Fuzzy Information Enrichment for Self-healing Recommendation Systems of COVID-19 Patient

The global emergency caused by the Covid-19 pandemic does not yet have a registered drug. Many studies suggest strengthening the immune system in the human body as an alternative solution to treating Covid-19 before the discovery of drugs. This study reports on various types of potential treatments and factors associated with the immune response to the virus. The analysis shows that the effectiveness of the treatment depends on the current preferences of the Covid-19 patient. Therefore, this study aims to use crowdsourced fuzzy information enrichment through Self-healing Recommender Systems (ShRS) to provide recommendations for the best treatment therapy. It is hoped that the proper treatment therapy will cure the healing of Covid-19 patients who are self-isolating. To demonstrate the ShRS, an illustrative example was conducted. We used a crowdsourcing approach to generate treatment therapy recommendations in Bojonegoro, an area with a high number of Covid-19 cases in Indonesia. Most contextual input parameters such as age category, physical condition, and nutritional status are fuzzy. Therefore, we perform ShRS in proposing fuzzy inference to compute a new score/rank with each treatment pooled in it. The purpose of this study is to build a more practical recommendation system because the use of website applications and gadgets can open up opportunities for the public to contribute to human care. This study proposes a system to uncover the best options for healing people infected with Covid-19. It can help health practitioners and the general public cope with self-healing during a pandemic as an alternative lifesaver

Implementation of Electre Method in Determining Tourism Places in North Sumatera

A tourist in determining the purpose of his tour, must be based on several criteria that are used as a determining factor in choosing. These criteria are taken into consideration by tourists in determining which tourist attractions will be chosen. Thus this study aims to assist tourists in choosing tourist attractions in North Sumatra based on the desired criteria and / or provide information to tourists about the best tourist attractions in North Sumatra in accordance with the desired criteria. The Elimination Et Choix Traduisant La Realita (ELECTRE) method which is a system that uses a multicriter decision making method based on the outranking concept by using a pairwise comparison of alternatives based on each appropriate criterion. This research resulted in a recommendation for natural tourist attractions in North Sumatra, namely Teluk Dalam tourism in the Nias Islands

Recommendations for Tourism Sites Using the Mamdani Fuzzy Logic Method and Floyd Warshall Algorithm (Case Study in Yogyakarta)

Tourism is one of the activities carried out for recreation or leisure in a place with a variety of purposes and objectives. In Indonesia, many cities provide attractive tourism places, and one of them is the city of Yogyakarta. Because it has interesting and diverse tourism places, Yogyakarta is in great demand by local and foreign tourists. Thus to be able to maximize the visits of tourists who come to Yogyakarta, we need a system that is able to provide information on tourist attractions to tourists precisely in accordance with what the tourists want. The proposed system uses the Fuzzy Logic method and Floyd Warshall Algorithm which are combined, so as to obtain results in the form of recommendations for tourist attractions based on the costs of tourists, the length of time and distance needed to reach the tourist attraction

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Literatura científica sobre tecnologías de la información y la comunicación en ecoturismo

The objective of the article is to identify the written production of a scientific nature related to the inclusion of information and communications technologies (ICTs) for the strengthening of nature tourism. Its methodological development was built on a work of scientific bibliometric analysis under a quantitative exploratory, three research questions were defined to establish the knowledge developed to date about study. As a result, the analysis, and graphs of the evolution of the publications were obtained; geographical origin, types of documents, themes, and technology trends associated with the use of ICT in nature tourism; In addition, the journals, authors, citations, and influential keywords related to the research were analyzed. The results presented in this document encourage new research into the use of ICT as a cross-cutting element in tourism activities developed by communities that promote sustainable development. To conclude with a quantitative analysis of each of the categories studied and the data obtained systematized in the period 2011 to the first half of 2020, based on publications in the Scopus databaseEl objetivo del artículo es identificar la producción científica en el uso de las tecnologías de la información y la comunicación (TIC) en el aporte e impulso del turismo de naturaleza. Su desarrollo metodológico se construyó a partir de un trabajo de análisis bibliométrico científico bajo un carácter exploratorio-cuantitativo, se definieron tres preguntas de investigación para establecer el conocimiento desarrollado hasta la fecha sobre el objeto de estudio, como resultados se obtuvieron los análisis y gráficas de la evolución de publicaciones, procedencia geográfica y tipos de documentos, al igual las temáticas y tendencias tecnologías asociadas y el análisis de revistas, autores, citas y palabras claves influyentes. Para concluir con un análisis cuantitativo de cada una de las categorías estudiadas y el examen de la inclusión de las TIC en el turismo de naturaleza en el periodo comprendido entre 2011 y primer semestre de 2020, basándose en las publicaciones en la base de datos de Scopu

A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users

Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented