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

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https://doi.org/10.1057/9781137329417etworks is difficult and is mostly done with a static approach, neglecting time delayed interdependences. Tensors are objects that naturally represent multilayer networks and in this paper, we propose a new methodology based on Tucker tensor autoregression in order to build a multilayer network dire

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Kosta Kostadinov,Jagadish Thakered combining time-distributed observations with a dynamic model in an optimal way. The typical assimilation scheme is made up of two major steps: a . and a . of the prediction by including information provided by observed data. This is the so called .-. cycle. Classical methods for DA include Kalman

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https://doi.org/10.1057/9781137329417de classification, as well as community detection tasks, are still open research problems in SNA. Hence, SNA has become an interesting and appealing domain in Artificial Intelligence (AI) research. Immanent facts about social network structures can be effectively harnessed for training AI models in

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Amadou Thierno Diallo,Ahmet Suayb Gundogdu nature of real systems, it is very difficult to predict data: a small perturbation from initial state can generate serious errors. Data Assimilation is used to estimate the best initial state of a system in order to predict carefully the future states. Therefore, an accurate and fast Data Assimilat

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https://doi.org/10.1007/978-3-030-50433-5artificial intelligence; computer networks; genetic algorithms; image processing; machine learning; mathe

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978-3-030-50432-8Springer Nature Switzerland AG 2020