scholarly journals Smart City Data Sensing during COVID-19: Public Reaction to Accelerating Digital Transformation

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3965
Author(s):  
Alexander A. Kharlamov ◽  
Aleksei N. Raskhodchikov ◽  
Maria Pilgun
Keyword(s):  
Text Analysis ◽  
Russian Language ◽  
Online Media ◽  
Multimodal Approach ◽  
Digital Services ◽  
Language Data ◽  
Database Size ◽  
Public Reaction ◽  
Network Technologies ◽  
Neural Network Technologies

The article presents the results of the analysis of the adaptation of metropolis IT technologies to solve operational problems in extreme conditions during the COVID-19 pandemic. The material for the study was Russian-language data from social networks, microblogging, blogs, instant messengers, forums, reviews, video hosting services, thematic portals, online media, print media and TV related to the first wave of the COVID-19 pandemic in Russia. The data were collected between 1 March 2020 and 1 June 2020. The database size includes 85,493,717 characters. To analyze the content of social media, a multimodal approach was used involving neural network technologies, text analysis, sentiment-analysis and analysis of lexical associations. The transformation of old digital services and applications, as well as the emergence of new ones were analyzed in terms of the perception of digital communications by actors.

scholarly journals Semantic representation and perception of network aggression: Urban digital communication

2020 ◽  
Vol 7 (4) ◽  
pp. 462-489
Author(s):  
Maria A. Pilgun ◽  
Keyword(s):  
Text Analysis ◽  
Social Stress ◽  
Construction Projects ◽  
Semantic Representation ◽  
Road Construction ◽  
Social Tension ◽  
Network Technologies ◽  
High Degree ◽  
Neural Network Technologies ◽  
Construction Works

The article presents the results of a study of digital content reflecting conflicting urban communications related to road construction in Moscow. The empirical base of the study was data from social networks, microblogs, blogs, messengers, forums, reviews, and videos dedicated to the construction of transport interchange hubs (TPU). Date of material collection: 1.08.19 00:00 — 30.09.19 23:59. We used a multimodal approach to analyze the content of social media using neural network technologies, text analysis, content analysis, sentimental analysis, and psycholinguistic techniques. As a result of the research, the analysis of residents’ perception of the implementation of these construction projects was carried out, as well as social stress in the construction areas was identified, and the risks of conflicts with the population of Moscow were assessed when planning and conducting construction works. The analysis showed that five objects (TPU Varshavskaya, TPU Dmitrovskaya, TPU Nekrasovka, TPU Pyatnitskoe shosse, TPU Ryazan) do not have risks during implementation, the content differs in the absence of aggression and social tension. TPU Nagatinskaya is characterized by an average degree of social stress. A high degree of social tension is caused by the projects of TPU Michurinsky Prospekt and TPU Khovrino. Thus, the analysis allowed us to predict the absence of conflicts with the population in the implementation of the projects of TPU Varshavskaya, TPU Dmitrovskaya, TPU Nekrasovka, TPU Pyatnitskoe highway, TPU Ryazan. The construction of TPU Nagatinskaya and TPU Khovrino causes social tension, but conflict is expected only in the virtual environment. The content of the TPU Michurinsky Prospekt should be defined as a conflict-prone digital zone with a high degree of social stress, and the escalation of conflict between residents and city authorities, builders, both in the online and offline space should be predicted.

A Medical Text Analysis System for German - Syntax Analysis

1991 ◽  
Vol 30 (04) ◽  
pp. 275-283 ◽  
Author(s):  
P. M. Pietrzyk
Keyword(s):  
Text Analysis ◽  
Free Text ◽  
New Approach ◽  
Syntax Analysis ◽  
Medical Text ◽  
Medical Language ◽  
Computerized Processing ◽  
Language Data ◽  
Analysis System ◽  
German Syntax

Abstract:Much information about patients is stored in free text. Hence, the computerized processing of medical language data has been a well-known goal of medical informatics resulting in different paradigms. In Gottingen, a Medical Text Analysis System for German (abbr. MediTAS) has been under development for some time, trying to combine and to extend these paradigms. This article concentrates on the automated syntax analysis of German medical utterances. The investigated text material consists of 8,790 distinct utterances extracted from the summary sections of about 18,400 cytopathological findings reports. The parsing is based upon a new approach called Left-Associative Grammar (LAG) developed by Hausser. By extending considerably the LAG approach, most of the grammatical constructions occurring in the text material could be covered.

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

2020 ◽  
Vol 96 (3s) ◽  
pp. 585-588
Author(s):  
С.Е. Фролова ◽  
Е.С. Янакова
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computer Vision ◽  
High Performance ◽  
Systems On Chip ◽  
High Performance Systems ◽  
On Chip ◽  
Network Technologies ◽  
Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.

scholarly journals Neural network technologies in system synthesis

2021 ◽  
Vol 1047 (1) ◽  
pp. 012099
Author(s):  
O E Filatova ◽  
Yu V Bashkatova ◽  
L S Shakirova ◽  
M A Filatov
Keyword(s):  
Neural Network ◽  
System Synthesis ◽  
Network Technologies ◽  
Neural Network Technologies

scholarly journals RESEARCH OF CLASSIFICATION METHOD OF TV3-117 ENGINE RATINGS OPERATIONS BASED ON NEURAL NETWORK TECHNOLOGIES

2018 ◽  
Vol 0 (4 (6)) ◽  
pp. 93-102
Author(s):  
Юрій Миколайович Шмельов ◽  
Сергій Ігорович Владов ◽  
Олексій Федорович Кришан ◽  
Станіслав Денисович Гвоздік ◽  
Людмила Іванівна Чижова
Keyword(s):  
Neural Network ◽  
Classification Method ◽  
Network Technologies ◽  
Neural Network Technologies

Prospects for the implementation of neural network technologies in radar information processing systems

2021 ◽  
Author(s):  
E.V. Egorova ◽  
A.N. Rybakov ◽  
M.H. Aksyaitov
Keyword(s):  
Neural Network ◽  
Information Processing ◽  
Network Technology ◽  
Network Methods ◽  
External Conditions ◽  
Network Technologies ◽  
Radar Information ◽  
Neural Network Technologies ◽  
Neural Network Technology

Conducted studies of the phased implementation of neural network technologies in the practice of processing radar information, providing for a gradual increase in the level of neural network methods in processing systems, have shown that the use of neural network technologies can improve the quality of radar information processing in the most difficult conditions that require high computing power, when the dynamics of changes in external conditions is very is high and traditional approaches to the creation of processing systems are not able to provide the required level of efficiency. The need to develop theoretical provisions for neural network processing of radar information was revealed, while the main features of information processing in radars determine the relevance of research devoted to preventing the reduction in the quality of radar images in conditions of a large number of targets and a complex «jamming» environment based on the rational use of neural network technology. Analysis of the phased implementation of neural network technologies in radar information processing systems, as well as the use of neural network technology for processing radar information in terms of search and research, makes it possible to increase the efficiency of neural network methods for all processing tasks. Assessment of the required performance of computational tools allows us to single out the main neural network paradigms, the use of which gives a tangible increase in the efficiency of radar information processing, such as multilayer perceptron, Hopfield associative memory and self-organizing Kohonen network, while it is possible to rank the proposed methods in accordance with the required performance, undemanding to computing power and implemented on existing or promising computing facilities with software implementation of neural network paradigms. The analysis of possible directions for improving the quality of radar information processing does not claim to fully cover the entire multifaceted area of such studies. In this paper, only the most universal and widespread neural network paradigms are considered and the main part of possible areas of their application is analyzed. However, the proposed options show that the use of neural network technologies in critical tasks will improve the efficiency of radar information processing for complex, rapidly changing external conditions. The use of the principles of self-learning and the developed apparatus for the synthesis of neural network methods will reduce the duration and complexity of theoretical research, the conduct of which is a necessary and mandatory part of the traditional approach. In the course of further research, some of the proposed methods can be refined, as well as the emergence of new methods that make it possible to more fully use the advantages of neural network technology. Carrying out further research work in these areas will give a powerful stimulating impetus for the creation in the future of highly efficient methods for processing radar information, which can be implemented on the available element base.

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