Michael Z. Zgurovsky - Big Data: Conceptual Analysis and Applications

Studies in Big Data 58 Michael Z. Zgurovsky Yuriy P. Zaychenko Big Data: Conceptual Analysis and Applications Studies in Big Data Volume 58 Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland The series Studies in Big Data (SBD) publishes new developments and advances in the various areas of Big Data- quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence including neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems.

Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. ** Indexing: The books of this series are submitted to ISI Web of Science, DBLP, Ulrichs, MathSciNet, Current Mathematical Publications, Mathematical Reviews, Zentralblatt Math: MetaPress and Springerlink. More information about this series at http://www.springer.com/series/11970 Michael Z. Zgurovsky Yuriy P. Zaychenko Big Data: Conceptual Analysis and Applications Michael Z. Zgurovsky Yuriy P.

Zaychenko National Technical University of Ukraine National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Igor Sikorsky Kyiv Polytechnic Institute Kyiv, Ukraine Kyiv, Ukraine ISSN 2197-6503 ISSN 2197-6511 (electronic) Studies in Big Data ISBN 978-3-030-14297-1 ISBN 978-3-030-14298-8 (eBook) https://doi.org/10.1007/978-3-030-14298-8 Library of Congress Control Number: 2019933181 Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made.

The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Preface The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition, large-scale neural networks with applications for Deep Learning systems were used. Application of hybrid neuro-fuzzy networks for analyzing stock markets was presented.

The analysis of big historical, economic and physical data revealed the hidden Fibonacci pattern about the course of systemic world conflicts and their connection with the Kondratieff big economic cycles and the Schwabe-Wolf solar activity cycles. Now we give a brief description of the main practical problems solved in this book related to the intellectual analysis of big data. First of all large dimensions of modern neural networks with applications for 3-D images recognition and automatic speech recognition demanded development of new efficient training methods called Deep Learning (DL). But the most serious drawback of deep learning networks is a problem of determination of its proper structure and how to choose adequate number of their layers. For solution of DL problems arising in BD novel approaches and methods are developed and presented based on the application method of self-organization, also known as the Group Method of Data Handling (GMDH). Several classes of hybrid GMDH neuro-fuzzy networks are considered algorithms of their structure synthesis based on GMDH are suggested and analyzed.

Training algorithms for hybrid deep networks are free from problem of gradient vanishing or explosion and besides, the application of GMDH enables to reduce dimensionality of training DN and accelerate the convergence of training. Secondly, the application of hybrid GMDH- neuro-fuzzy networks for at the stock markets is presented. Problems of images in 2-D and 3-D which also refer stock prices forecasting to sphere of BD analytics are considered. For its solution, v vi Preface last years convolutional neural networks (CNN) are widely applied. New class of hybrid fuzzy CNN network is suggested in which CNN VGG is used as informative features extractor and fuzzy neural network NEFClass is used as classifier. Besides, for cutting dimensionality of classification problem and reducing of number of feature principal component method (PCM) was applied and investigated.