Lakhmi C. Jain

Knowledge-based and intelligent information and engineering systems

Adaptive Resonance Theory (ART) neural networks model real-time prediction, search, learning, and recognition. ART networks function both as models of human cognitive information processing [1,2,3] and as neural systems for technology transfer [4]. A neural computation central to both the scientific and the technological analyses is the ART matching rule [5], which models the interaction between topdown expectation and bottom-up input, thereby creating a focus of attention which, in turn, determines the nature of coded memories. Sites of early and ongoing transfer of ART-based technologies include industrial venues such as the Boeing Corporation [6] and government venues such as MIT Lincoln Laboratory [7]. A recent report on industrial uses of neural networks [8] states: “[The] Boeing ... Neural Information Retrieval System is probably still the largest-scale manufacturing application of neural networks. It uses [ART] to cluster binary templates of aeroplane parts in a complex hierarchical network that covers over 100,000 items, grouped into thousands of self-organised clusters. Claimed savings in manufacturing costs are in millions of dollars per annum.” At Lincoln Lab, a team led by Waxman developed an image mining system which incorporates several models of vision and recognition developed in the Boston University Department of Cognitive and Neural Systems (BU/CNS). Over the years a dozen CNS graduates (Aguilar, Baloch, Baxter, Bomberger, Cunningham, Fay, Gove, Ivey, Mehanian, Ross, Rubin, Streilein) have contributed to this effort, which is now located at Alphatech, Inc. Customers for BU/CNS neural network technologies have attributed their selection of ART over alternative systems to the models defining design principles. In listing the advantages of its THOT technology, for example, American Heuristics Corporation (AHC) cites several characteristic computational capabilities of this family of neural models, including fast on-line (one-pass) learning, “vigilant” detection of novel patterns, retention of rare patterns, improvement with experience, “weights [which] are understandable in real world terms,” and scalability (www.heuristics.com). Design principles derived from scientific analyses and design constraints imposed by targeted applications have jointly guided the development of many variants of the basic networks, including fuzzy ARTMAP [9], ART-EMAP [10], ARTMAP-IC [11],

Evolutionary Multiobjective Optimization

Very often real-world applications have several multiple conflicting objectives. Recently there has been a growing interest in evolutionary multiobjective optimization algorithms that combine two major disciplines: evolutionary computation and the theoretical frameworks of multicriteria decision making. In this introductory chapter, some fundamental concepts of multiobjective optimization are introduced, emphasizing the motivation and advantages of using evolutionary algorithms. We then lay out the important contributions of the remaining chapters of this volume.

Designing classifier fusion systems by genetic algorithms

We suggest two simple ways to use a genetic algorithm (GA) to design a multiple-classifier system. The first GA version selects disjoint feature subsets to be used by the individual classifiers, whereas the second version selects (possibly) overlapping feature subsets, and also the types of the individual classifiers. The two GAs have been tested with four real data sets: heart, Satimage, letters, and forensic glasses. We used three-classifier systems and basic types of individual classifiers (the linear and quadratic discriminant classifiers and the logistic classifier). The multiple-classifier systems designed with the two GAs were compared against classifiers using: all features; the best feature subset found by the sequential backward selection method; and the best feature subset found by a CA. The GA design can be made less prone to overtraining by including penalty terms in the fitness function accounting for the number of features used.

Recurrent Neural Networks: Design and Applications

From the Publisher: With applications ranging from motion detection to financial forecasting, recurrent neural networks (RNNs) have emerged as an interesting and important part of neural network research. Recurrent Neural Networks: Design and Applications reflects the tremendous, worldwide interest in and virtually unlimited potential of RNNs - providing a summary of the design, applications, current research, and challenges of this dynamic and promising field.

Nearest neighbor classifier: simultaneous editing and feature selection

Nearest neighbor classifiers demand significant computational resources (time and memory). Editing of the reference set and feature selection are two diAerent approaches to this problem. Here we encode the two approaches within the same genetic algorithm (GA) and simultaneously select features and reference cases. Two data sets were used: the SATIMAGE data and a generated data set. The GA was found to be an expedient solution compared to editing followed by feature selection, feature selection followed by editing, and the individual results from feature selection and editing. ” 1999 Elsevier Science B.V. All rights reserved.

Advances in Swarm Intelligence

In this chapter, advances in techniques and applications of swarm intelligence are presented. An overview of different swarm intelligence models is described. The dynamics of each swarm intelligence model and the associated characteristics in solving optimization as well as other problems are explained. The application and implementation of swarm intelligence in a variety of different domains are discussed. The contribution of each chapter included in this book is also highlighted.

Serious Games and Edutainment Applications

The recent re-emergence of serious games as a branch of video games and as a promising frontier of education has introduced the concept of games designed for a serious purpose other than pure entertainment. To date the major applications of serious games include education and training, engineering, medicine and healthcare, military applications, city planning, production, crisis response, to name just a few. If utilised alongside, or combined with conventional training and educational approaches, serious games could provide a more powerful means of knowledge transfer in almost every application domain. Serious Games and Edutainment Applications offers an insightful introduction to the development and applications of games technologies in educational settings. It includes cutting-edge academic research and industry updates that will inform readers of current and future advances in the area. The book is suitable for both researchers and educators who are interested in using games for educational purposes, as well as game professionals requiring a thorough understanding of issues involved in the application of video games technology into educational settings. It is also applicable to programmers, game artists, and management contemplating or involved in the development of serious games for educational or training purposes.

Fuzzy and Neuro-Fuzzy Systems in Medicine

From the Publisher: Fuzzy and Neuro-Fuzzy Systems in Medicine provides a thorough review of state-of-the-art techniques and practices, defines and explains relevant problems, as well as provides solutions to these problems. After an introduction, the book progresses from one topic to another - with a linear development from fundamentals to applications.

Computational Intelligence: A Compendium

Computational Intelligence: A Compendium presents a well structured overview about this rapidly growing field with contributions of leading experts in Computational Intelligence. The main focus of the compendium is on applied methods tired-and-proven effective to realworld problems, which is especially useful for practitioners, researchers, students and also newcomers to the field. The 25 chapters are grouped into the following themes: I. Overview and Background II. Data Preprocessing and Systems Integration III. Artificial Intelligence IV. Logic and Reasoning V. Ontology VI. Agents VII. Fuzzy Systems VIII. Artificial Neural Networks IX. Evolutionary Approaches X. DNA and Immune-based Computing.

Recent advances in intelligent decision technologies

Intelligent decision technologies (IDTs) combine artificial intelligence (AI) based in computer science, decision support based in information technology, and systems development based in engineering science. IDTs integrate these fields with a goal of enhancing and improving individual and organizational decision making. This session of the 11th International Conference on Knowledge-Based & Intelligent Information & Engineering Systems (KES) presents current research in IDTs and their growing impact on decision making.