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.

Multilingual Agents in a Dynamic Environment

Abstract: Experiments conducted by the Knowledge-Based Intelligent Information and Engineering Systems (KES) Centre use Java to gain its many advantages, especially in a distributed and dynamically scalable environment. Interoperability within and across ubiquitous computing operations has evolved to a level where plug ‘n′ play protocols that invoke common interfaces, provide the flexibility required for effective multi-lingual communications. One example includes: dynamic agent functionality within simulations that automatically adapt to incoming data and/or languages via scripts or messaging to achieve data management and inference. This has been shown using demonstrations at the Centre herein. Many aspects of the model involve web centric transactions, which involve data mining or the use of other types of Intelligent Decision Support System (IDSS). Section One of this paper provides an introduction, Section Two introduces the basic concepts of Decision Support System (DSS), Section Three discusses Intelligent Decision Support System (IDSS) enhancements, Section Four explains how agents use a multi-lingual dynamic environment,while Section Five highlights conclusions and future research direction.

Shadow watermark extraction system

Abstract: A new watermarking scheme which provides the ability of sharing secret with multi-users is proposed It splits the original watermark into two shares and embeds one share into the cover image to increase the security A polarization procedure is performed to establish a polarity stream from the cover image The second share and the polarity stream are used to generate a master key and several normal keys In our system, only the super user can reveal the genuine watermark directly Other users possess the normal keys can obtain shadow watermarks merely By combining the shadow watermarks together, the real watermark can be recovered

Multimedia Services in Intelligent Environments: An Introduction

Abstract: This chapter presents an overview of multimedia services in intelligent environments and outlines the contents of the book. Multimedia services is the term chosen to describe services which rely on the coordinated and secure storage, processing, transmission, and retrieval of multiple forms of information. The term refers to various levels of data processing and includes applications areas such as digital libraries, e-learning, e-government, e-commerce, e-entertainment, e-health, or e-legal services. Besides the introductory chapter, this book includes 14 additional chapters. Nine of these chapters attempt to cover various aspects of low level data processing in multimedia services in intelligent environments, such as storage, recognition and classification, transmission, information retrieval, and information securing. Four additional chapters present intermediate level multimedia services in noise and hearing monitoring and measuring, augmented reality, automated lecture rooms and rights management and licensing. Finally, Chap. 15 is devoted to a high-level intelligent recommender service in scientific digital libraries.

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.

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.