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Author: search.filters.author.Majhi B.

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    On the Linear Antenna Array Synthesis Techniques for Sum and Difference Patterns Using Flower Pollination Algorithm
    (2018) Chakravarthy V.V.S.S.S.; Chowdary P.S.R.; Panda G.; Anguera J.; And�jar A.; Majhi B.
    Enhancement of performance of antenna array in terms of directive characteristics requires simultaneous control of both side-lobe level (SLL) and beam width (BW). The population-based evolutionary computing techniques are best suited for such array synthesis problems. In this paper, a novel evolutionary computing tool known as flower pollination algorithm (FPA) is applied to linear array synthesis problem. In addition, the performance of both amplitude only and amplitude�spacing-based methods is assessed. The proposed method estimates the weights of each objective so that the generated radiation patterns with desired SLL and BW are produced. The performance and the efficiency of FPA-based method are evaluated and also compared with those obtained using genetic algorithm and particle swarm optimization. The problem of synthesizing patterns with narrow and wide nulls is also investigated. Multiple narrow band nulls as well wide nulls are produced using the FPA with amplitude only method. The investigation has been made for both sum and difference patterns. � 2017, King Fahd University of Petroleum & Minerals.
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    Comparative performance study of wore segmentation techniques for handwritten Odia documents
    (2017) Pradhan A.; Behera S.; Panda G.; Majhi B.
    Word segmentation of handwritten documents is a vital step in the Optical Character Recognition system as its accuracy greatly influences the overall recognition performance. In the literature, various methods have been proposed for word segmentation of handwritten documents of various languages. However, it is observed that for Odia, which is an important Indian language, very little work has been reported on word segmentation. Hence, the objective of this paper is to employ two standard existing methods to segment words of Odia handwritten documents and compare the segmentation performance of these methods with the lone Water Reservoir Algorithm available in the literature and finally rank those methods based on their segmentation performance. It is observed that out of three methods, the Tree Structure method performs the best comparing four different performance measures. � 2016 IEEE.
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    Sentiment analysis of Twitter data for predicting stock market movements
    (2017) Pagolu V.S.; Reddy K.N.; Panda G.; Majhi B.
    Predicting stock market movements is a well-known problem of interest. Now-a-days social media is perfectly representing the public sentiment and opinion about current events. Especially, Twitter has attracted a lot of attention from researchers for studying the public sentiments. Stock market prediction on the basis of public sentiments expressed on Twitter has been an intriguing field of research. Previous studies have concluded that the aggregate public mood collected from Twitter may well be correlated with Dow Jones Industrial Average Index (DJIA). The thesis of this work is to observe how well the changes in stock prices of a company, the rises and falls, are correlated with the public opinions being expressed in tweets about that company. Understanding author's opinion from a piece of text is the objective of sentiment analysis. The present paper have employed two different textual representations, Word2vec and N-gram, for analyzing the public sentiments in tweets. In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from Twitter and analyze the correlation between stock market movements of a company and sentiments in tweets. In an elaborate way, positive news and tweets in social media about a company would definitely encourage people to invest in the stocks of that company and as a result the stock price of that company would increase. At the end of the paper, it is shown that a strong correlation exists between the rise and falls in stock prices with the public sentiments in tweets. � 2016 IEEE.
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    An improved approach for prediction of Parkinson's disease using machine learning techniques
    (2017) Challa K.N.R.; Pagolu V.S.; Panda G.; Majhi B.
    Parkinson's disease (PD) is one of the major public health problems in the world. It is a well-known fact that around one million people suffer from Parkinson's disease in the United States whereas the number of people suffering from Parkinson's disease worldwide is around 5 millions. Thus, it is important to predict Parkinson's disease in early stages so that early plan for the necessary treatment can be made. People are mostly familiar with the motor symptoms of Parkinson's disease, however an increasing amount of research is being done to predict the Parkinson's disease from non-motor symptoms that precede the motor ones. If early and reliable prediction is possible then a patient can get a proper treatment at the right time. Non-motor symptoms considered are Rapid Eye Movement (REM) sleep Behaviour Disorder (RBD) and olfactory loss. Developing machine learning models that can help us in predicting the disease can play a vital role in early prediction. In this paper we extend a work which used the non-motor features such as RBD and olfactory loss. Along with this the extended work also uses important biomarkers. In this paper we try to model this classifier using different machine learning models that have not been used before. We developed automated diagnostic models using Multilayer Perceptron, BayesNet, Random Forest and Boosted Logistic Regression. It has been observed that Boosted Logistic Regression provides the best performance with an impressive accuracy of 97.159 % and the area under the ROC curve was 98.9%. Thus, it is concluded that this models can be used for early prediction of Parkinson's disease. � 2016 IEEE.
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    Constrained LMMSE-based object-specific reconstruction in compressive sensing
    (2017) Tripathy S.R.; Panda G.; Majhi B.
    In many applications like surveillance, it is essential to detect the presence of specific objects by seeing an image or video without being concerned about details of the scene. The reconstruction algorithms proposed in the compressive sensing literature try to iteratively reconstruct the full image. Hence, those are computationally expensive. If some prior knowledge of the object is available, then a closed-form reconstruction algorithm can be formulated. The goal is to reconstruct the object of interest efficiently without being bothered about the quality of reconstruction of the scene. To address this situation, a constraint is formulated and incorporated into linear minimum mean square error (LMMSE) estimator to form a closed-form solution. This compact solution is capable of meaningfully reconstructing the object relative to the scene. In the proposed method, an illconditioned matrix inversion problem has been faced and overcome by regularization method. To boost the speed of the algorithm, a modified Euler method is proposed for finding the regularization parameter. To further speedup the reconstruction process, larger images are divided into several pieces and each piece is reconstructed separately using constrained LMMSE. For a given number of measurements, the simulation-based results demonstrate acceptable quality of reconstruction with minimal computational effort. � The Institution of Engineering and Technology 2017.
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    Prediction based mean-variance model for constrained portfolio assets selection using multiobjective evolutionary algorithms
    (2016) Mishra S.K.; Panda G.; Majhi B.
    In this paper, a novel prediction based mean-variance (PBMV) model has been proposed, as an alternative to the conventional Markowitz mean-variance model, to solve the constrained portfolio optimization problem. In the Markowitz mean-variance model, the expected future return is taken as the mean of the past returns, which is incorrect. In the proposed model, first the expected future returns are predicted, using a low complexity heuristic functional link artificial neural network (HFLANN) model and the portfolio optimization task is carried out by using multi-objective evolutionary algorithms (MOEAs). In this paper, swarm intelligence based, multiobjective optimization algorithm, namely self-regulating multiobjective particle swarm optimization (SR-MOPSO) has also been proposed and employed efficiently to solve this important problem. The Pareto solutions obtained by applying two other competitive MOEAs and using the proposed PBMV models and Markowitz mean-variance model have been compared, considering six performance metrics and the Pareto fronts. Moreover, in the present study, the nonparametric statistical analysis using the Sign test and Wilcoxon rank test are also carried out, to compare the performance of the algorithms pair wise. It is observed that, the proposed PBMV model based approach provides better Pareto solutions, maintaining adequate diversity, and also quite comparable to the Markowitz model. From the simulation result, it is observed that the self regulating multiobjective particle swarm optimization (SR-MOPSO) algorithm based on PBMV model, provides the best Pareto solutions amongst those offered by other MOEAs. � 2016 Elsevier B.V.
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    Forecasting of currency exchange rates using an adaptive ARMA model with differential evolution based training
    (2014) Rout M.; Majhi B.; Majhi R.; Panda G.
    To alleviate the limitations of statistical based methods of forecasting of exchange rates, soft and evolutionary computing based techniques have been introduced in the literature. To further the research in this direction this paper proposes a simple but promising hybrid prediction model by suitably combining an adaptive autoregressive moving average (ARMA) architecture and differential evolution (DE) based training of its feed-forward and feed-back parameters. Simple statistical features are extracted for each exchange rate using a sliding window of past data and are employed as input to the prediction model for training its internal coefficients using DE optimization strategy. The prediction efficiency is validated using past exchange rates not used for training purpose. Simulation results using real life data are presented for three different exchange rates for one-fifteen months' ahead predictions. The results of the developed model are compared with other four competitive methods such as ARMA-particle swarm optimization (PSO), ARMA-cat swarm optimization (CSO), ARMA-bacterial foraging optimization (BFO) and ARMA-forward backward least mean square (FBLMS). The derivative based ARMA-FBLMS forecasting model exhibits worst prediction performance of the exchange rates. Comparisons of different performance measures including the training time of the all three evolutionary computing based models demonstrate that the proposed ARMA-DE exchange rate prediction model possesses superior short and long range prediction potentiality compared to others. � 2013 Elsevier B.V.
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    Robust incremental adaptive strategies for distributed networks to handle outliers in both input and desired data
    (2014) Sahoo U.K.; Panda G.; Mulgrew B.; Majhi B.
    Conventional distributed strategies based on least error squares cost function are not robust against outliers present in the desired and input data. This manuscript employs the generalized-rank (GR) technique as a cost function instead of least error squares cost function to control the effects of outliers present both in input and desired data. A novel indicator function and median based approach are proposed to decrease the computational complexity requirement at the sensor nodes. Further to increase the convergence speed a sign regressor GR norm is also proposed and used. Simulation based experiments show that the performance obtained using proposed methods is robust against outliers in the desired and input data. � 2013 Elsevier B.V.
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    Development of robust distributed learning strategies for wireless sensor networks using rank based norms
    (2014) Sahoo U.K.; Panda G.; Mulgrew B.; Majhi B.
    Distributed signal processing is an important area of research in wireless sensor networks (WSNs) which aims to increase the lifetime of the entire network. In WSNs the data collected by nodes are affected by both additive white Gaussian noise (AWGN) and impulsive noise. The classical square error based distributed techniques used for parameter estimation are sensitive to impulse noise and provide inferior estimation performance. In this paper, novel robust distributed learning strategies are proposed based on the Wilcoxon norm and its variants. The Wilcoxon norm based learning strategy provides very slow convergence speed. In order to circumvent this improved distributed learning strategies based on the notion of the Wilcoxon norm are proposed for different types of environmental data. These algorithms require less computational complexity compared to previous ones. In addition these algorithms offer faster convergence rate in the presence of biased input data. Simulation based experiments demonstrate that the proposed techniques provide faster convergence speed than the previously reported techniques in both biased and unbiased input data. � 2014 Elsevier B.V.
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    On the development and performance evaluation of a multiobjective GA-based RBF adaptive model for the prediction of stock indices
    (2014) Majhi B.; Rout M.; Baghel V.
    This paper develops and assesses the performance of a hybrid prediction model using a radial basis function neural network and non-dominated sorting multiobjective genetic algorithm-II (NSGA-II) for various stock market forecasts. The proposed technique simultaneously optimizes two mutually conflicting objectives: the structure (the number of centers in the hidden layer) and the output mean square error (MSE) of the model. The best compromised non-dominated solution-based model was determined from the optimal Pareto front using fuzzy set theory. The performances of this model were evaluated in terms of four different measures using Standard and Poor 500 (S&P500) and Dow Jones Industrial Average (DJIA) stock data. The results of the simulation of the new model demonstrate a prediction performance superior to that of the conventional radial basis function (RBF)-based forecasting model in terms of the mean average percentage error (MAPE), directional accuracy (DA), Thelis' U and average relative variance (ARV) values. � 2013 King Saud University.