Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2010: 295 (Studies in Computational Intelligence) by Roger Lee, Liz Bacon, Wencai Du | Springer | English | Paperback | 2010 Edition

Overview

This volume brings together rigorous research from the 11th Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2010), held in London, United Kingdom. Edited by Roger Lee, Liz Bacon, and Wencai Du, the book presents fifteen carefully selected and peer-reviewed papers that explore cutting-edge developments across computer science disciplines. Published by Springer as part of the Studies in Computational Intelligence series (volume 295), this illustrated paperback edition offers Indian researchers, postgraduate students, and professionals a concise yet deep dive into topics like shape decomposition, neural network-based prediction models, model-driven code generation, and machine learning for case-based maintenance.

Key Topics Covered

• Shape Decomposition: A refined morphological shape decomposition method by Cai Luyuan and colleagues.
• Effort and Error Prediction: Using artificial neural networks (ANNs) to reduce margin of error in embedded software development projects, proposed by Kazunori Iwata et al.
• Model-Driven Code Generation: A tool by Viliam Šimko et al. that generates system code from plain English use-cases.
• Case Base Maintenance: Abir Smiti and Zied Elouedi employ machine learning to preserve system competence.
• Broadcasting Schemes: Simulation-based analysis of broadcasting protocols in mobile networks by Shagufta Henna and Thomas Erlebach.

Why This Book Matters for Indian Readers

India's growing software and AI sectors demand access to high-quality, peer-reviewed research. This volume provides a snapshot of international academic collaboration, offering insights that can inform both classroom learning and industry R&D. The focus on practical techniques—such as ANN-based prediction and automated code generation—makes it especially relevant for professionals working in embedded systems, network design, and intelligent systems.

Who Should Read This Book

• Postgraduate students in computer science, AI, and software engineering.
• Researchers exploring neural networks, model-driven development, and distributed computing.
• Industry professionals seeking state-of-the-art methods for software quality and network efficiency.

Edition Details

This is a 2010 illustrated paperback edition from Springer, published in English. With 166 pages of concentrated content, it is an ideal reference for academic libraries and personal collections alike.