Software Engineering Artificial Intelligence Networking And Parallel Distributed Computing 2010

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Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2010

Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2010
Author :
Publisher : Springer
Total Pages : 166
Release :
ISBN-10 : 9783642132650
ISBN-13 : 3642132650
Rating : 4/5 (650 Downloads)

Book Synopsis Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2010 by : Liz Bacon

Download or read book Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2010 written by Liz Bacon and published by Springer. This book was released on 2010-05-11 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: th The purpose of the 11 Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2010) held on June 9 – 11, 2010 in London, United Kingdom was to bring together researchers and scientists, businessmen and entrepreneurs, teachers and students to discuss the numerous fields of computer science, and to share ideas and information in a meaningful way. Our conference officers selected the best 15 papers from those papers accepted for presentation at the conference in order to publish them in this volume. The papers were chosen based on review scores submitted by members of the program committee, and underwent further rounds of rigorous review. In Chapter 1, Cai Luyuan et al. Present a new method of shape decomposition based on a refined morphological shape decomposition process. In Chapter 2, Kazunori Iwata et al. propose a method for reducing the margin of error in effort and error prediction models for embedded software development projects using artificial neural networks (ANNs). In Chapter 3, Viliam Šimko et al. describe a model-driven tool that allows system code to be generated from use-cases in plain English. In Chapter 4, Abir Smiti and Zied Elouedi propose a Case Base Maintenance (CBM) method that uses machine learning techniques to preserve the maximum competence of a system. In Chapter 5, Shagufta Henna and Thomas Erlebach provide a simulation based analysis of some widely used broadcasting schemes within mobile ad hoc networks (MANETs) and propose adaptive extensions to an existing broadcasting algorithm.


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