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Nathoo F.S.,University of Victoria | Ghosh P.,Information Systems Management Institute
Statistics in Medicine | Year: 2013

Mixed models incorporating spatially correlated random effects are often used for the analysis of areal data. In this setting, spatial smoothing is introduced at the second stage of a hierarchical framework, and this smoothing is often based on a latent Gaussian Markov random field. The Markov random field provides a computationally convenient framework for modeling spatial dependence; however, the Gaussian assumption underlying commonly used models can be overly restrictive in some applications. This can be a problem in the presence of outliers or discontinuities in the underlying spatial surface, and in such settings, models based on non-Gaussian spatial random effects are useful. Motivated by a study examining geographic variation in the treatment of acute coronary syndrome, we develop a robust model for smoothing small-area health service utilization rates. The model incorporates non-Gaussian spatial random effects, and we develop a formulation for skew-elliptical areal spatial models. We generalize the Gaussian conditional autoregressive model to the non-Gaussian case, allowing for asymmetric skew-elliptical marginal distributions having flexible tail behavior. The resulting new models are flexible, computationally manageable, and can be implemented in the standard Bayesian software WinBUGS. We demonstrate performance of the proposed methods and comparisons with other commonly used Gaussian and non-Gaussian spatial prior formulations through simulation and analysis in our motivating application, mapping rates of revascularization for patients diagnosed with acute coronary syndrome in Quebec, Canada. © 2012 John Wiley & Sons, Ltd.

Panigrahi P.K.,Information Systems Management Institute
Proceedings - 4th International Conference on Computational Intelligence and Communication Networks, CICN 2012 | Year: 2012

Unsolicited e-mail (Spam) has become a major issue for each e-mail user. In recent days it is very difficult to filter spam emails as these emails are written or generated in a very special way so that anti-spam filters cannot detect such emails. This Paper compares and discusses performance measures of certain categories of supervised machine learning techniques such as Bayes algorithms, lazy algorithms, tree algorithms, neural network, and support vector machines for classifying a spam e-mail corpus maintained by UCI Machine Learning Repository. The objective of this study is to consider the content of the emails, learn a finite dataset available and to develop a classification model that will able to predict whether an e-mail is spam or not. © 2012 IEEE.

Srivastava P.R.,Information Systems Management Institute
International Journal of Bio-Inspired Computation | Year: 2016

Software testing is prime concern for the software industry and researchers. In the software testing process test cases play an important and significant role. Optimisations of test cases are essential to test the software effectively. Finding maximum number of faults and rectifying them before actual software release is most complex and critical during software development process. This paper deals with software test case optimisation using bacteriologic algorithm (BA) and requirement mapping-based approach. Test case optimisation deals with selecting effective test cases having maximum code coverage and fault detection capability, consequently minimising and prioritising the test cases. Copyright © 2016 Inderscience Enterprises Ltd.

Thakurta R.,Information Systems Management Institute
Software Quality Journal | Year: 2013

Non-functional requirements (NFRs) determine the characteristics of a software product or service as a whole. The research described in this paper presents a quantitative framework involving respondents of both the project and the business organization, in order to determine the priority of a list of NFRs to be considered for implementation during a software development. The framework also provides a quantitative basis for evaluating the extent of value addition that can be achieved while deciding upon whether or not to consider a particular non-functional requirement for inclusion to the project's requirement set. The assessment process also indicates the extent to which different business values are perceived important by representatives of business organizations, and their perception of the importance of the different NFRs. The work distinguishes from others by explicitly considering dependencies among NFRs in the evaluation process. The final results are expected to be beneficial to both the business and the project organization by identifying and implementing the desired NFRs that contribute to business value in a cost-effective manner. © 2012 Springer Science+Business Media New York.

Thakurta R.,Information Systems Management Institute
Requirements Engineering | Year: 2016

The importance of prioritizing requirements stems from the fact that not all requirements can usually be met with available time and resource constraints. Efficient and trustworthy methods for prioritizing requirements are therefore in high demand. In this article, we present results of a systematic mapping study in order to appreciate the different considerations that have influenced prioritization of software requirements, identify the various types of artifacts proposed toward prioritizing software requirements, and examine certain characterizations of these artifacts. The results emphasize the heightened attention the domain of requirement prioritization has received in recent years. On the basis of this study, we are able to provide the following inferences regarding possible future research trajectories in software requirement prioritization artifacts: (1) focus on frameworks and tools; (2) emphasis on specialization; and (3) proposition of theory-based artifacts. Additional research possibilities are also pointed out at the end and are expected to stimulate further research on the topic. © 2016 Springer-Verlag London

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