Titlebook: Computational Science – ICCS 2020; 20th International C Valeria V. Krzhizhanovskaya,Gábor Závodszky,Jo?o T Conference proceedings 2020 Spri

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/233084.jpg

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https://doi.org/10.1007/978-1-4020-6598-9ging technologies are used to help wildfire analysts determine fire behavior and spread aiming at a more accurate prediction results and efficient use of resources in fire fighting. Natural hazards simulations need to deal with data input uncertainty and their impact on prediction results, usually r

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https://doi.org/10.1007/978-1-4020-6598-9A) methods aim to best approximate model states with imperfect measurements, where particle Filters (PFs) are commonly used with discrete-event simulations. In this paper, we study three critical conditions of DA using PFs: (1) the time interval of iterations, (2) the number of particles and (3) the

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Ivo Allegrini,Francesca Costabilelatforms such as Foursquare, Twitter, and Yelp has motivated a considerable amount of research focused on POI recommendations within the last decade. Inspired by the success of deep neural networks in many fields, researchers are increasingly interested in using neural networks such as Recurrent Neu

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Global Warming Affecting Mexican Firms, We then generated a dynamic graph with our visualization tool and performed malware attribution analysis. We found: 1) malware distribution networks form clusters rather than a single network; 2) those cluster sizes follow the Power Law; 3) there is a correlation between cluster size and the number

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D. Prabhakar,P. K. Garg,Anoop Kumar Shuklaon various scales can help to improve quality of living, enhance urban management, and advance the development of smart cities. But it is widely known that the performance of algorithms for data mining and analysis heavily relies on the quality of input data. The main aim of this paper is helping LB

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Integrated Water Resource Management,ystem. The PI-ESN is trained by using (i) data, which contains no information on the unmeasured state, and (ii) the physical equations of a prototypical chaotic dynamical system. Non-noisy and noisy datasets are considered. First, it is shown that the PI-ESN can accurately reconstruct the unmeasured