This paper aims to investigate the use of transfer learning architectures in the detection of COVID-19 from CT lung scans. The study evaluates the performances of various transfer learning architectures, as well as the effects of the standard Histogram Equalization and Contrast Limited Adaptive Histogram Equalization. The findings of this study suggest that transfer learning-based frameworks are an alternative to the contemporary methods used to detect the presence of the virus in patients. The highest performing model, the VGG-19 implemented with the Contrast Limited Adaptive Histogram Equalization, on a SARS-CoV-2 dataset, achieved an accuracy and recall of 95.75% and 97.13%, respectively.

The modelling of agriculture with ontologies has been of interest to many authors in the past years. However, no research, currently, has focused on building a knowledge base ontology for the Climate Smart Agriculture (CSA) domain. This study attempts to fill this gap through the development of a Climate Smart Agriculture Ontology (OntoCSA). Information was gathered from secondary sources including websites, published research articles and reports as well as related ontologies, to formalize the OntoCSA ontology in Description Logics (DLs). The OntoCSA ontology was developed in Web Ontology Language (OWL) with Protégé. Furthermore, the OntoCSA ontology was successfully validated with the HermiT reasoner within Protégé. The resulting OntoCSA ontology is a machine-readable model of CSA that can be leveraged in web-based applications for the storage, open and automated access and sharing of CSA information/data, for research and dissemination of best practices

No abstract