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

Temporal Exploration in 2D Visualization of Emotions on Twitter Stream

As people freely express their opinions toward a product on Twitter streams without being bound by time, visualizing time pattern of customers emotional behavior can play a crucial role in decision-making. We analyze how emotions are fluctuated in pattern and demonstrate how we can explore it into useful visualizations with an appropriate framework. We manually customized the current framework in order to improve a state-of-the-art of crawling and visualizing Twitter data. The data, post or update on status on the Twitter website about iPhone, was collected from U.S.A, Japan, Indonesia, and Taiwan by using geographical bounding-box and visualized it into two-dimensional heat map, interactive stream graph, and context focus via brushing visualization. The results show that our proposed system can explore uniqueness of temporal pattern of customers emotional behavior