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

Recommendation of Tourism Resources Supported by Crowdsourcing

Context-aware recommendation of personalised tourism resources is possible because of personal mobile devices and powerful data filtering algorithms. The devices contribute with computing capabilities, on board sensors, ubiquitous Internet access and continuous user monitoring, whereas the filtering algorithms provide the ability to match the profile (interests and the context) of the tourist against a large knowledge bases of tourism resources. While, in terms of technology, personal mobile devices can gather user-related information, including the user context and access multiple data sources, the creation and maintenance of an updated knowledge base of tourism-related resources requires a collaborative approach due to the heterogeneity, volume and dynamic nature of the resources. The current PhD thesis aims to contribute to the solution of this problem by adopting a Crowdsourcing approach for the collaborative maintenance of the knowledge base of resources, Trust and Reputation for the validation of uploaded resources as well as publishers, Big Data for user profiling and context-aware filtering algorithms for the personalised recommendation of tourism resources

A Crowdsourcing Mode of Tourism Customization Based on Sharing Economy

China’s latest innovations of Internet Economy are partly reflected in video living broadcast, shared bicycles etc. In recent years, tourism industry in China obtains rapid development by utilizing Internet. However, it is still difficult to meet the growing tourist demands. In order to solve this problem, in this paper, we put forward a Tourism Crowdsourcing Model (TCM), which utilizes the public creativity to meet the increasing demands for personalized tourism. Firstly, the main problems of the tourism industry are analyzed. Secondly, the pattern of TCM is elaborated, and a matching algorithm between the tourist requirements and the workers’ abilities is well designed to find the qualified service providers efficiently and accurately. Finally, an example is given to verify the feasibility and effectiveness of the TCM based on shared economy. The results shows that TCM has some significant advantages to satisfy the tourism personalized needs by motivating the public to participate in the tourism industry initiatively

IoT in Arctic Tourism – A Case-study of the Tourist Information Office in Narvik

Source at: https://fruct.org/publications/volume-32/acm32/</a

Geoinformatics in Citizen Science

The book features contributions that report original research in the theoretical, technological, and social aspects of geoinformation methods, as applied to supporting citizen science. Specifically, the book focuses on the technological aspects of the field and their application toward the recruitment of volunteers and the collection, management, and analysis of geotagged information to support volunteer involvement in scientific projects. Internationally renowned research groups share research in three areas: First, the key methods of geoinformatics within citizen science initiatives to support scientists in discovering new knowledge in specific application domains or in performing relevant activities, such as reliable geodata filtering, management, analysis, synthesis, sharing, and visualization; second, the critical aspects of citizen science initiatives that call for emerging or novel approaches of geoinformatics to acquire and handle geoinformation; and third, novel geoinformatics research that could serve in support of citizen science

Context-Aware Recommendation Systems in Mobile Environments

Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed