Snehasis Mukhopadhyay

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Simple sequence repeat (SSR) markers are widely used in many plant and animal genomes due to their abundance, hypervariability, and suitability for high-throughput analysis. Development of SSR markers using molecular methods is time consuming, laborious, and expensive. Use of computational approaches to mine ever-increasing sequences such as expressed(More)
The NARMA model is an exact representation of the input-output behavior of finite-dimensional nonlinear discrete-time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate methods(More)
This paper considers the problem of using approximate methods for realizing the neural controllers for nonlinear multivariable systems. In [1] the NARMA-L1 and NARMA-L2 models were introduced as approximations of he NARMA model used for the representation of a SISO nonlinear dynamical systems. The advantage obtained from using NARMA-L1 and NARMA-L2 models(More)
Detection of Shifts in User Interests for Personalized Information Filtering W. Lam*, S. Mukhopadhyay, J. Mostafa**, and M. Palakal Computer and Information Science Purdue University School of Science at Indianapolis 723 W. Michigan St. SL280 Indianapolis, IN 46202 *Department of Management Sciences S306 Pappajohn Building The University of Iowa Iowa City,(More)
s were used. Figure 16-1. Graph showing relationships between genes in Known Pathway. The higher the Association strength the closer the genes appear on the graph. In this way the related genes are clustered together and can be picked out. A graphical presentation of the unknown pathway (Table 16-2) is shown in Figure 16-1. The relationship discovery aspect(More)
In information-filtering environments, uncertainties associated with changing interests of the user and the dynamic document stream must be handled efficiently. In this article, a filtering model is proposed that decomposes the overall task into subsystem functionalities and highlights the need for multiple adaptation techniques to cope with uncertainties.(More)
In the information age, lters are going to play a dominant role. Isolated lters are incapable of dealing with a large number of incoming documents. Collaborative ltering creates a symbiosis of information ltering and web based technologies. However, collaboration has associated costs. Each service ooered and provided by a lter must be appropriately(More)
Modeling users in information filtering systems is a difficult challenge due to dimensions such as nature, scope, and variability of interests. Numerous machine-learning approaches have been proposed for user modeling in filtering systems. The focus has been primarily on techniques for user model capture and representation, with relatively simple(More)