Assignment Task
Task
The proposed system uses real-time visual object recognition of food ingredients and recommends cooking recipes related to recognized food ingredients. Users can instantly learn about the associated cooking recipes. The goal of the proposed system is to help elders decide on a cooking recipe in grocery stores or the kitchen. This task develops a system that allows users to choose healthier foods by recommending healthier recipes and foods that meet their needs and likes and dislikes. This system is also beneficial for health-conscious people. This article proposes a recipe recommendation system used for supervised learning to analyze data using the deep learning method of convolutional neural networks (CNN).
Main objectives
- The goal of the application is to provide a platform where elders find low calories meal recipe for their favorite food and its nutritional value. The recipe contains several ingredients, cooking procedures, and categories. The development of low calories meal recommender system using AI technology may lead to the creation of a global network that will be able to both actively support, and monitor the personalized supply of nutrients.
- Users can able to select the food items by text, voice or by capture/upload image of an ingredient without any restrictions and using the deep learning neural network (DNN) application to suggest low calories meal plan. A dietary pattern consistent with current guidelines to consume relatively high amounts of vegetables, whole grains, fish may be associated with superior nutritional status, quality of life, and survival in older adults.
- Specific objectives
- • The objective is to help in the innovation of new dishes and to help people allergic to certain ingredients by recommending alternate ingredients.
- • Nutritional values, e.g., amount of fat or protein included in a recipe or expected by a user.
- • Preparation time and difficulty of a meal.
- • Variety of low calories’ meal plan, in terms of used ingredients and the category of a meal
- The system recognizes the ingredients in the pictures taken by the built-in camera and searches the online recipe database for recipes that require the ingredients identified. The object detection method uses a linear kernel SVM with a set of features as image features that use SURFs and color histograms extracted from multiple images rather than individually, and a one-to-rest strategy as a classifier.
- 1. Point the smartphone camera at food or enter the ingredient name or by voice.
- 2. Continuously recognize food ingredients in captured images or entered name.
- 3. Search the meal plan from the recipe database.
- 4. Display the low calories meal plan.
- • Web scraping
- Web scraping is used to extract the data from a website that provides low food calories recipe recommendations personalized to the individual's taste, various recipes, ingredients, and also the flavor components of all the ingredients. This data is then stored in the CSV file for further operations.
- • Alternative ingredient
- Alternative Ingredient recommendation is used to suggest an alternative ingredient if a distinct ingredient is not present or cannot be used in the recipe. Word2vec model is created to perform this function and prescribe alternative ingredients.
- Tool and Technology (Mobile Application)
- Language: Python
- Framework: Flask
- IDE: Android Studio
- Wrap only low calories meal plan (recipes) and store the recipes in csv or json file
- To analyze the recipes use Neural Network classifier, data mining
- Image classification linear kernel SVM with a set of features as image features that use SURFs and color histograms for the images of the ingredient (raw ingredients like vegetable: carrot, beans)
- Visual recognition by built in camera and suggest low calories meal plan
- Voice recognition of the ingredient and suggest low calories meal plan (algorithm to be used vector quantization (VQ) or dynamic time warping (DTW) or artificial neural network (ANN))
- Upload a image of the ingredient and suggest low calories meal plan
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