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Titlebook: Artificial Intelligence; 17th Russian Confere Sergei O. Kuznetsov,Aleksandr I. Panov Conference proceedings 2019 Springer Nature Switzerlan

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41#
發(fā)表于 2025-3-28 15:47:48 | 只看該作者
Klaus P. Herrmann,Lars Hannes Larssontion and show the dependence of stable activity patterns on the parameters of agents. Networks of MIMO-agents with similar properties can be used not only to describe the interaction of users of social networks, but also in modeling the transfer of heterogeneous information in telecommunications net
42#
發(fā)表于 2025-3-28 20:57:28 | 只看該作者
Dino Celli,Onome Scott-Emuakpor,Tommy Georgelaves and their transfer to the slave-maker nests, and slaves adaptation in the new nest. It is proposed that this behavior is based on the “friend-alien” identification and is an evolutionary development of food and territorial behavior. The paper describes previously created methods, models, and m
43#
發(fā)表于 2025-3-28 23:23:56 | 只看該作者
44#
發(fā)表于 2025-3-29 04:28:29 | 只看該作者
45#
發(fā)表于 2025-3-29 07:21:00 | 只看該作者
46#
發(fā)表于 2025-3-29 12:58:11 | 只看該作者
Delayed and Chronic Perilunate Dislocationsical and semantic constraints, nested constructs, alternatives, and discontinuity. The lexicon of indicators will be used for automatic annotation of argument indicators in unannotated text, as well as for experiments in argument mining.
47#
發(fā)表于 2025-3-29 18:56:47 | 只看該作者
Axial Dislocations or Fracture-Dislocationsare accessible in the internet, but the real life problem domain could differ from the origin domain where the network was learned. In this paper an approach to vocabulary expansion for neural network language model by means of hierarchical clustering is presented. This technique allows one to adopt
48#
發(fā)表于 2025-3-29 20:46:58 | 只看該作者
Artificial Intelligence978-3-030-30763-9Series ISSN 1865-0929 Series E-ISSN 1865-0937
49#
發(fā)表于 2025-3-30 00:44:57 | 只看該作者
Jay Carroll,Shuman Xia,Garrett J Patakyon in the time series area was shown and a neural network architecture was proposed. The neural network for exchange trade forecasting using Python’s Keras Library was developed and trained. The basic parameters setting of algorithm have been carried out.
50#
發(fā)表于 2025-3-30 05:53:01 | 只看該作者
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