درخت حوزه‌های تخصصی

In today's world, the Internet of Things (IoT), which is a fairly new technology, has become a popular topic for discussion. Meanwhile, the increasing demand for personalized healthcare with the assistance of new technologies has created new applications called e-health IoT applications; however, researchers are still attempting to find its applications, therefore they have not been able to focus on comparing these applications. We have aimed at understanding the benefits of e-health IoT applications in comparison to one another. Therefore, this study is an attempt to provide a list of e-health IoT applications for individuals and to prioritize them. The Fuzzy Analytical Hierarchy Process (FAHP) method has been used, which is a method for Multi-Criteria Decision Making (MCDM) and a useful tool for prioritizing multiple alternatives based on criteria. Eight important criteria, based on a comprehensive literature review and experts’ opinions, were determined. Then, by using the FAHP method, the weight of each criterion was calculated. As a result, seven applications identified for individuals were prioritized based on the weight of each criterion and the score of each application in each criterion. Health Effectiveness, Empowerment, Safety, Privacy, and Peace of Mind are the most important criteria in e-health IoT applications for individuals; Cost Saving, round-the-clock Access, and Time-Saving are in the next levels of importance. The results also show that Chronic disease management, Medication reminders, Health monitoring, Air quality, Fall detection, Sleep control and Fitness were respectively ranked as first, second, third, fourth, fifth, sixth and seventh among the IoT applications.

Recommender systems are important tools for users to identify their preferred items and for businesses to improve their products and services. In recent years, the use of online services for selection and reservation of hotels have witnessed a booming growth. Customer’ reviews have replaced the word of mouth marketing, but searching hotels based on user priorities is more time-consuming. This study is aimed at designing a recommender system based on the explicit and implicit preferences of the customers in order to increase prediction’s accuracy. In this study, we have combined sentiment analysis with the Collaborative Filtering (CF) based on deep learning for user groups in order to increase system accuracy. The proposed system uses Natural Language Processing (NLP) and supervised classification approach to analyze sentiments and extract implicit features. In order to design the recommender system, the Singular Value Decomposition (SVD) was used to improve scalability. The results show that our proposed method improves CF performance.

Nowadays with the ever-increasing growth in social media platforms and the creation of different forms of online activism, the word known as “Campaign” has become a familiar and useful term in people’s everyday lives. Campaigns with all kinds of social aims especially using Hashtags are run on social media platforms by individuals, charities, NGOs, governments, municipalities and brand companies and with peoples' participation and value co-creation, appropriate results are achieved and the desired changes are made in people’s attitudes and the behavior of individuals, businesses, or governments. Therefore, there seems to be a new form of a virtual public sphere where there is action, dialog and social mobilization, which leads to the formation of public opinion and social change in the digital era. In this paper, using a critical review method, we are going to investigate a public sphere's literature and study its core features (1. access, 2. dialog, 3. action, and 4. public opinion) in digital campaigns and online platforms. Then, in the last part, we are going to map those in a real case study in order to further clarify the arguments and show how social campaigns on online platforms contribute to the formation of a public sphere in the digital era.