Iob And Personalized Fitness Plans And Workout Recommendations

Iob And Personalized Fitness Plans And Workout Recommendations – Developing ecotourism in coastal indigenous communities: a comparison of the case studies of La Ventanilla and La Escobilla in Oaxaca, Mexico

Analysis of classification and detection performance of motion blur images of photovoltaic panels based on deblurring and deep learning techniques

Iob And Personalized Fitness Plans And Workout Recommendations

Iob And Personalized Fitness Plans And Workout Recommendations

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Iob And Personalized Fitness Plans And Workout Recommendations

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Iob And Personalized Fitness Plans And Workout Recommendations

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By Khasim Vali Dudekula Khasim Vali Dudekula Scilit Google Scholar 1, * , Hussain Syed Hussain Syed Scilit Google Scholar 1, * , Mohammad Iqbal Mehboob Basha Mohammad Iqbal Mehboob Basha Scilit Google Scholar 1, Sudhakar Ilango Swamy Sudhakar Ilango Swamykan Scilit Google Scholar 1, Purna Prakash Kasaraneni Purna Prakash Kasaraneni Scilit Google Scholar 1, Yellapragada Venkata Pawan Kumar Yellapragada Venkata Pawan Kumar Scilit Google Flant Aymenorg, Google Flant Aymenorg 2. Scholar 3 and Ahmad Taher Azar Ahmad Taher Azar Skillit Google Scholar 4, 5

Iob And Personalized Fitness Plans And Workout Recommendations

Energy Processes, Environment and Electrical Systems Unit, National Engineering School of Gabes, University of Gabes, Gabès 6072, Tunisia

Zahbro Resistance Band Set Of 5

Received: 21 November 2022 / Revised: 14 January 2023 / Accepted: 15 January 2023 / Published: 25 January 2023

Iob And Personalized Fitness Plans And Workout Recommendations

Smart home culture is growing rapidly around the world and is driving smart home users to use smart devices. A smart television (TV) is a device integrated with intelligent technology. Smart TV users maintain their interest in programs. However, automatic user-to-user program recommendation is still being explored. Several articles have discussed recommender systems, but they are related to different applications. Although there are some works to recommend programs to smart TV users (single-user and multi-user), the camera module of the smart TV to capture and verify the user’s image to recommend individual programs has not been discussed. Therefore, this paper proposes a personalized program recommendation system based on convolutional neural network (CNN) for smart TV users. To implement this proposed approach, a CNN algorithm is trained for feature extraction and human face detection on the “Celebface Attribute Dataset” and “Labeled Faces in the Wild-People” datasets. The trained CNN model is applied to the user image captured using the Smart TV camera module. Additionally, the captured image is matched to the user’s image in the “synthetic dataset”. Based on this correspondence, a hybrid filtering technique is proposed and implemented; A related program is therefore recommended. The proposed CNN algorithm achieved almost 95% of the training performance. Furthermore, the performance of hybrid filtering is approximately 85% from a single-user perspective and approximately 81% from a multi-user perspective. From this, it is observed that hybrid filtering outperforms traditional content-based filtering and collaborative filtering techniques.

Artificial intelligence (AI); convolutional neural network (CNN); hybrid filtering; machine learning; program recommendation system; smart devices; home automation; Intelligent Television (TV)

Iob And Personalized Fitness Plans And Workout Recommendations

The Importance Of High Intensity, Low Impact Workouts

A recommender system, also called a recommender system, filters information and accordingly predicts the rating or preference that a customer will give to an item. These systems are mainly used in business applications. They allow the user to have a better product discovery experience by receiving personalized program recommendations. Currently, the smart home culture is growing day by day [1, 2, 3, 4], where all devices are expected to work intelligently to meet the continuous needs of users. Furthermore, by operating intelligently, they are expected to save electricity. Inserting smart technology [5] into a regular television (TV) turns it into a smart TV and has become one of the smart devices in the smart home. Additionally, apps on smart TVs offer users a seamless, flexible and controlled programming experience. It is necessary to test the usability of these applications through an automated model [6]. Generally, Smart TV programs are shared by all family members, but individual program interfaces based on user preferences are needed [7]. The reason for this requirement is that family members will have their own preferences in viewing programs. The main objective of the research is to develop a personalized program recommendation system for individuals or groups of members based on their preferences. Recent technological developments have enabled artificial intelligence (AI)-based recommendation systems to play a key role in many applications [8, 9, 10]. Furthermore, various machine learning algorithms have been applied for recommendation systems in the field of artificial intelligence [11]. As discussed in Section 1.1, increasing importance for their implementation is given to recommender systems running multiple applications.

Recommender systems are not limited to content and collaborative filtering, but also extend to contextual situations. In this regard, an in-depth investigation has been conducted using computational intelligence methods such as genetic algorithm, particle swarm optimization, artificial neural network, K-Nearest Nevers, Bayesian theory, Support Vector Machine, Fuzzy Sets etc. to improve the traditional design. situations [12, 13, 14, 15, 16, 17, 18, 19, 20]. Furthermore, a situation-aware recommendation system based on recurrent neural networks has been developed for personalized healthcare applications [21]. A personality-aware recommender system was discussed in [22] for personality calculation based on models of personality types and traits. A personalized channel recommendation system based on viewers’ individual preferences was implemented in [23] for live streaming platforms. This was implemented using user preference clustering and hybrid user preference clustering. Furthermore, a comprehensive investigation was conducted on technologies such as association rules, matrix factorization, hybrid recommendations, etc. for personalized news recommendation systems [24]. A framework including incremental feature scanning with multiple windows and a hierarchical behavior structure was applied to associate multiple modes of a transportation system with a recommender system [25]. A recommender system using a support vector machine has been developed to facilitate tourists by providing support in their decision making [26]. An object-to-object recommendation system based on k-means clustering was discussed in [27] to create image mosaicking. A nutrient recommendation system based on an improved genetic algorithm has been discussed to suggest suitable nutrients based on crop yield and fertility [28]. A hierarchical recommender system using item description and deep sequential recommendation based on reviews has been introduced in e-commerce applications to recommend products to customers based on certain reviews [29]. A home energy recommendation system based on deep reinforcement learning was developed based on user activities and feedback [30]. A personalized demand-side recommendation system using a non-intrusive appliance load monitoring technique to save energy in residential appliances connected to a smart grid was discussed in [31]. A comprehensive investigation has been conducted on the characteristics and challenges of recommender systems when integrated with blockchain technology [32]. All the above-mentioned recommendation systems were focused on user preferences and recommendations were followed accordingly. However, you also need to focus on how to predict and create a list of user dislikes. To predict this list, an approach called signed network-based inference was discussed in [33]. Furthermore, it is also important to know the quality of recommendations, which reveals the operating conditions of the recommendation system. In this aspect, an investigation was conducted on useful metrics and strategies for evaluating further recommendations to know the effectiveness of the recommendation system [34]. The literature works cited above discuss recommender systems related to various applications and fields. In addition to the above, literature works related to smart TV recommender systems are discussed as follows.

Iob And Personalized Fitness Plans And Workout Recommendations

Various opportunities, challenges, and directions for future research in personalized content recommendation systems for smart TVs were examined in detail. Furthermore, the limitations of different recommendation approaches, such as content-based filtering, collaborative filtering, context-based filtering, etc., have been discussed in [35]. A new formula and age-sex matrix methodology were applied in [36] to generate user profiles to improve the results of recommender systems by detecting the dominance of users in a group. A “deepTV” neural network-based visual environment model has been applied to live TV recommendation systems.

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