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.

Innovations in web applications using artificial intelligence paradigm

Abstract: Artificial Intelligence techniques are increasingly being used when devising smart Web applications for efficiently presenting the information on the Web to the user. The motivation for this trend is the growth of the internet and the increasing difficulty for users to navigate the web to find useful information and applications. This chapter shows how artificial intelligence techniques can be utilized to improve the Web and the benefits that can be obtained by integrating the Web and Artificial Intelligence.

How to assess human visual performance on an operational task

Abstract: A methodology is presented for the assessment of human operator performance in a detection and identification task, using two sets of infrared images of natural outdoor scenes with everyday objects used as targets. It includes measures of effectiveness such as operator detection rate, identification rate, false alarm rate, response time, confidence levels and image quality ratings. This robust methodology could be used in the evaluation of any image improvement technique or to evaluate different imaging techniques or technologies.

Perceptual Grouping of Contours via Gated Diffusion of Boundary Signals

Abstract: In this paper we present a vision based neural network architecture that models perceptual grouping of boundaries The network employs a novel concept of contour interaction via diffusion of boundary signals from edge pre-processed images This diffusion process is gated by a feedback signal from a layer of neurons, which are sensitive to the direction of surface The network is based on human pre-attentive vision and in particular on the recent neuro-physiological findings that cells in extrastriate cortex V2 perform the coding of border ownership The proposed mechanism thus leads to grouping of boundaries with surfaces that own them The model is implemented for figure-ground separation and in this paper we demonstrate its performance of perceptual grouping by testing it on a number of synthetic images

Recent Advances in Computational Physics

Abstract: The chapter presents a brief description of chapters that contribute to the recent advances in computational physics in different areas, i.e., computational aerodynamics, hydrodynamics, dynamics of plasma, solid mechanics, elastic and acoustic wave phenomena, seismic prospecting, seismic resistance, train movement, medicine, and biology. The first part of the book is devoted to the numerical solutions of problems of aerodynamics, hydrodynamics, and dynamics of plasma. The second part of the book deals with numerical methods and its applications in the area of solid mechanics. The third part of the book covers computational methods in medicine and biology.

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.