Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2010 [electronic resource] /
International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed ComputingCorporate Author
International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing(11th :2010 :London, England)
International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
Lee, Roger.editor.
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.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
Shape Decomposition for Graph Representation -- Improving Accuracy of an Artificial Neural Network Model to Predict Effort and Errors in Embedded Software Development Projects -- From Textual Use-Cases to Component-Based Applications -- COID: Maintaining Case Method Based on Clustering, Outliers and Internal Detection -- Congestion and Network Density Adaptive Broadcasting in Mobile Ad Hoc Networks -- Design and Performance Evaluation of a Machine Learning-Based Method for Intrusion Detection -- Evolution of Competing Strategies in a Threshold Model for Task Allocation -- Efficient Mining of High Utility Patterns over Data Streams with a Sliding Window Method -- Resolving Sluices in Urdu -- Context Sensitive Gestures -- A Recommendation Framework for Mobile Phones Based on Social Network Data -- Language Modeling for Medical Article Indexing.
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.
Computational intelligence.
Artificial intelligence.
Computational Intelligence.
Artificial Intelligence.
Studies in Computational Intelligence