Babita Majhi

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The present paper introduces the use of BFO and ABFO techniques to develop an efficient forecasting model for prediction of various stock indices. The structure used in these forecasting models is a simple linear combiner. The connecting weights of the adaptive linear combiner based models are optimized using ABFO and BFO by minimizing its mean square error(More)
This paper outlines the basic concept and principles of two simple and powerful swarm intelligence tools: the particle swarm optimization (PSO) and the Bacterial Foraging Optimization (BFO). The adaptive identification of an unknown plant has been formulated as an optimization problem and then solved using the PSO and BFO techniques. Using this new approach(More)
The paper develops an efficient but simple adaptive nonlinear classifier for recognition of handwritten Odiya numerals. The standard gradient and curvature features are extracted and nonlinearly mapped by sine/cosine expansions. These nonlinear inputs are fed to a low complexity classifier. The simulation results show excellent classification accuracy when(More)
Source direction of arrival (DOA) estimation is one of the challenging problem in wireless sensor network. Several methods based on maximum likelihood (ML) criteria has been established in literature. Generally, to obtain the exact ML (EML) solutions, the DOAs must be estimated by optimizing a complicated nonlinear multimodal function over a(More)
Distributed wireless sensor networks have been proposed as a solution to environment sensing, target tracking, data collection and others. Energy efficiency, high estimation accuracy, and fast convergence are important goals in distributed estimation algorithms for WSN. This paper studies the problem of robust adaptive estimation in impulsive noise(More)
The rapid development of internet usage has paved the way towards the use of online shopping. In this paper an investigation has been made on the behavior of Indian consumers towards online shopping using Functional link artificial neural network (FLANN) and the result is compared with that obtained from the conventional statistical based approach,(More)