This paper reports the development and web deployment of an inference model for Coronavirus COVID-19 using machine vision on chest radiographs (CXR). The transfer learning from the Residual Network (RESNET-50) was leveraged for model development on CXR images from healthy individuals, bacterial and viral pneumonia, and COVID-19 positives patients. The performance metrics showed an accuracy of 99%, a recall valued of 99.8%, a precision of 99% and an F1 score of 99.8% for COVID-19 inference. The model was further successfully validated on CXR images from an independent repository. The implemented model was deployed with a web graphical user interface for inference (https://medicsinference.onrender.com ) for the medical research community; an associated cron job is scheduled to continue the learning process when novel and validated information becomes available.

The k-Nearest Neighbor classifier is a non-complex and widely applied data classification algorithm which does well in real-world applications. The overall classification accuracy of the k-Nearest Neighbor algorithm largely depends on the choice of the number of nearest neighbors(k). The use of a constant k value does not always yield the best solutions especially for real-world datasets with an irregular class and density distribution of data points as it totally ignores the class and density distribution of a test point’s k-environment or neighborhood. A resolution to this problem is to dynamically choose k for each test instance to be classified. However, given a large dataset, it becomes very tasking to maximize the k-Nearest Neighbor performance by tuning k. This work proposes the use of Simulated Annealing, a metaheuristic search algorithm, to select optimal k, thus eliminating the prospect of an exhaustive search for optimal k. The results obtained in four different classification tasks demonstrate a significant improvement in the computational efficiency against the k-Nearest Neighbor methods that perform exhaustive search for k, as accurate nearest neighbors are returned faster for k-Nearest Neighbor classification, thus reducing the computation time.

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This research performs a literature survey of remote user authentication researches based on smart card and external memory. The main security confidence of smart card based schemes is temper-resistance property. Other reasons are small physical size, portability, convenience of non-volatile memory, and security provided by a single chip computer embedded in a plastic card. The most efficient schemes are those that used hash function or ECC. The high cost of the cards and readers and their deployment remains a burden to issuers or users. This is what motivates the use of external memory instead of smart card. But the problem of non-temper resistance property associated with external memory limited researches in that direction. There are also, absence of other activities that are essential in user authentication such as forgot/reset password and re-registration in case the external memory or smart card is stolen or lost in all the reviewed researches.

Ontologies are widely used in modeling the real world for the purpose of information sharing and reasoning. Traditional ontologies contain only concepts and relations that describe asserted facts about the world. Modeling in a dynamic world requires taking into consideration the uncertainty that may arise in the domain. In this paper, the concept of soft sets initiated by Molodtsov and the concept of rough sets introduced by Pawlack are used to define a way of instantiating ontologies of vague domains. We define ontological algebraic operations and their properties while taking into consideration the uncertain nature of domains. We show that, by doing so, intra ontological operations and their properties are preserved and formalized as operations in a vague set of objects and can be proved algebraically.