http://localhost:8080/xmlui/handle/123456789/220 2026-04-26T08:24:51Z 2026-04-26T08:24:51Z T., Rahul Majhi, R. http://localhost:8080/xmlui/handle/123456789/3218 2025-02-26T05:21:53Z 2014-01-01T00:00:00Z

Title: An adaptive nonlinear approach for estimation of consumer satisfaction and loyalty in mobile phone sector of India Authors: T., Rahul; Majhi, R. Abstract: To facilitate business growth assessment of customer's, satisfaction and loyalty levels in mobile sector are two important issues which need in-depth investigation. These two levels of customers are non linearly related to their corresponding attributes. The past studies have mostly assumed linear relation and have mostly used regression based models for estimation of these levels and the results are not encouraging. To overcome this limitation, the present study has developed simple non linear models for accurate estimation of these two parameters using their related key factors and results obtained are shown to be much better. This paper has also observed the positive effect of satisfaction on the loyalty estimation of customers. Employing the proposed nonlinear adaptive models, the service provider can also predict the satisfaction and loyalty levels of each of its customers which help the organization to determine the number of possible future churners. Description: NITW

2014-01-01T00:00:00Z Mishra, Sudhansu Kumar Panda, Ganapati Majhi, Ritanjali http://localhost:8080/xmlui/handle/123456789/3207 2025-02-06T09:50:01Z 2014-01-01T00:00:00Z

Title: Constrained portfolio asset selection using multiobjective bacteria foraging optimization Authors: Mishra, Sudhansu Kumar; Panda, Ganapati; Majhi, Ritanjali Abstract: Portfolio asset selection (PAS) is a challenging and interesting multiobjective task in the field of computational finance, and is receiving the increasing attention of researchers, fund management companies and individual investors in the last few decades. Selecting a subset of assets and corresponding optimal weights from a set of available assets, is a key issue in the PAS problem. A Markowitz model is generally used to solve this optimization problem, where the total profit is maximized, while the total risk is to be minimized. However, this model does not consider the practical constraints, such as the minimum buy in threshold, maximum limit, cardinality etc. The Practical constraints are incorporated in this study to meet a real world financial scenario. In the proposed work, the PAS problem is formulated in a multiobjective framework, and solved using the multiobjective bacteria foraging optimization (MOBFO) algorithm. The performance of the proposed approach is compared with a set of competitive multiobjective evolutionary algorithms using six performance metrics, the Pareto front and computational time. On examining the performance metrics, it is concluded that the proposed MOBFO algorithm is capable of identifying a good Pareto solution, maintaining adequate diversity. The proposed algorithm is also successfully applied to different cardinality constraint conditions, for six different market indices. Description: NITW

2014-01-01T00:00:00Z Bathini, Dharma Raju Vohra, Neharika http://localhost:8080/xmlui/handle/123456789/3010 2025-01-28T06:15:20Z 2015-01-01T00:00:00Z

Title: Volunteering: The Role of Individual-level Psychological Variables Authors: Bathini, Dharma Raju; Vohra, Neharika Description: NITW

2015-01-01T00:00:00Z Majhi, B. Rout, M. Majhi, R.; Panda, G Fleming, P.J. http://localhost:8080/xmlui/handle/123456789/2472 2025-01-06T10:26:56Z 2012-11-01T00:00:00Z

Title: New robust forecasting models for exchange rates prediction Authors: Majhi, B.; Rout, M.; Majhi, R.;; Panda, G; Fleming, P.J. Abstract: This paper introduces two robust forecasting models for efficient prediction of different exchange rates for future months ahead. These models employ Wilcoxon artificial neural network (WANN) and Wilcoxon functional link artificial neural network (WFLANN). The learning algorithms required to train the weights of these models are derived by minimizing a robust norm called Wilcoxon norm. These models offer robust exchange rate predictions in the sense that the training of weight parameters of these models are not influenced by outliers present in the training samples. The Wilcoxon norm considers the rank or position of an error value rather than its amplitude. Simulation based experiments have been conducted using real life data and the results indicate that both models, unlike conventional models, demonstrate consistently superior prediction performance under different densities of outliers present in the training samples. Further, comparison of performance between the two proposed models reveals that both provide almost identical performance but the later involved low computational complexity and hence is preferable over the WANN model. Description: NITW

2012-11-01T00:00:00Z