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The distributed computing attempts to improve performance in large-scale computing problems by resource sharing. Moreover, rising low-cost computing power coupled with advances in communications/networking and the advent of big data, now enables new distributed computing paradigms such as Cloud, Jungle and Fog computing.Cloud computing brings a number of advantages to consumers in terms of accessibility and elasticity. It is based on centralization of resources that possess huge processing power and storage capacities. Fog computing, in contrast, is pushing the frontier of computing away from centralized nodes to the edge of a network, to enable computing at the source of the data. On the other hand, Jungle computing includes a simultaneous combination of clusters, grids, clouds, and so on, in order to gain maximum potential computing power.To understand these new buzzwords, reviewing these paradigms together can be useful. Therefore, this paper describes the advent of new forms of distributed computing. It provides a definition for Cloud, Jungle and Fog computing, and the key characteristics of them are determined. In addition, their architectures are illustrated and, finally, several main use cases are introduced.

The development of medical assisting tools based on artificial intelligence advances is essential in the global fight against COVID-19 outbreak and the future of medical systems. In this study, we introduce ai-corona, a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using chest CT scans. Our framework incorporates an EfficientNetB3-based feature extractor. We employed three datasets; the CC-CCII set, the MasihDaneshvari Hospital (MDH) cohort, and the MosMedData cohort. Overall, these datasets constitute 7184 scans from 5693 subjects and include the COVID-19, non-COVID abnormal (NCA), common pneumonia (CP), non-pneumonia, and Normal classes. We evaluate ai-corona on test sets from the CC-CCII set, MDH cohort, and the entirety of the MosMedData cohort, for which it gained AUC scores of 0.997, 0.989, and 0.954, respectively. Our results indicates ai-corona outperforms all the alternative models. Lastly, our framework’s diagnosis capabilities were evaluated as assistant to several experts. Accordingly, We observed an increase in both speed and accuracy of expert diagnosis when incorporating ai-corona’s assistance.

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