The main task of using neural networks is the prompt and accurate solution of various creative tasks, including the analysis and synthesis of news flows, while maintaining the continuity of learning. The result of such processing can be digests, filtered news streams, as well as event forecasts that allow for proactivity in management decisions. Known methods of news processing by neural networks and technical solutions that implement them do not fully provide a solution to the problems that arise in this area. It is necessary to expand their functionality, and improve the space-time signal binding in recurrent neural networks. When processing news flows, simultaneously with continuous training of recurrent neural networks, selection, recognition, restoration, prediction and synthesis of news should be carried out. To reduce the severity of the problem, a promising method of multifunctional processing of news flows is proposed using recurrent neural networks with a logical organization of layers and continuous learning. The method is based on the development of associative processing of textual information in streaming recurrent neural networks with controlled elements. The key features of this method are the multifunctional processing of information flows with changing laws of news appearance. The method provides for operational selection, recognition, restoration, forecasting and synthesis of news based on deep associative continuous processing of links between text elements. The neural network system that implements the proposed method differs from the known solutions by new elements, connections between them, as well as by the functions performed. The results of the experiments confirmed the extended functionality of the method. New features of processing news texts by streaming RNNs are revealed. The proposed solutions can be used to create a new generation of intelligent systems not only for word processing, but also for other types of information.
Preserving the cultural and historical heritage of various world nations, and their thorough presentation is a long-term commitment of scholars and researchers working in many areas. From centuries every generation is aimed at keeping record about its labor, so that it could be revised and studied by the next generations. New information and multimedia technologies have been developed during the past couple of years, which introduced new methods of preservation, maintenance and distribution of the huge amounts of collected material. This article aims to present the virtual museum, an advanced system managing diverse collections of digital objects that are organized in various ways by a complex specialized functionality. The management of digital content requires a well-designed architecture that embeds services for content presentation, management, and administration. All elements of the system architecture are interrelated, thus the accuracy of each element is of great importance. These systems suffer from the lack of tools for intelligent data curation with the capacity to validate data from different sources and to add value to data. This paper proposes a solution for intelligent data curation that can be implemented in a virtual museum in order to provide opportunity to observe the valuable historical specimens in a proper way. The solution is focused on the process of validation and verification to prevent the duplication of records for digital objects, in order to guarantee the integrity of data and more accurate retrieval of knowledge.
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