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Soft Computing Applications and Intelligent Systems: Second International Multi-Conference on Artificial Intelligence Technology, M-CAIT 2013, Shah Alam, August 28-29, 2013. Proceedings (Communications in Computer and Information Science #378)
by Shahrul Azman Noah Siti Norul Huda Sheikh Abdullah Haslina Arshad Azuraliza Abu Bakar Zulaiha Ali Othman Kairuddin Omar Zalinda OthmanThis book constitutes the refereed proceedings of the International Second International Multi-Conference on Artificial Intelligence Technology, M-CAIT 2013, held in Shah Alam, in August 2013. The 25 revised full papers presented were carefully reviewed and selected from 110 submissions. M-CAIT 2013 hosted four special tracks in a single event: Intelligence Computation on Pattern Analysis and Robotics (ICPAIR 2013), Data Mining and Optimization (DMO 2013), Semantic Technology and Information Retrieval (STAIR 2013) and Industrial Computing & Applied Informatics (IComp 2013). The papers address issues of state-of-the-art research, development, implementation and applications within the four focus areas in CAIT: pattern recognition, data mining and optimization, knowledge technology and industrial computing.
Soft Computing Applications for Group Decision-making and Consensus Modeling (Studies in Fuzziness and Soft Computing #357)
by Mikael Collan Janusz KacprzykThis book offers a concise introduction and comprehensive overview of the state of the art in the field of decision-making and consensus modeling, with a special emphasis on fuzzy methods. It consists of a collection of authoritative contributions reporting on the decision-making process from different perspectives: from psychology to social and political sciences, from decision sciences to data mining, and from computational sciences in general, to artificial and computational intelligence and systems. Written as a homage to Mario Fedrizzi for his scholarly achievements, creative ideas and long lasting services to different scientific communities, it introduces key theoretical concepts, describes new models and methods, and discusses a range of promising real-world applications in the field of decision-making science. It is a timely reference guide and a source of inspiration for advanced students and researchers
Soft Computing Applications in Business (Studies in Fuzziness and Soft Computing #230)
by Bhanu PrasadSoft computing techniques are widely used in most businesses. This book consists of several important papers on the applications of soft computing techniques for the business field. The soft computing techniques used in this book include (or very closely related to): Bayesian networks, biclustering methods, case-based reasoning, data mining, Dempster-Shafer theory, ensemble learning, evolutionary programming, fuzzy decision trees, hidden Markov models, intelligent agents, k-means clustering, maximum likelihood Hebbian learning, neural networks, opportunistic scheduling, probability distributions combined with Monte Carlo methods, rough sets, self organizing maps, support vector machines, uncertain reasoning, other statistical and machine learning techniques, and combinations of these techniques. The businesses or business problems addressed in this book include (or very closely related to): analysis of correlations between currency exchange rates, analysis of USA banks and Moody’s bank financial strength rating, arrears management, business risk identification, company audit fee evaluation, dental treatments, business internal control, intelligent tutoring systems and educational assessment, modeling agent behavior, motor insurance industry, personal loan defaults, pricing strategies for increasing the market share, pricing strategies in supply chain management, probabilistic sales forecasting, user relevance feedback analysis for online text retrieval, and world crude oil spot price forecasting.
Soft Computing Applications in Industry (Studies in Fuzziness and Soft Computing #226)
by Bhanu PrasadSoftcomputing techniques play a vital role in the industry. This book presents several important papers presented by some of the well-known scientists from all over the globe. The main techniques of soft computing presented include ant-colony optimization, artificial immune systems, artificial neural networks, Bayesian models. The book includes various examples and application domains such as bioinformatics, detection of phishing attacks, and fault detection of motors.
Soft Computing Applications in Modern Power and Energy Systems: Select Proceedings of EPREC 2023, Volume 4 (Lecture Notes in Electrical Engineering #1107)
by Om Hari Gupta Narayana Prasad Padhy Sukumar KamalasadanThis book includes select proceedings of EPREC 2023. It provides rigorous discussions, case studies, and recent developments in the areas of soft computing and its applications in power systems enabled with power electronics-based equipment, energy systems, and the energy community. The other topics to be covered are optimal planning, analysis, operation, and control related to modern power and energy systems, and applications of various soft computing methodologies. The readers find this book useful for enhancing their knowledge and skills in the domain areas.
Soft Computing Applications in Optimization, Control, and Recognition (Studies in Fuzziness and Soft Computing)
by Patricia Melin Oscar CastilloSoft computing includes several intelligent computing paradigms, like fuzzy logic, neural networks, and bio-inspired optimization algorithms. This book describes the application of soft computing techniques to intelligent control, pattern recognition, and optimization problems. The book is organized in four main parts. The first part deals with nature-inspired optimization methods and their applications. Papers included in this part propose new models for achieving intelligent optimization in different application areas. The second part discusses hybrid intelligent systems for achieving control. Papers included in this part make use of nature-inspired techniques, like evolutionary algorithms, fuzzy logic and neural networks, for the optimal design of intelligent controllers for different kind of applications. Papers in the third part focus on intelligent techniques for pattern recognition and propose new methods to solve complex pattern recognition problems. The fourth part discusses new theoretical concepts and methods for the application of soft computing to many different areas, such as natural language processing, clustering and optimization.
Soft Computing Applications in Sensor Networks
by Sankar K. Pal Sudip MisraThis book uses tutorials and new material to describe the basic concepts of soft-computing which potentially can be used in real-life sensor network applications. It is organized in a manner that exemplifies the use of an assortment of soft-computing applications for solving different problems in sensor networking. Written by worldwide experts, the chapters provide a balanced mixture of different problems concerning channel access, routing, coverage, localization, lifetime maximization and target tracking using emerging soft-computing applications.
Soft Computing Applications in Sensor Networks
by Sudip Misra and Sankar K. PalThis book uses tutorials and new material to describe the basic concepts of soft-computing which potentially can be used in real-life sensor network applications. It is organized in a manner that exemplifies the use of an assortment of soft-computing applications for solving different problems in sensor networking. Written by worldwide experts, the chapters provide a balanced mixture of different problems concerning channel access, routing, coverage, localization, lifetime maximization and target tracking using emerging soft-computing applications.
Soft Computing Approach for Mathematical Modeling of Engineering Problems
by Ali AhmadianThis book describes different mathematical modeling and soft computing techniques used to solve practical engineering problems. It gives an overview of the current state of soft computing techniques and describes the advantages and disadvantages of soft computing compared to traditional hard computing techniques. Through examples and case studies the editors demonstrate and describe how problems with inherent uncertainty can be addressed and eventually solved through the aid of numerical models and methods. The chapters address several applications and examples in bioengineering science, drug delivery, solving inventory issues, Industry 4.0, augmented reality and weather forecasting. Other examples include solving fuzzy-shortest-path problems by introducing a new distance and ranking functions. Because, in practice, problems arise with uncertain data and most of them cannot be solved exactly and easily, the main objective is to develop models that deliver solutions with the aid of numerical methods. This is the reason behind investigating soft numerical computing in dynamic systems. Having this in mind, the authors and editors have considered error of approximation and have discussed several common types of errors and their propagations. Moreover, they have explained the numerical methods, along with convergence and consistence properties and characteristics, as the main objectives behind this book involve considering, discussing and proving related theorems within the setting of soft computing. This book examines dynamic models, and how time is fundamental to the structure of the model and data as well as the understanding of how a process unfolds • Discusses mathematical modeling with soft computing and the implementations of uncertain mathematical models • Examines how uncertain dynamic systems models include uncertain state, uncertain state space and uncertain state’s transition functions • Assists readers to become familiar with many soft numerical methods to simulate the solution function’s behaviorThis book is intended for system specialists who are interested in dynamic systems that operate at different time scales. The book can be used by engineering students, researchers and professionals in control and finite element fields as well as all engineering, applied mathematics, economics and computer science interested in dynamic and uncertain systems. Ali Ahmadian is a Senior Lecturer at the Institute of IR 4.0, The National University of Malaysia.Soheil Salahshour is an associate professor at Bahcesehir University.
Soft Computing Approach for Mathematical Modeling of Engineering Problems
by Ali Ahmadian Soheil SalahshourThis book describes different mathematical modeling and soft computing techniques used to solve practical engineering problems. It gives an overview of the current state of soft computing techniques and describes the advantages and disadvantages of soft computing compared to traditional hard computing techniques. Through examples and case studies the editors demonstrate and describe how problems with inherent uncertainty can be addressed and eventually solved through the aid of numerical models and methods. The chapters address several applications and examples in bioengineering science, drug delivery, solving inventory issues, Industry 4.0, augmented reality and weather forecasting. Other examples include solving fuzzy-shortest-path problems by introducing a new distance and ranking functions. Because, in practice, problems arise with uncertain data and most of them cannot be solved exactly and easily, the main objective is to develop models that deliver solutions with the aid of numerical methods. This is the reason behind investigating soft numerical computing in dynamic systems. Having this in mind, the authors and editors have considered error of approximation and have discussed several common types of errors and their propagations. Moreover, they have explained the numerical methods, along with convergence and consistence properties and characteristics, as the main objectives behind this book involve considering, discussing and proving related theorems within the setting of soft computing. This book examines dynamic models, and how time is fundamental to the structure of the model and data as well as the understanding of how a process unfolds • Discusses mathematical modeling with soft computing and the implementations of uncertain mathematical models • Examines how uncertain dynamic systems models include uncertain state, uncertain state space and uncertain state’s transition functions • Assists readers to become familiar with many soft numerical methods to simulate the solution function’s behaviorThis book is intended for system specialists who are interested in dynamic systems that operate at different time scales. The book can be used by engineering students, researchers and professionals in control and finite element fields as well as all engineering, applied mathematics, economics and computer science interested in dynamic and uncertain systems. Ali Ahmadian is a Senior Lecturer at the Institute of IR 4.0, The National University of Malaysia.Soheil Salahshour is an associate professor at Bahcesehir University.
Soft Computing Approach to Pattern Classification and Object Recognition: A Unified Concept
by Kumar S. RaySoft Computing Approach to Pattern Classification and Object Recognition establishes an innovative, unified approach to supervised pattern classification and model-based occluded object recognition. The book also surveys various soft computing tools, fuzzy relational calculus (FRC), genetic algorithm (GA) and multilayer perceptron (MLP) to provide a strong foundation for the reader. The supervised approach to pattern classification and model-based approach to occluded object recognition are treated in one framework , one based on either a conventional interpretation or a new interpretation of multidimensional fuzzy implication (MFI) and a novel notion of fuzzy pattern vector (FPV). By combining practice and theory, a completely independent design methodology was developed in conjunction with this supervised approach on a unified framework, and then tested thoroughly against both synthetic and real-life data. In the field of soft computing, such an application-oriented design study is unique in nature. The monograph essentially mimics the cognitive process of human decision making, and carries a message of perceptual integrity in representational diversity. Soft Computing Approach to Pattern Classification and Object Recognition is intended for researchers in the area of pattern classification and computer vision. Other academics and practitioners will also find the book valuable.
Soft Computing as Transdisciplinary Science and Technology: Proceedings of the fourth IEEE International Workshop WSTST´05 (Advances in Intelligent and Soft Computing #29)
by Ajith Abraham Mario Köppen Yasuhiko Dote Takeshi Furuhashi Azuma Ohuchi Yukio OhsawaThis book presents the proceedings of the Fourth International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST '05), May 25-27, 2005, Muroran, Japan. It brings together the original work of international soft computing/computational intelligence researchers, developers, practitioners, and users. This proceedings provide contributions to all areas of soft computing including intelligent hybrid systems, agent-based systems, intelligent data mining, decision support systems, cognitive and reactive distributed artificial intelligence (AI), internet modelling, human interface, and applications in science and technology.
Soft Computing Based Modeling in Intelligent Systems (Studies in Computational Intelligence #196)
by Valentina Emilia Balas János Fodor Annamária R. Várkonyi-KóczyThe book “Soft Computing Based Modeling in Intelligent Systems”contains the - tended works originally presented at the IEEE International Workshop SOFA 2005 and additional papers. SOFA, an acronym for SOFt computing and Applications, is an international wo- shop intended to advance the theory and applications of intelligent systems and soft computing. Lotfi Zadeh, the inventor of fuzzy logic, has suggested the term “Soft Computing.” He created the Berkeley Initiative of Soft Computing (BISC) to connect researchers working in these new areas of AI. Professor Zadeh participated actively in our wo- shop. Soft Computing techniques are tolerant to imprecision, uncertainty and partial truth. Due to the large variety and complexity of the domain, the constituting methods of Soft Computing are not competing for a comprehensive ultimate solution. Instead they are complementing each other, for dedicated solutions adapted to each specific pr- lem. Hundreds of concrete applications are already available in many domains. Model based approaches offer a very challenging way to integrate a priori knowledge into procedures. Due to their flexibility, robustness, and easy interpretability, the soft c- puting applications will continue to have an exceptional role in our technologies. The applications of Soft Computing techniques in emerging research areas show its mat- ity and usefulness. The IEEE International Workshop SOFA 2005 held Szeged-Hungary and Arad- Romania in 2005 has led to the publication of these two edited volumes. This volume contains Soft Computing methods and applications in modeling, optimisation and prediction.
Soft Computing Based Optimization and Decision Models: To Commemorate the 65th Birthday of Professor José Luis "Curro" Verdegay (Studies in Fuzziness and Soft Computing #360)
by David A. Pelta Carlos Cruz CoronaThis book offers a timely snapshot of current soft-computing research and solutions to decision-making and optimization problems, which are ubiquitous in the current social and technological context, addressing fields including logistics, transportation and data analysis. Written by leading international experts from the United States, Brazil and Cuba, as well as the United Kingdom, France, Finland and Spain, it discusses theoretical developments in and practical applications of soft computing in fields where these methods are crucial to obtaining better models, including: intelligent transportation systems, maritime logistics, portfolio selection, decision- making, fuzzy cognitive maps, and fault detection. The book is dedicated to Professor José L. Verdegay, a pioneer who has been actively pursuing research in fuzzy sets theory and soft computing since 1982, in honor of his 65th birthday.
Soft Computing Evaluation Logic: The LSP Decision Method and Its Applications (Wiley - IEEE)
by Jozo DujmovićA novel approach to decision engineering, with a verified framework for modeling human reasoning Soft Computing Evaluation Logic provides an in-depth examination of evaluation decision problems and presents comprehensive guidance toward the use of the Logic Scoring of Preference (LSP) method in modeling complex decision criteria. Fully aligned with current developments in computational intelligence, the discussion covers the design and use of LSP criteria for evaluation and comparison in diverse areas, such as search engines, medical conditions, real estate, space management, habitat mitigation projects in ecology, and land use and residential development suitability maps, with versatile transfer to other similar decision-modeling contexts. Human decision making is rife with fuzziness, imprecision, uncertainty, and half-truths—yet humans make evaluation decisions every day. In this book, such decision processes are observed, analyzed, and modeled. The result is graded logic, a soft computing mathematical infrastructure that provides both formal logic and semantic generalizations of classical Boolean logic. Graded logic is used for logic aggregation in the context of evaluation models consistent with observable properties of human reasoning. The LSP method, based on graded logic and logic aggregation, is a vital component of an industrial-strength decision engineering framework. Thus, the book: Provides detailed theoretical background for graded logic Provides a theory of logic aggregators Explains the LSP method for designing complex evaluation criteria and their use Shows techniques for evaluation, comparison, and selection of complex systems, as well as the cost/suitability analysis, optimization, sensitivity analysis, tradeoff analysis, and missingness-tolerant aggregation Includes a survey of available LSP software tools, including ISEE, ANSY and LSP.NT. With quantitative modeling of human reasoning, novel approaches to modeling decision criteria, and a verified decision engineering framework applicable to a broad array of applications, this book is an invaluable resource for graduate students, researchers, and practitioners working within the decision engineering realm.
Soft Computing Evaluation Logic: The LSP Decision Method and Its Applications (Wiley - IEEE)
by Jozo DujmovićA novel approach to decision engineering, with a verified framework for modeling human reasoning Soft Computing Evaluation Logic provides an in-depth examination of evaluation decision problems and presents comprehensive guidance toward the use of the Logic Scoring of Preference (LSP) method in modeling complex decision criteria. Fully aligned with current developments in computational intelligence, the discussion covers the design and use of LSP criteria for evaluation and comparison in diverse areas, such as search engines, medical conditions, real estate, space management, habitat mitigation projects in ecology, and land use and residential development suitability maps, with versatile transfer to other similar decision-modeling contexts. Human decision making is rife with fuzziness, imprecision, uncertainty, and half-truths—yet humans make evaluation decisions every day. In this book, such decision processes are observed, analyzed, and modeled. The result is graded logic, a soft computing mathematical infrastructure that provides both formal logic and semantic generalizations of classical Boolean logic. Graded logic is used for logic aggregation in the context of evaluation models consistent with observable properties of human reasoning. The LSP method, based on graded logic and logic aggregation, is a vital component of an industrial-strength decision engineering framework. Thus, the book: Provides detailed theoretical background for graded logic Provides a theory of logic aggregators Explains the LSP method for designing complex evaluation criteria and their use Shows techniques for evaluation, comparison, and selection of complex systems, as well as the cost/suitability analysis, optimization, sensitivity analysis, tradeoff analysis, and missingness-tolerant aggregation Includes a survey of available LSP software tools, including ISEE, ANSY and LSP.NT. With quantitative modeling of human reasoning, novel approaches to modeling decision criteria, and a verified decision engineering framework applicable to a broad array of applications, this book is an invaluable resource for graduate students, researchers, and practitioners working within the decision engineering realm.
Soft Computing for Biological Systems
by Hemant J. Purohit Vipin Chandra Kalia Ravi Prabhakar MoreThis book explains how the biological systems and their functions are driven by genetic information stored in the DNA, and their expression driven by different factors. The soft computing approach recognizes the different patterns in DNA sequence and try to assign the biological relevance with available information.The book also focuses on using the soft-computing approach to predict protein-protein interactions, gene expression and networks. The insights from these studies can be used in metagenomic data analysis and predicting artificial neural networks.
Soft Computing for Business Intelligence (Studies in Computational Intelligence #537)
by Rafael Espin Rafael Bello Pérez Angel Cobo Jorge Marx Gómez Ariel Racet ValdésThe book Soft Computing for Business Intelligence is the remarkable output of a program based on the idea of joint trans-disciplinary research as supported by the Eureka Iberoamerica Network and the University of Oldenburg.It contains twenty-seven papers allocated to three sections: Soft Computing, Business Intelligence and Knowledge Discovery, and Knowledge Management and Decision Making. Although the contents touch different domains they are similar in so far as they follow the BI principle “Observation and Analysis” while keeping a practical oriented theoretical eye on sound methodologies, like Fuzzy Logic, Compensatory Fuzzy Logic (CFL), Rough Sets and other soft computing elements.The book tears down the traditional focus on business, and extends Business Intelligence techniques in an impressive way to a broad range of fields like medicine, environment, wind farming, social collaboration and interaction, car sharing and sustainability.
Soft Computing for Complex Multiple Criteria Decision Making (International Series in Operations Research & Management Science #85)
by Ignacy KaliszewskiThis book concentrates on providing technical tools to make the user of Multiple Criteria Decision Making (MCDM) methodologies independent of bulky optimization computations. These bulky computations have been a necessary, but limiting, characteristic of interactive MCDM methodologies and algorithms. The book removes these limitations of MCDM problems by reducing a problem's computational complexity. The result is a wider and more functional general framework for presenting, teaching, implementing and applying a wide range of MCDM methodologies.
Soft Computing for Control of Non-Linear Dynamical Systems (Studies in Fuzziness and Soft Computing #63)
by Oscar Castillo Patricia MelinThis book presents a unified view of modelling, simulation, and control of non linear dynamical systems using soft computing techniques and fractal theory. Our particular point of view is that modelling, simulation, and control are problems that cannot be considered apart, because they are intrinsically related in real world applications. Control of non-linear dynamical systems cannot be achieved if we don't have the appropriate model for the system. On the other hand, we know that complex non-linear dynamical systems can exhibit a wide range of dynamic behaviors ( ranging from simple periodic orbits to chaotic strange attractors), so the problem of simulation and behavior identification is a very important one. Also, we want to automate each of these tasks because in this way it is more easy to solve a particular problem. A real world problem may require that we use modelling, simulation, and control, to achieve the desired level of performance needed for the particular application.
Soft Computing for Data Analytics, Classification Model, and Control (Studies in Fuzziness and Soft Computing #413)
by Oscar Castillo Deepak Gupta Ashish Khanna Aditya KhampariaThis book presents a set of soft computing approaches and their application in data analytics, classification model, and control. The basics of fuzzy logic implementation for advanced hybrid fuzzy driven optimization methods has been covered in the book. The various soft computing techniques, including Fuzzy Logic, Rough Sets, Neutrosophic Sets, Type-2 Fuzzy logic, Neural Networks, Generative Adversarial Networks, and Evolutionary Computation have been discussed and they are used on variety of applications including data analytics, classification model, and control. The book is divided into two thematic parts. The first thematic section covers the various soft computing approaches for text classification and data analysis, while the second section focuses on the fuzzy driven optimization methods for the control systems. The chapters has been written and edited by active researchers, which cover hypotheses and practical considerations; provide insights into the design of hybrid algorithms for applications in data analytics, classification model, and engineering control.
Soft Computing for Data Mining Applications (Studies in Computational Intelligence #190)
by K. R. Venugopal K.G Srinivasa L. M. PatnaikThe authors have consolidated their research work in this volume titled Soft Computing for Data Mining Applications. The monograph gives an insight into the research in the ?elds of Data Mining in combination with Soft Computing methodologies. In these days, the data continues to grow - ponentially. Much of the data is implicitly or explicitly imprecise. Database discovery seeks to discover noteworthy, unrecognized associations between the data items in the existing database. The potential of discovery comes from the realization that alternate contexts may reveal additional valuable information. The rate at which the data is storedis growing at a phenomenal rate. Asaresult,traditionaladhocmixturesofstatisticaltechniquesanddata managementtools are no longer adequate for analyzing this vast collection of data. Severaldomainswherelargevolumesofdataarestoredincentralizedor distributeddatabasesincludesapplicationslikeinelectroniccommerce,bio- formatics, computer security, Web intelligence, intelligent learning database systems,?nance,marketing,healthcare,telecommunications,andother?elds. E?cient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the ca- bility of computers to search huge amounts of data in a fast and e?ective manner. However,the data to be analyzed is imprecise and a?icted with - certainty. In the case of heterogeneous data sources such as text and video, the data might moreover be ambiguous and partly con?icting. Besides, p- terns and relationships of interest are usually approximate. Thus, in order to make the information mining process more robust it requires tolerance toward imprecision, uncertainty and exceptions.
Soft Computing for Hybrid Intelligent Systems (Studies in Computational Intelligence #154)
by Oscar Castillo Patricia Melin Witold PedryczWe describe in this book, new methods and applications of hybrid intelligent systems using soft computing techniques. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and evolutionary al- rithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in five main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of intelligent control, which are basically papers that use hybrid systems to solve particular problems of control. The second part contains papers with the main theme of pattern recognition, which are basically papers using soft computing techniques for achieving pattern recognition in different applications. The third part contains papers with the themes of intelligent agents and social systems, which are papers that apply the ideas of agents and social behavior to solve real-world problems. The fourth part contains papers that deal with the hardware implementation of intelligent systems for solving particular problems. The fifth part contains papers that deal with modeling, simulation and optimization for real-world applications.
Soft Computing for Image and Multimedia Data Processing
by Siddhartha Bhattacharyya Ujjwal MaulikProper analysis of image and multimedia data requires efficient extraction and segmentation techniques. Among the many computational intelligence approaches, the soft computing paradigm is best equipped with several tools and techniques that incorporate intelligent concepts and principles. This book is dedicated to object extraction, image segmentation, and edge detection using soft computing techniques with extensive real-life application to image and multimedia data. The authors start with a comprehensive tutorial on the basics of brain structure and learning, and then the key soft computing techniques, including evolutionary computation, neural networks, fuzzy sets and fuzzy logic, and rough sets. They then present seven chapters that detail the application of representative techniques to complex image processing tasks such as image recognition, lighting control, target tracking, object extraction, and edge detection. These chapters follow a structured approach with detailed explanations of the problems, solutions, results, and conclusions. This is both a standalone textbook for graduates in computer science, electrical engineering, system science, and information technology, and a reference for researchers and engineers engaged with pattern recognition, image processing, and soft computing.
Soft Computing for Image Processing (Studies in Fuzziness and Soft Computing #42)
by Sankar K. Pal Ashish Ghosh Malay K. KunduAny task that involves decision-making can benefit from soft computing techniques which allow premature decisions to be deferred. The processing and analysis of images is no exception to this rule. In the classical image analysis paradigm, the first step is nearly always some sort of segmentation process in which the image is divided into (hopefully, meaningful) parts. It was pointed out nearly 30 years ago by Prewitt (1] that the decisions involved in image segmentation could be postponed by regarding the image parts as fuzzy, rather than crisp, subsets of the image. It was also realized very early that many basic properties of and operations on image subsets could be extended to fuzzy subsets; for example, the classic paper on fuzzy sets by Zadeh [2] discussed the "set algebra" of fuzzy sets (using sup for union and inf for intersection), and extended the defmition of convexity to fuzzy sets. These and similar ideas allowed many of the methods of image analysis to be generalized to fuzzy image parts. For are cent review on geometric description of fuzzy sets see, e. g. , [3]. Fuzzy methods are also valuable in image processing and coding, where learning processes can be important in choosing the parameters of filters, quantizers, etc.