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.

Industrial Applications of Neural Networks

Abstract: From the Publisher: Industrial Applications of Neural Networks explores the success of neural networks in different areas of engineering endeavors. Each chapter shows how the power of neural networks can be exploited in modern engineering applications.

Computational intelligence in games

Abstract: Introduction to computational intelligence paradigms.- Evolving a neural network to play checkers without human expertise.- Retrograde analysis of patterns versus metaprogramming.- Learning to evaluate Go positions via temporal difference methods.- Model-based reinforcement learning for evolving soccer strategies.- Fuzzy rule-based strategy for a market selection game.-

Recent advances in intelligent paradigms and applications

Abstract: 1. Intelligent systems: architectures and perspectives.- 2. Hybrid architecture for autonomous robots, based on representations, perception and intelligent control.- 3. An intuitionistic fuzzy set based approach to intelligent data analysis: an application to medical diagnosis.- 4. A fuzzy inference methodology based on the fuzzification of set inclusion.- 5. A fuzzy approach to job-shop scheduling problem based on imprecise processing times.- 6. On efficient representation of expert knowledge by fuzzy logic.- 7. Discovering efficient learning rules for feedforward neural networks using genetic programming.- 8. Neuro-fuzzy methods for modeling and identification.- 9. Constrained two dimensional bin packing using a genetic algorithm.- 10. Sequential and distributed evolutionary algorithms for combinatorial optimization problems.- 11. Embodied emotional agent in intelligent training system.- 12. Optimizing intelligent agent's constraint satisfaction with neural networks.

Cooperative multi-robot path planning using differential evolution

Abstract: This paper provides an alternative approach to the co-operative multi-robot path planning problem using parallel differential evolution algorithms. Both centralized and distributed realizations for multi-robot path planning have been studied, and the performances of the methods have been compared with respect to a few pre-defined yardsticks. The distributed approach to this problem out-performs its centralized version for multi-robot planning. Relative performance of the distributed version of the differential evolution algorithm has been studied with varying numbers of robots and obstacles. The distributed version of the algorithm is also compared with a PSO-based realization, and the results are competitive.

Virtual, Augmented Reality and Serious Games for Healthcare 1

Abstract: There is a tremendous interest among researchers for the development of virtual, augmented reality and games technologies due to their widespread applications in medicine and healthcare. To date the major applications of these technologies include medical simulation, telemedicine, medical and healthcare training, pain control, visualisation aid for surgery, rehabilitation in cases such as stroke, phobia and trauma therapies. Many recent studies have identified the benefits of using Virtual Reality, Augmented Reality or serious games in a variety of medical applications. This research volume on Virtual, Augmented Reality and Serious Games for Healthcare 1 offers an insightful introduction to the theories, development and applications of virtual, augmented reality and digital games technologies in medical and clinical settings and healthcare in general. It is divided into six sections: section one presents a selection of applications in medical education and healthcare management; Section two relates to the nursing training, health literacy and healthy behaviour; Section three presents the applications of Virtual Reality in neuropsychology; Section four includes a number of applications in motor rehabilitation; Section five aimed at therapeutic games for various diseases; and the final section presents the applications of Virtual Reality in healing and restoration. This book is directed to the healthcare professionals, scientists, researchers, professors and the students who wish to explore the applications of virtual, augmented reality and serious games in healthcare further.

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.