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Artificial Immune Systems and Their Applications Springer Berlin Heidelberg New York Barcelona Hongkong London Milan Paris Singapore Tokyo Dipankar Dasgupta (Ed.) Artificial Immune Systems and Their Applications Springer Berlin Heidelberg New York Barcelona HongKong London Milan Paris Singapore Tokyo Dipankar Dasgupta (Ed.) Artificial Immune Systems and Their Applications With 100 Figures and 15 TabIes , Springer Editor: Dipankar Dasgupta University of Memphis Mathematical Sciences Department Memphis, TN 38152-6429, USA [email protected] ACM Computing Classification (1998): F.l.l, F.2.2, 1.2, 1.6, EA, J.3 ISBN-13: 978-3-642-64174-9 e-ISBN-13: 978-3-642-59901-9 DOI: 10.1007/978-3-642-59901-9 Library of Congress Cataloging-in-Publication Data Artificial immune systems and their applications / [edited by] Dipankar Dasgupta p. cm. Includes bibliographical references and index. ISBN 3-540-64390-7 (alk. paper) 1. Immune system - Computer simulation. 2. Artificial Intelligence.1. Dasgupta, D. (Dipankar), 1958- QRI82.2.C65A78 1998 616.07'9'OI13-dc21 98-35558 CIP This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German copyright law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer­ Verlag. Violations are liable for prosecution under the German Copyright Law. © Springer-Verlag Berlin Heidelberg 1999 Softcover reprint ofthe hardcover 1st edition 1999 The use of general descriptive names, trademarks, ete. in this publieation does not imply, even in the absence of a specifie statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: design & production GmbH, Heidelberg Typesetting: Camera ready copy from the editor using a Springer TEX macro package SPIN: 10675823 45/3142 - 543210 - Printed on acid-free paper Preface The natural immune system is a complex adaptive system which efficiently employs several mechanisms for defense against foreign pathogens. The main role of the immune system is to recognize all cells (or moleeules ) within the body and categorize those cells as self or non-self. The non-self cells are further categorized in order to induce an appropriate type of defensive mechanism. From an information-processing perspective, the immune system is a highly parallel intelligent system. It uses learning, memory, and associative retrieval to solve recognition and classification tasks. In particular, it learns to recognize relevant patterns (antigenic peptide), memorize patterns that have been seen previously, and use combinatorics (within gene libraries) to con­ struct pattern detectors (V-regions in antibody) efficiently for distinguishing between foreign antigens and the body's own cells. Moreover, the identifica­ tion of antigens is not done by a single recognizing set but rather a system level mutual recognition through antigen-antibody re action as a network. So the overall behavior of the immune system is an emergent property of many local interactions. The natural immune system is a subject of great research interest be­ cause of its importance, complexity and poorly understood alternative mech­ anisms. However, its general features provide an excellent model of adaptive processes operating at the local level and of useful behavior emerging at the global level. There exist several theories (some are contradictory) to explain immunological phenomena and computer models to simulate various compo­ nents of the immune system. There is also a growing number of intelligent methodologies (inspired by the immune system) toward real-world problem solving. These methods are labeled with different names - Artificial Immune Systems, Immunity-Based Systems, Immunological Computation, etc. The scope of this field includes (but is not limited to) the following: * Computational methods based on Immunological Principles * Immunity-Based Cognitive Models * Artificial Immune Systems for Pattern Recognition * Immunity-Based Systems for Anomaly or Fault Detection * Immunity-Based Multi-Agent Systems VI Preface * Immunity-Based Systems for Self-organization * Immunity-Based approach for Collective Intelligence * Immunity-Based Systems for Search and Optimization * The Immune System as Autonomous Decentralized System * Immunity-Based approach for Artificial Life * Immunity-Based Systems for Computer & Internet Security * The Immune System as a metaphor for Learning System * Immunological Computation for Data Mining * Artificial Immune Systems for Fraud Detection * Immunity-Based Systems in Image & Signal Processing As the field is growing, researchers started to organize scientific meetings which can serve as a forum for presenting and disseminating current research activities in the field. The first international workshop on "Immunity-based Systems" was held in Japan on December 10, 1996. Subsequently, there was a special track on "Artificial Immune Systems and Their Applications", or­ ganized by the editor of this book, at the IEEE International Conference on Systems, Man, and Cyberneties (SMC' 97), Orlando, October 12-15, 1997. A similar event on this topies is also planned to organize at SMC' 98 to be held at San Diego, California. It seems particularly significant at this point of time, to put together important works in an edited volume in order to provide new researchers with a more thorough and systematic account of this rapidly emerging field. We feel that Artificial Immune Systems will so on receive similar attention like other biologically-motivated approaches, such as genetic algorithms, neural networks, cellular automata, etc. This is the first book that focuses on immunological computation tech­ niques and their applications in many areas including computer security, data mining, machine learning, fault detection, etc. Though the emphasis of the book is on the computational aspects of the immune system, biological models are also considered since they are important to understand the immunological mechanisms and derive computational algorithms. The book will be useful for academician, researchers and practitioners in any scientific discipline who are interested in the models and applications of immunity-based systems. This volume consists of three parts: introduction, models of artificial im­ mune systems and applications. Various chapters emphasize in-depth analysis of various immune system models and their relation to information processing and problem solving. The chapter by Dasgupta in the introductory section covers important immunologieal principles and their computational aspects. It also provides an overview of immunity-based computational models and their applications in pattern recognition, fault detection and diagnosis, computer security, and others. Bersini's chapter describes the double plasticity of the immune network that allows the system to conduct its self-assertion role while being in con­ stant shift according to the organic's ontogenie changes and in response to Preface VII the environmental coupling. The author illustrates three application areas where the endogenous double level of adaptability, weakly inspired by the double plasticity in immune networks, allows to learn rapidly a satisfactory solution. In part 11, first chapter argues that the immune system is composed of two distinct compartments, a Central Immune System (CIS) and a Periph­ eral Immune System (PIS). The PIS is composed of lymphocyte clones and is appropriate for reactions to immunizing antigens, whereas the CIS is ap­ propriate for body antigens. The chapter also reviews the second generation immune network and proposes a third generation network model with an ef­ fort to establish a productive relationship between theory and experiments of the immune system. Segal and Bar-Or view the immune system as an autonomous decentral­ ized system. They propose three different models of such distributed systems and make some useful comparisons between the immune system and other autonomous decentralized systems. The chapter by Chowdhury presents mathematical models for describing the population dynamics of the immunocompetent cells in a unified man­ ner by incorporating intra-clonal as weH as inter-clonal interactions in both discrete and continuous formulation. Smith et al. argue that immunological memory belongs to the same class of associative memories as Kanerva's sparse distributed memory (SDM). They show the correspondence between Band T ceHs in the immune system and hard locations in a SDM. In particular, their work demonstrates that Band T cells perform a sparse coverage of aH possible antigens in the same way that hard locations perform a sparse coverage of aH possible addresses in a SDM. Next two chapters in this part provide more biological insight of the hu­ man immune system. In particular, in Tan and Xiang's chapter astate space model is developed to estimate and predict the number of free HIV and T ceHs in HIV-infected individuals using the Kalman filter method. Their mod­ els express HIV pathogenesis in terms of stochastic differential equations. Based on observed RNA virus co pies over time, their models are validated by comparing the Kalman filter estimates of the RN A virus copies with the observed ones from patient data. According to the authors, these models may be useful for monitoring the dynamic behavior of
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