In the past few years, people's life is affecting badly by spread of coronavirus due to a lack information about virus and proper management control it. The government also looking for ways get that how beneficial their preventive measures .So, they can Know whether need be modified or not. effect seen number people affected, being treated, dead. These are data based on which our application wi...

In this paper, we propose an arc-search corrector-predictor interior-point method for solving $P_*(kappa)$-linear complementarity problems. The proposed algorithm searches the optimizers along an ellipse that is an approximation of the central path. The algorithm generates a sequence of iterates in the wide neighborhood of central path introduced by Ai and Zhang. The algorithm does not de...

Traditional branch predictors exploit correlations between pattern history and branch outcome to predict branches, but there is a stronger and more natural correlation between path history and branch outcome. I exploit this correlation with piecewise linear branch prediction, an idealized branch predictor that develops a set of linear functions, one for each program path to the branch to be pre...

In this paper, the (m+1)-step Adams-Bashforth, Adams-Moulton, and Predictor-Correctormethods are used to solve rst-order linear fuzzy ordinary dierential equations. The conceptsof fuzzy interpolation and generalised strongly dierentiability are used, to obtaingeneral algorithms. Each of these algorithms has advantages over current methods. Moreover,for each algorithm a convergence formula can b...

We study the problem of minimizing the expected loss of a linear predictor while constraining the sparsity of the predictor, i.e. bounding the number of features used by the predictor. While this problem is generally NP-hard, we describe several approximation algorithms. We analyze the performance of our algorithms, focusing on the characterization of the trade-off between accuracy and sparsity...

In this paper, we propose a context-based adaptive predictor for use in lossless image coding. Most often, lossless image coders utilize non-adaptive linear predictors for the sake of simplicity and to reduce the complexity of the coder. In DPCM-based lossless image coders, adaptivity can result in significant improvements in the performance. However, adaptive prediction is faced with a number ...