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7.223  Articles
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Online lectures are mandatory to deal with the implementation of education during the COVID-19 pandemic. This significant change certainly creates a different experience for students. Regarding online learning, several public health experts and ophthalmol... see more

Despite the fact that the ensemble is usually more accurate than a single network, existing ensemble techniques tend to create unreasonably large ensembles that increase the use of memory and computation costs. The ensemble's pruning solves this problem. ... see more

Deterministic model forecasts do not convey to the end users the forecast uncertainty the models possess as a result of physics parameterizations, simplifications in model representation of physical processes, and errors in initial conditions. This lack o... see more

Kalman filter is an algorithm to estimate the state variable of dynamical stochastic system. The square root ensemble Kalman filter is an modification of Kalman filter. The square root ensemble Kalman filter is proposed to keep the computational stability... see more

A superkine variant of interleukin-2 with six site mutations away from the binding interface developed from the yeast display technique has been previously characterized as undergoing a distal structure alteration which is responsible for its super-potenc... see more

Availability of diverse types of high-throughput data increases the opportunities for researchers to develop computational methods to provide a more comprehensive view for the mechanism and therapy of cancer. One fundamental goal for oncology is to divide... see more

Mobile Service selection is an important but challenging problem in service and mobile computing. Quality of service (QoS) predication is a critical step in service selection in 5G network environments. The traditional methods, such as collaborative filte... see more

Recently, the Entropy Ensemble Filter (EEF) method was proposed to mitigate the computational cost of the Bootstrap AGGregatING (bagging) method. This method uses the most informative training data sets in the model ensemble rather than all ensemble membe... see more

In this paper, a matrix-free posterior ensemble Kalman filter implementation based on a modified Cholesky decomposition is proposed. The method works as follows: the precision matrix of the background error distribution is estimated based on a modified Ch... see more

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