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Abstract. Traditionally, to handle security for stand-alone computers and small networks, user authentication and access control mechanisms would be almost enough. However, considering distributed networks such as the Internet and pervasive environments, these kinds of approaches are confronted with flexibility challenges and scalability problems. This is mainly because open environments lack a central control, and users in them are not predetermined. In such ubiquitous computing environments, issues concerning security and trust become crucial. Adding trust to the existing security infrastructures would enhance the security of these environments. Although many trust models are proposed to deal with trust issues in pervasive environments, none of them considers the semantic relations exist among pervasive elements and especially among trust categories. Employing Semantic Web concepts, we propose a computational trust model based on the ontology structure, considering the mentioned semantic relations. In this model, each entity can calculate its trust in other entities and use the calculated trust values to make decisions about granting or rejecting collaborations. Using ontology structure can make the model extendible to encompass other pervasive features such as context awareness in a simple way.

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Introduction: Our aim in this study was to summarize information on the use of intelligent models for predicting and diagnosing the Coronavirus disease 2019 (COVID-19) to help early and timely diagnosis of the disease.Material and Methods: A systematic literature search included articles published until 20 April 2020 in PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv databases. The search strategy consisted of two groups of keywords: A) Novel coronavirus, B) Machine learning. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. Studies were critically reviewed for risk of bias using prediction model risk of bias assessment tool.Results: We gathered 1650 articles through database searches. After the full-text assessment 31 articles were included. Neural networks and deep neural network variants were the most popular machine learning type. Of the five models that authors claimed were externally validated, we considered external validation only for four of them. Area under the curve (AUC) in internal validation of prognostic models varied from .94 to .97. AUC in diagnostic models varied from 0.84 to 0.99, and AUC in external validation of diagnostic models varied from 0.73 to 0.94. Our analysis finds all but two studies have a high risk of bias due to various reasons like a low number of participants and lack of external validation.Conclusion: Diagnostic and prognostic models for COVID-19 show good to excellent discriminative performance. However, these models are at high risk of bias because of various reasons like a low number of participants and lack of external validation. Future studies should address these concerns. Sharing data and experiences for the development, validation, and updating of COVID-19 related prediction models is needed.