Uncertainty Modeling In Knowledge Engineering And Decision Making
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Uncertainty Modeling In Knowledge Engineering And Decision Making - Proceedings Of The 10th International Flins Conference by Cengiz Kahraman,Faik Tunc Bozbura,Etienne E Kerre Pdf
FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to Computational Intelligence for applied research. The contributions to the 10th of FLINS conference cover state-of-the-art research, development, and technology for computational intelligence systems, both from the foundations and the applications points-of-view.
Uncertainty Modelling in Knowledge Engineering and Decision Making by Xianyi Zeng,Jie Lu,Etienne E Kerre,Luis Martinez,Ludovic Koehl Pdf
FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to include Computational Intelligence for applied research. The contributions to the 12th of FLINS conference cover state-of-the-art research, development, and technology for computational intelligence systems, both from the foundations and the applications points-of-view.
Uncertainty Modeling in Knowledge Engineering and Decision Making by Anonim Pdf
FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to Computational Intelligence for applied research. The contributions to the 10th of FLINS conference cover state-of-the-art research, development, and technology for computational intelligence systems, both from the foundations and the applications points-of-view. Sample Chapter(s). Foreword (55 KB). Evaluation of Manufacturing Technology of Photovoltaic Cells (124 KB). Contents: Decision Making and Decision Support Systems; Uncertainty Modeling; Foundations of Computational Intelligence; Statistics, Data Analysis and Data Mining; Intelligent Information Processing; Productivity and Reliability; Applied Research. Readership: Graduate students, researchers, and academics in artificial intelligence/machine learning, information management, decision sciences, databases/information sciences and fuzzy logic.
Integrated Uncertainty in Knowledge Modelling and Decision Making by Van-Nam Huynh,Masahiro Inuiguchi,Bac Le,Bao Nguyen Le,Thierry Denoeux Pdf
This book constitutes the refereed proceedings of the 5th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2016, held in Da Nang, Vietnam, in November/December 2016. The IUKM symposia aim to provide a forum for exchanges of research results and ideas, and experience of application among researchers and practitioners involved with all aspects of uncertainty modelling and management.
Integrated Uncertainty in Knowledge Modelling and Decision Making by Zengchang Qin,Van-Nam Huynh Pdf
This book constitutes the refereed proceedings of the International Symposium on Integrated Uncertainty in Knowledge Modeling and Decision Making, IUKM 2013, held in Beijing China, in July 2013. The 19 revised full papers were carefully reviewed and selected from 49 submissions and are presented together with keynote and invited talks. The papers provide a wealth of new ideas and report both theoretical and applied research on integrated uncertainty modeling and management.
Integrated Uncertainty in Knowledge Modelling and Decision Making by Van-Nam Huynh,Masahiro Inuiguchi,Thierry Demoeux Pdf
This book constitutes the refereed proceedings of the 4th International Symposium on Integrated Uncertainty in Knowledge Modeling and Decision Making, IUKM 2015, held in Nha Trang, Vietnam, in October 2015. The 40 revised full papers were carefully reviewed and selected from 58 submissions and are presented together with three keynote and invited talks. The papers provide a wealth of new ideas and report both theoretical and applied research on integrated uncertainty modeling and management
Integrated Uncertainty in Knowledge Modelling and Decision Making by Yongchuan Tang,Van-Nam Huynh,Jonathan Lawry Pdf
This book constitutes the refereed proceedings of the International Symposium on Integrated Uncertainty in Knowledge Modeling and Decision Making, IUKM 2011, held in Hangzhou, China, in October 2011. The 21 revised full papers presented together with 1 keynote lecture and 5 invited talks were carefully reviewed and selected from 55 submissions. The papers provide a wealth of new ideas and report both theoretical and applied research on integrated uncertainty modeling and management.
Uncertainty Modeling and Analysis in Engineering and the Sciences by Bilal M. Ayyub,George J. Klir Pdf
Engineers and scientists often need to solve complex problems with incomplete information resources, necessitating a proper treatment of uncertainty and a reliance on expert opinions. Uncertainty Modeling and Analysis in Engineering and the Sciences prepares current and future analysts and practitioners to understand the fundamentals of knowledge a
Data Science and Knowledge Engineering for Sensing Decision Support by Jun Liu,Jie Lu,Yang Xu,Luis Martinez,Etienne E Kerre Pdf
FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to include Computational Intelligence for applied research. The contributions of the FLINS conference cover state-of-the-art research, development, and technology for computational intelligence systems, with special focuses on data science and knowledge engineering for sensing decision support, both from the foundations and the applications points-of-view.
Applied Research in Uncertainty Modeling and Analysis by Bilal M. Ayyub Pdf
The application areas of uncertainty are numerous and diverse, including all fields of engineering, computer science, systems control and finance. Determining appropriate ways and methods of dealing with uncertainty has been a constant challenge. The theme for this book is better understanding and the application of uncertainty theories. This book, with invited chapters, deals with the uncertainty phenomena in diverse fields. The book is an outgrowth of the Fourth International Symposium on Uncertainty Modeling and Analysis (ISUMA), which was held at the center of Adult Education, College Park, Maryland, in September 2003. All of the chapters have been carefully edited, following a review process in which the editorial committee scrutinized each chapter. The contents of the book are reported in twenty-three chapters, covering more than . . ... pages. This book is divided into six main sections. Part I (Chapters 1-4) presents the philosophical and theoretical foundation of uncertainty, new computational directions in neural networks, and some theoretical foundation of fuzzy systems. Part I1 (Chapters 5-8) reports on biomedical and chemical engineering applications. The sections looks at noise reduction techniques using hidden Markov models, evaluation of biomedical signals using neural networks, and changes in medical image detection using Markov Random Field and Mean Field theory. One of the chapters reports on optimization in chemical engineering processes.
Uncertainty, Modeling, and Decision Making in Geotechnics by Kok-Kwang Phoon,Takayuki Shuku,Jianye Ching Pdf
Uncertainty, Modeling, and Decision Making in Geotechnics shows how uncertainty quantification and numerical modeling can complement each other to enhance decision-making in geotechnical practice, filling a critical gap in guiding practitioners to address uncertainties directly. The book helps practitioners acquire a working knowledge of geotechnical risk and reliability methods and guides them to use these methods wisely in conjunction with data and numerical modeling. In particular, it provides guidance on the selection of realistic statistics and a cost-effective, accessible method to address different design objectives, and for different problem settings, and illustrates the value of this to decision-making using realistic examples. Bringing together statistical characterization, reliability analysis, reliability-based design, probabilistic inverse analysis, and physical insights drawn from case studies, this reference guide from an international team of experts offers an excellent resource for state-of-the-practice uncertainty-informed geotechnical design for specialist practitioners and the research community.
Integrated Uncertainty in Knowledge Modelling and Decision Making by Hirosato Seki,Canh Hao Nguyen,Van-Nam Huynh,Masahiro Inuiguchi Pdf
This book constitutes the refereed proceedings of the 7th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2019, held in Nara, Japan, in March 2019. The 37 revised full papers presented were carefully reviewed and selected from 93 submissions. The papers deal with all aspects of uncertainty modelling and management and are organized in topical sections on uncertainty management and decision support; econometrics; machine learning; machine learning applications; and statistical methods.
Modeling with Rules Using Semantic Knowledge Engineering by Grzegorz J. Nalepa Pdf
This book proposes a consistent methodology for building intelligent systems. It puts forward several formal models for designing and implementing rules-based systems, and presents illustrative case studies of their applications. These include software engineering, business process systems, Semantic Web, and context-aware systems on mobile devices. Rules offer an intuitive yet powerful method for representing human knowledge, and intelligent systems based on rules have many important applications. However, their practical development requires proper techniques and models - a gap that this book effectively addresses.
Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach by Bilal Ayyub,Madan M. Gupta Pdf
Uncertainty has been of concern to engineers, managers and . scientists for many centuries. In management sciences there have existed definitions of uncertainty in a rather narrow sense since the beginning of this century. In engineering and uncertainty has for a long time been considered as in sciences, however, synonymous with random, stochastic, statistic, or probabilistic. Only since the early sixties views on uncertainty have ~ecome more heterogeneous and more tools to model uncertainty than statistics have been proposed by several scientists. The problem of modeling uncertainty adequately has become more important the more complex systems have become, the faster the scientific and engineering world develops, and the more important, but also more difficult, forecasting of future states of systems have become. The first question one should probably ask is whether uncertainty is a phenomenon, a feature of real world systems, a state of mind or a label for a situation in which a human being wants to make statements about phenomena, i. e. , reality, models, and theories, respectively. One cart also ask whether uncertainty is an objective fact or just a subjective impression which is closely related to individual persons. Whether uncertainty is an objective feature of physical real systems seems to be a philosophical question. This shall not be answered in this volume.
