Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource PHD PhD PhD Thesis Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Relation A related resource 61000134 Title A name given to the resource Development of personalized diet and exercise recommender system based on clinical data Subject The topic of the resource Computer Science and Engineering Description An account of the resource The present health scenario indicates that thyroid diseases are a common challenge experienced by most individuals. According to the statistics in India, one out of eight women suffer from thyroid-related conditions. Hyperthyroid, hypothyroid, or thyroid cancer are categories of thyroid disorder. It is imperative to maintain optimum levels of secretion of the thyroid hormones as the imbalance could lead to thyroid diseases. Therefore, thyroid patients must be vigilant regarding their iodine intake and follow a customized daily diet and exercise plan. The diet plan, along with balanced iodine levels, must also be able to meet the patient's nutritional needs. A personalized diet plan could help thyroid patients to be more aware and focused on their body metabolism. Existing recommender systems usually provide generic diet recommendations, and unfortunately, it may not be beneficial to patients suffering from a specific disease. Content-based Neighborhood-Conditional RBM (CB-NCRBM) model has posited to recommend Top-3 diet and exercise plans for thyroid patients. The proposed model considers the joint probability distribution of different scores using the user profile. Similarly, preference and health scores are estimated based on content features. The model feeds these scores as visible units to conditional RBM. The proposed model also integrates several content-based features such as users' physiological profiles, thyroid disease information, food, and exercise preferences. The proposed recommender model validates the experimental results using recommendation error and classification accuracy metrics. The proposed hybrid model outperforms several popularly used recommendation models, such as collaborative filtering, content-based, and pure RBM models. The system also provides a feedback loop to enhance the quality of the recommended diet and exercise plans based on user experience. Creator An entity primarily responsible for making the resource Sheshrao, Vairale Vaishali Source A related resource from which the described resource is derived Author's Submission Publisher An entity responsible for making the resource available Christ(Deemed to be University) Date A point or period of time associated with an event in the lifecycle of the resource 2020-01-01 Contributor An entity responsible for making contributions to the resource Shukla, Samiksha Rights Information about rights held in and over the resource Open Access Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource PhD Identifier An unambiguous reference to the resource within a given context <a href="http://hdl.handle.net/10603/313848" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/313848</a>