Javed Mostafa

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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)
OBJECTIVE The amount of information for clinicians and clinical researchers is growing exponentially. Text summarization reduces information as an attempt to enable users to find and understand relevant source texts more quickly and effortlessly. In recent years, substantial research has been conducted to develop and evaluate various summarization(More)
We investigate the modeling of changes in user interest in information filtering systems. A new technique for tracking user interest shifts based on a Bayesian approach is developed. The interest tracker is integrated into a profile learning module of a filtering system. We present an analytical study to establish the rate of convergence for the profile(More)
This paper proposes a method for identifying protein names in biomedical texts with an emphasis on detecting protein name boundaries. We use a probabilistic model which exploits several surface clues characterizing protein names and incorporates word classes for generalization. In contrast to previously proposed methods, our approach does not rely on(More)
We studied decentralized search in information networks and focused on the impact of network clustering on the findability of relevant information sources. We developed a multiagent system to simulate peer-to-peer networks, in which peers worked with one another to forward queries to targets containing relevant information, and evaluated the effectiveness,(More)
It is crucial to study basic principles that support adaptive and scalable retrieval functions in large networked environments such as the Web, where information is distributed among dynamic systems. We conducted experiments on decentralized IR operations on various scales of information networks and analyzed effectiveness, efficiency, and scalability of(More)
Information retrieval (IR) functions serve a critical role in many digital library systems. There are numerous mature IR algorithms that have been implemented and it will be a waste of resources and time to re-implement them. The implemented IR algorithms can be distributed or their functions made available through the framework of web services. Web(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)