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in Image Denoising 9.5 Results and Discussions 9.6 Conclusion and Future Scope References

      14  10 Performance Analysis of Nature-Inspired Algorithms in Breast Cancer Diagnosis 10.1 Introduction 10.2 Related Works 10.3 Dataset: Wisconsin Breast Cancer Dataset (WBCD) 10.4 Ten-Fold Cross-Validation 10.5 Naive Bayesian Classifier 10.6 K-Means Clustering 10.7 Support Vector Machine (SVM) 10.8 Swarm Intelligence Algorithms 10.9 Evaluation Metrics 10.10 Results and Discussion 10.11 Conclusion References

      15  11 Applications of Cuckoo Search Algorithm for Optimization Problems 11.1 Introduction 11.2 Related Works 11.3 Cuckoo Search Algorithm 11.4 Applications of Cuckoo Search 11.5 Conclusion and Future Work References

      16  12 Mapping of Real-World Problems to Nature-Inspired Algorithm Using Goal-Based Classification and TRIZ 12.1 Introduction and Background 12.2 Motivations Behind NIA Exploration 12.3 Novel TRIZ + NIA Approach 12.4 Examples to Support the TRIZ + NIA Approach 12.5 A Solution of NP-H Using NIA 12.6 Conclusion References

      17  Index

      18  Also of Interest

      19  End User License Agreement

      Guide

      1  Cover

      2  Table of Contents

      3  Title page

      4  Copyright

      5  Preface

      6  Begin Reading

      7  Index

      8  Also of Interest

      9  End User License Agreement

      List of Illustrations

      1 Chapter 1Figure 1.1 Category of NIC.

      2 Chapter 2Figure 2.1 Genetic algorithm flow chart. Image source: Moustafa Alzantot [5].Figure 2.2 Definition of gene, chromosome, and population. Image source: Vijini ...Figure 2.3 Crossover point. Image source: Patacchiola [11].Figure 2.4 Development of offspring. Image source: Patacchiola [11].Figure 2.5 Mutation. Image source: GeneHunter.Figure 2.6 Traveling salesman problem. Image source: Jessica Yu (2014)—Traveling...Figure 2.7 Blackjack—a casino game.Figure 2.8 Pong against AI—evolving agents. Image source: Pong using pixels—Andr...Figure 2.9 Snake AI—game. Image source: Greg Sharma (2018)—Via Siltherin.Figure 2.10 Genetic algorithm’s role in neural network. Image source: Suryansh [...Figure 2.11 Association rules generation. Image source: Berkani, L., Chebahi, Y....Figure 2.12 Structure of a chromosome in GACD. Image source: Sanghamitra Bandyop...Figure 2.13 Grizzly bear.Figure 2.14 Bear’s nasal cavity. https://grizzlybearblog.wordpress.com/2010/11/1...Figure 2.15 Top view of a grizzly bear skull. https://in.pinterest.com/ericgreb/...Figure 2.16 ABO gist. https://www.ijser.org/researchpaper/Business-Intelligence-...Figure 2.17 Flow chart of overall proposed application.Figure 2.18 Artificial bear optimization: Pseudocode algorithm.Figure 2.19 Performance evaluation of ABO for banking customer profile and cance...

      3 Chapter 3Figure 3.1 Global and local optima for f(x).Figure 3.2 Data flow of ACO approach.Figure 3.3 Work flow of surgical process.Figure 3.4 Hospital dataset.Figure 3.5 Waiting time and completion time.Figure 3.6 Components for scheduling.Figure 3.7 Dataset waste management.Figure 3.8 Routing systems for waste collection.Figure 3.9 Working flow of the system.Figure 3.10 Comparison of heuristic value with phenomenon value.Figure 3.11 Functional approach of swarm intelligence.Figure 3.12 Process flow.

      4 Chapter 4Figure 4.1 Existing technique for secure image encoding.Figure 4.2 Block diagram of the proposed technique.Figure 4.3 DWT analysis and synthesis.Figure 4.4 Filter bank structure of the 2D DWT analysis.Figure 4.5 Filter bank structure

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