The COVID-19 data is critical to support countries and healthcare organizations for effective planning and evidence-based practices to counter the pressures of cost reduction, improved coordination, and outcome and produce more with less. Several COVID-19 datasets are published on the web to support stakeholders in gaining valuable insights for better planning and decision-making in healthcare. However, the datasets are produced in heterogeneous proprietary formats, which create data silos and make data discovery and reuse difficult. Further, the data integration for analysis is difficult and is usually performed by the domain experts manually, which is time-consuming and error-prone. Therefore, an explicit, flexible, and widely acceptable methodology to represent, store, query, and visualize COVID-19 data is needed. In this paper, we have presented the design and development of the Linked Open COVID-19 Data system for structuring and transforming COVID-19 data into semantic format using explicitly developed ontology and publishing on the web using Linked Open Data (LOD) principles. The key motivation of this research is the evaluation of LOD technology as a potential option and application of the available Semantic Web tools (i.e., Protégé, Excel2RDF, Fuseki, Silk, and Sgvizler) for building LOD-based COVID-19 information systems. We have also underpinned several use-case scenarios exploiting the LOD format of the COVID-19 data, which could be used by applications and services for providing relevant information to the end-users. The effectiveness of the proposed methodology and system is evaluated using the system usability scale and descriptive statistical methods and results are found promising.

No abstract

The challenge of segmentation for noisy images, especially those that have light in their backgrounds, is still exists in many advanced state-of-the-art segmentation models. Furthermore, it is significantly difficult to segment such images. In this article, we provide a novel variational model for the simultaneous restoration and segmentation of noisy images that have intensity inhomogeneity and high contrast background illumination and light. The suggested concept combines the multi-phase segmentation technology with the statistical approach in terms of local region knowledge and details of circular regions that are, in fact, centered at every pixel to enable in-homogeneous image restoration. The suggested model is expressed as a fuzzy set and is resolved using the multiplier alternating direction minimization approach. Through several tests and numerical simulations with plausible assumptions, we have evaluated the accuracy and resilience of the proposed approach over various kinds of real and synthesized images in the existence of intensity inhomogeneity and light in the background. Additionally, the findings are contrasted with those from cutting-edge two-phase and multi-phase methods, proving the superiority of our proposed approach for images with noise, background light, and inhomogeneity.

In image segmentation and in general in image processing, noise and outliers distort contained information posing in this way a great challenge for accurate image segmentation results. To ensure a correct image segmentation in presence of noise and outliers, it is necessary to identify the outliers and isolate them during a denoising pre-processing or impose suitable constraints into a segmentation framework. In this paper, we impose suitable removing outliers constraints supported by a well-designed theory in a variational framework for accurate image segmentation. We investigate a novel approach based on the power mean function equipped with a well established theoretical base. The power mean function has the capability to distinguishes between true image pixels and outliers and, therefore, is robust against outliers. To deploy the novel image data term and to guaranteed unique segmentation results, a fuzzy-membership function is employed in the proposed energy functional. Based on qualitative and quantitative extensive analysis on various standard data sets, it has been observed that the proposed model works well in images having multi-objects with high noise and in images with intensity inhomogeneity in contrast with the latest and state-of-the-art models.