Development of a Prognostic Model of the User’s Information Image Using Automated Tools for Processing Data from Social Networks

2019 ◽  
pp. 405-413 ◽  
Author(s):  
Alexandr Tropnikov ◽  
Anna Uglova ◽  
Boris Nizomutdinov
Keyword(s):  
Social Networks ◽  
Prognostic Model ◽  
Processing Data ◽  
Automated Tools ◽  
Information Image

scholarly journals Information image of the art museum: current state and prospects

2020 ◽  
pp. 93-97
Author(s):  
D. D. Rodionova ◽  
◽  
E. P. Vykhodtsev ◽  
◽  
Keyword(s):  
Social Networks ◽  
Art Museum ◽  
Art Museums ◽  
The State ◽  
Museum Collection ◽  
Group Design ◽  
Museum Information ◽  
Current State ◽  
Information Image ◽  
State Of Activity

The paper provides an overview of the state of activity of art museums in the regions of Siberia in different social networks. Also, the authors monitor the presence of the official website of the museum and its presence in social networks. In addition, an attempt to give a definition «informational image of the museum» is made, and a selected list of art museums in the regions of Siberia is provided. Within the framework of the study, the authors developed recommendations for improving the information image of an art museum such as: group design, museum information, a fixed record, museum collection materials and publications. Moreover, the authors give the best examples of using the site and social networks by the museums to increase users’ activity during a pandemic. So, the authors prove that the official website and work with social networks is an integral part of museum activities aimed at improving a modern museum.

scholarly journals Metrics for Personal Profiles of Social Network Users

2020 ◽  
Vol 79 ◽  
pp. 01012
Author(s):  
Konstantin Sergeevich Nikolaev ◽  
Fail Mubarakovich Gafarov ◽  
Pavel Nikolaevich Ustin
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Web Application ◽  
Machine Learning Methods ◽  
Processing Data ◽  
Behavioral Predictors ◽  
Technical Details ◽  
Genre Preferences ◽  
Selection Of

This paper discusses the technical details of obtaining and processing data to determine a set of characteristics of texts from social networks, genre preferences in movies and music genres for students of Kazan Federal University who have different academic performance (successful, average, not-successful). The selection of such characteristics is carried out using machine learning methods (Word2Vec, tSNE). The data obtained is used in the development of a functional psychometric model of cognitive behavioral predictors of an individual’s activity within the framework of their educational activities. We also developed a web application for visualizing the obtained data using the Flask engine.

Fuzzy-Based Techniques in Human-Like Processing of Social Network Data

2015 ◽  
Vol 23 (Suppl. 1) ◽  
pp. 1-14 ◽  
Author(s):  
Lotfi A. Zadeh ◽  
Ali M. Abbasov ◽  
Shahnaz N. Shahbazova
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Fuzzy Sets ◽  
Possibility Theory ◽  
Linguistic Terms ◽  
Social Network Data ◽  
Processing Data ◽  
Source Of Information ◽  
Analysis Of Social Networks ◽  
Theory Of Fuzzy Sets

Social networks have gained a lot attention. They are perceived as a vast source of information about their users. Variety of different methods and techniques has been proposed to analyze these networks in order to extract valuable information about the users – things they do and like/dislike. A lot of effort is put into improvement of analytical methods in order to grasp a more accurate and detailed image of users. Such information would have an impact on many aspects of everyday life of people – from politics, via professional life, to shopping and entertainment. The theory of fuzzy sets and systems, introduced in 1965, has the ability to handle imprecise and ambiguous information, and to cope with linguistic terms. The theory has evolved into such areas like possibility theory and computing with words. It is very suitable for processing data in a human-like way, and providing the results in a human-oriented manner. The paper presents a short survey of works that use fuzzy-based technologies for analysis of social networks. We pose an idea that fuzzy-based techniques allow for introduction of humancentric and human-like data analysis processes. We include here detailed descriptions of a few target areas of social network analysis that could benefit from applications of fuzzy sets and systems methods.

scholarly journals CONTENT BASED IMAGE RETRIEVAL USING MULTI-SVM TECHNIQUE FROM NOISY AND NON NOISY IMAGES.

2020 ◽  
Vol 9 (10) ◽  
pp. 49-52
Keyword(s):  
Image Retrieval ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Mining Machine ◽  
Image Mining ◽  
Query Image ◽  
Processing Data ◽  
Average Accuracy ◽  
Information Image ◽  
New System

Image mining is a technique which handles the mining of information, image data association, or additional patterns not unambiguously stored in the images. It exploits methods from computer vision, image retrieval, image processing, data mining, machine learning, database, and artificial intelligence. In the proposed work, we have developed a new system that can retrieve the images from a dataset on the basis of contents of the query image. Here, ‘Content-Based’ means that the search will analyze the actual contents of the image. The existing system does not evaluate the results upon attacks but in proposed system the results are also being evaluated on adding noise to the images and blurring the images. The overall average accuracy of the proposed system is 96% whereas that of existing system is 85%. Performance of the existing systems is checked on the maximum of 1000 images whereas the performance of the proposed system is checked on more than 5000 images.

scholarly journals CONTENT BASED IMAGE RETRIEVAL USING MULTI-SVM TECHNIQUE FROM NOISY AND NON NOISY IMAGES

2020 ◽  
Vol 9 (8) ◽  
pp. 93-96
Keyword(s):  
Image Retrieval ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Mining Machine ◽  
Image Mining ◽  
Query Image ◽  
Processing Data ◽  
Average Accuracy ◽  
Information Image ◽  
New System

Image mining is a technique which handles the mining of information, image data association, or additional patterns not unambiguously stored in the images. It exploits methods from computer vision, image retrieval, image processing, data mining, machine learning, database, and artificial intelligence. In the proposed work, we have developed a new system that can retrieve the images from a dataset on the basis of contents of the query image. Here, ‘Content-Based’ means that the search will analyze the actual contents of the image. The existing system does not evaluate the results upon attacks but in proposed system the results are also being evaluated on adding noise to the images and blurring the images. The overall average accuracy of the proposed system is 96% whereas that of existing system is 85%. Performance of the existing systems is checked on the maximum of 1000 images whereas the performance of the proposed system is checked on more than 5000 images.

scholarly journals Research and analysis of messages of users of social networks using BigData technology

2019 ◽  
pp. 504-509
Author(s):  
I A Rytsarev ◽  
A V Kupriyanov ◽  
D V Kirsh ◽  
R A Paringer
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real Time ◽  
World Cup ◽  
Word Cloud ◽  
Processing Data ◽  
The Social ◽  
The World ◽  
The Subject ◽  
The City

In this paper is dedicated to the World Cup held in the city of Samara from June 15 to July 15, 2018. As part of the work, a multithreaded collection in real time was organized, filtering and processing messages from users of the social network Twitter within the host city and its surroundings from May 15 to August 15, 2018. Then, a study was conducted of the texts of user messages on the subject of the popularity of topics and the construction of a “word cloud”. The second study was the construction of a diagram of the dynamics of the number of messages in different languages. As part of the work, modules for collecting, filtering and processing data using BigData technology were implemented.

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