Computational intelligence in business analytics :

By: Sztandera, Les MMaterial type: TextTextPublication details: Noida : Pearson Education, 2014Description: xi, 138 pages : illustrationsISBN: 9780133552089 (hbk. : alk. paper); 013355208X (hbk. : alk. paper); 9789332540354Subject(s): Computational intelligence | Business intelligence | Artificial intelligenceDDC classification: 006.3
Contents:
Machine generated contents note: ch. 1 Overview -- Learning Objectives -- 1.1. Introduction -- 1.2.A Need for Computational Intelligence in Business Analytics -- 1.3. Differentiating Your Business Through Computational Intelligence -- Exercises -- ch. 2 Computational Intelligence Foundations -- Learning Objectives -- 2.1. Introduction -- 2.2. Artificial Neural Networks -- 2.3. Fuzzy Sets and Systems -- 2.4. Genetic Algorithms -- 2.5. Neuro-Fuzzy Systems -- Conclusions -- Exercises -- ch. 3 Computational Intelligence Versus Statistical Approaches -- Learning Objectives -- 3.1. Introduction -- 3.2. Adding Value to Business Through Utilization of Computational Intelligence -- Exercises -- ch. 4 Computational Intelligence at Work -- Learning Objectives -- 4.1. Introduction -- 4.2. Role of Analytics in Medical Informatics -- 4.3. Extracting Information from Failure Equipment Notifications: Use of Fuzzy Sets to Determine Optimal Inventory. Contents note continued: 4.4. The Use of Computational Intelligence in the Design of Polymers and in Property Prediction -- Exercises -- ch. 5 Future of Computational Intelligence -- Learning Objectives -- 5.1. Prospects for the Future -- Exercises.
Summary: Using computational intelligence methods, you can drive far more value from business analytics, and account far more effectively for the real-world uncertainties and complexities you face in making key decisions. This text teaches you the computational intelligence concepts and methods you need to fully leverage these powerful techniques. This book illuminates today's key computational intelligence tools, knowledge, and strategies for analysis, exploration, and knowledge generation. This text demystifies artificial neural networks, genetic algorithms, and fuzzy systems, and guides you through using them to model, discover, and interpret new patterns that cannot be found through statistical methods alone. To demonstrate these techniques at work, this book is packed with relevant case studies and examples.
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Machine generated contents note: ch. 1 Overview --
Learning Objectives --
1.1. Introduction --
1.2.A Need for Computational Intelligence in Business Analytics --
1.3. Differentiating Your Business Through Computational Intelligence --
Exercises --
ch. 2 Computational Intelligence Foundations --
Learning Objectives --
2.1. Introduction --
2.2. Artificial Neural Networks --
2.3. Fuzzy Sets and Systems --
2.4. Genetic Algorithms --
2.5. Neuro-Fuzzy Systems --
Conclusions --
Exercises --
ch. 3 Computational Intelligence Versus Statistical Approaches --
Learning Objectives --
3.1. Introduction --
3.2. Adding Value to Business Through Utilization of Computational Intelligence --
Exercises --
ch. 4 Computational Intelligence at Work --
Learning Objectives --
4.1. Introduction --
4.2. Role of Analytics in Medical Informatics --
4.3. Extracting Information from Failure Equipment Notifications: Use of Fuzzy Sets to Determine Optimal Inventory. Contents note continued: 4.4. The Use of Computational Intelligence in the Design of Polymers and in Property Prediction --
Exercises --
ch. 5 Future of Computational Intelligence --
Learning Objectives --
5.1. Prospects for the Future --
Exercises.

Using computational intelligence methods, you can drive far more value from business analytics, and account far more effectively for the real-world uncertainties and complexities you face in making key decisions. This text teaches you the computational intelligence concepts and methods you need to fully leverage these powerful techniques. This book illuminates today's key computational intelligence tools, knowledge, and strategies for analysis, exploration, and knowledge generation. This text demystifies artificial neural networks, genetic algorithms, and fuzzy systems, and guides you through using them to model, discover, and interpret new patterns that cannot be found through statistical methods alone. To demonstrate these techniques at work, this book is packed with relevant case studies and examples.

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