Showing papers by "Lakhmi C. Jain published in 2007"

Recent advances in intelligent decision technologies

Abstract: 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.

Intelligent Multimedia Data Hiding: New Directions

Abstract: This book presents the latest research in the area of multimedia data hiding paradigms. The book is divided into four parts and an appendix. The first part introduces multimedia signal processing and information hiding techniques. It includes multimedia representation, need for multimedia, digital watermarking fundamentals and requirements of watermarking. The second part describes the recent advances in multimedia signal processing. The third part presents information hiding techniques including steganography, secret sharing and watermarking. The final part of this book includes practical applications of intelligent multimedia signal processing and data hiding systems. Appendix includes source codes and/or executables related to the topics in some chapters. Interested readers are invited to use these programs.

Advances in Evolutionary Computing for System Design

Abstract: Evolutionary computing paradigms offer robust and powerful adaptive search mechanisms for system design. This book includes thirteen chapters covering a wide area of topics in evolutionary computing and applications including: Introduction to evolutionary computing in system design; evolutionary neuro-fuzzy systems; evolution of fuzzy controllers; genetic algorithms for multi-classifier design; evolutionary grooming of traffic; evolutionary particle swarms; fuzzy logic systems using genetic algorithms; evolutionary algorithms and immune learning for neural network-based controller design; distributed problem solving using evolutionary learning; evolutionary computing within grid environment; evolutionary game theory in wireless mesh networks; hybrid multiobjective evolutionary algorithms for the sailor assignment problem; evolutionary techniques in hardware optimization. This book will be useful to researchers in intelligent systems with interest in evolutionary computing, application engineers and system designers. The book can also be used by students and lecturers as an advanced reading material for courses on evolutionary computing.

Advanced Techniques in Knowledge Discovery and Data Mining

Abstract: That's it, a book to wait for in this month. Even you have wanted for long time for releasing this book advanced techniques in knowledge discovery and data mining; you may not be able to get in some stress. Should you go around and seek fro the book until you really get it? Are you sure? Are you that free? This condition will force you to always end up to get a book. But now, we are coming to give you excellent solution.

Advanced Intelligent Paradigms in Computer Games

Abstract: This book presents a sample of the most recent research concerning the application of computational intelligence techniques and internet technology in computer games. The contents include: COMMONS GAME in intelligent environment; adaptive generation of dilemma-based interactive narratives; computational intelligence in racing games; evolutionary algorithms for board game players with domain knowledge; the ChessBrain project; electronic market games; EVE s entropy; capturing player enjoyment in computer games.

Evolutionary Neuro-Fuzzy Systems and Applications

Abstract: In recent years, the use of hybrid soft computing methods has shown that in various applications the synergism of several techniques is superior to a single technique For example, the use of a neural fuzzy system and an evolutionary fuzzy system hybridises the approximate reasoning mechanism of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms Evolutionary neural systems hybridise the neurocomputing approach with the solution-searching ability of evolutionary computing Such hybrid methodologies retain limitations that can be overcome with full integration of the three basic soft computing paradigms, and this leads to evolutionary neural fuzzy systems The objective of this chapter is to provide an account of hybrid soft computing systems, with special attention to the combined use of evolutionary algorithms and neural networks in order to endow fuzzy systems with learning and adaptive capabilities After an introduction to basic soft computing paradigms, the various forms of hybridisation are considered, which results in evolutionary neural fuzzy systems The chapter also describes a particular approach that jointly uses neural learning and genetic optimisation to learn a fuzzy model from the given data and to optimise it for accuracy and interpretability

Artificial Intelligence in Recognition and Classification of Astrophysical and Medical Images

Abstract: This section describes a number of geometrical and intensity procedures which are frequently used in the processing of solar images. Geometrical operations have a range of applications from compensating for different sizes and viewpoints of images, to displaying an image in a different co-ordinate system. Intensity operations ranging from removing radial and non-radial background illumination variations to removing dust lines; image de-blurring can be useful preliminary steps before the application of feature detection algorithms. 2.1.1 Geometrical Standardization This section describes procedures which are used to change the shape or remap a digital image by applying geometrical transformations such as the one shown in Fig. 2.1. A common approach, used when the transformation is invertible, is to first apply the forward transformation to define the space occupied by the new transformed image. This space is then scanned pixel by pixel, and for each pixel the inverse transformation is applied to determine where it came from in the original image. This position generally falls between the integer pixel locations in the original position and an interpolation procedure is applied to determine the appropriate image value at that point. “Image Resampling and Interpolation” describes in detail this commonly used approach to applying a geometrical transformation to an image and “Removal of Nonradial Background Illumination and Removal of Dust Lines” describe specific examples in which different geometrical transformations are applied to solar images. www.springerlink.com © Springer-Verlag Berlin Heidelberg 2007 S.S. Ipson et al.: Image Standardization and Enhancement, Studies in Computational Intelligence (SCI) 46, 19–58 (2007) Image Resampling and Interpolation The general mathematical form of a continuous geometrical transformation applied to an image is ) , ( ) , ( y x g y y x f x = ′ = ′ , (2.1) where the image intensity at the point (x, y) in the original image has been moved to the point (x′, y′) in the transformed image under the functions f and g. A simple case is the affine transformation (illustrated in Fig. 2.1) which can be used to standardize the position and size of a solar image and is sometimes used to correct the shape of the solar disc from an instrumentally distorted elliptical shape back to the true circular shape. The affine transformation is expressed in matrix form with homogeneous coordinates as follows:

Reinforcement learning of competitive skills with soccer agents

Abstract: Reinforcement learning plays an important role in Multi-Agent Systems. The reasoning and learning ability of agents is the key for autonomous agents. Autonomous agents are required to be able to adapt and learn in uncertain environments via communication and collaboration (in both competitive and cooperative situations). For real-time, non-deterministic and dynamic systems, it is often extremely complex and difficult to formally verify their properties a priori. In this paper, we adopt the reinforcement learning algorithms to verify goal-oriented agents' competitive and cooperative learning abilities for decision making. In doing so, a simulation testbed is applied to test the learning algorithms in the specified scenarios. In addition, the function approximation technique known as tile coding (TC), is used to generate value functions, which can avoid the value function growing exponentially with the number of the state values.

Agent and Multi-Agent Systems: Technologies and Applications First KES International Symposium, KES-AMSTA 2007, Wroclaw, Poland, May 31-June 1, 2007, ... / Lecture Notes in Artificial Intelligence)

Abstract: This book constitutes the refereed proceedings of the First International Symposium on Agent and Multi-Agent Systems - Technologies and Applications, KES-AMSTA 2007, held in Wroclaw, Poland in May/June 2007. China in November 2006. The 110 revised papers presented were carefully reviewed and selected from 464 submissions and contain 4 papers from the doctoral track, and 38 papers from 3 special sessions. The papers are organized in topical sections representing the following conference tracks: methodological aspects of agent systems, agent-oriented Web applications, mobility aspects of agent systems and ontology management, multi-agent resource allocation, negotiating agents, agents for network management, agent approaches to robotic systems, market agents and other applications; the doctoral track; special sessions on mobile agent application and its development, agent on networked media and its applications on next-generation convergence network, as well as on intelligent and secure agent for digital content management.

Intelligent Machines: An Introduction

Abstract: In this chapter, an introduction to intelligent machine is presented. An explanation on intelligent behavior, and the difference between intelligent and repetitive natural or programmed behavior is provided. Some learning techniques in the field of Artificial Intelligence in constructing intelligent machines are then discussed. In addition, applications of intelligent machines to a number of areas including aerial navigation, ocean and space exploration, and humanoid robots are presented.

Hybrid filtering methods applied in web-based movie recommendation system

Abstract: In this paper web-based movie recommendation system using hybrid filtering methods is presented. The recommender systems deliver one of the methods for increasing the web-based systems attractiveness and usability. We can distinguish three basic filtering methods that are applied in recommender systems: demographic, content-based, and collaborative. The combination of these approaches that is called hybrid method.

Soft computing approach to contextual determination of grounding sets for simple modalities

Abstract: Four strategies for computing the grounding sets are suggested. An original model for grounding of simple modalities is briefly outlined and the need for its contextualization is discussed. References are made to works in which soft computing methods are presented to make effective implementation of these strategies possible for the case of software agents.

An architecture for agent coordination and cooperation

Abstract: Agent coordination and cooperation co-exist in a multiagent system, and are cognitively linked. This link is emphasized further by incorporating their atomic composition with their ability to perceive and gather information from the environment around them. As a result, a new generation of coordination/cooperation architectures is starting to emerge. From each of their definitions and current implementations, we show how the relationship between the Belief-Desire-Intention (BDI) architecture and the Observe-Orient-Decide-Act (OODA) loop can enable the use of Coordinative Cooperation within the Agent Coordination and Cooperation Cognitive Model. More importantly, we show the relationship between coordination, cooperation, BDI and OODA. This paper also discusses the current developments of the model and how the BDI and OODA architectures can affect coordination and cooperation within a Multi-Agent System (MAS). We recommend how these concepts can be designed and implemented in a MAS.

Building a decision making framework using agent teams

Abstract: This article describes preliminary work performed to gain an understanding of how to implement Decision support system that collaborate between intelligent agents in a Multi-Agent System MAS when human interaction is involved. A condensed description of previous research shows how developments in the agent software frameworks can be implemented using reasoning and learning models. Cooperation is a type of relationship that is evident within structured teams where collaboration involves the creation of temporary relationships between those agents and/or humans to achieve their respective goals. Due to the inherent physical separation between humans and agents, the concept of collaboration has been identified as the means of realizing human-agent teams to assist with decision making. An example application is also provided to demonstrate this research.

Convergence analysis on approximate reinforcement learning

Abstract: Temporal difference (TD) learning is a form of approximate reinforcement learning using an incremental learning updates. For large, stochastic and dynamic systems, however, it is still on open question for lacking the methodology to analyse the convergence and sensitivity of TD algorithms. Meanwhile, analysis on convergence and sensitivity of parameters are very expensive, such analysis metrics are obtained only by running an experiment with different parameter values. In this paper, we utilise the TD(λ) learning control algorithm with a linear function approximation technique known as tile coding in order to help soccer agent learn the optimal control processes. The aim of this paper is to propose a methodology for analysing the performance for adaptively selecting a set of optimal parameter values in TD(λ) learning algorithm.

Intelligent decision support feedback using Multi-Agent System in a defence maintenance environment

Abstract: Safety and Airworthiness of airborne platforms rest heavily on the maintainability and reliability to maximise the availability and reduce logistics down time. Most of the test and maintenance data currently produced is either paper-based or discarded and generally fails to provide preventive analysis. Improvements could be made by creating an expert system using intelligent agents. Data Mining techniques and intelligent agents could be employed to create an expert system within the Integrated Logistics Support (ILS), thereby creating a feedback mechanism. This concept would develop into an Intelligent Decision Support System (IDSS) that extrapolates forecasts and warnings. This paper presents the design and development of that agent-based Intelligent Decision Support System (IDSS).

Size invariant shape recognition by modulated competitive neural circuit

Abstract: This paper addresses the problem of size invariant shape recognition based on scale transformation within a modulated competition neural layer. In this paper we discuss the advantages of the application of a neural network to pattern recognition and study how the traditional automatic target recognition can fail to recognise a known pattern due to either a size change or background clutter and distortion. The model construction is based on neurophysiology experiments on vision systems. The Neural Circuit Simulation studies undertaken demonstrate the effectiveness of the proposed model in recognising 2D objects in non-ideal visual conditions.

Introduction to Computational Intelligence in Healthcare

Abstract: This chapter presents introductory remarks on computational intelligence in healthcare practice, and it provides a brief outline for each of the succeeding chapters in the remainder of this book.

Introduction to Evolutionary Computing in System Design

Abstract: In this chapter, an introduction on the use of evolutionary computing techniques, which are considered as global optimization and search techniques inspired from biological evolutions, in the domain of system design is presented. A variety of evolutionary computing techniques are first explained, and the motivations of using evolutionary computing techniques in tackling system design tasks are then discussed. In addition, a number of successful applications of evolutionary computing to system design tasks are described.

An Introduction to Intelligent Multimedia Data Hiding

Abstract: 1 Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan, R.O.C. huang.hc@gmail.com 2 Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 807, Taiwan, R.O.C. jspan@cc.kuas.edu.tw 3 California Institute of Technology & NASA’s Jet Propulsion Lab, Pasadena, CA, USA wai-chi.fang@jpl.nasa.gov 4 University of South Australia, Adelaide, Australia Lakhmi.Jain@unisa.edu.au

Asymmetric Clustering Based on Self-Similarity

Abstract: This paper proposes a clustering method for asymmetric similarity data. In this method, systematic asymmetry in the data is explained by using self-similarity of objects. We exploit an additive fuzzy clustering model for capturing the classification structure in the data. Moreover, the symmetric similarity data is restored by using the result of the clustering method. Therefore, we can exploit many data analyses in which objective data is symmetric similarity data. Several numerical examples are shown in order to show the better performance of the proposed method.

Advanced Computational Intelligence Paradigms in Healthcare - 1 (Studies in Computational Intelligence)

Abstract: Spend your few moment to read a book even only few pages. Reading book is not obligation and force for everybody. When you don't want to read, you can get punishment from the publisher. Read a book becomes a choice of your different characteristics. Many people with reading habit will always be enjoyable to read, or on the contrary. For some reasons, this advanced computational intelligence paradigms in healthcare 1 studies in computational intelligence tends to be the representative book in this website.

A differential geometry-based neurodynamical classifier udc531/534:611.69:514.76(045)=111

Abstract: A new model for a neurodynamical classifier is proposed. The classifier is viewed as a generalized bi-directional associative memory (GBAM) (11) and is described in the language of differential geometry (12-14). GBAM is a tensor-field system resembling a two-phase biological neural oscillator in which an excitatory neural field excites an inhibitory neural field, which reciprocally inhibits the excitatory one. GBAM equations have been directly implemented in the computer algebra system 'Mathematica' and tested on two different sets of data related to detection of breast cancer.• The GBAM classifier outperformed other neural classifiers.