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Mathematics
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Data Analytics in Intelligent Systems
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Data Analytics in Intelligent Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 6210

Special Issue Editor

Dr. Vadim Moshkin

E-Mail Website
Guest Editor
Department of Information Systems, Ulyanovsk State Technical University, Severny Venets Str., 32, Ulyanovsk 432027, Russia
Interests: natural language processing; data mining; knowledge base; decision support systems; time series; fuzzy sets; machine learning; intelligent systems; artificial Intelligence; ontology; inference

Special Issue Information

Dear Colleagues,

Currently, effective decision making requires intelligent systems that are able to quickly analyze big data.

This Special Issue aims to discover new methods and models of artificial intelligence that are used in information systems to solve applied problems in various subject areas, including economics, manufacturing, medicine, social sciences, sports, etc.

This Special Issue will accept papers presenting original theoretical results as well as various AI applications and survey articles. High-quality papers and survey articles of excellent merit in the following fields, among other relevant areas, are welcome:

  • Intelligent systems;
  • Inference;
  • Knowledge bases;
  • Scientific computing;
  • Nature language processing;
  • Machine learning;
  • Neural networks;
  • Hybrid methods;
  • Fuzzy systems and soft computing.

Dr. Vadim Moshkin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

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17 pages, 2424 KiB  
Article
Advancing Semantic Classification: A Comprehensive Examination of Machine Learning Techniques in Analyzing Russian-Language Patient Reviews
by Irina Kalabikhina, Vadim Moshkin, Anton Kolotusha, Maksim Kashin, German Klimenko and Zarina Kazbekova
Mathematics 2024, 12(4), 566; https://doi.org/10.3390/math12040566 - 13 Feb 2024
Viewed by 957
Abstract
Currently, direct surveys are used less and less to assess satisfaction with the quality of user services. One of the most effective methods to solve this problem is to extract user attitudes from social media texts using natural language text mining. This approach [...] Read more.
Currently, direct surveys are used less and less to assess satisfaction with the quality of user services. One of the most effective methods to solve this problem is to extract user attitudes from social media texts using natural language text mining. This approach helps to obtain more objective results by increasing the representativeness and independence of the sample of service consumers being studied. The purpose of this article is to improve existing methods and test a method for classifying Russian-language text reviews of patients about the work of medical institutions and doctors, extracted from social media resources. The authors developed a hybrid method for classifying text reviews about the work of medical institutions and tested machine learning methods using various neural network architectures (GRU, LSTM, CNN) to achieve this goal. More than 60,000 reviews posted by patients on the two most popular doctor review sites in Russia were analysed. Main results: (1) the developed classification algorithm is highly efficient—the best result was shown by the GRU-based architecture (val_accuracy = 0.9271); (2) the application of the method of searching for named entities to text messages after their division made it possible to increase the classification efficiency for each of the classifiers based on the use of artificial neural networks. This study has scientific novelty and practical significance in the field of social and demographic research. To improve the quality of classification, in the future, it is planned to expand the semantic division of the review by object of appeal and sentiment and take into account the resulting fragments separately from each other. Full article
(This article belongs to the Special Issue Data Analytics in Intelligent Systems)
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27 pages, 4251 KiB  
Article
Multicriteria Assessment Method for Network Structure Congestion Based on Traffic Data Using Advanced Computer Vision
by Roman Ekhlakov and Nikita Andriyanov
Mathematics 2024, 12(4), 555; https://doi.org/10.3390/math12040555 - 12 Feb 2024
Cited by 1 | Viewed by 717
Abstract
Overloading of network structures is a problem that we encounter every day in many areas of life. The most associative structure is the transport graph. In many megacities around the world, the so-called intelligent transport system (ITS) is successfully operating, allowing real-time monitoring [...] Read more.
Overloading of network structures is a problem that we encounter every day in many areas of life. The most associative structure is the transport graph. In many megacities around the world, the so-called intelligent transport system (ITS) is successfully operating, allowing real-time monitoring and making changes to traffic management while choosing the most effective solutions. Thanks to the emergence of more powerful computing resources, it has become possible to build more complex and realistic mathematical models of traffic flows, which take into account the interactions of drivers with road signs, markings, and traffic lights, as well as with each other. Simulations using high-performance systems can cover road networks at the scale of an entire city or even a country. It is important to note that the tool being developed is applicable to most network structures described by such mathematical apparatuses as graph theory and the applied theory of network planning and management that are widely used for representing the processes of organizing production and enterprise management. The result of this work is a developed model that implements methods for modeling the behavior of traffic flows based on physical modeling and machine learning algorithms. Moreover, a computer vision system is proposed for analyzing traffic on the roads, which, based on vision transformer technologies, provides high accuracy in detecting cars, and using optical flow, allows for significantly faster processing. The accuracy is above 90% with a processing speed of more than ten frames per second on a single video card. Full article
(This article belongs to the Special Issue Data Analytics in Intelligent Systems)
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33 pages, 9935 KiB  
Article
Computer Model for an Intelligent Adjustment of Weather Conditions Based on Spatial Features for Soil Moisture Estimation
by Luis Pastor Sánchez-Fernández, Diego Alberto Flores-Carrillo and Luis Alejandro Sánchez-Pérez
Mathematics 2024, 12(1), 152; https://doi.org/10.3390/math12010152 - 2 Jan 2024
Cited by 1 | Viewed by 849
Abstract
In this paper, an intelligent weather conditions fuzzy adjustment based on spatial features (IWeCASF) is developed. It is indispensable for our regional soil moisture estimation approach, complementing a point estimation model of soil moisture from the literature. The point estimation model requires the [...] Read more.
In this paper, an intelligent weather conditions fuzzy adjustment based on spatial features (IWeCASF) is developed. It is indispensable for our regional soil moisture estimation approach, complementing a point estimation model of soil moisture from the literature. The point estimation model requires the weather conditions at the point where an estimate is made. Therefore, IWeCASF’s aim is to determine these weather conditions. The procedure begins measuring them at only one checkpoint, called the primary checkpoint. The model determines the weather conditions anywhere within a region through image processing algorithms and fuzzy inference systems. The results are compared with the measurement records and with a spatial interpolation method. The performance is similar to or better than interpolation, especially in the rain, where the model developed is more accurate due to the certainty of replication. Additionally, IWeCASF does not require more than one measurement point. Therefore, it is a more appropriate approach to complement the point estimation model for enabling a regional soil moisture estimation. Full article
(This article belongs to the Special Issue Data Analytics in Intelligent Systems)
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16 pages, 1466 KiB  
Article
Time Series Forecasting during Software Project State Analysis
by Anton Romanov, Nadezhda Yarushkina, Alexey Filippov, Pavel Sergeev, Ilya Andreev and Sergey Kiselev
Mathematics 2024, 12(1), 47; https://doi.org/10.3390/math12010047 - 22 Dec 2023
Viewed by 621
Abstract
Repositories of source code and their hosting platforms are important data sources for software project development and management processes. These sources allow for the extraction of historical data points for the product development process evaluation. Extracted data points reflect the previous development experience [...] Read more.
Repositories of source code and their hosting platforms are important data sources for software project development and management processes. These sources allow for the extraction of historical data points for the product development process evaluation. Extracted data points reflect the previous development experience and allow future planning and active development tracking. The aim of this research is to create a predictive approach to control software development based on a time series extracted from repositories and hosting platforms. This article describes the method of extracting parameters from repositories, the approach to creating time series models and forecasting their behavior. Also, the article represents the proposed approach for software project analyses based on fuzzy logic principles. The novelty of this approach is the ability to perform an expert evaluation of different stages of software product development based on the forecasted values of interested parameters and a fuzzy rule base. Full article
(This article belongs to the Special Issue Data Analytics in Intelligent Systems)
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21 pages, 1361 KiB  
Article
Associations between the Avatar Characteristics and Psychometric Test Results of VK Social Media Users
by Valeriia Stoliarova, Fedor Bushmelev and Maxim Abramov
Mathematics 2023, 11(20), 4300; https://doi.org/10.3390/math11204300 - 16 Oct 2023
Viewed by 1042
Abstract
Online social media has an increasing influence on people’s lives, providing tools for communication and self–representation. People’s digital traces are gaining attention as a reflection of their personality traits, enhancing the personality computing tasks in various areas. This study aims at the identification [...] Read more.
Online social media has an increasing influence on people’s lives, providing tools for communication and self–representation. People’s digital traces are gaining attention as a reflection of their personality traits, enhancing the personality computing tasks in various areas. This study aims at the identification of statistical associations between psychometric scores from three questionnaires—the Big Five Inventory, Plutchik’s Lifestyle Index and the Eysenck Personality Questionnaire—and a set of graphical features of avatar images from the VK online social media that include the pixel characteristics from the HSV and RGB color models and the number of persons and faces depicted in an avatar. The problem is considered from the statistical point of view. The dependency between psychometric scores and the number of faces/persons is assessed with the Kruskal–Wallis test with Dunn test pairwise comparisons. The color-pixel characteristics that are associated with the psychometric scores are selected with several fits of the regularized regression with L2 and MCP penalties. The data for the study were collected via a specially developed application for the online social media platform VK. The results of the analysis support existing research on how colors express personality and discover certain color-pixel image characteristics that could be used for personality computing models. Full article
(This article belongs to the Special Issue Data Analytics in Intelligent Systems)
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Review

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25 pages, 3086 KiB  
Review
Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting
by Devi Munandar, Budi Nurani Ruchjana, Atje Setiawan Abdullah and Hilman Ferdinandus Pardede
Mathematics 2023, 11(13), 2975; https://doi.org/10.3390/math11132975 - 3 Jul 2023
Cited by 1 | Viewed by 1368
Abstract
The issue of climate change holds immense significance, affecting various aspects of life, including the environment, the interaction between soil conditions and the atmosphere, and agriculture. Over the past few decades, a range of spatio-temporal and Deep Neural Network (DNN) techniques had been [...] Read more.
The issue of climate change holds immense significance, affecting various aspects of life, including the environment, the interaction between soil conditions and the atmosphere, and agriculture. Over the past few decades, a range of spatio-temporal and Deep Neural Network (DNN) techniques had been proposed within the field of Machine Learning (ML) for climate forecasting, using spatial and temporal data. The forecasting model in this paper is highly complex, particularly due to the presence of nonlinear data in the residual modeling of General Space-Time Autoregressive Integrated Moving Average (GSTARIMA), which represented nonstationary data with time and location dependencies. This model effectively captured trends and seasonal data with time and location dependencies. On the other hand, DNNs proved reliable for modeling nonlinear data that posed challenges for spatio-temporal approaches. This research presented a comprehensive overview of the integrated approach between the GSTARIMA model and DNNs, following the six-stage Data Analytics Lifecycle methodology. The focus was primarily on previous works conducted between 2013 and 2022. The review showed that the GSTARIMA–DNN integration model was a promising tool for forecasting climate in a specific region in the future. Although spatio-temporal and DNN approaches have been widely employed for predicting the climate and its impact on human life due to their computational efficiency and ability to handle complex problems, the proposed method is expected to be universally accepted for integrating these models, which encompass location and time dependencies. Furthermore, it was found that the GSTARIMA–DNN method, incorporating multivariate variables, locations, and multiple hidden layers, was suitable for short-term climate forecasting. Finally, this paper presented several future directions and recommendations for further research. Full article
(This article belongs to the Special Issue Data Analytics in Intelligent Systems)
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