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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.


Papers
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Book ChapterDOI
01 Jan 2020
TL;DR: In this article, the authors present a brief description of the application of image processing in different practical fields, from radar systems to medical applications, and present a review of some of the most relevant work.
Abstract: The chapter presents a brief description of chapters on image processing in different practical fields, from radar systems to medical applications. In spite of the fact that images can be multidimensional, additional dimensions extend the possibilities of methods and applications.

3 citations

Book
30 May 2008
TL;DR: A conceptual model for Holonic Manufacturing Execution is presented that draws together research threads from both holonics and multi-agent systems, and the Team Programming paradigm is introduced, which is something of interest to the wider complex systems community as a new paradigm for complex systems development.
Abstract: This research book presents a conceptual model for Holonic Manufacturing Execution that draws together research threads from both holonics and multi-agent systems. In holonics, we found the notion of a holon as an entity being both a whole in itself and part of a larger system, and in multi-agent systems, we found the modelling approach of expressing software behaviour in terms of Beliefs, Desires and Intentions. The conceptual model brings focus to the execution facet of Holonic Manufacturing, and forms a bridge between the areas of planning / scheduling and control; the areas of predominant concerns in the past decade of active research on Holonic Manufacturing. We present the model by mapping it onto two current BDI programming frameworks, and use this for two separate implementations of an execution systems for an industrial strength robotic assembly cell. This work also introduces the Team Programming paradigm, which is something of interest to the wider complex systems community as a new paradigm f or complex systems development.

3 citations

Book ChapterDOI
01 Jan 2020
TL;DR: In this paper, a basic outline of the deep learning process and five methods for exploiting DL with the Convolutional Neural Network (CNN) is presented, as well as a summary of the chapters in this book.
Abstract: This chapter provides an introduction to deep learners and deep learner descriptors for medical applications. A basic outline of the deep learning (DL) process and five methods for exploiting DL with the Convolutional Neural Network (CNN), is presented, as well as a summary of the chapters in this book.

2 citations

Book ChapterDOI
01 Jan 2002
TL;DR: A novel scheme is presented to detect and recognise a logo in a given document(s) and makes correct judgements regarding their identity.
Abstract: A novel scheme is presented to detect and recognise a logo in a given document(s). Another area of interest will be dealing with distorted logos. This refers to logos, which are scaled, rotated, and have a brightness or contrast variation from the original logo. The system recognises these logos and makes correct judgements regarding their identity. The success rate for this system is about 75 to 80 percent

2 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).

13,246 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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.

10,141 citations

Book
01 Jan 1995
TL;DR: In this article, Nonaka and Takeuchi argue that Japanese firms are successful precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies, and they reveal how Japanese companies translate tacit to explicit knowledge.
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.

7,448 citations

01 Jan 2009

7,241 citations