Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world.
Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Dedication - p. v 6. Contributors - p. xxi 7. About the Editors - p. xxvii 8. Chapter 1: Gravitational Search Algorithm With Chaos - p. xxviii 9. Chapter 2: Textures and Rough Sets - p. 16 10. Chapter 3: Hydrological Time Series Forecasting Using Three Different Heuristic Regression Techniques - p. 43 11. Chapter 4: A Reflection on Image Classifications for Forest Ecology Management: Towards Landscape Mapping and Monitoring - p. 65 12. Chapter 5: An Intelligent Hybridization of ABC and LM Algorithms With Constraint Engineering Applications - p. 85 13. Chapter 6: Network Intrusion Detection Model Based on Fuzzy-Rough Classifiers - p. 107 14. Chapter 7: Efficient System Reliability Analysis of Earth Slopes Based on Support Vector Machine Regression Model - p. 125 15. Chapter 8: Predicting Short-Term Congested Traffic Flow on Urban Motorway Networks - p. 143 16. Chapter 9: Object Categorization Using Adaptive Graph-Based Semi-supervised Learning - p. 165 17. Chapter 10: Hemodynamic Model Inversion by Iterative Extended Kalman Smoother - p. 179 18. Chapter 11: Improved Sparse Approximation Models for Stochastic Computations - p. 199 19. Chapter 12: Symbol Detection in Multiple Antenna Wireless Systems via Ant Colony Optimization - p. 223 20. Chapter 13: Application of Particle Swarm Optimization to Solve Robotic Assembly Line Balancing Problems - p. 237 21. Chapter 14: The Cuckoo Optimization Algorithm and Its Applications - p. 267 22. Chapter 15: Hybrid Intelligent Model Based on Least Squares Support Vector Regression and Artificial Bee Colony Optimization for Time-Series Modeling and Forecasting Horizontal Displacement of Hydropower Dam - p. 277 23. Chapter 16: Modeling the Axial Capacity of Bored Piles Using Multi-Objective Feature Selection, Functional Network and Multivariate Adaptive Regression Spline - p. 293 24. Chapter 17: Transient Stability Constrained Optimal Power Flow Using Chaotic Whale Optimization Algorithm - p. 309 25. Chapter 18: Slope Stability Evaluation Using Radial Basis Function Neural Network, Least Squares Support Vector Machines, and Extreme Learning Machine - p. 332 26. Chapter 19: Alternating Decision Trees - p. 344 27. Chapter 20: Scene Understanding Using Deep Learning - p. 371 28. Chapter 21: Deep Learning for Coral Classification - p. 382 29. Chapter 22: A Deep Learning Framework for Classifying Sounds of Mysticete Whales - p. 401 30. Chapter 23: Unsupervised Deep Learning for Data-Driven Reliability and Risk Analysis of Engineered Systems - p. 415 31. Chapter 24: Applying Machine Learning Algorithms in Landslide Susceptibility Assessments - p. 431 32. Chapter 25: MDHS–LPNN: A Hybrid FOREX Predictor Model Using a Legendre Polynomial Neural Network with a Modified Differential Harmony Search Technique - p. 457 33. Chapter 26: A Neural Model of Attention and Feedback for Computing Perceived Brightness in Vision - p. 486 34. Chapter 27: Support Vector Machine: Principles, Parameters, and Applications - p. 513 35. Chapter 28: Evolving Radial Basis Function Networks Using Moth–Flame Optimizer - p. 535 36. Chapter 29: Application of Fuzzy Methods in Power System Problems - p. 550 37. Chapter 30: Application of Particle Swarm Optimization Algorithm in Power System Problems - p. 570 38. Chapter 31: Optimum Design of Composite Concrete Floors Using a Hybrid Genetic Algorithm - p. 579 39. Chapter 32: A Comparative Study of Image Segmentation Algorithms and Descriptors for Building Detection - p. 589 40. Chapter 33: Object-Oriented Random Forest for High Resolution Land Cover Mapping Using Quickbird-2 Imagery - p. 606 41. Index - p. 621
Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world.
Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Dedication - p. v 6. Contributors - p. xxi 7. About the Editors - p. xxvii 8. Chapter 1: Gravitational Search Algorithm With Chaos - p. xxviii 9. Chapter 2: Textures and Rough Sets - p. 16 10. Chapter 3: Hydrological Time Series Forecasting Using Three Different Heuristic Regression Techniques - p. 43 11. Chapter 4: A Reflection on Image Classifications for Forest Ecology Management: Towards Landscape Mapping and Monitoring - p. 65 12. Chapter 5: An Intelligent Hybridization of ABC and LM Algorithms With Constraint Engineering Applications - p. 85 13. Chapter 6: Network Intrusion Detection Model Based on Fuzzy-Rough Classifiers - p. 107 14. Chapter 7: Efficient System Reliability Analysis of Earth Slopes Based on Support Vector Machine Regression Model - p. 125 15. Chapter 8: Predicting Short-Term Congested Traffic Flow on Urban Motorway Networks - p. 143 16. Chapter 9: Object Categorization Using Adaptive Graph-Based Semi-supervised Learning - p. 165 17. Chapter 10: Hemodynamic Model Inversion by Iterative Extended Kalman Smoother - p. 179 18. Chapter 11: Improved Sparse Approximation Models for Stochastic Computations - p. 199 19. Chapter 12: Symbol Detection in Multiple Antenna Wireless Systems via Ant Colony Optimization - p. 223 20. Chapter 13: Application of Particle Swarm Optimization to Solve Robotic Assembly Line Balancing Problems - p. 237 21. Chapter 14: The Cuckoo Optimization Algorithm and Its Applications - p. 267 22. Chapter 15: Hybrid Intelligent Model Based on Least Squares Support Vector Regression and Artificial Bee Colony Optimization for Time-Series Modeling and Forecasting Horizontal Displacement of Hydropower Dam - p. 277 23. Chapter 16: Modeling the Axial Capacity of Bored Piles Using Multi-Objective Feature Selection, Functional Network and Multivariate Adaptive Regression Spline - p. 293 24. Chapter 17: Transient Stability Constrained Optimal Power Flow Using Chaotic Whale Optimization Algorithm - p. 309 25. Chapter 18: Slope Stability Evaluation Using Radial Basis Function Neural Network, Least Squares Support Vector Machines, and Extreme Learning Machine - p. 332 26. Chapter 19: Alternating Decision Trees - p. 344 27. Chapter 20: Scene Understanding Using Deep Learning - p. 371 28. Chapter 21: Deep Learning for Coral Classification - p. 382 29. Chapter 22: A Deep Learning Framework for Classifying Sounds of Mysticete Whales - p. 401 30. Chapter 23: Unsupervised Deep Learning for Data-Driven Reliability and Risk Analysis of Engineered Systems - p. 415 31. Chapter 24: Applying Machine Learning Algorithms in Landslide Susceptibility Assessments - p. 431 32. Chapter 25: MDHS–LPNN: A Hybrid FOREX Predictor Model Using a Legendre Polynomial Neural Network with a Modified Differential Harmony Search Technique - p. 457 33. Chapter 26: A Neural Model of Attention and Feedback for Computing Perceived Brightness in Vision - p. 486 34. Chapter 27: Support Vector Machine: Principles, Parameters, and Applications - p. 513 35. Chapter 28: Evolving Radial Basis Function Networks Using Moth–Flame Optimizer - p. 535 36. Chapter 29: Application of Fuzzy Methods in Power System Problems - p. 550 37. Chapter 30: Application of Particle Swarm Optimization Algorithm in Power System Problems - p. 570 38. Chapter 31: Optimum Design of Composite Concrete Floors Using a Hybrid Genetic Algorithm - p. 579 39. Chapter 32: A Comparative Study of Image Segmentation Algorithms and Descriptors for Building Detection - p. 589 40. Chapter 33: Object-Oriented Random Forest for High Resolution Land Cover Mapping Using Quickbird-2 Imagery - p. 606 41. Index - p. 621
