattle in organic beef and milk production systems. It provides an important source of peer-reviewed references on the organic feeding of cattle from the international scientific literature. Details on permitted feed ingredients, with an emphasis on those grown or available locally, and on suitable dietary formulations are included in the book. It will be of interest to the advisory personnel that service the organic milk and beef industries, researchers, university and college teachers, students, veterinarians, regulatory agencies, feed manufacturers and feed supply companies.
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Salud Universal en el Siglo XXI: 40 años de Alma-Ata”. Informe de la Comisión de Alto Nivel. Organización Panamericana de la Salud, 2019. http://dx.doi.org/10.37774/9789275320778.

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[Introducción]. Con motivo de los 40 años transcurridos desde la Declaración de Alma-Ata, el 11 y 12 de diciembre de 2017 la Organización Panamericana de la Salud (OPS) convocó en Quito el Foro Regional “Salud Universal en el Siglo XXI: 40 años de Alma-Ata”. Como parte de este movimiento regional la Directora de la OPS, la Dra. Carissa F. Etienne tomó la iniciativa de crear una Comisión de Alto Nivel, denominada “Salud Universal en el Siglo XXI: 40 años de Alma-Ata”, presidida por la Dra. Michelle Bachelet y el Embajador Sr. Néstor Méndez, y conformada por un grupo interdisciplinario de expertos regionales. Entre ellos había representantes de la comunidad, la academia y actores políticos, como ex ministros de salud y líderes de sindicatos y movimientos de diferentes grupos sociales. El objetivo de la Comisión fue elaborar recomendaciones para la Directora de OPS que permitieran hacer efectivo el derecho a la salud de las personas, entendido como un derecho humano fundamental, a partir del análisis de los avances y los desafíos que tienen los sistemas de salud en la Región de las Américas. El presente documento refleja el posicionamiento de la Comisión en torno a la Atención Primaria de Salud (APS) y la búsqueda de soluciones para hacer efectivo el derecho a la salud, además del enfoque utilizado para orientar el debate, el análisis y las recomendaciones sobre cómo garantizar este derecho. El documento se basa en los reportes elaborados por cinco grupos temáticos: a) modelo de atención de salud, b) modelo institucional, c) modelo de financiamiento, d) salud y protección social y e) recursos humanos de salud, los cuales están disponibles como anexos a este informe. Estos grupos temáticos fueron liderados por los miembros de la Comisión, y reunieron a un gran número de expertos académicos y movimientos sociales de diferentes países de la Región. La Comisión presenta diez recomendaciones para lograr la salud para todas y todos en la Región de las Américas en el contexto del siglo XXI. [Introduction]. To mark the 40th anniversary of the Declaration of Alma-Ata, the Pan American Health Organization (PAHO) convened the Regional Forum “Universal Health in the 21st Century: 40 Years of Alma-Ata” on December 11-12, 2017, in Quito, Ecuador. As part of this regional movement, PAHO Director Dr. Carissa F. Etienne convened a High-Level Commission: Universal Health in the 21st Century: 40 Years of Alma-Ata, chaired by Dr. Michelle Bachelet and Ambassador Néstor Mendez, and made up of an interdisciplinary group of regional experts, including representatives from communities and academia, as well as political actors, such as former health ministers, trade union leaders, and representatives of different social movements. The objective of the Commission was to develop recommendations for the PAHO Director on how to give effect to the right to health as a fundamental human right, based on an analysis of the progress and challenges faced by health systems in the Region of the Americas. This document reflects the Commission’s position regarding primary health care (PHC), the search for solutions to ensure the right to health, and the approach taken in discussions, analysis, and recommendations on how to guarantee this right. It is based on reports prepared by the five thematic groups addressing: a) health care model, b) institutional model, c) financing model, d) health and social protection, and e) human resources for health (see annexes to the present report). The thematic groups were led by members of the Commission, bringing together a great number of academic experts and social movements from different countries in the Region. In this report, the Commission presents 10 recommendations for achieving health for all in the Region of the Americas in the 21st century.
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Book chapters on the topic "Diet Recommendation System"

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Polo-Rodriguez, Aurora, Maria Ariza, Ana Rivas, Miguel Angel Carvajal, and Javier Medina-Quero. "A Recommendation Ubiquitous System to Personalize Diet for Children with Obesity." In Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022). Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21333-5_27.

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Chen, Rung-Ching, Yung-Da Lin, Chia-Ming Tsai, and Huiqin Jiang. "Constructing a Diet Recommendation System Based on Fuzzy Rules and Knapsack Method." In Recent Trends in Applied Artificial Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38577-3_50.

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Ali, Syed Imran, Muhammad Bilal Amin, Seoungae Kim, and Sungyoung Lee. "A Hybrid Framework for a Comprehensive Physical Activity and Diet Recommendation System." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94523-1_9.

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Sharath, P. T., Sasi Gowtham Reddy Sathi, and K. Anita Davamani. "Personal Nutritionist Gui for Diet Recommendation System Using Ensemble Machine Learning Technique Based on User Health Information." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-75771-6_28.

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Sharawat, Kirti, and Sanjay Kumar Dubey. "Diet Recommendation for Diabetic Patients Using MCDM Approach." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5903-2_26.

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Gaurav, Prashant, and Sanjay Kumar Dubey. "Diet Recommendation to Respiratory Disease Patient Using Decision-Making Approach." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3600-3_7.

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Rabethge, Nico, and Franz Kummert. "Developing a Human-centred AI-based System to Assist Sorting Laundry." In Informatik aktuell. Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-43705-3_3.

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ZusammenfassungThis paper presents the development of a human-centred AI system for the classification of laundry according to washing categories such as color and type. The system aims to provide a solution that is both accurate and easy to use for individuals with varying levels of technical expertise. The development process involved a human-centred approach, including user research and testing, to ensure that the system meets the needs and expectations of its users. The system uses a combination of computer vision techniques and machine learning algorithms to analyze images of dirty laundry and provide recommendations for the appropriate washing category.In addition to the development of the system itself, this paper also focuses on the explanation of the AI. The aim is to increase transparency and promote understanding of how the system makes its decisions. This is achieved through the use of visualizations and explanations that make the inner workings of the AI more accessible to users.The results of testing demonstrate that the system is effective in accurately classifying dirty laundry. The explanation of the AI has yet to receive more feedback, whether users report that it increased their trust in the system and find it easy to use. The development of a human-centered AI system for laundry classification has the potential to improve the efficiency and accuracy of laundry sorting while also promoting understanding and trust in AI systems.Zusammenfassung. In diesem Beitrag wird die Entwicklung eines menschenzentrierten KI-Systems für die Klassifizierung von Wäsche nach Waschkategorien wie Farbe und Typ vorgestellt. Das System zielt darauf ab, eine Lösung zu bieten, die sowohl einfach wie auch möglichst genau für Personen mit unterschiedlichem technischem Fachwissen zu bedienen sein soll.Das System nutzt eine Kombination aus Computer-Vision-Techniken und Algorithmen des Deep Learning, um Bilder von schmutziger Wäsche zu analysieren und Empfehlungen für die richtige Waschkategorie zu geben. Neben der Entwicklung des Systems selbst geht es in diesem Beitrag auch um die Erklärung der KI und das Aktive Lernen. Ziel ist es, die Transparenz zu erhöhen und das Verständnis dafür zu fördern, wie das System seine Entscheidungen trifft. Dies wird durch den Einsatz von Visualisierungen und Erklärungen erreicht, die den Nutzern die Funktionsweise der KI näher bringen. Durch das Aktive Lernen wird der Aufwand für das Annotierten der Daten verringert, welches für jede Wäscherei aufgrund unterschiedlicher Bedürfnisse erneut durchgeführt werden müsste.Die Testergebnisse zeigen, dass das System in der Lage ist, bestimmte Attribute schmutziger Wäsche zuverlässig zu klassifizieren. Es sind zukünftig Nutzerstudien notwendig, welche überprüfen, ob das Sytem tatsächlich das Vertrauen in das System stärkt und es einfach zu bedienen ist. Die Entwicklung eines menschenzentrierten KI-Systems zur Wäscheklassifizierung hat das Potenzial, die Effizienz und Genauigkeit der Wäschesortierung zu verbessern und gleichzeitig das Verständnis und Vertrauen in KI-Systeme zu fördern.
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Espín, Vanesa, María V. Hurtado, Manuel Noguera, and Kawtar Benghazi. "Semantic-Based Recommendation of Nutrition Diets for the Elderly from Agroalimentary Thesauri." In Flexible Query Answering Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40769-7_41.

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Neufeld, Lynnette M., Sheryl Hendriks, and Marta Hugas. "Healthy Diet: A Definition for the United Nations Food Systems Summit 2021." In Science and Innovations for Food Systems Transformation. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15703-5_3.

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AbstractThe aim of this chapter is to propose a definition of “healthy diets” and provide related evidence, thus permitting the alignment of terminology for the Food Systems Summit and beyond.Diets are combinations of foods and beverages (referred to as foods hereafter, for simplicity) consumed by individuals. However, the specific combination of foods that make up healthy diets is context-specific and depends on many cultural, economic, and other factors. We provide a definition and overview of approaches that have been used to translate this into food-based recommendations. We also provide a brief review highlighting evidence, gaps and controversies related to defining healthy diets. The evidence for potential solutions to making healthy diets more available, affordable, and their production environmentally sustainable is the subject of much literature (Herforth 2020; Chaudhary et al. 2018; Smetana et al. 2019; Badiane and Makombe 2020; Program of Accompanying Research for Agricultural Innovation 2020), and is not discussed here in detail.
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Ema, Arisa, and Takashi Suyama. "Factors Influencing Trust and Use of Recommendation AI: A Case Study of Diet Improvement AI in Japan." In The International Library of Ethics, Law and Technology. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34804-4_10.

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AbstractTo use AI systems that are trustworthy, it is necessary to consider not only AI technologies, but also a model that takes into account factors such as guidelines, assurance through audits and standards and user interface design. In this paper, we conducted a questionnaire survey focusing on (1) AI intervention, (2) data management, and (3) purpose of use. The survey was conducted on a case study of an AI service for dietary habit improvement recommendations among Japanese people. The results suggest that how the form of communication between humans and AI is designed may affect whether users trust and use AI.
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Conference papers on the topic "Diet Recommendation System"

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Subhedar, Arjun, Pranav Bhutada, Ansh Avi Khanna, Ritik Kumar Gupta, and Raghuram Bharadwaj Diddigi. "Personalised Diet Recommendation System Using Bandits." In 2024 IEEE International Conference on Future Machine Learning and Data Science (FMLDS). IEEE, 2024. https://doi.org/10.1109/fmlds63805.2024.00089.

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P, Arun Prakash, Harisankar A, Dhanush B, Gnanajothi T, and IJJU Hemanth Kumar. "Diet Recommendation System Using Supervised Machine Learning." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10716981.

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Bagaskara, Afrizal, Azka Noor Rizqi, and Teguh Prasandy. "Balance Diet Recommendation System Using Fuzzy Logic." In 2024 9th International Conference on Business and Industrial Research (ICBIR). IEEE, 2024. https://doi.org/10.1109/icbir61386.2024.10875955.

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Lakshmi, K., K. Deeba, Vani Harave, and Sneha Bharti. "Life Expectancy Prediction And Diet Recommendation System for Cardiovascular and Diabetes Disease Using Machine Learning." In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2024. http://dx.doi.org/10.1109/ickecs61492.2024.10616598.

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Agrawal, Prerna, Savita Gandhi, and Manish Agarwal. "DiabChatbot: A Machine Learning Chatbot for Early Diagnosis of Type II/Mellitus Diabetes and Diet Recommendation in an Indian Scenario." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10716933.

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K, Ananthajothi, Suganthi M, Sujitha P, and Visalatchi R. "Diet Recommendation System Using ML." In International Conference on Recent Trends in Data Science and its Applications (ICRTDA 2023). River Publishers, 2023. http://dx.doi.org/10.13052/rp-9788770040723.097.

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Chibber, Yuvraj, Dushyant Betala, and Srividhya S. "Diet Recommendation System Using Machine Learning." In International Conference on Recent Trends in Data Science and its Applications (ICRTDA 2023). River Publishers, 2023. http://dx.doi.org/10.13052/rp-9788770040723.216.

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Kovasznai, Gergely. "Developing an expert system for diet recommendation." In 2011 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2011. http://dx.doi.org/10.1109/saci.2011.5873056.

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Muhd Hafeez Khan, Hadirah Khan, Sharifalillah Nordin, and Mohd Ramadan Ab Hamid. "Diet Recommendation Expert System for Hypertension Patients." In 2023 IEEE 8th International Conference on Recent Advances and Innovations in Engineering (ICRAIE). IEEE, 2023. http://dx.doi.org/10.1109/icraie59459.2023.10468502.

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Balpande, Mangesh, Jinesh Sharma, Aaryan Nair, Mihir Khandelwal, and Shejal Dhanray. "AI Based Gym Trainer and Diet Recommendation System." In 2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON). IEEE, 2023. http://dx.doi.org/10.1109/indiscon58499.2023.10270066.

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Reports on the topic "Diet Recommendation System"

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Winkler-Portmann, Simon. Umsetzung einer wirksamen Compliance in globalen Lieferketten am Beispiel der Anforderungen aus der europäischen Chemikalien-Regulierung an die Automobilindustrie. Sonderforschungsgruppe Institutionenanalyse, 2020. http://dx.doi.org/10.46850/sofia.9783941627796.

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This publication based on a master thesis explores the challenges of the automotive industry regarding the European chemical regulations REACH and CLP, as well as potential improvements of the current compliance activities and the related incentives and barriers. It answers the research question: "To what extent should the compliance activities of actors in the automotive supply chain be extended in order to meet the requirements of European chemicals regulation; and where would it help to strengthen incentives in enforcement and the legal framework?“. The study’s structure is based on the transdisciplinary delta analysis of the Society for Institutional Analysis at the Darmstadt University of Applied Sciences. It compares the target state of the legal requirements and the requirements for corresponding compliance with the actual state of the actual compliance measures of the automotive players and attempts to identify their weak points (the delta). The main sources for the analysis are the legal texts and relevant court decisions as well as guideline-based expert interviews with automotive players based on Gläser &amp; Laudel. As objects of the analysis, there are in addition answers to random enquiries according to Article 33 (2) REACH as well as the recommendations and guidelines of the industry associations. The analysis identifies the transmission of material information in the supply chain as a key problem. The global database system used for this purpose, the IMDS, shows gaps in the framework conditions. This results in compliance risk due to the dynamically developing regulation. In addition, the study identifies an incompliance of the investigated automobile manufacturers with regard to Art. 33 REACH. In answering the research question, the study recommends solutions to the automotive players that extend the current compliance activities. In addition, it offers tables and process flow diagrams, which structure the duties and required compliance measures and may serve as basic audit criteria. The analysis is carried out from an external perspective and looks at the entire industry. It therefore cannot cover all the individual peculiarities of each automotive player. As a result, the identified gaps serve only as indications for possible further compliance risks.
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Survey of health and social care setting food businesses on implementation of the FSA Listeriosis Guidance. Food Standards Agency, 2023. http://dx.doi.org/10.46756/sci.fsa.djg946.

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Food safety is a crucial component of protecting the wellbeing of those in the care of health and social care organisations. Incidents, such as the 2019 listeriosis outbreak associated with pre-packed sandwiches supplied to hospitals in England, from which seven patients died of listeriosis, underline the risk of the disease and the serious consequences that a breach in standards can have. Vulnerable consumers - whose immune systems are weakened in some way - are particularly susceptible to listeriosis and the disease has a high hospitalisation and fatality rate, compared to infections with other bacterial pathogens. The bacterium which causes listeriosis, Listeria monocytogenes, is acutely challenging to control as it has the potential to grow at low temperatures and can survive freezing. As such, L. monocytogenes must be controlled in any health or social care (HSC) organisation that provides chilled ready-to-eat food for vulnerable groups. The Food Standards Agency (FSA) guidance on ‘Reducing the risk of vulnerable groups contracting listeriosis (Opens in a new window)’ concentrates on preventing the spread of listeriosis, from preparation to consumption, in chilled ready-to-eat food. The review set up following the 2019 listeriosis outbreak - the Independent Review of NHS Hospital Food (Opens in a new window), contained recommendations on food safety for NHS trusts to take on board. The FSA also committed to assess its own guidance in response to the 2019 outbreak. Social research was commissioned as part of the FSA’s response. This report covers findings from 39 respondents within NHS Trusts and 445 from Health and Social Care (HSC) (non- NHS Trust) settings, such as nursing homes, home care service providers and hospices, in England, Wales and Northern Ireland. The research objectives for the surveys of health and social care settings and NHS Trusts were to: Measure awareness of the FSA guidance on listeriosis Find out how well the FSA guidance on listeriosis is implemented Understand barriers to implementing the guidance in full Understand good practice in implementing the guidance Understand HSC stakeholders’ perceptions of the effectiveness and suitability of the guidance
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