Insight into Fuzzy Modeling
Insight into Fuzzy Modeling

Author: Vilém Novák

Publisher: John Wiley & Sons

Published: 2016-04-04

Total Pages: 268

ISBN-13: 1119193184

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Provides a unique and methodologically consistent treatment of various areas of fuzzy modeling and includes the results of mathematical fuzzy logic and linguistics This book is the result of almost thirty years of research on fuzzy modeling. It provides a unique view of both the theory and various types of applications. The book is divided into two parts. The first part contains an extensive presentation of the theory of fuzzy modeling. The second part presents selected applications in three important areas: control and decision-making, image processing, and time series analysis and forecasting. The authors address the consistent and appropriate treatment of the notions of fuzzy sets and fuzzy logic and their applications. They provide two complementary views of the methodology, which is based on fuzzy IF-THEN rules. The first, more traditional method involves fuzzy approximation and the theory of fuzzy relations. The second method is based on a combination of formal fuzzy logic and linguistics. A very important topic covered for the first time in book form is the fuzzy transform (F-transform). Applications of this theory are described in separate chapters and include image processing and time series analysis and forecasting. All of the mentioned components make this book of interest to students and researchers of fuzzy modeling as well as to practitioners in industry. Features: Provides a foundation of fuzzy modeling and proposes a thorough description of fuzzy modeling methodology Emphasizes fuzzy modeling based on results in linguistics and formal logic Includes chapters on natural language and approximate reasoning, fuzzy control and fuzzy decision-making, and image processing using the F-transform Discusses fuzzy IF-THEN rules for approximating functions, fuzzy cluster analysis, and time series forecasting Insight into Fuzzy Modeling is a reference for researchers in the fields of soft computing and fuzzy logic as well as undergraduate, master and Ph.D. students. Vilém Novák, D.Sc. is Full Professor and Director of the Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Czech Republic. Irina Perfilieva, Ph.D. is Full Professor, Senior Scientist, and Head of the Department of Theoretical Research at the Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Czech Republic. Antonín Dvorák, Ph.D. is Associate Professor, and Senior Scientist at the Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Czech Republic.

Uncertainty Modelling in Data Science
Uncertainty Modelling in Data Science

Author: Sébastien Destercke

Publisher: Springer

Published: 2018-07-24

Total Pages: 246

ISBN-13: 3319975471

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This book features 29 peer-reviewed papers presented at the 9th International Conference on Soft Methods in Probability and Statistics (SMPS 2018), which was held in conjunction with the 5th International Conference on Belief Functions (BELIEF 2018) in Compiègne, France on September 17–21, 2018. It includes foundational, methodological and applied contributions on topics as varied as imprecise data handling, linguistic summaries, model coherence, imprecise Markov chains, and robust optimisation. These proceedings were produced using EasyChair. Over recent decades, interest in extensions and alternatives to probability and statistics has increased significantly in diverse areas, including decision-making, data mining and machine learning, and optimisation. This interest stems from the need to enrich existing models, in order to include different facets of uncertainty, like ignorance, vagueness, randomness, conflict or imprecision. Frameworks such as rough sets, fuzzy sets, fuzzy random variables, random sets, belief functions, possibility theory, imprecise probabilities, lower previsions, and desirable gambles all share this goal, but have emerged from different needs. The advances, results and tools presented in this book are important in the ubiquitous and fast-growing fields of data science, machine learning and artificial intelligence. Indeed, an important aspect of some of the learned predictive models is the trust placed in them. Modelling the uncertainty associated with the data and the models carefully and with principled methods is one of the means of increasing this trust, as the model will then be able to distinguish between reliable and less reliable predictions. In addition, extensions such as fuzzy sets can be explicitly designed to provide interpretable predictive models, facilitating user interaction and increasing trust.

Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications

Author: Jesús Medina

Publisher: Springer

Published: 2018-05-29

Total Pages: 773

ISBN-13: 3319914790

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This three volume set (CCIS 853-855) constitutes the proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2017, held in Cádiz, Spain, in June 2018. The 193 revised full papers were carefully reviewed and selected from 383 submissions. The papers are organized in topical sections on advances on explainable artificial intelligence; aggregation operators, fuzzy metrics and applications; belief function theory and its applications; current techniques to model, process and describe time series; discrete models and computational intelligence; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy logic and artificial intelligence problems; fuzzy mathematical analysis and applications; fuzzy methods in data mining and knowledge discovery; fuzzy transforms: theory and applications to data analysis and image processing; imprecise probabilities: foundations and applications; mathematical fuzzy logic, mathematical morphology; measures of comparison and entropies for fuzzy sets and their extensions; new trends in data aggregation; pre-aggregation functions and generalized forms of monotonicity; rough and fuzzy similarity modelling tools; soft computing for decision making in uncertainty; soft computing in information retrieval and sentiment analysis; tri-partitions and uncertainty; decision making modeling and applications; logical methods in mining knowledge from big data; metaheuristics and machine learning; optimization models for modern analytics; uncertainty in medicine; uncertainty in Video/Image Processing (UVIP).

Information Processing and Management of Uncertainty in Knowledge-Based Systems
Information Processing and Management of Uncertainty in Knowledge-Based Systems

Author: Davide Ciucci

Publisher: Springer Nature

Published: 2022-07-04

Total Pages: 825

ISBN-13: 3031089715

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This two-volume set (CCIS 1601-1602) constitutes the proceedings of the 19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2021, held in Milan, Italy, in July 2022. The 124 papers were carefully reviewed and selected from 188 submissions. The papers are organized in topical sections as follows: aggregation theory beyond the unit interval; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy mathematical analysis and its applications; generalized sets and operators; information fusion techniques based on aggregation functions, pre-aggregation functions, and their generalizations; interval uncertainty; knowledge acquisition, representation and reasoning; logical structures of opposition and logical syllogisms; mathematical fuzzy logics; theoretical and applied aspects of imprecise probabilities; data science and machine learning; decision making modeling and applications; e-health; fuzzy methods in data mining and knowledge discovery; soft computing and artificia intelligence techniques in image processing; soft methods in statistics and data analysis; uncertainty, heterogeneity, reliability and explainability in AI; weak and cautious supervised learning.

Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003
Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003

Author: Okyay Kaynak

Publisher: Springer

Published: 2003-08-03

Total Pages: 1164

ISBN-13: 3540449892

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The refereed proceedings of the Joint International Conference on Artificial Neural Networks and International Conference on Neural Information Processing, ICANN/ICONIP 2003, held in Istanbul, Turkey, in June 2003. The 138 revised full papers were carefully reviewed and selected from 346 submissions. The papers are organized in topical sections on learning algorithms, support vector machine and kernel methods, statistical data analysis, pattern recognition, vision, speech recognition, robotics and control, signal processing, time-series prediction, intelligent systems, neural network hardware, cognitive science, computational neuroscience, context aware systems, complex-valued neural networks, emotion recognition, and applications in bioinformatics.

Advances in Computational Intelligence
Advances in Computational Intelligence

Author: Hans-Paul Schwefel

Publisher: Springer Science & Business Media

Published: 2013-03-09

Total Pages: 327

ISBN-13: 3662056097

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The 30 coherently written chapters by leading researchers presented in this anthology are devoted to basic results achieved in computational intelligence since 1997. The book provides complete coverage of the core issues in the field, especially in fuzzy logic and control as well as for evolutionary optimization algorithms including genetic programming, in a comprehensive and systematic way. Theoretical and methodological investigations are complemented by prototypic applications for design and management tasks in electrical engineering, mechanical engineering, and chemical engineering. This book will become a valuable source of reference for researchers active in computational intelligence. Advanced students and professionals interested in learning about and applying advanced techniques of computational intelligence will appreciate the book as a useful guide enhanced by numerous examples and applications in a variety of fields.

Information Processing and Management of Uncertainty in Knowledge-Based Systems
Information Processing and Management of Uncertainty in Knowledge-Based Systems

Author: Marie-Jeanne Lesot

Publisher: Springer Nature

Published: 2020-06-05

Total Pages: 839

ISBN-13: 3030501531

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This three volume set (CCIS 1237-1239) constitutes the proceedings of the 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, in June 2020. The conference was scheduled to take place in Lisbon, Portugal, at University of Lisbon, but due to COVID-19 pandemic it was held virtually. The 173 papers were carefully reviewed and selected from 213 submissions. The papers are organized in topical sections: homage to Enrique Ruspini; invited talks; foundations and mathematics; decision making, preferences and votes; optimization and uncertainty; games; real world applications; knowledge processing and creation; machine learning I; machine learning II; XAI; image processing; temporal data processing; text analysis and processing; fuzzy interval analysis; theoretical and applied aspects of imprecise probabilities; similarities in artificial intelligence; belief function theory and its applications; aggregation: theory and practice; aggregation: pre-aggregation functions and other generalizations of monotonicity; aggregation: aggregation of different data structures; fuzzy methods in data mining and knowledge discovery; computational intelligence for logistics and transportation problems; fuzzy implication functions; soft methods in statistics and data analysis; image understanding and explainable AI; fuzzy and generalized quantifier theory; mathematical methods towards dealing with uncertainty in applied sciences; statistical image processing and analysis, with applications in neuroimaging; interval uncertainty; discrete models and computational intelligence; current techniques to model, process and describe time series; mathematical fuzzy logic and graded reasoning models; formal concept analysis, rough sets, general operators and related topics; computational intelligence methods in information modelling, representation and processing.

Gaining Insight into User and Search Engine Behaviour by Analyzing Web Logs
Gaining Insight into User and Search Engine Behaviour by Analyzing Web Logs

Author: Jeeva Jose

Publisher: Anchor Academic Publishing

Published: 2016-10-04

Total Pages: 209

ISBN-13: 3960675879

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Web Usage Mining, also known as Web Log Mining, is the result of user interaction with a Web server including Web logs, click streams and database transaction or the visits of search engine crawlers at a Website. Log files provide immense source of information about the behavior of users as well as search engine crawlers. Web Usage Mining concerns usage of common browsing patterns i.e. pages requested in sequence from Web logs. These patterns can be utilized to enhance the design and modification of a Website. Analyzing and discovering user behavior is helpful for understanding what online information users inquire and how they behave. The analyzed result can be used in intelligent online applications, refining Websites, improving search accuracy when seeking information and lead decision makers towards better decisions in changing markets like putting advertisements in ideal places. Similarly, the crawlers or spiders are accessing the Websites to index new and updated pages. These traces help to analyze the behavior of search engine crawlers. The log files are unstructured files and of huge size. These files need to be extracted and pre-processed before any data mining functionality to follow. Pre-processing is done in unique ways for each application. Two pre-processing algorithms are proposed based on indiscernibility relations in rough set theory which generates Equivalence Classes. The first algorithm generates a pre-processed file with successful user requests while the second one generates a pre-processed file for pre-fetching and caching purposes. Two algorithms are proposed to extract usage analytics. The first algorithm identifies the origin of visits, the top referring sites and the most popular keywords used by the visitor to arrive at a Website. The second algorithm extracts user agents like browser with its version and operating system with its version used by a visitor to access a Website. In this study, clustering of users based on Entry Pages to a Website is done to analyze the deep linked traffic at a Website. The Top Ten Entry Pages, the traffic and the temporal information of the Top Ten Entry Pages are also studied.

Econometrics for Financial Applications
Econometrics for Financial Applications

Author: Ly H. Anh

Publisher: Springer

Published: 2017-12-18

Total Pages: 1089

ISBN-13: 3319731505

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This book addresses both theoretical developments in and practical applications of econometric techniques to finance-related problems. It includes selected edited outcomes of the International Econometric Conference of Vietnam (ECONVN2018), held at Banking University, Ho Chi Minh City, Vietnam on January 15-16, 2018. Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. An extremely important part of economics is finances: a financial crisis can bring the whole economy to a standstill and, vice versa, a smart financial policy can dramatically boost economic development. It is therefore crucial to be able to apply mathematical techniques of econometrics to financial problems. Such applications are a growing field, with many interesting results – and an even larger number of challenges and open problems.

Computational Intelligence
Computational Intelligence

Author: Rudolf Kruse

Publisher: Springer Nature

Published: 2022-03-26

Total Pages: 629

ISBN-13: 3030422275

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This textbook provides a clear and logical introduction to the field, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. This enhanced third edition has been fully revised and expanded with new content on deep learning, scalarization methods, large-scale optimization algorithms, and collective decision-making algorithms. Features: provides supplementary material at an associated website; contains numerous classroom-tested examples and definitions throughout the text; presents useful insights into all that is necessary for the successful application of computational intelligence methods; explains the theoretical background underpinning proposed solutions to common problems; discusses in great detail the classical areas of artificial neural networks, fuzzy systems and evolutionary algorithms; reviews the latest developments in the field, covering such topics as ant colony optimization and probabilistic graphical models.