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Готові списки джерел за темами / Diet Recommendation System / Статті в журналах

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Статті в журналах з теми "Diet Recommendation System"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

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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Анотація:

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 m

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2

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 sy

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3

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 ar

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4

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 recommendat

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5

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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6

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-friend

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7

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 an

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8

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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9

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 the

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10

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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Анотація:

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

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11

Zingade, D. S., Umar Shaikh, Shreyas Saisekhar, Umang Koul, and Keshav Vaswani. "An Online Diet Recommendation System Based On Artificial Intelligence." International Journal of Computer Sciences and Engineering 7, no. 4 (2019): 1126–30. http://dx.doi.org/10.26438/ijcse/v7i4.11261130.

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12

Nagur Vali Shaik, Lokesh Karthik Varma Penmetsa, Spandana Salveru, Praisey Bathula, and Sahas Manikanta Madishetty. "Medicine recommendation system (Health Harbour)." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 195–203. https://doi.org/10.30574/wjarr.2025.25.2.0380.

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Health Harbour is a machine-learning-based system designed to assist users in identifying possible health conditions and finding suitable medications. By analyzing symptoms entered by the user, it simplifies the process of symptom-based diagnosis and provides helpful health insights. Built using Python and Scikit-Learn, the model is trained on a dataset of 187 symptoms and achieves an impressive accuracy of 99.6%. Users can input four key symptoms, and the system will predict potential illnesses while suggesting appropriate medications. Additionally, it offers diet recommendations, necessary p

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13

Nagur Vali Shaik, Lokesh Karthik Varma Penmetsa, Spandana Salveru, Praisey Bathula, and Sahas Manikanta Madishetty. "Medicine recommendation system (Health Harbour)." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 195–203. https://doi.org/10.30574/wjarr.2025.25.2.0382.

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Анотація:

Health Harbour is a machine-learning-based system designed to assist users in identifying possible health conditions and finding suitable medications. By analyzing symptoms entered by the user, it simplifies the process of symptom-based diagnosis and provides helpful health insights. Built using Python and Scikit-Learn, the model is trained on a dataset of 187 symptoms and achieves an impressive accuracy of 99.6%. Users can input four key symptoms, and the system will predict potential illnesses while suggesting appropriate medications. Additionally, it offers diet recommendations, necessary p

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14

Minal, Pardey Yogita Puttewar* Janhavi Keche Vaishnavi Paghrut Vaishnavi Jayale. "Research on Nutrition Deficiency Analysis and Diet Plan Recommendation System." International Journal of Scientific Research and Technology 2, no. 5 (2025): 237–48. https://doi.org/10.5281/zenodo.15385892.

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Nutrition is the source of energy that is required to carry out all the processes of the human body. “Nutritional deficiency” consists of severely reduced levels of one or more nutrients, making the body unable to normally perform its functions and thus leading to an increased risk of several diseases like cancer, diabetes, and heart disease. This paper presents a Nutrition Deficiency Analysis and Diet Plan Recommendation System developed using Python with a backend SQLite3 database and deployed through Flask. The system is designed to identify nutritional deficiencies and generate

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15

Nidhi Waghela, Jahanvi Mistry, Melony Bharucha, and Ms. Monali Parikh. "Diet Recommendation System Using K-Means Clustering Algorithm of Machine Learning." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 567–71. http://dx.doi.org/10.32628/cseit2410445.

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Анотація:

In today’s world, many people suffer from range of illnesses due to lack of nutrients in their daily diet. It’s not always simple to recommend diet right away. The majority of individuals in the today’s world are fanatically trying to reduce weight, gain weight, or keep their health in check. The study relies on a database that has various amount of nutrients. As a result of the circumstance, we set out to create a program that would help out individuals to become healthy. Only three orts of good are recommended weight loss, weight gain, and staying healthy. The diet recommendation system leve

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16

Naik, Pratiksha Ashok. "Intelligent Food Recommendation System Using Machine Learning." Volume 5 - 2020, Issue 8 - August 5, no. 8 (2020): 616–19. http://dx.doi.org/10.38124/ijisrt20aug414.

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The buying behavior of the consumer is affected by the suggestions given to the items. Recommendations can be made in the form of a review or ranking given to a specific product. Calories consumed by people contains carbohydrates, fats, proteins, minerals and vitamins, and any malnutrition causes severe health problems. In this paper, we propose a recommendation system which is trained on the basis of the recommendations received by the customer who has already used the product. Software recommends the product to the customer on the basis of the experience of the consumer using the same produc

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17

Kingsley Orevaoghene, Eniforo, Chika Yinka-Banjo, and Emmanuel John Anagu. "Fuzzy Logic Based Personalised Diet Recommendation engine for Dietary Prevention and Control of Diabetes." Journal of Engineering, Computational and Applied Sciences (JECAS) 8, no. 1 (2024): 1–13. https://doi.org/10.64290/jecas.v8i1.894.

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Dietary management is very important not only to prevent Type 2 diabetes mellitus but also to treat it. Despite the fact that there are great strides in knowledge dietary strategies, many patients continue to rely on generic advice instead of individualized plans. There is limited integration of technology-based tools into routine care to assist healthcare providers in delivering personalized dietary recommendations. This research developed a personalized system that aligns dietary recommendations with the health conditions and preferences of diabetes patients. The system used Fuzzy Logic inte

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18

Sree, A. Varsha. "HEALTH DIET PLANNER USING PYTHON." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04548.

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Abstract— This project implements a machine learning-based diet recommendation system using a Decision Tree Classifier. The model is trained on a dataset containing various health parameters, including age, gender, BMI, disease type, severity, physical activity level, and dietary restrictions, to predict an appropriate dietary plan. The system encodes categorical variables, splits the dataset for training and testing, and achieves predictions with high accuracy. The trained model is saved for future use, enabling real-time diet recommendations based on user input. The model suggests personaliz

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19

Chhipa, Shubham, Vishal Berwal, Tushar Hirapure, and Soumi Banerjee. "Recipe Recommendation System Using TF-IDF." ITM Web of Conferences 44 (2022): 02006. http://dx.doi.org/10.1051/itmconf/20224402006.

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A Recipe Recommendation System is being proposed in this following paper. Food recommendation is a new area, with few systems that are focus on analysing and user preferences and constraints such as ingredients available at their side being deployed in real settings in the form of web application or mobile application [4]. The proposed model is a mobile application which allows users to search recipes using ingredients available at them including vegetables. For this work we have find a dataset which is a collection of Indian cuisines recipes and apply the content-based recommendation using Te

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20

Hoe, Mah Chun, and Ts Vasuky Mohanan. "MealCompass: A Food Recommendation System with Machine Learning." Journal of International Conference Proceedings 8, no. 1 (2025): 400–417. https://doi.org/10.32535/jicp.v8i1.3998.

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Анотація:

Post-pandemic, Malaysians face “choice overload” when eating out. Additionally, the rising incidence of diabetes and obesity in Malaysia emphasizes the need for healthier eating options. To address these problems, MealCompass recommends food to users based on different user-defined criteria. Moreover, it aims to enable users to find healthier options and allow restaurant owners to provide nutritional information on food items served, which studies have proved an increase in selection of healthier choices by 13.5%. A hybrid recommendation system is proven to be more effective compared to using

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21

Akinbohun, Folake. "Development of Models by Energy Expended and Age Classifications for Diet Recommendation System." European Journal of Computer Science and Information Technology 12, no. 6 (2024): 24–34. http://dx.doi.org/10.37745/ejcsit.2013/vol12n62434.

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People today are more conscious of their health and constantly looking for methods to improve their health status. Due to the unavailability and inaccessibility of dieticians to recommend diet, people do not know how to plan their diet well, thereby people’s health are compromised because of unbalanced diet and misappropriation of diet. There is need to develop models that recommend diet on various classifications on the basis of energy expended daily and age. The models are mathematically represented using arithmetic expressions on different classifications: Energy expended or job/activities,

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22

Wadhwan, Ankita, Priyanka Chawla, Sandeep Kaur, and Usha Mittal. "IntelliHealth: A Machine Learning Driven Disease Detectionand Diet recommendation System." Journal of Computer Science 21, no. 6 (2025): 1251–65. https://doi.org/10.3844/jcssp.2025.1251.1265.

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23

Samita Bhandari, Simon Guljarilal Bansal, Sushmitha Santhosh, and Isha Pramod Lakhekar. "AI-Powered Fitness and Diet Recommendation System: A Personalized Approach to Health and Wellness." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 03 (2025): 534–39. https://doi.org/10.47392/irjaem.2025.0085.

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With the rise of technology in healthcare, personalized fitness and diet recommendations have gained significant attention. This paper presents an AI-powered fitness and diet recommendation system that leverages machine learning (ML) and generative AI to provide tailored workout plans, meal suggestions, and health-tracking features[1]. The system analyzes user-specific parameters such as age, weight, height, fitness goals, and dietary preferences to generate customized recommendations. Implemented using React, Redux, Node.js, Flask, and MongoDB, the platform integrates AI-driven insights to en

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24

Manoharan, Dr Samuel, and Prof. Sathish. "Patient Diet Recommendation System Using K Clique and Deep learning Classifiers." June 2020 2, no. 2 (2020): 121–30. http://dx.doi.org/10.36548/jaicn.2020.2.005.

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There are several systems designed for the purpose of recommending. The recommending system has gained its prominence even in the medical industry for suggesting the diets for the patient’s, medicines to be taken, treatments to be taken etc. The recommendation system mainly enhances the robustness, extends protection against the many disease and improves the quality of living of an individual. So to automatically suggest the foods based on their health conditions and the level of sugar, blood pressure, protein, fat, cholesterol, age etc. the paper puts forth k-clique embedded deep learning cla

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25

Khan, Abdus Salam, and Achim Hoffmann. "Building a case-based diet recommendation system without a knowledge engineer." Artificial Intelligence in Medicine 27, no. 2 (2003): 155–79. http://dx.doi.org/10.1016/s0933-3657(02)00113-6.

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26

J. Watane, Ms Rushali, and Prof Nitin R.Chopde. "A Survey on - Healthy Diet Recommendation System using Web Data Mining." International Journal of Engineering Trends and Technology 10, no. 2 (2014): 105–7. http://dx.doi.org/10.14445/22315381/ijett-v10p220.

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27

V., Shital, and S. S. Sambare. "Study of Diet Recommendation System based on Fuzzy Logic and Ontology." International Journal of Computer Applications 132, no. 12 (2015): 20–24. http://dx.doi.org/10.5120/ijca2015907625.

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28

Orue-Saiz, Iñigo, Miguel Kazarez, and Amaia Mendez-Zorrilla. "Systematic Review of Nutritional Recommendation Systems." Applied Sciences 11, no. 24 (2021): 12069. http://dx.doi.org/10.3390/app112412069.

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In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society. There are many apps created to count calories based on what we eat, or to estimate calorie consumption according to the sport we do, or to recommend recipes, but very few are capable of giving personalized recommendations. This review tries to see what studies exist and what recommendation systems are used for this purpose, over the last 5 years in the main databases. Among the results obtained, it is observed that the existing works focus on the recomme

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29

Moz, Shahadat Hoshen, Md Apu Hosen, Md Noornobi Sohag Santo, Sk Shalauddin Kabir, Md Nasim Adnan, and Syed Md. Galib. "Precision cardiodiet: transforming cardiac care with artificial intelligence-driven dietary recommendations." Radioelectronic and Computer Systems, no. 4 (December 6, 2023): 20–31. http://dx.doi.org/10.32620/reks.2023.4.02.

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The subject matter of this research revolves around addressing the escalating global health threat posed by cardiovascular diseases, which have become a leading cause of mortality in recent times. The goal of this study was to develop a comprehensive diet recommendation system tailored explicitly for cardiac patients. The primary task of this study is to assist both medical practitioners and patients in developing effective dietary strategies to counter heart-related ailments. To achieve this goal, this study leverages the capabilities of machine learning (ML) to extract valuable insights from

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30

Gallo, Ignazio, Nicola Landro, Riccardo La Grassa, and Andrea Turconi. "Food Recommendations for Reducing Water Footprint." Sustainability 14, no. 7 (2022): 3833. http://dx.doi.org/10.3390/su14073833.

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Most existing food-related research efforts focus on recipe retrieval, user preference-based food recommendation, kitchen assistance, or nutritional and caloric estimation of dishes, ignoring personalized and conscious food recommendations resources of the planet. Therefore, in this work, we present a personalized food recommendation scheme, mapping the ingredients to the most resource-friendly dishes on the planet and in particular, selecting recipes that contain ingredients that consume as little water as possible for their production. The system proposed here is able to understand the user’

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31

Virgiani, Igga Febrian, Z. K. A. Baizal, and Ramanti Dharayani. "Healthy Menu Recommendation for Malnutrition Patients Based on Ontology." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 1 (2023): 392. http://dx.doi.org/10.30865/mib.v7i1.5543.

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Анотація:

A healthy diet is one of the keys to creating a healthy lifestyle, but at this time the selection of a healthy and nutritious meal menu in the society is difficult to do because of the limited nutritional information contained in a food. A healthy diet can help a person to get balanced nutrition, good nutritional intake can increase the body's immunity, and make a normal or healthy body weight so that it can increase work productivity and prevention of chronic diseases. To overcome this problem, we propose the use of ontology and Semantic Web Rule Language (SWRL) to build a healthy menu recomm

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32

Roy, Febin, Ashish Shaji, Vinu Sherimon, and Malak Majid Salim Al Amri. "STAY-HEALTHY: AN EXPERT SYSTEM TO SUGGEST A HEALTHY DIET." International Journal of Engineering Science Technologies 6, no. 1 (2022): 11–17. http://dx.doi.org/10.29121/ijoest.v6.i1.2022.262.

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Анотація:

In this time of sudden outbreaks of illnesses and new viruses, people try to seek out more healthy and better lives to protect their fitness in all viable ways. As ways as an amateur character care, he/she isn't aware of the shape of ingredients and therefore the big variety of energy to eat which could lead on him/her to a healthful life, especially people that are suffering from persistent non-Communicable illnesses (NCD) which include cardiovascular illnesses, hypertension, diabetes etc. This study proposes the development of a knowledgeable gadget that shows a customized everyday weight lo

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33

Joshi, Saurav, Filip Ilievski, and Jay Pujara. "Knowledge-Powered Recommendation for an Improved Diet Water Footprint." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23805–7. http://dx.doi.org/10.1609/aaai.v38i21.30571.

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According to WWF, 1.1 billion people lack access to water, and 2.7 billion experience water scarcity at least one month a year. By 2025, two-thirds of the world's population may be facing water shortages. This highlights the urgency of managing water usage efficiently, especially in water-intensive sectors like food. This paper proposes a recommendation engine, powered by knowledge graphs, aiming to facilitate sustainable and healthy food consumption. The engine recommends ingredient substitutes in user recipes that improve nutritional value and reduce environmental impact, particularly water

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34

Chang, I.-Cheng, Nguyen Minh Trang, Ken Chang, and Kenrick Albert. "Diet advisor: an image-based food intake analysis and meal recommendation system." IET Conference Proceedings 2024, no. 28 (2025): 37–39. https://doi.org/10.1049/icp.2025.0183.

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35

Jadhav, Swati, Sandip Shinde, Vivek Ghuge, Divija Godse, Mitrajeet Golsangi, and Pravin Harne. "A Software Development Lifecycle Case Study on: Diet Recommendation System based on User Activities." ITM Web of Conferences 50 (2022): 01009. http://dx.doi.org/10.1051/itmconf/20225001009.

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This paper focuses on the methods of software engineering which can be helpful to various people working on huge projects with teams. This paper considers diet application as a case study. The main aim of the proposed system (diet application) is to give its users a healthy and balanced diet. The application is made in Flutter to ensure it reaches most of the audience and maximum people can receive its benefits. This paper is mostly based on the process used to create the application and all the views, processes, and architecture of the same. Some of the methods which are mentioned in the pape

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Tang, Jianchen, Bing Huang, and Mingshan Xie. "Anticancer Recipe Recommendation Based on Cancer Dietary Knowledge Graph." European Journal of Cancer Care 2023 (October 18, 2023): 1–13. http://dx.doi.org/10.1155/2023/8816960.

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Many recipes contain ingredients with various anticancer effects, which can help users to prevent cancer, as well as provide treatment for cancer patients, effectively slowing the disease. Existing recipe knowledge graph recommendation systems obtain entity feature representations by mining latent connections between recipes and between users and recipes to enhance the performance of the recommendation system. However, it ignores the influence of time on user taste preferences, fails to capture the dependency between them from the user’s dietary records, and is unable to more accurately predic

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K, Renuka Devi, Bhavithra J, and Saradha A. "DIET RECOMMENDATION FOR GLYCEMIC PATIENTS USING IMPROVED KMEANS AND KRILL-HERD OPTIMIZATION." ICTACT Journal on Soft Computing 10, no. 3 (2020): 2096–101. https://doi.org/10.21917/ijsc.2020.0298.

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Maintaining nutrition for glycemic (diabetic) patients in order to retain the blood glucose level is one of the important activity to be followed. Stimulating the amount of carbohydrates, protein, vitamins, and minerals will result in a healthy diet. So, there is a necessity for recommendation of nutrition to those diabetic patients nowadays. Recommender Systems (RS) play a vital role in urging relevant suggestions to the users. To promote improvised and optimized results, Optimization technique plays a significant role in refining the parameters of chosen algorithm. To optimize and to upgrade

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Iwendi, Celestine, Suleman Khan, Joseph Henry Anajemba, Ali Kashif Bashir, and Fazal Noor. "Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model." IEEE Access 8 (2020): 28462–74. http://dx.doi.org/10.1109/access.2020.2968537.

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BEDERIANA, Shyti, Stergu ARGESTA, Valera DHURATA, and Papajani BLERINA. "Food Recommendation System for a Healthy Liver Using Machine Learning." Eurasia Proceedings of Science Technology Engineering and Mathematics 28 (August 15, 2024): 438–47. http://dx.doi.org/10.55549/epstem.1523634.

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Nowadays, the work routine, the problems we face, make us not pay proper attention to healthy eating. As a result, people get sick. We have created through mathematical knowledge (statistics, probability, linear algebra, geometry), combining the concepts of Machine Learning (Content Based Filtering, TD IDF Vectorizer Algorithm, Conditional Independence, Count Vectorization) with Python language, a Recommendation System for all people suffering from liver. Liver is one of the most important organs of our body, because it makes 500 essential functions in the organism. But we have to be aware abo

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Ladyzhets, Viktor, and Svitlana Terenchuk. "Models, Methods and Tools of Planning Human Diet." Management of Development of Complex Systems, no. 53 (March 17, 2023): 39–44. http://dx.doi.org/10.32347/2412-9933.2023.53.39-44.

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In this article was taken to consideration an issue of planning a person's diet and nutrition and were determined factors that significantly affect the choice of food. It was covered approaches studies on the basis of which modern systems of recommendations for a balanced diet work. A classification of decision support systems regarding the choice of diet and diet is provided based on the data on the basis of which they provide recommendations. The limitations of the linear combination of the user's choice factors and the elements characterizing his profile are shown when modeling a large numb

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Misbahul Munir, Muhammad, Ade Pujianto, and Haechal Aulia Muhali Lamuru. "Optimisasi Algoritma Genetika dengan Particle Swarm Optimization (PSO) untuk Sistem Rekomendasi Diet Gizi bagi Penderita Diabetes." Jurnal Riset Sistem dan Teknologi Informasi (RESTIA) 1, no. 2 (2023): 38–48. http://dx.doi.org/10.30787/restia.v1i2.1289.

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Diabetes, especially diabetic nephropathy, is a global health problem that is increasing in prevalence. This disease can cause various serious complications and even death. Despite the high cure rate associated with diabetes, it is important to improve the human body's immune system to reduce the risk of developing diabetes or diabetic nephropathy. One approach that can help is maintaining a diet with good nutritional coverage. This research aims to develop an artificial intelligence (AI) system that can provide recommendations for a good nutritional diet menu for diabetes sufferers. We propos

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Ter, Zheng Bin, Naveen Palanichamy, and Jayapradha J. "Generative AI-based Meal Recommender System." Journal of Informatics and Web Engineering 4, no. 2 (2025): 315–38. https://doi.org/10.33093/jiwe.2025.4.2.20.

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Maintaining a balanced diet is essential for overall well-being, yet many individuals face challenges in meal planning due to time constraints, limited nutritional knowledge, and difficulty aligning meals with personal dietary needs. Traditional meal recommender systems often rely on predefined plans or collaborative filtering techniques, limiting their adaptability and personalization. This study presents a generative AI-based Meal Recommender System utilizing Variational Autoencoders (VAEs) to generate personalized and nutritionally balanced meal plans. The system processes user inputs, such

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Vipparla, Aruna. "CaviScanNet: AI-Powered Cavity Detection, Segmentation, and Diagnosis with BERT Recommendations." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50143.

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Abstract.This paper introduces a deep learning-based system for dental X-ray analysis aimed at automating cavity detection, severity classification, and providing personalized recommendations. Using Mask R-CNN, the system detects cavities and segments their affected areas, while ResNet-50 classifies the severity of caries into superficial, medium, or deep categories. A fine-tuned BERT-based recommendation system then offers tailored advice based on severity and potential causes such as poor hygiene or diet. The solution reduces manual diagnostic effort, enhances accuracy, and provides actionab

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Xie, Weiguang, and Hongliang Lou. "Implementation of Key Technologies for a Healthy Food Culture Recommendation System Using Internet of Things." Mobile Information Systems 2022 (August 31, 2022): 1–12. http://dx.doi.org/10.1155/2022/9675452.

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With the development of catering culture, the types of diets are changing with each passing day. The types of food are becoming more and more abundant, and the concept of healthy eating has become more and more prominent in people’s thinking. The development of Internet of Things technology enables people to live a variety of information in the network, and the amount of information increases sharply. With the development of the Internet of Things, the number of online recipes has increased significantly, and a number of choices are now available to people to find suitable recipes. It is still

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Namgung, Kwon, Tae-Hwan Kim, and Youn-Sik Hong. "Menu Recommendation System Using Smart Plates for Well-balanced Diet Habits of Young Children." Wireless Communications and Mobile Computing 2019 (November 14, 2019): 1–10. http://dx.doi.org/10.1155/2019/7971381.

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A well-balanced diet habit of a wide variety of foods and adequate nutrition can help to maintain proper growth and healthy life for young children. In Korea, young children aged 3 to 6 years use their own plates to eat lunch served in the kindergarten or childcare facilities. In this paper, we propose a smart plate that can easily measure how much food children have eaten. The smart plate has five load cell sensors to measure the weight of five places. Using them, the amount of food intake can be determined by measuring the weight of food before and after meals, respectively. This helps to kn

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Olutunde, Timothy, Chukwuemeka Lawrence Ani, and Godwin Aondofa Adesue. "Leveraging Machine Learning for Personalized Dietary Recommendations, Nutritional Patterns, and Health Outcome Predictions." Journal of Science Research and Reviews 1, no. 2 (2024): 43–56. https://doi.org/10.70882/josrar.2024.v1i2.40.

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Unhealthy dietary patterns are key contributors to chronic diseases such as obesity, diabetes, and cardiovascular conditions. This study employs machine learning (ML) techniques to analyze dietary intake, identify patterns, and assess their relationships with health outcomes. The aim is to provide personalized dietary recommendations and insights to promote healthier eating habits. Data for this research were sourced from a Kaggle dataset on foods and nutrients and the National Health and Nutrition Examination Survey (NHANES) on health outcomes. Preprocessing steps included data cleaning, feat

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Harwalkar, Mukund, Sneha Jadhav, Sakshi Birdar, and Sanjeevini Joshi. "VITAMIN DEFICIENCY AND FOOD RECOMMENDATION USING MACHINE LEARNING." International Journal of Engineering Applied Sciences and Technology 7, no. 5 (2022): 95–98. http://dx.doi.org/10.33564/ijeast.2022.v07i05.016.

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study by WHO reports that inadequate and imbalanced intake of food causes around 9% of heart attack deaths, about 11% of ischemic heart disease deaths, and 14% of gastrointestinal cancer deaths worldwide. Moreover, around billions children are suffering from different types deficiency from Vitamin-A to vitamin k deficiency, 0.2 billion people are suffering from iron deficiency (anaemia), and 0.7 billion people are suffering from iodine deficiency. The main objective of this work to recommend a diet to different individual. The recommender system deals with a large volume of information present

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Gajalakshmi N, Andalpriya C, Raja Lakshmi K, and Nuttrenai V. "Fit AI-Personalized Diet and Fitness Planner." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 03 (2025): 508–13. https://doi.org/10.47392/irjaem.2025.0080.

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Maintaining a healthy lifestyle is becoming increasingly challenging due to hectic schedules, unhealthy eating habits, and the lack of personalized diet and fitness guidance. Generic health plans often fail to address individual requirements, leading to ineffective results and poor adherence. To overcome these challenges, FitAI: Personalized Diet and Fitness Planner is developed as an AI-powered web application that provides customized diet and fitness recommendations based on user-specific data. The system collects key user inputs, including age, height, weight, gender, exercise frequency, di

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Uduakobong, M. Umoren, A. Ojokoh Bolanle, and O. S. Ijarotimi. "Addressing Malnutrition in School-Aged Children with a Diet Recommender System." International Journal of Innovative Science and Research Technology 7, no. 9 (2022): 1704–18. https://doi.org/10.5281/zenodo.7223141.

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Automated recommender systems have been developed to make up for human inadequacies in decision making while solving the information overload problem. They have also found profound use in the area of diet and nutrition. Nevertheless, child nutrition in recommender systems is yet under researched, with very few works found in this area. This research work employs a switching hybrid recommendation technique that is a combination of user-based collaborative filtering and human expert knowledge for both healthy and malnourished children; to cater for the nutrition needs of children on a large and

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Mrs. Nirupa V, Gagguturi Afree, Pathan Ashraf Khan, Marannagari Harini, and Salladhi Jaswanth Kumar. "Automated Detection and Recommendation System for Parkinson’s Disease Using Machine Learning." International Research Journal of Innovations in Engineering and Technology 09, Special Issue ICCIS (2025): 150–54. https://doi.org/10.47001/irjiet/2025.iccis-202524.

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Abstract - Parkinson’s Disease (PD) is a chronic neurological condition that significantly affects speech and motor control. Early diagnosis plays a vital role in symptom management and slowing disease progression. This project presents an automated machine learningbased system for early detection and severity classification of Parkinson’s Disease using voice signal features. Key voice measurements such as jitter, shimmer, and harmonic-to-noise ratio are extracted from biomedical voice data to train multiple classifiers. An ensemble model combining XGBoost, K-Nearest Neighbors, Decision Tree,

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