This paper discusses mixing of chaotic systems as a dependable method for secure communication. Distribution of the entropy function for steady state as well as plaintext input sequences are analyzed. It is shown that the mixing of chaotic sequences results in a sequence that does not have any state dependence on the information encrypted by them. The… (More)
A Bayesian classifier that up-weights the differences in the attribute values is discussed. Using four popular datasets from the UCI repository, some interesting features of the network are illustrated. The network is suitable for classification problems.
In this paper we describe the use of a new artificial neural network, called the difference boosting neural network (DBNN), for automated classification problems in astronomical data analysis. We illustrate the capabilities of the network by applying it to star galaxy classification using recently released, deep imaging data. We have compared our results… (More)
A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy. Abstract In today's interconnected world, threats from anywhere in the world can have serious global repercussions. In particular, two types of threats have a global impact: 1) cyber crime and 2) cyber and biological weapons. If a country's… (More)
Rainfall in Kerala State, the southern part of Indian Peninsula in particular is caused by the two monsoons and the two cyclones every year. In general, climate and rainfall are highly nonlinear phenomena in nature giving rise to what is known as the 'butterfly effect'. We however attempt to train an ABF neural network on the time series rainfall data and… (More)
Scientific investigations on processes on networks (more generally, dyadic relational data) often assume that the initial data collection method on relations between individuals is perfect – that is, the representation given to the connection is without noise or random variation. Other investigations on a network presume that there is an underlying process… (More)
The difference-boosting algorithm is used on letters dataset from the UCI repository to classify distorted raster images of English alphabets. In contrast to rather complex networks, the difference-boosting is found to produce comparable or better classification efficiency on this complex problem.
This technical report provides users and researchers information on the configuration and use of Construct, the CASOS dynamic network, agent-based, information and belief diffusion simulation of complex socio-technical systems. The report provides a Quick Start Guide to Construct, a detailed discussion of its configuration, and use through a sample problem… (More)
This report describes a preliminary Virtual Experiment (VE) utilizing multi-agent simulation that explores how information loss and information error impact decision accuracy in organizations. Results indicate that information loss and error exert interactive influences on decision accuracy. Moreover, the pattern of interaction suggests that information… (More)