A comparison between google cloud service and icloud

© 2019 IEEE. The availability of high speed networks and low cost storage devices and computer in addition to the adoption of Service-Oriented-Architecture has opened the door to Cloud Storage Services by many providers. During the recent couple of years, more companies are moving towards cloud storage due to the many reasons such as: scalable on demand disk storage space, backup and data replication and the ability to share and access data from anywhere and anytime. The main objective of the paper is to compare different kinds of Cloud storage service providers. It starts by offering a brief introduction to cloud storage followed by an outline of the general history of cloud services and then moves on to the specific history of two major cloud services: iCloud and Google cloud platform. Furthermore, the various features of the two cloud services are explored and a comparison is made between them

Cloud is Mine

In this section, we introduce the company and the proposed problems. We then describe the provided database and identify some of its limitations which, to our opinion, prevent any effective resolution of the problem (Section 2). We nevertheless propose a purely theoretical solution (Section 3), but do not put it in practice because of the incomplete data. Finally, we explain how our method could be extended and improved (Section 4)

A design of neuro-computational approach for double‐diffusive natural convection nanofluid flow

The artificial intelligence based neural networking with Back Propagated Levenberg-Marquardt method (NN-BPLMM) is developed to explore the modeling of double‐diffusive free convection nanofluid flow considering suction/injection, Brownian motion and thermophoresis effects past an inclined permeable sheet implanted in a porous medium. By applying suitable transformations, the PDEs presenting the proposed problem are transformed into ordinary ones. A reference dataset of NN-BPLMM is fabricated for multiple influential variants of the model representing scenarios by applying Lobatto III-A numerical technique. The reference data is trained through testing, training and validation operations to optimize and compare the approximated solution with desired (standard) results. The reliability, steadiness, capability and robustness of NN-BPLMM is authenticated through MSE based fitness curves, error through histograms, regression illustrations and absolute errors. The investigations suggest that the temperature enhances with the upsurge in thermophoresis impact during suction and decays for injection, whereas increasing Brownian effect decreases the temperature in the presence of wall suction and reverse behavior is seen for injection. The best measures of performance in form of mean square errors are attained as 7.1058×10−10,2.9262×10−10,1.1652×10−08,1.5657×10−10 and 5.5652×10−10 against 969, 824, 467, 277 and 650 iterations. The comparative study signifies the authenticity of proposed solver with the absolute errors about 10−7 to 10−3 for all influential parameters results

Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic.

Clinical evidence suggests that some patients diagnosed with coronavirus disease 2019 (COVID-19) experience a variety of complications associated with significant morbidity, especially in severe cases during the initial spread of the pandemic. To support early interventions, we propose a machine learning system that predicts the risk of developing multiple complications. We processed data collected from 3,352 patient encounters admitted to 18 facilities between April 1 and April 30, 2020, in Abu Dhabi (AD), United Arab Emirates. Using data collected during the first 24 h of admission, we trained machine learning models to predict the risk of developing any of three complications after 24 h of admission. The complications include Secondary Bacterial Infection (SBI), Acute Kidney Injury (AKI), and Acute Respiratory Distress Syndrome (ARDS). The hospitals were grouped based on geographical proximity to assess the proposed system's learning generalizability, AD Middle region and AD Western & Eastern regions, A and B, respectively. The overall system includes a data filtering criterion, hyperparameter tuning, and model selection. In test set A, consisting of 587 patient encounters (mean age: 45.5), the system achieved a good area under the receiver operating curve (AUROC) for the prediction of SBI (0.902 AUROC), AKI (0.906 AUROC), and ARDS (0.854 AUROC). Similarly, in test set B, consisting of 225 patient encounters (mean age: 42.7), the system performed well for the prediction of SBI (0.859 AUROC), AKI (0.891 AUROC), and ARDS (0.827 AUROC). The performance results and feature importance analysis highlight the system's generalizability and interpretability. The findings illustrate how machine learning models can achieve a strong performance even when using a limited set of routine input variables. Since our proposed system is data-driven, we believe it can be easily repurposed for different outcomes considering the changes in COVID-19 variants over time

ESGI 2016 – Cloud is Mine Report

info:eu-repo/semantics/nonPublishe