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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.
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)
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)
Rainfall prediction is very important to countries thriving on agro-based economy. In general, climate and rainfall are highly non-linear phenomena in nature giving rise to what is known as "butterfly effect". The parameters that are required to predict the rainfall are enormously complex and subtle so that uncertainty in a prediction using all these… (More)
Sensory quality attributes, consumption pattern and preference for some selected Nigerian meat types (beef, goat meat, mutton, grasscutter (Thryonomys swinderianus raptorum), African giant rat (Cricetomys gambianus--water house) were investigated. Sensory quality scores were carried out using a panel of thirty carefully screened consumers, based on a… (More)
It is shown that modifying the sigmoidal basis function of a multi-layer feedforward arti"cial neural network using a control parameter improves the network's ability to learn. The modi"cation is rendered by a gradient descent algorithm similar to the back-propagation. In doing so, the method retains all the goodies of the sigmoidal function and causes the… (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.
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)
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)