B. B. Misra

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Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the(More)
In this paper we present polynomial neural network (PNN) model using the group method of data handling to generate a nonlinear time series for classification of patterns. The proposed method considers nonlinear characteristics of the datasets and tries to evolve a polynomial using polynomial neural network that will approximate it to arbitrary token values(More)
The increasing demand of real-time multimedia applications in wireless environment requires stringent Quality of Service (QoS) provisioning to the Mobile Hosts (MI. The scenario becomes more complex during group communication from single source to multiple destinations. Fulfilling users demand with respect to delay, jitter, available bandwidth, packet loss(More)
With the rise of artificial intelligence technology and the growing interrelated markets of the last two decades offering unprecedented trading opportunities, technical analysis simply based on forecasting models is no longer enough. To meet the trading challenge in today's global market, technical analysis must be redefined. Before using the neural network(More)
Collection of information from critical situations where human beings find difficult to exist is a challenging task. In such scenarios tiny micro-sensors are densely deployed for monitoring the target objects or observing certain events to occur. The sensor nodes collect information from the target nodes, communicate with their neighbors and then forward(More)
Stock index forecasting has been a cornerstone and challenging task in computational statistics and financial mathematics since last few decades. Several machine learning methods have been proposed in order to forecast the future value of stocks effectively as well as efficiently. In this paper we considered an Artificial Neural Network (ANN) combined with(More)
The energy problem in wireless sensor networks (WSN) remains one of the major barriers preventing the complete exploitation of this technology. Sensor nodes are typically powered by batteries remains a limited resource to be consumed judiciously. Efficient energy management is thus a key requirement for optimal utilization of the sensor network technology.(More)
In this paper, we introduce a new topology of Optimal Polynomial Fuzzy Swarm Net (OPFSN) that is based on Swarm optimized multilayer perceptron with fuzzy polynomial neurons. The study offers a comprehensive design methodology involving mechanisms of Particle Swarm Optimization (PSO). The design laye of the conventional PNN uses extended group methods of(More)
In this paper we have used a local linear wavelet neural network (LLWNN) model for pattern classification. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. Particle swarm optimisation (PSO) technique(More)
The highly dynamic nonlinear and volatile nature of stock market has remained a challenging issue for the researchers in mathematical economics as well as in financial engineering. Despite the existence of a number of statistical and soft computing methodologies for stock market forecasting, there is still need for an efficient and accurate forecasting(More)