http://localhost:8080/xmlui/handle/123456789/1683 Sun, 26 Apr 2026 08:07:02 GMT 2026-04-26T08:07:02Z http://localhost:8080/xmlui/handle/123456789/2808 Title: Development and performance evaluation of neural network classifiers for Indian internet shoppers Authors: Majhi, R.; Majhi, B; Panda, G. Abstract: The rapid growth of usage of internet has paved the way towards the use of online shopping. Consumers’ behavior is one of the significant aspects that is considered by the service providers for the improvement of various services. Consumers are generally satisfied if their needs are fulfilled. In this paper an in depth investigation is made on the behavior of Indian consumers towards online shopping. Factor analysis is carried out to extract significant factors that affect online shopping of Indian consumers and these consumers are clustered based on their behavior, towards online shopping using hierarchical clustering. Employing the results of clustering in training of multilayer perceptron (MLP), functional link artificial neural network (FLANN) and radial basis function (RBF) networks efficient classifier models are developed. The performance of these classifiers are evaluated and compared with those obtained by conventional statistical based discriminant analysis. The simulation study demonstrates that the RBF network provides best classification performance of internet shoppers compared to those given by the FLANN, MLP and discriminant analysis based methods. The simulation study on the impact of different combination of inputs demonstrates that demographic input has least effect on classification performance. On the other hand the combination of psychological and cultural inputs play the most significant role in classification followed by psychological and then cultural inputs alone. Description: NITW Wed, 01 Feb 2012 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/2808 2012-02-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/1817 Title: Development and performance evaluation of FLANN based model for forecasting of stock markets Authors: Majhi, Ritanjali; Panda, G.; Sahoo, G. Abstract: A trigonometric functional link artificial neural network (FLANN) model for short (one day) as well as long term (one month, two months) prediction of stock price of leading stock market indices: DJIA and S&P 500 is developed in this paper. The proposed FLANN model employs the least mean square (LMS) as well as the recursive least square (RLS) algorithms in different experiments to train the weights of the model. The historical index data transformed into various technical Indicators as well as macro economic data as fundamental factors are considered as inputs to the proposed models. The mean absolute percentage error (MAPE) with respect to actual stock prices is selected as the performance index to gauge the quality of prediction of the models. Extensive simulation and test results show that the application of FLANN to the stock market prediction problem gives out results which are comparable to other neural network models. In addition the proposed models are structurally simple and requires less computation during training and testing as the model contains only one neuron and one layer. Between the two models proposed the FLANN-RLS requires substantially less experiments to train compared to the LMS based model. This feature makes the RLS-based FLANN model more suitable for online prediction. Description: NITW Thu, 01 Jan 2009 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/1817 2009-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/1742 Title: Efficient Sales Forecasting Using PSO Based Adaptive ARMA Model Authors: Majhi, Ritanjali; Mishra, Sashikala; Majhi, Babita; Panda, Ganapati; Rout, Minakshi Abstract: The paper proposes a new hybrid forecasting model using auto regressive moving average (ARMA) as basic architecture and particle swarm optimization (PSO) as learning algorithm. These two combinations have yielded an efficient prediction model for retail sales volumes. To facilitate comparison ARMA, functional link artificial neural network (FLANN) and MLP models are also simulated. The performance of the new model has been evaluated through simulation study and the results demonstrate the best prediction performance both for long and short ranges. Description: NITW Thu, 01 Jan 2009 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/1742 2009-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/1695 Title: Prediction of S&P 500 and DJIA stock indices using particle swarm optimization technique Authors: Majhi, Ritanjali; Panda, G; Sahoo, G.; Panda, Abhishek; Choubey, Arvind Abstract: The present paper introduces the particle swarm optimization (PSO) technique to develop an efficient forecasting model for prediction of various stock indices. The connecting weights of the adaptive linear combiner based model are optimized by the PSO so that its mean square error(MSE) is minimized. The short and long term prediction performance of the model is evaluated with test data and the results obtained are compared with those obtained from the multilayer perceptron (MLP) based model. It is in general observed that the proposed model is computationally more efficient, prediction wise more accurate and takes less training time compared to the standard MLP based model. Description: NITW Tue, 01 Jan 2008 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/1695 2008-01-01T00:00:00Z