Lakhmi C. Jain

Bio: Lakhmi C. Jain is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Artificial neural network & Intelligent decision support system. The author has an hindex of 41, co-authored 419 publications receiving 10015 citations. Previous affiliations of Lakhmi C. Jain include University of South Australia & University of Canberra.

Intelligent Adaptive Control: Industrial Applications

Abstract: From the Publisher: Intelligent Adaptive Control describes important techniques, developments, and applications of computational intelligence in system control. Intelligent Adaptive Control provides a state-of-the-art treatment of practical applications of computational intelligence in system control. The book cohesively covers introductory and advanced theory, design, implementation, and industrial useserving as a singular resource for the theory and application of intelligent control, particularly employing fuzzy logic, neural networks, and evolutionary computing.

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.

Intelligent agents in the evolution of Web and applications

Abstract: The Evolution of Intelligent Agents within the World Wide Web.- A Multi-agent System Based on Evolutionary Learning for the Usability Analysis of Websites.- Towards Norm-Governed Behavior in Virtual Enterprises.- e-JABAT - An Implementation of the Web-Based A-Team.- Adaptive and Intelligent Agents Applied in the Taking of Decisions Inside of a Web-Based Education System.- A Resource Discovery Method Based on Multiple Mobile Agents in P2P Systems.- Browsing Assistant for Changing Pages.- Considering Resource Management in Agent-Based Virtual Organization.- BDI Agents: Flexibility, Personalization, and Adaptation for Web-Based Support Systems.- Ontology for Agents and the Web.- Ontology Agents and Their Applications in the Web-Based Education Systems: Towards an Adaptive and Intelligent Service.- An Evolutionary Approach for Intelligent Negotiation Agents in e-Marketplaces.- Security of Intelligent Agents in the Web-Based Applications.

Fusion of intelligent agents for the detection of aircraft in SAR images

Abstract: Receiver operating curves are used in the analysis of 20 images using a novel automatic target recognition (ATR) fusion system. Fuzzy reasoning is used to improve the accuracy of the automatic detection of aircraft in synthetic aperture radar (SAR) images using a priori knowledge derived from color aerial photographs. The images taken by the two different sensors are taken at different times. In summarizing the results of our experiments using real and generated targets with noise for a probability of detection of 91.5 percent using the ATR fusion technique, we have improved our false alarm rates by approximately 17 percent over using texture classification.

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.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Abstract: How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.