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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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Journal ArticleDOI
TL;DR: The proposed work presented in this paper, maintains the same level of classification accuracy in minimum computation time, as it employs most prominent and reduced number of feature set for classification.
Abstract: The paper provides an alternative approach to protein structural class prediction employing artificial neural network. Existing works on protein structural class prediction are computationally intensive. The method employs SOFM for extraction of representative feature vectors, for the four different structural classes and then uses Principal Component Analysis for finding optimum feature vector dimension. Nearest neighborhood classification technique is finally utilised; to classify these protein datapoints to their respective classes. The proposed work presented in this paper, maintains the same level of classification accuracy in minimum computation time, as it employs most prominent and reduced number of feature set for classification.

10 citations

Book
17 Jan 2009
TL;DR: The book presents the latest research on the theory, models and applications of complex systems in knowledge-based environment and the real challenge is to reinvent theory to deal with complex systems.
Abstract: The book presents the latest research on the theory, models and applications of complex systems in knowledge-based environment The amount of information is increasing at an exponential rate Thus, our systems are getting complex day by day For example the world wide web carries practically infinite information The real challenge is to reinvent theory to deal with complex systems The provisional contents in this book will be based on the theory and practical applications of complex systems in knowledge-based environment The contents will be based on but not limited to: - Advanced Knowledge-Based paradigms in complex systems; - Information models and architectures of complex systems; - Intelligent agents in complex systems design; - Multi-media systems and practical applications

10 citations

Journal ArticleDOI
01 Aug 2008
TL;DR: A new approach for predicting tertiary structure of protein using artificial neural network and particle swarm optimization technique that reduces the dimensionality of the search space and computationally outperforms all other classical technique in atleast 80% cases.
Abstract: This paper describes a new approach for predicting tertiary structure of protein using artificial neural network and particle swarm optimization technique. The paper is concetrated around the well known Ab-initio approach for global minimisation of energy function. It predicts the native structure of protein by finding the main chain dihedral angles; through the optimization of CHARMM energy function, using particle swarm optimization algorithm. Side chain dihedral angle are predicted using three layered artificial neural network, realised with back propagation algorithm. This approach has merit as it reduces the dimensionality of the search space and computationally outperforms all other classical technique in atleast 80% cases.

10 citations

BookDOI
01 Jan 2014
TL;DR: The integration of software agents into elearning mediums strengthens e-learning systems and allows for higher quality learning outcomes.
Abstract: E-learning involves the use of digital technology applications in learning and teaching processes, and grows out of an interdisciplinary field, in which are contained a variety different approaches and components. Since the field of e-learning requires the use of high-level technology, it is very much affected by technological developments. Software agents are computer systems that have features such as autonomy, reactivity, intentionality and interactivity. The integration of software agents into elearning mediums strengthens e-learning systems and allows for higher quality learning outcomes.

10 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