Nature-Inspired Algorithms and Applications. Группа авторов

      1  Cover

      2  Title page

      3  Copyright

      4  Preface

      5  1 Introduction to Nature-Inspired Computing 1.1 Introduction 1.2 Aspiration From Nature 1.3 Working of Nature 1.4 Nature-Inspired Computing 1.5 General Stochastic Process of Nature-Inspired Computation References

      6  2 Applications of Hybridized Algorithms and Novel Algorithms in the Field of Machine Learning 2.1 Introduction of Genetic Algorithm 2.2 Introduction to Artificial Bear Optimization (ABO) 2.3 Performance Evaluation 2.4 What is Next? References

      7  3 Efficiency of Finding Best Solutions Through Ant Colony Optimization (ACO) Technique 3.1 Introduction 3.2 A Case Study on Surgical Treatment in Operation Room 3.3 Case Study on Waste Management System 3.4 Working Process of the System 3.5 Background Knowledge to be Considered for Estimation 3.6 Case Study on Traveling System 3.7 Future Trends and Conclusion References

      8  4 A Hybrid Bat-Genetic Algorithm–Based Novel Optimal Wavelet Filter for Compression of Image Data 4.1 Introduction 4.2 Review of Related Works 4.3 Existing Technique for Secure Image Transmission 4.4 Proposed Design of Optimal Wavelet Coefficients for Image Compression 4.5 Results and Discussion 4.6 Conclusion References

      9  5 A Swarm Robot for Harvesting a Paddy Field 5.1 Introduction 5.2 Second Case Study on Recommendation Systems 5.3 Third Case Study on Weight Lifting Robot 5.4 Background Knowledge of Harvesting Process 5.5 Future Trend and Conclusion References

      10  6 Firefly Algorithm 6.1 Introduction 6.2 Firefly Algorithm 6.3 Applications of Firefly Algorithm 6.4 Why Firefly Algorithm is Efficient 6.5 Discussion and Conclusion References

      11  7 The Comprehensive Review for Biobased FPA Algorithm 7.1 Introduction 7.2 Related Work to FPA 7.3 Limitations 7.4 Future Research 7.5 Conclusion References

      12  8 Nature-Inspired Computation in Data Mining 8.1 Introduction 8.2 Classification of NIC 8.3 Evolutionary Computation 8.4 Biological Neural Network 8.5 Molecular Biology 8.6 Immune System 8.7 Applications of NIC in Data Mining 8.8 Conclusion References

      13  9 Optimization Techniques for Removing Noise in Digital Medical Images 9.1 Introduction 9.2 Medical Imaging Techniques 9.3 Image Denoising 9.4