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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 2002
TL;DR: A common web-based AI resource that is designed as a virtual class, and computer-supported collaborative work environments is proposed.
Abstract: The combination of computers and electronic communication has the power to dramatically enhance the productivity of researchers/educators in a given area. A major step towards realizing that potential comes from combining the interests of scientific community to create an integrated common resource and a communication system to support scientific collaboration. This paper proposes a common web-based AI resource that is designed as a virtual class, and computer-supported collaborative work environments.

1 citations

Book ChapterDOI
01 Jan 2010
TL;DR: This chapter presents an introduction to the computational intelligence in medicine as well as a sample of recent advances in the field.
Abstract: This chapter presents an introduction to the computational intelligence in medicine as well as a sample of recent advances in the field. A very brief introduction of chapters included in the book is included.

1 citations

Journal ArticleDOI
TL;DR: The outcomes of collaborative research between three PhD students working in the Multi-Agent System MAS, Knowledge-Based System KBS, and aviation Situation Awareness SA domains confirmed it is possible to identify at least three specific behaviours.
Abstract: This paper presents the outcomes of collaborative research between three PhD students working in the Multi-Agent System MAS, Knowledge-Based System KBS, and aviation Situation Awareness SA domains. The aim of this research was to create a MAS that could be used to monitor pilot SA during flight. SA is a cognitive activity that is a critical function conducted by pilots to maintain knowledge of their environment during flight. Good SA ultimately enhances the safety of passengers by reducing the possibility of pilots contributing to a number of documented catastrophic incidents. A controlled experiment has been devised to enable these students to capture and analyse pilot behaviour in an attempt to passively monitor SA monitoring activities using a camera. The MAS consists of multiple agent capabilities that capture the pilots visual acuity and eye movements. This data is used to assess the perceived cognitive activity in real-time. All agents can communicate and share the knowledge captured in order to analyse the activity based on pattern-matching rules using an embedded KBS. The experiments confirmed it is possible to identify at least three specific behaviours. The agents were able to post-process the acquired data to distinguish significant differences between an expert pilot and trained volunteers.

1 citations

Book ChapterDOI
01 Jan 2019
TL;DR: This chapter presents an introduction to complex networks by giving several examples of technological, social, information and biological networks, and discusses complex networks that are in the focus of this monograph (software, ontology and co-authorship networks).
Abstract: Complex networks are graphs describing complex natural, conceptual and engineered systems. In this chapter we present an introduction to complex networks by giving several examples of technological, social, information and biological networks. Then, we discuss complex networks that are in the focus of this monograph (software, ontology and co-authorship networks). Finally, we briefly outline our main research contributions presented in the monograph.

1 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