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Balasore, India

Fakir Mohan University is a university situated in Balasore, Odisha, India. It was carved out of the Utkal University in 1999 and acts as an affiliating university. The university has 60 colleges and 150,000 students at the graduate and postgraduate levels. It is the only University in India that offers a postgraduate course in Ballistics. Wikipedia.


Panda J.J.,International Center for Genetic Engineering and Biotechnology | Dua R.,International Center for Genetic Engineering and Biotechnology | Mishra A.,International Center for Genetic Engineering and Biotechnology | Mittra B.,Fakir Mohan University | Chauhan V.S.,International Center for Genetic Engineering and Biotechnology
ACS Applied Materials and Interfaces | Year: 2010

Three-dimensional (3D) hydrogels incorporating a compendium of bioactive molecules can allow efficient proliferation and differentiation of cells and can thus act as successful tissue engineering scaffolds. Self-assembled peptide-based hydrogels can be worthy candidates for such applications as peptides are biocompatible, biodegradable and can be easily functionalized with desired moieties. Here, we report 3D growth and proliferation of mammalian cells (HeLa and L929) on a dipeptide hydrogel chemically functionalized with a pentapeptide containing Arg-Gly-Asp (RGD) motif. The method of functionalization is simple, direct and can be adapted to other functional moieties as well. The functionalized gel was noncytotoxic, exhibited enhanced cell growth promoting properties, and promoted 3D growth and proliferation of cells for almost 2 weeks, with simultaneous preservation of their metabolic activities. The presence of effective cell growth supporting properties in a simple and easy to functionalize dipeptide hydrogel is unique and makes it a promising candidate for tissue engineering and cell biological applications. © 2010 American Chemical Society.


Dehuri S.,Fakir Mohan University | Cho S.-B.,Yonsei University
Neural Computing and Applications | Year: 2010

Functional link neural network (FLNN) is a class of higher order neural networks (HONs) and have gained extensive popularity in recent years. FLNN have been successfully used in many applications such as system identification, channel equalization, short-term electric-load forecasting, and some of the tasks of data mining. The goals of this paper are to: (1) provide readers who are novice to this area with a basis of understanding FLNN and a comprehensive survey, while offering specialists an updated picture of the depth and breadth of the theory and applications; (2) present a new hybrid learning scheme for Chebyshev functional link neural network (CFLNN); and (3) suggest possible remedies and guidelines for practical applications in data mining. We then validate the proposed learning scheme for CFLNN in classification by an extensive simulation study. Comprehensive performance comparisons with a number of existing methods are presented. © Springer-Verlag London Limited 2009.


Chimankar D.A.,Fakir Mohan University | Sahoo H.,Dr. Babasaheb Ambedkar Marathwada University
Studies on Ethno-Medicine | Year: 2011

The National Family Health Survey (NFHS-III 2005-06) provided a gloomy picture of the status of maternal health indicators of Uttarakhand. The state has witnessed a higher proportion of high risk pregnancies. A large number of births take place outside the health system (67.4 percent), the majority being attended by untrained dais (midwives). These have resulted in higher maternal morbidity and mortality. Therefore, the present paper attempts to find out the possible factors influencing the use of maternal health care services, using the data from NFHS III. Both bi-variate and multivariate analysis have been carried out for the study by taking ante-natal care and delivery care as dependant variables. The result reveals that the educational level of women, birth order and wealth index are significant predictors in explaining ante-natal and delivery care. Controlling the effect of other variables, the predictive power of women's educational level, wealth index have been positively associated with antenatal care and also delivery care. © Kamla-Raj 2011.


Dehuri S.,Fakir Mohan University | Misra B.B.,Orissa Engineering College, Bhubaneswar | Ghosh A.,Indian Statistical Institute | Cho S.-B.,Yonsei University
Applied Soft Computing Journal | Year: 2011

A novel condensed polynomial neural network using particle swarm optimization (PSO) technique is proposed for the task of classification in this paper. In solving classification task classical algorithms such as polynomial neural network (PNN) and its variants need more computational time as the partial descriptions (PDs) grow over the training period layer-by-layer and make the network very complex. Unlike PNN the proposed network needs to generate the partial description for a single layer. The discrete PSO (DPSO) is used to select a relevant set of PDs as well as features with a hope to get better accuracy, which are in turn fed to the output neuron. The weights associated with the links from hidden to output neuron is optimized by PSO for continuous domain (CPSO). Performance of this model is compared with the results obtained from PNN. Simulation result shows that the performance of this model both in processing time and accuracy, is encouraging for harnessing its power in domain with large and complex data particularly in data mining area. © 2010 Elsevier B.V. All rights reserved.


Dehuri S.,Fakir Mohan University | Cho S.-B.,Yonsei University
International Journal of Fuzzy System Applications | Year: 2012

This paper proposes an algorithm for classification by learning fuzzy network with a sequence bound global particle swarm optimizer. The aim of this work can be achieved in two folded. Fold one provides an explicit mapping of an input features from original domain to fuzzy domain with a multiple fuzzy sets and the second fold discusses the novel sequence bound global particle swarm optimizer for evolution of optimal set of connection weights between hidden layer and output layer of the fuzzy network. The novel sequence bound global particle swarm optimizer can solve the problem of premature convergence when learning the fuzzy network plagued with many local optimal solutions. Unlike multi-layer perceptron with many hidden layers it has only single hidden layer. The output layer of this network contains one neuron. This network advocates a simple and understandable architecture for classification. The experimental studies show that the classification accuracy of the proposed algorithm is promising and superior to other alternatives such as multi-layer perceptron and radial basis function network. © 2012, IGI Global.

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