Foundations of Computational Intelligence Volume 2

1. Front Matter

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2. Approximate Reasoning - Theoretical Foundations and Applications

   1. Front Matter

      Pages 1-1

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   2. Approximate Reasoning - Theoretical Foundations

      1. Fuzzy Sets, Near Sets, and Rough Sets for Your Computational Intelligence Toolbox

         • James F. Peters

         Pages 3-25

      2. Fuzzy without Fuzzy: Why Fuzzy-Related Aggregation Techniques Are Often Better Even in Situations without True Fuzziness

         • Hung T. Nguyen, Vladik Kreinovich, François Modave, Martine Ceberio

         Pages 27-51

      3. Intermediate Degrees Are Needed for the World to Be Cognizable: Towards a New Justification for Fuzzy Logic Ideas

         • Hung T. Nguyen, Vladik Kreinovich, J. Esteban Gamez, François Modave, Olga Kosheleva

         Pages 53-74

      4. Paraconsistent Annotated Logic Program Before-after EVALPSN and Its Application

         • Kazumi Nakamatsu

         Pages 75-108

3. Approximate Reasoning - Success Stories and Real World Applications

   1. Front Matter

      Pages 109-109

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   2. A Fuzzy Set Approach to Software Reliability Modeling

      • P. Zeephongsekul

      Pages 111-131

   3. Computational Methods for Investment Portfolio: The Use of Fuzzy Measures and Constraint Programming for Risk Management

      • Tanja Magoč, François Modave, Martine Ceberio, Vladik Kreinovich

      Pages 133-173

   4. A Bayesian Solution to the Modifiable Areal Unit Problem

      • C. Hui

      Pages 175-196

   5. Fuzzy Logic Control in Communication Networks

      • Chrysostomos Chrysostomou, Andreas Pitsillides

      Pages 197-236

   6. Adaptation in Classification Systems

      • Abdelhamid Bouchachia

      Pages 237-258

   7. Music Instrument Estimation in Polyphonic Sound Based on Short-Term Spectrum Match

      • Wenxin Jiang, Alicja Wieczorkowska, Zbigniew W. Raś

      Pages 259-273

   8. Ultrasound Biomicroscopy Glaucoma Images Analysis Based on Rough Set and Pulse Coupled Neural Network

      • El-Sayed A. El-Dahshan, Aboul Ella Hassanien, Amr Radi, Soumya Banerjee

      Pages 275-293

   9. An Overview of Fuzzy C-Means Based Image Clustering Algorithms

      • Huiyu Zhou, Gerald Schaefer

      Pages 295-310

4. Back Matter

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Foundations of Computational Intelligence Volume 2: Approximation Reasoning: Theoretical Foundations and Applications Human reasoning usually is very approximate and involves various types of - certainties. Approximate reasoning is the computational modelling of any part of the process used by humans to reason about natural phenomena or to solve real world problems. The scope of this book includes fuzzy sets, Dempster-Shafer theory, multi-valued logic, probability, random sets, and rough set, near set and hybrid intelligent systems. Besides research articles and expository papers on t- ory and algorithms of approximation reasoning, papers on numerical experiments and real world applications were also encouraged. This Volume comprises of 12 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Intelligence techniques for - proximation reasoning. The Volume is divided into 2 parts: Part-I: Approximate Reasoning – Theoretical Foundations Part-II: Approximate Reasoning – Success Stories and Real World Applications Part I on Approximate Reasoning – Theoretical Foundations contains four ch- ters that describe several approaches of fuzzy and Para consistent annotated logic approximation reasoning. In Chapter 1, “Fuzzy Sets, Near Sets, and Rough Sets for Your Computational Intelligence Toolbox” by Peters considers how a user might utilize fuzzy sets, near sets, and rough sets, taken separately or taken together in hybridizations as part of a computational intelligence toolbox. In multi-criteria decision making, it is necessary to aggregate (combine) utility values corresponding to several criteria (parameters).

• Approximate Reasoning
• algorithm
• algorithms
• computational intelligence
• fuzzy
• fuzzy set
• fuzzy sets
• intelligence
• modeling

• Faculty of Computers and Information Information Technology Department, Cairo University, Orman, Giza

  Aboul-Ella Hassanien

• Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn,Washington, USA

  Ajith Abraham

• Soft Computing and Intelligent Information Systems Department of Computer Science and Artificial Intelligence ETS de Ingenierias Informática y de Telecomunicación, University of Granada, Granada, Spain

  Francisco Herrera

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