стратегический менеджмент учебник для вузов петрова
Главное меню:
Интересное
стратегический менеджмент учебник для вузов петрова, Digital Anti-Aging Healthcare: An Overview of the Applications of , YanCotta/anti-aging-epigenetics-ml-app - GitHub, (PDF) Hybrid Based Model Face Shape Classification Using Ensemble , Hybrid Based Model Face Shape Classification Using Ensemble Method for , (PDF) Metaverse for Digital Anti-Aging Healthcare: An Overview of , A Content-based Skincare Product Recommendation System, A-Recommendation-system-for-Facial-Skin-Care-using-Machine - GitHub.
Effective anti-aging healthcare is needed as the population ages. Diet management has proven to be a fundamental aspect of anti-aging healthcare, as research demonstrates that maintaining a healthy diet prevents and delays most age-related diseases []. Diet management incorporates dietary planning, monitoring and tracking, nutrient intake analysis, diet personalization, nutritional goal setting, and behavioral change support. Traditional diet management methods, such as self-reported food diaries and 24 h recalls, are fraught with limitations and face several challenges, since they are time-consuming, inaccurate, and less effective due to their dependency on an individual’s memory and honesty []. One common challenge is the need for an electronic medical system that can effectively monitor dietary restrictions to assess claims that they can provide older adults with anti-aging benefits []. Other challenges include the complexity in estimating required nutrient intake and predicting accurate, personalized dietary challenges caused by humans’ genetic uniqueness, subjectivity, and compliance due to carelessness and forgetfulness, delays in assessing the impact of the process, etc. []. While traditional diet control methods face challenges, digital technologies provide new and innovative ways to handle diet, thus enhancing anti-aging healthcare outcomes. Digital technologies have the potential to revolutionize healthcare and anti-aging healthcare. Integrating digital technologies in the diet control process has emerged as a promising solution to address the challenges faced in the traditional setup []. Integrating digital technology into nutritional assessments improves data accuracy, streamlines the process, and reduces the participant burden. This has huge potential to advance public health initiatives, clinical research, and personalized nutrition plans. The most common evolution in diet management using digital technologies is mobile food-/diet-tracking applications []. These applications manage users’ data and use advanced analytic methods to generate recommendations, allowing healthcare providers to track and assess patients’ conditions []. They enable remote monitoring of patients through sensor-based accessories and IoT technology to monitor hydration, stress levels, and lifestyle to make informed decisions about diet and nutrition []. Other technologies, such as AI, blockchain, cloud computing, edge computing, 5G connectivity, nanotechnologies, point-of-care technologies, and more, are used in the backend or in combination with these applications to ensure their efficiency. For example, 5G connectivity eases data transmission from the wearable device to the data server []. At the same time, blockchains are used for food safety, a crucial factor in smart nutrition systems []. Anti-aging practices such as healthy dietary patterns, body mass index preservation, appropriate weight loss, wellness, and healthy lifestyles are significantly impacted by digital technologies []. , The research further examines the effects of digital dietary control on anti-aging healthcare, including improved nutritional monitoring, personalized recommendations, and behavioral and sustainable changes in habits, leading to an expansion of longevity and health span., Personalized Anti-Aging Recommendation System Based on Genetics This repository contains the source code for a thesis project developing a Personalized Anti-Aging Recommendation System. The system analyzes an individual's genetic predispositions (via Single Nucleotide Polymorphisms, or SNPs) and lifestyle habits (e.g., nutrition, exercise, sleep, stress) to generate personalized risk .
This work presents a novel framework for a hairstyle recommender system that is based on face shape classifier. This framework enables an automatic hairstyle recommendation with a single face image., Computer-aid face shape classification system helps to enhance the beauty and attractiveness of the system, and furthermore it minimizes the cost and time spent by experts. In this paper, we have presented machine learning and engineering-based method to classify the face shape to recommend hairstyle., A strategy for digital anti-aging healthcare in the metaverse must meet a couple of challenges during its implementation and operation stages. Though the adoption of the healthcare solutions in the metaverse environment will help improve patients' conditions and maintain a healthy aging process, especially when assisted by the abovelisted , This proposal focuses on designing a skincare product recommendation system based on the user’s skin type and ingredient composition of a product. Content-based filtering is used to identify the chemical components of products and find products with similar ingredient compositions., In recent years, recommender systems, pivotal in both commercial and academic spheres, have spotlighted the 'recommendation system for facial skincare.' This automated system advises consumers on tailored facial skincare product choices, considering individual skin types, issues, and preferences., This will be achieved by integrating smart nutrition and cutting-edge digital technologies, leading to a reshaped anti-aging approach..