Context-aware POI Recommendation using Neutrosophic Set for Mobile Edge Computing

With the rapid development of mobile communication technology, there is a growing demand for high-quality point of interest(POI) recommendation. The POIs visited by users only account for a very small proportion. Thus traditional POI recommendation method is vulnerable to data sparsity and lacks a clear and effective explanation for POI ranking result. The POI selection made by the user is influenced by various contextual attributes. The challenge lies in representing accurately and aggregating multiple contextual information efficiently. We transform the POI recommendation into a contextual multi-attribute decision problem based on the neutrosophic set (NS) which is suitable for representing fuzzy decision information. We establish a unified framework of contextual information. Firstly, we propose a contextual multi-attribute NS transformation model of POI, including the NS model for single-dimensional attributes and the NS model for multi-dimensional attributes. And then through the aggregation of multi attribute NS, the POI that best conforms to user's preferences is recommended. Finally, the experimental results based on the Yelp dataset show that the proposed strategy performs better than the typical POI recommendation method in NDCG, accuracy, and recall rate.

Fuzzy Decision Tree Based Method in Decision-Making of COVID-19 Patients’ Treatment

A new method in decision-making of timing of tracheostomy in COVID-19 patients is developed and discussed in this paper. Tracheostomy is performed in critically ill coronavirus disease (COVID-19) patients. The timing of tracheostomy is important for anticipated prolonged ventilatory wean when levels of respiratory support were favorable. The analysis of this timing has been implemented based on classification method. One of principal conditions for the developed classifiers in decision-making of timing of tracheostomy in COVID-19 patients was a good interpretation of result. Therefore, the proposed classifiers have been developed as decision tree based because these classifiers have very good interpretability of result. The possible uncertainty of initial data has been considered by the application of fuzzy classifiers. Two fuzzy classifiers as Fuzzy Decision Tree (FDT) and Fuzzy Random Forest (FRF) have been developed for the decision-making in tracheostomy timing. The evaluation of proposed classifiers and their comparison with other show the efficiency of the proposed classifiers. FDT has best characteristics in comparison with other classifiers.

PREDICTION OF THE OCCURRENCE AND DEVELOPMENT OF FATAL VASCULAR COMPLICATIONS IN COVID-19 USING FUZZY MATHEMATICAL MODELS

Целью исследования является разработка метода прогнозирования возникновения и развития тромботических осложнений (тромботических прецедентов), провоцируемых действием новой коронавирусной инфекции (COVID-19) на организм человека, позволяющего усовершенствовать лечебно-диагностические мероприятия для пациентов с данной патологией. В качестве базового математического аппарата была выбрана методология синтеза гибридных нечетких решающих правил, хорошо зарекомендовавшая себя в процессе решения задач с нечётким описанием исследуемых классов со структурой данных аналогичной решаемой в работе задачи. В ходе проводимых исследований были синтезированы математические модели прогнозирования возникновения и развития тромботических прецедентов. Экспертное оценивание и математическое моделирование показали, что уверенность в правильном принятии решений по прогнозу появления и развития исследуемого класса тромботических осложнений превышает величину 0,9. В работе получены нечёткие математические модели прогнозирования возникновения и развития тромботических прецедентов у людей с подтверждённой коронавирусной инфекцией, для которой ведущим фактором риска является вторичный антифосфолипидный синдром с возникновением микроангиопатии. В ходе проведенных исследований была показана целесообразность использования полученных результатов в практике работы таких врачей, как иммунологи, инфекционисты, пульмонологи, кардиологи и сердечно-сосудистые хирурги The aim of the study is to develop a method for predicting the occurrence and development of thrombotic complications (thrombotic precedents) provoked by the action of a new coronavirus infection (COVID-19) on the human body, which allows improving therapeutic and diagnostic measures for patients with this pathology. The methodology of synthesis of hybrid fuzzy decision rules was chosen as the basic mathematical apparatus, which proved itself well in the process of solving problems with a fuzzy description of the classes under study with a data structure similar to the problem being solved in the work. In the course of the research, mathematical models for predicting the occurrence and development of thrombotic precedents were synthesized. Expert evaluation and mathematical modeling have shown that confidence in the correct decision-making on the prognosis of the occurrence and development of the studied class of thrombotic complications exceeds 0.9. The paper presents fuzzy mathematical models for predicting the occurrence and development of thrombotic precedents in people with confirmed coronavirus infection, for which the leading risk factor is secondary antiphospholipid syndrome with the occurrence of microangiopathy. In the course of the conducted studies, the expediency of using the results obtained in the practice of such doctors as immunologists, infectious disease specialists, pulmonologists, cardiologists and cardiovascular surgeons was shown