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Soft computing is used where a complex problem is not adequately specified for the use of conventional math and computer techniques. Soft computing has numerous real-world applications in domestic, commercial and industrial situations. This book elaborates on the most recent applications in various fields of engineering.

Currently the methods of Soft Computing are successfully used for risk analysis in: budgeting, e-commerce development, portfolio selection, Black-Scholes option pricing models, corporate acquisition systems, evaluating investments in advanced manufacturing technology, interactive fuzzy interval reasoning for smart web shopping, fuzzy scheduling and logistic. An essential feature of economic and financial problems it that there are always at least two criteria to be taken into account: profit maximization and risk minimization. Therefore, the economic and financial problems are multiple criteria ones. In this book, a new systematization of the problems of multiple criteria decision making is proposed which allows the author to reveal unsolved problems. The solutions of them are presented as well and implemented to deal with some important real-world problems such as investment project's evaluation, tool steel material selection problem, stock screening and fuzzy logistic. It is well known that the best results in real -world applications can be obtained using the synthesis of modern methods of soft computing. Therefore, the developed by the author new approach to building effective stock trading systems, based on the synthesis of fuzzy logic and the Dempster-Shafer theory, seems to be a considerable contribution to the application of soft computing method in economics and finance. An important problem of capital budgeting is the fuzzy evaluation of the Internal Rate of Return. In this book, this problem is solved using a new method which makes it possible to solve linear and nonlinear interval and fuzzy equations and systems of them. The developed new method allows the author to obtain an effective solution of the Leontjev's input-output problem in the interval setting.

The 14th online World Conference on Soft Computing in Industrial Applications provides a unique opportunity for soft computing researchers and practitioners to publish high quality papers and discuss research issues in detail without incurring a huge cost. The conference has established itself as a truly global event on the Internet. The quality of the conference has improved over the years. The WSC14 conference has covered new trends in soft computing to state of the art applications. The conference has also added new features such as community tools, syndication, and multimedia online presen.

Given the increasing trend in water scarcity, which threatens a number of regions worldwide, governments and water distribution system (WDS) operators have sought accurate methods of estimating water demands. While investigators have proposed stochastic and deterministic techniques to model water demands in urban WDS, the performance of soft computing techniques [e.g., Genetic Expression Programming (GEP)] and machine learning methods [e.g., Support Vector Machines (SVM)] in this endeavour remains to be evaluated. The present study proposed a new rationale and a novel technique in forecasting water demand. Phase space reconstruction was used to feed the determinants of water demand with proper lag times, followed by development of GEP and SVM models. The relative accuracy of the three best models was evaluated on the basis of performance indices: coefficient of determination (R2), mean absolute error (MAE), root mean square of error (RMSE), and Nash-Sutcliff coefficient (E). Results showed GEP models were highly sensitive to data classification, genetic operators, and optimum lag time. The SVM model that implemented a Polynomial kernel function slightly outperformed the GEP models. This study showed how phase space reconstruction could potentially improve water demand forecasts using soft computing techniques.

Intro -- STATISTICAL AND SOFT COMPUTING APPROACHES IN INSURANCE PROBLEMS -- STATISTICAL AND SOFT COMPUTING APPROACHES IN INSURANCE PROBLEMS -- LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA -- CONTENTS -- FOREWORD -- Chapter 1: A REVIEW OF COMPUTATIONAL INTELLIGENCE ALGORITHMS IN INSURANCE APPLICATIONS -- Abstract -- 1. Introduction -- 2. Fuzzy Logic-Based Computation -- 3. Neural Computation-Based and Related Algorithms -- 4. Evolutionary Computation-Based Algorithms -- 5. Physics-Inspired Methods -- 6. Computation Inspired by the Collective Behavior of Social Living Beings -- 7. Review: Computational Intelligence Algorithms in Insurance Problems -- 8. Some Experiments and Results: Prediction of Vehicles Accidents from Drivers and Cars' Data Using Soft-Computing Techniques -- 9. Conclusion -- Acknowledgment -- References -- Chapter 2: ROUGH SETS IN INSURANCE SECTOR -- Abstract -- 1. Introduction: Artificial Intelligence and Insurance Sector -- 2. Rough Set Theory -- 3. Rough Set in Insurance Sector -- 4. Conclusion -- Acknowledgment -- References -- Chapter 3: PREDICTION OF CLAIMS AND SELECTION OF RISK FACTORS IN AUTOMOBILE INSURANCE USING SUPPORT VECTOR MACHINES, GENETIC ALGORITHMS AND CLASSIFICATION TREES -- Abstract -- 1. Introduction -- 2. Problem Definition -- 3. The Algorithm -- 4. Results and Comparative -- 5. Concluding Remarks and Further Studies -- Acknowledgment -- References -- Chapter 4: TAIL VALUE AT RISK. AN ANALYSIS WITH THE NORMAL-POWER APPROXIMATION -- Abstract -- 1. Introduction -- 2. Tail Value at Risk for the Normal-Power Approximation -- 3. Analysis of the Precision of the Approximation -- 4. Insurance and Credit Risk Application -- 5. Conclusion -- Acknowledgment -- Appendix -- References -- Chapter 5: FINANCIAL APPLICATIONS OF MODAL INTERVAL ANALYSIS -- Abstract -- 1. The Set of Classical Intervals.

This paper presents the application of soft computing techniques to video processing. Especially, the research work has been focused on the de-interlacing task. It is necessary whenever the transmission standard uses an interlaced format but the receiver requires a progressive scanning, as happens in consumer displays such as LCDs and plasma. A simple hierarchical solution that combines three simple fuzzy logic-based constituents (interpolators) is presented in this paper. Each interpolator is specialized in one of three key image features for de-interlacing: motion, edges, and possible repetition of picture areas. The resulting algorithm offers better results than others with less or similar computational cost. A very interesting result is that our algorithm is competitive with motion-compensated algorithms. ; This work was partially supported by MOBY-DIC project FP7-INFSO-ICT-248858 (www.mobydic-project.eu) from European Community, TEC2008- 04920 project from the Spanish Ministry of Science and Innovation, and by P08-TIC-03674 project from the Andalusian regional Government. ; Peer Reviewed