ISBN: 9781088200032 - intelligent machine learning-based personalized nutrition recommendation for women (9 resultados)

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Paperback. Condición: Brand New. 114 pages. 6.00x0.24x9.00 inches. In Stock.

Paperback. Condición: new. Paperback. Personalized nutrition is increasingly influenced by advances in data science, artificial intelligence, and machine learning. Intelligent Machine Learning-Based Personalized Nutrition Recommendation for Women provides a focused technical examination of how computational methods can be applied to nutrition recommendation and individualized dietary decision support. The book brings together machine learning, nutrition science, data analysis, recommendation systems, health informatics, and personalized dietary modeling within a structured interdisciplinary framework.The book introduces the foundations of personalized nutrition and explains the importance of considering individual characteristics when developing dietary recommendations. Factors such as nutritional requirements, dietary patterns, food preferences, lifestyle characteristics, demographic information, and other relevant personal variables can influence the suitability of a nutrition recommendation. The text examines these considerations from a computational and nutrition-science perspective.A central focus is placed on machine learning-based recommendation methods. The book discusses how structured nutrition and user-related data can be represented, processed, and analyzed to identify patterns and generate personalized recommendations. Concepts including data preprocessing, feature selection, classification, prediction, clustering, recommendation models, model training, and evaluation are considered in relation to intelligent nutrition systems.The book further explores the relationship between machine learning and personalized dietary recommendations for women. Women's nutritional needs can vary across different stages of life and according to individual circumstances, making personalization an important consideration in computational nutrition systems. The discussion examines how user characteristics and dietary information can be incorporated into recommendation frameworks while emphasizing the importance of appropriate data representation and model evaluation. Intelligent Machine Learning-Based Personalized Nutrition Recommendation for Women explores the application of machine learning and intelligent recommendation methods to personalized nutrition. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. Personalized nutrition is increasingly influenced by advances in data science, artificial intelligence, and machine learning. Intelligent Machine Learning-Based Personalized Nutrition Recommendation for Women provides a focused technical examination of how computational methods can be applied to nutrition recommendation and individualized dietary decision support. The book brings together machine learning, nutrition science, data analysis, recommendation systems, health informatics, and personalized dietary modeling within a structured interdisciplinary framework.The book introduces the foundations of personalized nutrition and explains the importance of considering individual characteristics when developing dietary recommendations. Factors such as nutritional requirements, dietary patterns, food preferences, lifestyle characteristics, demographic information, and other relevant personal variables can influence the suitability of a nutrition recommendation. The text examines these considerations from a computational and nutrition-science perspective.A central focus is placed on machine learning-based recommendation methods. The book discusses how structured nutrition and user-related data can be represented, processed, and analyzed to identify patterns and generate personalized recommendations. Concepts including data preprocessing, feature selection, classification, prediction, clustering, recommendation models, model training, and evaluation are considered in relation to intelligent nutrition systems.The book further explores the relationship between machine learning and personalized dietary recommendations for women. Women's nutritional needs can vary across different stages of life and according to individual circumstances, making personalization an important consideration in computational nutrition systems. The discussion examines how user characteristics and dietary information can be incorporated into recommendation frameworks while emphasizing the importance of appropriate data representation and model evaluation. Intelligent Machine Learning-Based Personalized Nutrition Recommendation for Women explores the application of machine learning and intelligent recommendation methods to personalized nutrition. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condición: new. Paperback. Personalized nutrition is increasingly influenced by advances in data science, artificial intelligence, and machine learning. Intelligent Machine Learning-Based Personalized Nutrition Recommendation for Women provides a focused technical examination of how computational methods can be applied to nutrition recommendation and individualized dietary decision support. The book brings together machine learning, nutrition science, data analysis, recommendation systems, health informatics, and personalized dietary modeling within a structured interdisciplinary framework.The book introduces the foundations of personalized nutrition and explains the importance of considering individual characteristics when developing dietary recommendations. Factors such as nutritional requirements, dietary patterns, food preferences, lifestyle characteristics, demographic information, and other relevant personal variables can influence the suitability of a nutrition recommendation. The text examines these considerations from a computational and nutrition-science perspective.A central focus is placed on machine learning-based recommendation methods. The book discusses how structured nutrition and user-related data can be represented, processed, and analyzed to identify patterns and generate personalized recommendations. Concepts including data preprocessing, feature selection, classification, prediction, clustering, recommendation models, model training, and evaluation are considered in relation to intelligent nutrition systems.The book further explores the relationship between machine learning and personalized dietary recommendations for women. Women's nutritional needs can vary across different stages of life and according to individual circumstances, making personalization an important consideration in computational nutrition systems. The discussion examines how user characteristics and dietary information can be incorporated into recommendation frameworks while emphasizing the importance of appropriate data representation and model evaluation. Intelligent Machine Learning-Based Personalized Nutrition Recommendation for Women explores the application of machine learning and intelligent recommendation methods to personalized nutrition. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Personalized nutrition is increasingly influenced by advances in data science, artificial intelligence, and machine learning. Intelligent Machine Learning-Based Personalized Nutrition Recommendation for Women provides a focused technical examination of how computational methods can be applied to nutrition recommendation and individualized dietary decision support. The book brings together machine learning, nutrition science, data analysis, recommendation systems, health informatics, and personalized dietary modeling within a structured interdisciplinary framework.The book introduces the foundations of personalized nutrition and explains the importance of considering individual characteristics when developing dietary recommendations. Factors such as nutritional requirements, dietary patterns, food preferences, lifestyle characteristics, demographic information, and other relevant personal variables can influence the suitability of a nutrition recommendation. The text examines these considerations from a computational and nutrition-science perspective.A central focus is placed on machine learning-based recommendation methods. The book discusses how structured nutrition and user-related data can be represented, processed, and analyzed to identify patterns and generate personalized recommendations. Concepts including data preprocessing, feature selection, classification, prediction, clustering, recommendation models, model training, and evaluation are considered in relation to intelligent nutrition systems.The book further explores the relationship between machine learning and personalized dietary recommendations for women. Women's nutritional needs can vary across different stages of life and according to individual circumstances, making personalization an important consideration in computational nutrition systems. The discussion examines how user characteristics and dietary information can be incorporated into recommendation frameworks while emphasizing the importance of appropriate data representation and model evaluation.…

Taschenbuch. Condición: Neu. Intelligent Machine Learning-Based Personalized Nutrition Recommendation for Women | Trisha | Taschenbuch | Englisch | 2026 | Nala Kala | EAN 9781088200032 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…