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

  • Roger Lee
  • Jixin Ma
  • Liz Bacon
  • Wencai Du
  • Miltos Petridis

Part of the Studies in Computational Intelligence book series (SCI, volume 295)

Table of contents

  1. Front Matter
  2. Cai Luyuan, Zhao Meng, Liu Shang, Mao Xiaoyan, Bai Xiao
    Pages 1-10
  3. Viliam Šimko, Petr Hnětynka, Tomáš Bureš
    Pages 23-37
  4. Chowdhury Farhan Ahmed, Syed Khairuzzaman Tanbeer, Byeong-Soo Jeong
    Pages 99-113
  5. Mohammad Abid Khan, Alamgir Khan, Mushtaq Ali
    Pages 115-126
  6. Daniel Demski, Roger Lee
    Pages 127-137
  7. Alper Ozcan, Sule Gunduz Oguducu
    Pages 139-149
  8. Jihen Majdoubi, Mohamed Tmar, Faiez Gargouri
    Pages 151-161
  9. Back Matter

About this book

Introduction

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.

Keywords

artificial intelligence computational intelligence computer design development distributed computing intelligence machine learning modeling network proving science software software development software engineering

Editors and affiliations

  • Roger Lee
    • 1
  • Jixin Ma
    • 2
  • Liz Bacon
    • 2
  • Wencai Du
    • 3
  • Miltos Petridis
    • 2
  1. 1.Software Engineering & Information Technology Institute, Computer Science DepartmentCentral Michigan UniversityMt. PleasantU.S.A.
  2. 2.The University of GreenwichLondonUK
  3. 3.Hainan UniversityHainanChina

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-13265-0
  • Copyright Information Springer Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-13264-3
  • Online ISBN 978-3-642-13265-0
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book
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