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Journal articles on the topic 'Intelligent data analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 May 2022

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1

Ziegel, Eric R., Michael Berthold, and David J. Hand. "Intelligent Data Analysis." Technometrics 42, no. 4 (2000): 442. http://dx.doi.org/10.2307/1270975.

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2

Berthold, Michael, and David I. Hand. "Intelligent Data Analysis." Technometrics 42, no. 4 (2000): 442. http://dx.doi.org/10.1080/00401706.2000.10485739.

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3

Patil, Pradnya, David C. Brown, and Nabil I. Hachem. "Intelligent data analysis systems." Journal of Intelligent Manufacturing 4, no. 2 (1993): 121–37. http://dx.doi.org/10.1007/bf00123906.

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4

Ventura, S., C. Romero, and A. Abraham. "Foreword: Intelligent data analysis." Journal of Computer and System Sciences 80, no. 1 (2014): 1–2. http://dx.doi.org/10.1016/j.jcss.2013.03.003.

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5

Zabezhailo, M. I., and E. V. Sinyakova. "On the intelligence of the intelligent data analysis." Automatic Documentation and Mathematical Linguistics 48, no. 2 (2014): 43–50. http://dx.doi.org/10.3103/s0005105514020046.

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6

Famili, A., Wei-Min Shen, Richard Weber, and Evangelos Simoudis. "Data Preprocessing and Intelligent Data Analysis." Intelligent Data Analysis 1, no. 1 (1997): 3–23. http://dx.doi.org/10.3233/ida-1997-1102.

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FAMILI, A., W. SHEN, R. WEBER, and E. SIMOUDIS. "Data preprocessing and intelligent data analysis." Intelligent Data Analysis 1, no. 1-4 (1997): 3–23. http://dx.doi.org/10.1016/s1088-467x(98)00007-9.

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8

Chun, Yang-ha, and Moon-ki Cho. "An Empirical Study of Intelligent Security Analysis Methods Utilizing Big Data." Webology 19, no. 1 (2022): 4672–81. http://dx.doi.org/10.14704/web/v19i1/web19311.

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The frequency of cyber-terrorist attacks targeting broadcasting companies or key financial institutions, in which PCs affected by malicious code obstruct normal operations, has been increasing. With the widespread availability of internet technology, the frequency of intelligent cyber-attacks in virtual cyber environments, such as APT attacks, continues to rise. Through an intelligent analysis of cyber threats which are still unknown, it is possible to prevent security accidents before they occur through the use of intelligent security analysis methods based on big data. The researcher conduct

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9

Hand, David J., Douglas H. Fisher, and Michael R. Berthold. "Advances in intelligent data analysis." Intelligent Data Analysis 4, no. 2 (2000): 93–95. http://dx.doi.org/10.3233/ida-2000-4201.

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Robins, V., J. Abernethy, N. Rooney, and E. Bradley. "Topology and intelligent data analysis." Intelligent Data Analysis 8, no. 5 (2004): 505–15. http://dx.doi.org/10.3233/ida-2004-8507.

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Bellazzi, Riccardo, and Blaž Zupan. "Intelligent Data Analysis – Special Issue." Methods of Information in Medicine 40, no. 05 (2001): 362–64. http://dx.doi.org/10.1055/s-0038-1634193.

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Holmes, John H., and Niels Peek. "Intelligent data analysis in biomedicine." Journal of Biomedical Informatics 40, no. 6 (2007): 605–8. http://dx.doi.org/10.1016/j.jbi.2007.10.001.

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13

Li, ZhuoJun. "Accurate Digital Marketing Communication Based on Intelligent Data Analysis." Scientific Programming 2022 (January 4, 2022): 1–10. http://dx.doi.org/10.1155/2022/8294891.

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In digital marketing, the core advantages of scientific and technological means such as artificial intelligence and big data analysis gradually appear and pay attention to them. This paper studies the accuracy of digital marketing and proposes an intelligent algorithm based on data analysis, which improves the effect of marketing communication. Through the combination of intelligent algorithms and big data analysis, the data are convincing. Through the comparison and improvement of intelligent algorithm logistic regression and XGBoost, this paper puts forward an improved algorithm of XGBoost b

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14

Kart, Özge, Gamze Özçelik, and Alp Kut. "Intelligent Data Analysis on a Cloud for Small and Medium Sized Enterprises." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 04, no. 04 (2016): 01–05. http://dx.doi.org/10.9756/sijcsea/v4i4/04040090401.

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15

A., Mohammed Raheel Basha. "Data Analytics Using Intelligent Optimization Technique: Profitability Analysis of Banks Using Fuzzy Logic Decision Making Technique." Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (2020): 349–73. http://dx.doi.org/10.5373/jardcs/v12sp3/20201271.

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16

Amant, Robert St, and Paul R. Cohen. "Intelligent Support for Exploratory Data Analysis." Journal of Computational and Graphical Statistics 7, no. 4 (1998): 545. http://dx.doi.org/10.2307/1390682.

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17

Grusho, A. A., N. A. Grusho, M. I. Zabezhailo, and E. E. Timonina. "Intelligent data analysis in information security." Automatic Control and Computer Sciences 50, no. 8 (2016): 722–25. http://dx.doi.org/10.3103/s0146411616080307.

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18

Hand, David J. "Intelligent Data Analysis: Issues and Opportunities." Intelligent Data Analysis 2, no. 2 (1998): 67–79. http://dx.doi.org/10.3233/ida-1998-2202.

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19

Tatur, M. M., M. M. Lukashevich, D. Y. Pertsev, and N. A. Iskra. "INTELLIGENT DATA ANALYSIS AND CLOUD COMPUTING." Doklady BGUIR, no. 6 (October 3, 2019): 62–71. http://dx.doi.org/10.35596/1729-7648-2019-124-6-62-71.

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In this paper the term of «intelligent data analysis» is discussed, the cloud computing concept is described. The system developed and deployed on the computer cluster by ECM department of BSUIR is shown as an example of the intelligent data analysis by means of cloud computing. Some results of research with the help of this system are given.

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20

Liu, Xiaohui. "Intelligent data analysis: issues and challenges." Knowledge Engineering Review 11, no. 4 (1996): 365–71. http://dx.doi.org/10.1017/s0269888900008055.

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Two phenomena have probably affected modern data analysts' lives more than anything else. First, the size of real-world data sets is getting increasingly large, especially during the last decade or so. Second, modern computational methods and tools are being developed which add further capability to traditional statistical analysis tools. These two developments have created a new range of problems and challenges for analysts, as well as new opportunities for intelligent systems in data analysis.

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21

Tsai, Chun Wei, Ya Lan Yang, Ming Chao Chiang, and Chu Sing Yang. "Intelligent big data analysis: a review." International Journal of Big Data Intelligence 1, no. 4 (2014): 181. http://dx.doi.org/10.1504/ijbdi.2014.066957.

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22

HAND, D. "Intelligent data analysis: Issues and opportunities." Intelligent Data Analysis 2, no. 1-4 (1998): 67–79. http://dx.doi.org/10.1016/s1088-467x(99)80001-8.

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23

Pawlak, Zdzisław. "Rough sets and intelligent data analysis." Information Sciences 147, no. 1-4 (2002): 1–12. http://dx.doi.org/10.1016/s0020-0255(02)00197-4.

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24

Corchado, Emilio, Michał Woźniak, Ajith Abraham, André C. P. L. F. de Carvalho, and Václav Snášel. "Recent trends in intelligent data analysis." Neurocomputing 126 (February 2014): 1–2. http://dx.doi.org/10.1016/j.neucom.2013.07.001.

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25

St. Amant, Robert, and Paul R. Cohen. "Intelligent Support for Exploratory Data Analysis." Journal of Computational and Graphical Statistics 7, no. 4 (1998): 545–58. http://dx.doi.org/10.1080/10618600.1998.10474794.

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26

Yang, Bo, Dong Tian, and Guihua Shan. "Tobacco Spatial Data Intelligent Visual Analysis." Electronics 11, no. 7 (2022): 995. http://dx.doi.org/10.3390/electronics11070995.

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A multi-module visualization framework is designed and a visual analysis system called TobaccoGeoVis is implemented to analyze tobacco spatial data efficiently. The proposed system provides a visualization technology for overlaying multiple graphics on a map to enrich the form of tobacco spatial data visualization. The system also adopts artificial intelligence algorithms and multi-view linkage interactive methods and provides flexible data-attribute field mapping and graphical parameter configuration to analyze tobacco spatial data. We demonstrated that the system is user-friendly and the app

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27

Xiao, Qin. "Big Data analysis: Intelligent Transportation Development Engine." Urban Transportation & Construction 1, no. 1 (2014): 11. http://dx.doi.org/10.18686/utc.v1i1.3.

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<p>With the development of the times, people have unwittingly entered the information age. The information age has become a large amount of data bursting characteristics of the new era. In this feature people still seek to improve the production and quality of life. For the development of intelligent transportation needs of people's lives and make the real world, but in the construction of intelligent transportation among a large number of information data also adds to its change and difficulty, how to build an intelligent era of big data, security, low-cost, efficient and convenient of

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28

Borovkov, Alexey I., Marina V. Bolsunovskaya, and Aleksei M. Gintciak. "Intelligent Data Analysis for Infection Spread Prediction." Sustainability 14, no. 4 (2022): 1995. http://dx.doi.org/10.3390/su14041995.

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Intelligent data analysis based on artificial intelligence and Big Data tools is widely used by the scientific community to overcome global challenges. One of these challenges is the worldwide coronavirus pandemic, which began in early 2020. Data science not only provides an opportunity to assess the impact caused by a pandemic, but also to predict the infection spread. In addition, the model expansion by economic, social, and infrastructural factors makes it possible to predict changes in all spheres of human activity in competitive epidemiological conditions. This article is devoted to the u

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29

V., Dr Sellam. "An Intelligent Smart Ranked Feature Construction Analysis based on Multi-dimensional Data Streams." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 2600–2605. http://dx.doi.org/10.37200/ijpr/v24i5/pr201960.

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30

Zhao, Liang, Wen Tao, Guangwen Wang, Lida Wang, and Guichang Liu. "Intelligent anti-corrosion expert system based on big data analysis." Anti-Corrosion Methods and Materials 68, no. 1 (2021): 17–28. http://dx.doi.org/10.1108/acmm-10-2020-2384.

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Purpose The paper aims to develop an intelligent anti-corrosion expert system based on browser/server (B/S) architecture to realize an intelligent corrosion management system. Design/methodology/approach The system is based on Java EE technology platform and model view controller (MVC) three-tier architecture development model. The authors used an extended three-dimensional interpolation model to predict corrosion rate, and the model is verified by cross-validation method. Additionally, MySQL is used to realize comprehensive data management. Findings The proposed anti-corrosion system thorough

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31

Berthold, Michael R., Elizabeth Bradley, and Rudolf Kruse. "Guest Editorial: "Advances in intelligent data analysis"." Intelligent Data Analysis 8, no. 5 (2004): 437–38. http://dx.doi.org/10.3233/ida-2004-8501.

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32

Baranov, A. A., L. S. Namazova-Baranova, I. V. Smirnov, et al. "Technologies for Complex Intelligent Clinical Data Analysis." Annals of the Russian academy of medical sciences 71, no. 2 (2016): 160–71. http://dx.doi.org/10.15690/vramn663.

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The paper presents the system for intelligent analysis of clinical information. Authors describe methods implemented in the system for clinical information retrieval, intelligent diagnostics of chronic diseases, patient’s features importance and for detection of hidden dependencies between features. Results of the experimental evaluation of these methods are also presented.Background: Healthcare facilities generate a large flow of both structured and unstructured data which contain important information about patients. Test results are usually retained as structured data but some data is retai

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33

Simeonov, Vasil, Lidia Wolska, Agnieszka Kuczyńska, et al. "Sediment-quality assessment by intelligent data analysis." TrAC Trends in Analytical Chemistry 26, no. 4 (2007): 323–31. http://dx.doi.org/10.1016/j.trac.2006.12.004.

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34

Gross, Deborah S., Robert Atlas, Jeffrey Rzeszotarski, et al. "Environmental chemistry through intelligent atmospheric data analysis." Environmental Modelling & Software 25, no. 6 (2010): 760–69. http://dx.doi.org/10.1016/j.envsoft.2009.12.001.

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35

Farouk, Ahmed, and Dou Zhen. "Big data analysis techniques for intelligent systems." Journal of Intelligent & Fuzzy Systems 37, no. 3 (2019): 3067–71. http://dx.doi.org/10.3233/jifs-179109.

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36

Kordon, Arthur K. "Hybrid intelligent systems for industrial data analysis." International Journal of Intelligent Systems 19, no. 4 (2004): 367–83. http://dx.doi.org/10.1002/int.10169.

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37

Peng, Wei, Xinlei Zhang, and Xin Li. "Intelligent Behavior Data Analysis for Internet Addiction." Scientific Programming 2019 (November 29, 2019): 1–12. http://dx.doi.org/10.1155/2019/2753152.

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Internet addiction refers to excessive internet use that interferes with daily life. Due to its negative impact on college students’ study and life, discovering students’ internet addiction tendencies and making correct guidance for them timely is necessary. However, at present, the research methods used in analyzing students’ internet addiction are mainly questionnaires and statistical analysis, which relies on the domain experts heavily. Fortunately, with the development of the smart campus, students’ behavior data such as consumption and trajectory information in the campus are stored. With

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38

Perner, Petra. "Intelligent data analysis in medicine—Recent advances." Artificial Intelligence in Medicine 37, no. 1 (2006): 1–5. http://dx.doi.org/10.1016/j.artmed.2005.10.003.

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39

Wang, Xiaomeng, Detlef D. Nauck, Martin Spott, and Rudolf Kruse. "Intelligent data analysis with fuzzy decision trees." Soft Computing 11, no. 5 (2006): 439–57. http://dx.doi.org/10.1007/s00500-006-0108-0.

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40

Spott, Martin, and Detlef Nauck. "Towards the automation of intelligent data analysis." Applied Soft Computing 6, no. 4 (2006): 348–56. http://dx.doi.org/10.1016/j.asoc.2005.11.002.

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41

Juuso, Esko K. "Intelligent temporal analysis of coronavirus statistical data." Open Engineering 11, no. 1 (2021): 1223–32. http://dx.doi.org/10.1515/eng-2021-0118.

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Abstract The coronavirus COVID-19 is affecting around the world with strong differences between countries and regions. Extensive datasets are available for visual inspection and downloading. The material has limitations for phenomenological modeling but data-based methodologies can be used. This research focuses on the intelligent temporal analysis of datasets in developing compact solutions for early detection of levels, trends, episodes, and severity of situations. The methodology has been tested in the analysis of daily new confirmed COVID-19 cases and deaths in six countries. The datasets

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42

Abidi, Syed Sibte Raza, and Samina Raza Abidi. "Intelligent health data analytics: A convergence of artificial intelligence and big data." Healthcare Management Forum 32, no. 4 (2019): 178–82. http://dx.doi.org/10.1177/0840470419846134.

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Healthcare is a living system that generates a significant volume of heterogeneous data. As healthcare systems are pivoting to value-based systems, intelligent and interactive analysis of health data is gaining significance for health system management, especially for resource optimization whilst improving care quality and health outcomes. Health data analytics is being influenced by new concepts and intelligent methods emanating from artificial intelligence and big data. In this article, we contextualize health data and health data analytics in terms of the emerging trends of artificial intel

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43

Qian, Wang, Mi Na, Yi Zenan, Sun Ming Yue, and Liu Qing. "Access Data Analysis Technology and Implementation of Electric Power Big Data Achievement Sharing Platform through Artificial Intelligence." Journal of Physics: Conference Series 2083, no. 3 (2021): 032065. http://dx.doi.org/10.1088/1742-6596/2083/3/032065.

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Abstract With the rapid development of the information age, the data generated by all walks of life is showing an increasing trend of “blowout”. According to statistics, the total amount of data generated by mankind in the past 40,000 years is less than the total amount of data generated from 2010 to 2013 alone. The global big data reserves reached 8.61ZB in 2015 alone, and the growth of data in the future will reach an unpredictable value, entering the data age in an all-round way. Traditional power data computing technology and intelligent analysis technology are undergoing profound changes,

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44

Tan, C., and S. Yan. "SPATIOTEMPORAL DATA ORGANIZATION AND APPLICATION RESEARCH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1363–66. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1363-2017.

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Organization and management of spatiotemporal data is a key support technology for intelligence in all fields of the smart city. The construction of a smart city cannot be realized without spatiotemporal data. Oriented to support intelligent applications，this paper proposes an organizational model for spatiotemporal data, and details the construction of a spatiotemporal big data calculation, analysis, and service framework for highly efficient management and intelligent application of spatiotemporal data for the entire data life cycle.

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45

Wang, Xiao-Ling, Jun Liu, Zi-Qi Li, and Zhi-Lin Luan. "Application of Physical Examination Data on Health Analysis and Intelligent Diagnosis." BioMed Research International 2021 (June 13, 2021): 1–10. http://dx.doi.org/10.1155/2021/8828677.

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Analysis and diagnosis according to the collected physical data are an important part in the physical examination. Through the data analysis of the physical examination results and expert diagnoses, the physical condition of a specific physical examination unit can be achieved which may guide individual health development. However, in general, the application of physical examination data is insufficient in most of the current physical examination organizations. Therefore, in the present study, statistical analysis and intelligent diagnosis were applied to maximize the utilization of physical e

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46

Grishanova, I. Y., and J. V. Rogushina. "Technological solutions for intelligent analysis of Big Data. Programming languages." PROBLEMS IN PROGRAMMING, no. 4 (December 2018): 045–58. http://dx.doi.org/10.15407/pp2018.04.045.

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47

Ren, Xuejiao, and Xiaozhou Ding. "Motivation Analysis of Technological Startups Business Models Based on Intelligent Data Mining and Analysis." Journal of Electrical and Computer Engineering 2022 (May 17, 2022): 1–12. http://dx.doi.org/10.1155/2022/9316534.

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To improve the motivation analysis effect of the technological startups business model, this study combines intelligent data mining technology to analyze related factors and proposes a method of adjusting parameters of machine-learning model based on Bayesian optimization algorithm. Moreover, this study integrates the prior information and sample information of the machine learning algorithm performance function and builds an intelligent analysis model based on the elements of the technological startups business model. Finally, this article designs experiments to evaluate the effect of intelli

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48

Podvesovskii, A. G., E. V. Karpenko, D. G. Lagerev, and A. N. Baburin. "ENSEMBLE MODELS FOR INTELLIGENT ANALYSIS OF SOCIOLOGICAL DATA." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 207 (September 2021): 43–52. http://dx.doi.org/10.14489/vkit.2021.09.pp.043-052.

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The paper investigates an approach to sociological information processing based on the use of intelligent data analysis methods applied to the task of processing the results of a questionnaire survey. The advantages of intelligent analysis of sociological data in comparison with traditional statistical processing are discussed, as well as the implementation features and applicability limits of various intelligent data analysis methods in solving problems of association, clustering and classification. Structure and features of representation of respondents’ survey data are considered, the appro

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49

Terentyev, Alexander N., Petr I. Bidyuk, and L. A. Korshevnyuk. "Bayesian Network as Instrument of Intelligent Data Analysis." Journal of Automation and Information Sciences 39, no. 8 (2007): 28–38. http://dx.doi.org/10.1615/jautomatinfscien.v39.i8.40.

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50

하성호, HanKuk Hong, and 김태훈. "Intelligent Data Envelopment Analysis (IDEA) for Efficiency Enhancement." E-Business Studies 9, no. 3 (2008): 183–97. http://dx.doi.org/10.15719/geba.9.3.200809.183.

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