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|a 9781394165513
|q (electronic bk. : oBook)
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|a 9781394165490
|q (electronic book)
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|a 1394165498
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|q (hardcover)
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|a 10.1002/9781394165513
|2 doi
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|a AU@
|b 000072898398
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|a (OCoLC)1338838293
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|a 9781786307712
|b O'Reilly Media
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|a QA76.9.Q36
|b D38 2022
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|a 001.42
|2 23/eng/20220915
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|a UAMI
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|a Data analysis and related applications.
|n 1,
|p Computational, algorithmic and applied economic data analysis /
|c edited by Konstantinos N. Zafeiris, [and four others].
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|a Computational, algorithmic and applied economic data analysis
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|a London :
|b ISTE Ltd ;
|a Hoboken, NJ :
|b John Wiley & Sons, Inc.,
|c 2022.
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300 |
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|a 1 online resource (478 pages)
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336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a Big data, artificial intelligence and data analysis set ;
|v volume 9
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504 |
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|a Includes bibliographical references and index.
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|a Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1 -- 1. Performance of Evaluation of Diagnosis of Various Thyroid Diseases Using Machine Learning Techniques -- 1.1. Introduction -- 1.2. Data understanding -- 1.3. Modeling -- 1.4. Findings -- 1.5. Conclusion -- 1.6. References -- 2. Exploring Chronic Diseases' Spatial Patterns: Thyroid Cancer in Sicilian Volcanic Areas -- 2.1. Introduction -- 2.2. Epidemiological data and territory -- 2.3. Methodology -- 2.3.1. Spatial inhomogeneity and spatial dependence -- 2.3.2. Standardized incidence ratio (SIR)
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|a 2.3.3. Local Moran's I statistic -- 2.4. Spatial distribution of TC in eastern Sicily -- 2.4.1. SIR geographical variation -- 2.4.2. Estimate of the spatial attraction -- 2.5. Conclusion -- 2.6. References -- 3. Analysis of Blockchain-based Databases in Web Applications -- 3.1. Introduction -- 3.2. Background -- 3.2.1. Blockchain -- 3.2.2. Blockchain types -- 3.2.3. Blockchain-based web applications -- 3.2.4. Blockchain consensus algorithms -- 3.2.5. Other consensus algorithms -- 3.3. Analysis stack -- 3.3.1. Art Shop web application -- 3.3.2. SQL-based application
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|a 3.3.3. NoSQL-based application -- 3.3.4. Blockchain-based application -- 3.4. Analysis -- 3.4.1. Adding records -- 3.4.2. Query -- 3.4.3. Functionality -- 3.4.4. Security -- 3.5. Conclusion -- 3.6. References -- 4. Optimization and Asymptotic Analysis of Insurance Models -- 4.1. Introduction -- 4.2. Discrete-time model with reinsurance and bank loans -- 4.2.1. Model description -- 4.2.2. Optimization problem -- 4.2.3. Model stability -- 4.3. Continuous-time insurance model with dividends -- 4.3.1. Model description -- 4.3.2. Optimal barrier strategy -- 4.3.3. Special form of claim distribution
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|a 4.3.4. Numerical analysis -- 4.4. Conclusion and further research directions -- 4.5. References -- 5. Statistical Analysis of TrafficVolume in the 25 de Abril Bridge -- 5.1. Introduction -- 5.2. Data -- 5.3. Methodology -- 5.3.1. Main limit results -- 5.3.2. Block maxima method -- 5.3.3. Largest order statistics method -- 5.3.4. Estimation of other tail parameters -- 5.4. Results and conclusion -- 5.5. Acknowledgements -- 5.6. References -- 6. Predicting the Risk of Gestational Diabetes Mellitus through Nearest Neighbor Classification -- 6.1. Introduction -- 6.2. Nearest neighbor methods
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|a 6.2.1. Background of the NN methods -- 6.2.2. The k-nearest neighbors method -- 6.2.3. The fixed-radius NN method -- 6.2.4. The kernel-NN method -- 6.2.5. Algorithms of the three considered NN methods -- 6.2.6. Parameter and distance metric selection -- 6.3. Experimental results -- 6.3.1. Dataset description -- 6.3.2. Variable selection and data splitting -- 6.3.3. Results -- 6.3.4. A discussion and comparison of results -- 6.4. Conclusion -- 6.5. References -- 7. Political Trust in National Institutions: The Significance of Items' Level of Measurement in the Validation of Constructs
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|a 7.1. Introduction
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|a Online resource; title from digital title page (viewed on September 15, 2022).
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|a The scientific field of data analysis is constantly expanding due to the rapid growth of the computer industry and the wide applicability of computational and algorithmic techniques, in conjunction with new advances in statistical, stochastic and analytic tools. There is a constant need for new, high-quality publications to cover the recent advances in all fields of science and engineering. This book is a collective work by a number of leading scientists, computer experts, analysts, engineers, mathematicians, probabilists and statisticians who have been working at the forefront of data analysis and related applications. The chapters of this collaborative work represent a cross-section of current concerns, developments and research interests in the above scientific areas. The collected material has been divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with related applications.
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|a O'Reilly
|b O'Reilly Online Learning: Academic/Public Library Edition
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650 |
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|a Quantitative research.
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|a Recherche quantitative.
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|a Quantitative research
|2 fast
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|a Zafeiris, Konstantinos N.,
|e editor.
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|a Skiadas, Christos H.,
|e editor.
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|a Dimotikalis, Yannis,
|e editor.
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|a Karagrigoriou, Alex,
|e editor.
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|a Karagrigoriou-Vonta, Christiana,
|e editor.
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776 |
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|i Print version:
|a Zafeiris, Konstantinos N.
|t Data Analysis and Related Applications, Volume 1.
|d Newark : John Wiley & Sons, Incorporated, ©2022
|z 9781786307712
|
830 |
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|a Innovation, entrepreneurship and management series.
|p Big data, artificial intelligence and data analysis set ;
|v v. 9.
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856 |
4 |
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|u https://learning.oreilly.com/library/view/~/9781786307712/?ar
|z Texto completo (Requiere registro previo con correo institucional)
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938 |
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|a YBP Library Services
|b YANK
|n 18078041
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|a ProQuest Ebook Central
|b EBLB
|n EBL7069543
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|a 92
|b IZTAP
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