Nanocomputers and Swarm Intelligence

Organizations, both private and public, are now evolving in a globalized information society that has been accelerated by digitization. They find themselves drawn into a spiral of transformations fueled by the incessant reinvention of information and communication technologies (ICT) that are changing digital uses and practices. They transform through the mediating action of ICTs, work activities and associated action situations.

Platform and Collective Intelligence analyzes a specific declination of an organization that has become irreversibly reticular: the platform organization. The network, at the heart of this new conception, proposes a model combining cybernetics and computing. The organization can thus be seen as an interface for contact, via its information systems, for employees or citizens, whatever their geographical location.

With a view going beyond technocentrism and technological determinism, this book combines collective intelligence and sociotechnics with the platform to arrive at the notion of organizational experience.

This book focuses on ambient intelligence and addresses various issues related to data management, networking and HCI in this context. Taking a holistic view, it covers various levels of abstraction, ranging from fundamental to advanced concepts and brings together the contributions of various specialists in the field. Moreover, the book covers the key areas of computer science concerned with the emergence of ambient intelligence (e.g. interaction, middleware, networks, information systems, etc.). It even goes slightly beyond the borders of computer science with contributions related to smart materials and ethics. The authors cover a broad spectrum, with some chapters dedicated to the presentation of basic concepts and others focusing on emerging applications in various fields such as health, transport and tourism.

The digitalization of archives produces a huge mass of structured documents (Big Data). Due to the proactive approach of public institutions (libraries, archives, administrations …), this data is more and more accessible. This book aims to present and analyze concrete examples of collective intelligence at the service of digital archives.

The classic text–now updated with a new interpretive approach to the WAIS?-III Assessing Adolescent and Adult Intelligence, the classic text from Alan Kaufman and Elizabeth Lichtenberger, has consistently provided the most comprehensive source of information on cognitive assessment of adults and adolescents. The newly updated Third Edition provides important enhancements and additions that highlight the latest research and interpretive methods for the WAIS?-III. Augmenting the traditional «sequential» and «simultaneous» WAIS?-III interpretive methods, the authors present a new approach derived from Cattell-Horn-Carroll (CHC) theory. This approach combines normative assessment (performance relative to age peers) with ipsative assessment (performance relative to the person's own mean level). Following Flanagan and Kaufman's work to develop a similar CHC approach for the WISC?-IV, Kaufman and Lichtenberger have applied this system to the WAIS?-III profile of scores along with integrating recent WAIS?-III literature. Four appendices present the new method in depth. In addition to a detailed description, the authors provide a blank interpretive worksheet to help examiners make the calculations and decisions needed for applying the additional steps of the new system, and norms tables for the new WAIS?-III subtest combinations added in this approach. Assessing Adolescent and Adult Intelligence remains the premier resource for the field, covering not only the WAIS?-III but also the WJ III?, the KAIT, and several brief measures of intelligence, as well as laying out a relevant, up-to-date discussion of the discipline. The new, theory-based interpretive approach for the WAIS?-III makes this a vital resource for practicing psychologists, as well as a comprehensive text for graduate students.

The rough and fuzzy set approaches presented here open up many new frontiers for continued research and development Computational Intelligence and Feature Selection provides readers with the background and fundamental ideas behind Feature Selection (FS), with an emphasis on techniques based on rough and fuzzy sets. For readers who are less familiar with the subject, the book begins with an introduction to fuzzy set theory and fuzzy-rough set theory. Building on this foundation, the book provides: A critical review of FS methods, with particular emphasis on their current limitations Program files implementing major algorithms, together with the necessary instructions and datasets, available on a related Web site Coverage of the background and fundamental ideas behind FS A systematic presentation of the leading methods reviewed in a consistent algorithmic framework Real-world applications with worked examples that illustrate the power and efficacy of the FS approaches covered An investigation of the associated areas of FS, including rule induction and clustering methods using hybridizations of fuzzy and rough set theories Computational Intelligence and Feature Selection is an ideal resource for advanced undergraduates, postgraduates, researchers, and professional engineers. However, its straightforward presentation of the underlying concepts makes the book meaningful to specialists and nonspecialists alike.

The development of a research, teaching, or application of competitive (economic) intelligence requires a strategic and transverse vision in regards to related issues. It is essential to integrate the role of culture when interpreting results, either from the training of a specialist or in respect to a country or region. The authors of this book, members of an expert group supported by the CNRS in France, bring all of their talents together to create a comprehensive book that does just this and more.

AI techniques are being successfully used in the fields of health to increase the efficacy of therapies and avoid the risks of false diagnosis, therapeutic decision-making, and outcome prediction in many clinical cases, thanks to the rapid advancement of technology. The acquisition, analysis, and application of a vast amount of information required to solve complex problems is a challenge for modern health therapies. The 21 chapters in this integrate several aspects of computational intelligence like machine learning and deep learning from diversified perspectives. The purpose of the book is to endow to different communities with their innovative advances in theory, analytical approaches, numerical simulation, statistical analysis, modeling, advanced deployment, case studies, analytical results, computational structuring and significance progress in healthcare applications.

With the idea of “deep learning” having now become the key to this new generation of solutions, major technological players in the business intelligence sector have taken an interest in the application of Big Data. In this book, the author explores the recent technological advances associated with digitized data flows, which have recently opened up new horizons for AI. The reader will gain insight into some of the areas of application of Big Data in AI, including robotics, home automation, health, security, image recognition and natural language processing.

The aim of the book is to analyse and understand the impacts of artificial intelligence in the fields of national security and defense; to identify the political, geopolitical, strategic issues of AI; to analyse its place in conflicts and cyberconflicts, and more generally in the various forms of violence; to explain the appropriation of artificial intelligence by military organizations, but also law enforcement agencies and the police; to discuss the questions that the development of artificial intelligence and its use raise in armies, police, intelligence agencies, at the tactical, operational and strategic levels.

Presents recent advances in both models and systems for intelligent decision making. Organisations often face complex decisions requiring the assessment of large amounts of data. In recent years Multicriteria Decision Aid (MCDA) and Artificial Intelligence (AI) techniques have been applied with considerable success to support decision making in a wide range of complex real-world problems. The integration of MCDA and AI provides new capabilities relating to the structuring of complex decision problems in static and distributed environments. These include the handling of massive data sets, the modelling of ill-structured information, the construction of advanced decision models, and the development of efficient computational optimization algorithms for problem solving. This book covers a rich set of topics, including intelligent decision support technologies, data mining models for decision making, evidential reasoning, evolutionary multiobjective optimization, fuzzy modelling, as well as applications in management and engineering. Multicriteria Decision Aid and Artificial Intelligence: Covers all of the recent advances in intelligent decision making. Includes a presentation of hybrid models and algorithms for preference modelling and optimisation problems. Provides illustrations of new intelligent technologies and architectures for decision making in static and distributed environments. Explores the general topics on preference modelling and learning, along with the coverage of the main techniques and methodologies and applications. Is written by experts in the field. This book provides an excellent reference tool for the increasing number of researchers and practitioners interested in the integration of MCDA and AI for the development of effective hybrid decision support methodologies and systems. Academics and post-graduate students in the fields of operational research, artificial intelligence and management science or decision analysis will also find this book beneficial.