The IV Latin American Congress on Biomedical Engineering, CLAIB2007, corresponds to the triennial congress for the Regional Bioengineering Council for Latin America (CORAL), it is supported by the International Federation for Medical and Biological Engineering (IFMBE) and the Engineering in Medicine, Biology Society (IEEE-EMBS). This time the Venezuela Society of Bioengineering (SOVEB) organized the conference, with the slogan Bioengineering solution for Latin America health.
This book describes in a detailed fashion the application of hybrid intelligent systems using soft computing techniques for intelligent control and mobile robotics. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The prudent combination of SC techniques can produce powerful hybrid intelligent systems that are capable of solving real-world problems. This is illustrated in this book with a wide range of applications, with particular emphasis in intelligent control and mobile robotics. 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 theory and algorithms, which are basically papers that propose new models and concepts, which can be the basis for achieving intelligent control and mobile robotics. The second part contains papers with the main theme of intelligent control, which are basically papers using bio-inspired techniques, like evolutionary algorithms and neural networks, for achieving intelligent control of non-linear plants. The third part contains papers with the theme of optimization of fuzzy controllers, which basically consider the application of bio-inspired optimization methods to automate the de-sign process of optimal type-1 and type-2 fuzzy controllers. The fourth part contains papers that deal with the application of SC techniques in times series prediction and intelligent agents. The fifth part contains papers with the theme of computer vision and robotics, which are papers considering soft computing methods for applications related to vision and robotics.
Introduction The International Federation for Information Processing (IFIP) is a non-profit umbrella organization for national societies working in the field of information processing. It was founded in 1960 under the auspices of UNESCO. It is organized into several technical c- mittees. This book represents the proceedings of the 2008 conference of technical committee 8 (TC8), which covers the field of infor- tion systems. TC8 aims to promote and encourage the advancement of research and practice of concepts, methods, techniques and issues related to information systems in organisations. TC8 has established eight working groups covering the following areas: design and evaluation of information systems; the interaction of information systems and the organization; decision support systems; e-business information systems: multi-disciplinary research and practice; inf- mation systems in public administration; smart cards, technology, applications and methods; and enterprise information systems. Further details of the technical committee and its working groups can be found on our website (ifiptc8. dsi. uminho. pt). This conference was part of IFIP’s World Computer Congress in Milan, Italy which took place 7-10 September 2008. The occasion celebrated the 32nd anniversary of IFIP TC8. The call for papers invited researchers, educators, and practitioners to submit papers and panel proposals that advance concepts, methods, techniques, tools, issues, education, and practice of information systems in organi- tions. Thirty one submissions were received.
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. - Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects - Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields - Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.
Written for practitioners of data mining, data cleaning and database management. Presents a technical treatment of data quality including process, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge. Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches. Uses case studies to illustrate applications in real life scenarios. Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques. Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining.
Management Information Systems provides comprehensive and integrative coverage of essential new technologies, information system applications, and their impact on business models and managerial decision-making in an exciting and interactive manner. The twelfth edition focuses on the major changes that have been made in information technology over the past two years, and includes new opening, closing, and Interactive Session cases.