Business Applications and Computational Intelligence, 1st Edition

  • Published By:
  • ISBN-10: 1591407044
  • ISBN-13: 9781591407041
  • Grade Level Range: College Freshman - College Senior
  • 481 Pages | eBook
  • Original Copyright 2005 | Published/Released October 2006
  • This publication's content originally published in print form: 2005

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Computational intelligence has a long history of applications to business - expert systems have been used for decision support in management, neural networks and fuzzy logic have been used in process control, a variety of techniques have been used in forecasting, and data mining has become a core component of customer relationship management in marketing. While there is literature on this field, it is spread over many disciplines and in many different publications, making it difficult to find the pertinent information in one source.

Business Applications and Computational Intelligence addresses the need for a compact overview of the diversity of applications in a number of business disciplines, and consists of chapters written by leading international researchers. Chapters cover most fields of business, including: marketing, data mining, e-commerce, production and operations, finance, decision-making, and general management. Business Applications and Computational Intelligence provides a comprehensive review of research into computational intelligence applications in business, creating a powerful guide for both newcomers and experienced researchers.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Business Applications and Computational Intelligence Table of Contents.
1: Introduction.
2: Computational Intelligence Applications in Business: A Cross-Section of the Field.
3: Making Decisions with Data: Using Computational Intelligence within a Business Environment.
4: Computational Intelligence as a Platform for a Data Collection Methodology in Management Science.
5: Marketing Applications.
6: Heuristic Genetic Algorithm for Product Portfolio Planning.
7: Modeling Brand Choice Using Boosted and Stacked Neural Networks.
8: Applying Information Gathering Techniques in Business-to-Consumer and Web Scenarios.
9: Web Mining System for Mobile-Phone Marketing.
10: Production and Operations Applications.
11: Artificial Intelligence in Electricity Market Operations and Management.
12: Reinforcement Learning-Based Intelligent Agents for Improved Productivity in Container Vessel Berthing Applications.
13: Optimization Using Horizon-Scan Technique: A Practical Case of Solving an Industrial Problem.
14: Data Mining Applications.
15: Visual Data Mining for Discovering Association Rules.
16: Analytical Customer Requirement Analysis Based on Data Mining.
17: Visual Grouping of Association Rules by Clustering Conditional Probabilities for Categorical Data.
18: Support Vector Machines for Business Applications.
19: Algorithms for Data Mining.
20: Management Applications.
21: A Tool for Assisting Group Decision-Making for Consensus Outcomes in Organizations.
22: Analyzing Strategic Stance in Public Services Management: An Exposition of NCaRBS in a Study of Long-Term Care Systems.
23: The Analytic Network Process—Dependence and Feedback in Decision-Making: Theory and Validation Examples.
24: Financial Applications.
25: Financial Classification Using an Artificial Immune System.
26: Development of Machine Learning Software for High Frequency Trading in Financial Markets.
27: Online Methods for Portfolio Selection.
28: Postscript.
29: Ankle Bones, Rogues, and Sexual Freedom for Women: Computational Intelligence in Historical Context.
About the Authors.