Artificial intelligence (AI) is an evolving set of technologies used for solving a wide range of applied issues. The core of AI is machine learning (ML)—a complex of algorithms and methods that address the problems of classification, clustering, and forecasting. The practical application of AI&ML holds promising prospects. Therefore, the researches in this area are intensive. However, the industrial applications of AI and its more intensive use in society are not widespread at the present time. The challenges of widespread AI applications need to be considered from both the AI (internal problems) and the societal (external problems) perspective. This consideration will identify the priority steps for more intensive practical application of AI technologies, their introduction, and involvement in industry and society. The article presents the identification and discussion of the challenges of the employment of AI technologies in the economy and society of resource-based countries. The systematization of AI&ML technologies is implemented based on publications in these areas. This systematization allows for the specification of the organizational, personnel, social and technological limitations. This paper outlines the directions of studies in AI and ML, which will allow us to overcome some of the limitations and achieve expansion of the scope of AI&ML applications.

/pdf/review-of-artificial-intelligence-and-machine-learning-e5ieghly.pdf

Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges

The subject of the article is the architecture of information systems of telecom operators (BSS/OSS), requirements for it and the processes of its transformation. The goal is to transform the existing information environment of the operator into an open digital ecosystem, in accordance with business and technological requirements for the functionality and architecture of information systems of communication operators, which will introduce new business models, services and products, communication channels, and increase competitiveness. The following tasks were solved at the study: analysis of the current state of telecommunications and information systems used by telecom operators; analysis of requirements for business support and operational information systems, collection and analysis of requirements for the architecture of modern information systems in the field of telecommunications, analysis of the transformation process from the existing BSS/OSS architecture of the communication operator into the target architecture of the digital communications provider, elaboration of a recommendations for an intermediate architecture. To solve these problems, methods of system and business analysis were used, in particular, methods of analysis of software requirements, technologies for developing software application architecture. The following results were obtained. The current state of the telecommunications services industry, the features of the existing information systems of telecommunications operators (BSS/OSS) and the requirements for the architecture of such systems was analyzed. Strategies of transition to modern architecture was considered and analyzed. The target architecture of the digital service provider's information ecosystem and the process of transforming the existing BSS/OSS architecture into the target architecture were considered. Intermediate migration architecture was proposed. Conclusions: the transformation to modern information architecture will ensure compliance of BSS/OSS systems with market requirements and new technologies, adapt to their changes, allow operators to increase competitiveness, reduce operating and capital costs, create technological conditions for open digital ecosystem and transform the operator into a digital services provider. It was recommended to use an intermediate architecture based on the open standardized TMF API for such transformation.

Processes of the telecom operator’s information architecture transformation

The paper presents the results of a correlation analysis between the information trends in the electronic media of Kazakhstan and indicators of the epidemiological situation of COVID-19 according to the World Health Organization (WHO). The developed method is based on topic modeling and some other methods of processing natural language texts. The method allows for calculating the correlations between media topics, moods, the results of full-text search queries, and objective WHO data. The analysis of the results shows how the attitudes of society towards the problems of COVID-19 changed from 2021–2022. Firstly, the results reflect a steady trend of decreasing interest of electronic media in the topic of the pandemic, although to an unequal extent for different thematic groups. Secondly, there has been a tendency to shift the focus of attention to more pragmatic issues, such as remote learning problems, remote work, the impact of quarantine restrictions on the economy, etc.

/pdf/analysis-of-the-correlation-between-mass-media-publication-1oudvdh0.pdf

Analysis of the Correlation between Mass-Media Publication Activity and COVID-19 Epidemiological Situation in Early 2022

Interpretable Dense Embedding for Large-Scale Textual Data via Fast Fuzzy Clustering

As a large social structure, the health care system is often reflected in media publications. This creates a significant impact on society's attitude towards the system and the state in general. In order to predict and correct state policies, media actions, and identify media shortcomings, it is necessary to analyze the image portrayed by the media and the public's attitude towards it. In this article, we present the results of a multidirectional analysis of a corpus of media publications related to health care. We propose a method for analyzing the information image of health care formed by the mass media based on a topic model of a text corpus. The method evaluates reader interest in various healthcare topics, the dynamics of changes in publication sentiment, and the main information trends. The article presents the results of analyzing a corpus of mass media publications in Kazakhstan from January 2020 to January 2023. 

Exploring the health care system’s representation in the media through hierarchical topic modeling

Mass media is one of the most important elements influencing the information environment of society. The mass media is not only a source of information about what is happening but is often the authority that shapes the information agenda, the boundaries, and forms of discussion on socially relevant topics. A multifaceted and, where possible, quantitative assessment of mass media performance is crucial for understanding their objectivity, tone, thematic focus and, quality. The paper presents a corpus of Kazakhstan media, which contains over 4 million publications from 36 primary sources (which has at least 500 publications). The corpus also includes more than 2 million texts of Russian media for comparative analysis of publication activity of the countries, also about 4000 sections of state policy documents. The paper briefly describes the natural language processing and multiple-criteria decision-making methods, which are the algorithmic basis of the text and mass media evaluation method, and describes the results of several research cases, such as identification of propaganda, assessment of the tone of publications, calculation of the level of socially relevant negativity, comparative analysis of publication activity in the field of renewable energy. Experiments confirm the general possibility of evaluating the socially significant news, identifying texts with propagandistic content, evaluating the sentiment of publications using the topic model of the text corpus since the area under receiver operating characteristics curve (ROC AUC) values of 0.81, 0.73 and 0.93 were achieved on abovementioned tasks. The described cases do not exhaust the possibilities of thematic, tonal, dynamic, etc., analysis of the considered corpus of texts. The corpus will be interesting to researchers considering both multiple publications and mass media analysis, including comparative analysis and identification of common patterns inherent in the media of different countries.

https://www.mdpi.com/2306-5729/6/3/31/pdf

KazNewsDataset: Single Country Overall Digital Mass Media Publication Corpus

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Viktors I. Gopejenko

Author Tools

Chat about Author

Papers

KazNewsDataset: Single Country Overall Digital Mass Media Publication Corpus