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E-grāmata: Hybrid Computational Intelligence: Research and Applications [Taylor & Francis e-book]

Edited by (VB - Technical University of Ostrava, Czech Republic), Edited by (RCC Institute of Information Technology, Kolkata, India), Edited by (RCC Institute of Information Technology, Kolkata, India), Edited by (Maulana Abul Kalam Azad University, Kolkata, India)
  • Formāts: 240 pages, 47 Tables, black and white; 81 Illustrations, black and white
  • Izdošanas datums: 30-Sep-2019
  • Izdevniecība: CRC Press
  • ISBN-13: 9780429453427
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Hybrid Computational Intelligence: Research and ApplicationsPalielināt
  • Formāts: 240 pages, 47 Tables, black and white; 81 Illustrations, black and white
  • Izdošanas datums: 30-Sep-2019
  • Izdevniecība: CRC Press
  • ISBN-13: 9780429453427
Citas grāmatas par šo tēmu:
Taylor & Francis e-booksMore info about Taylor & Francis e-books
Rokasgrāmatas mājas lapa: https://www.taylorfrancis.com/books/9780429453427

Hybrid computational intelligent techniques are efficient in dealing with the real-world problems encountered in engineering fields. The primary objective of this book is to provide an exhaustive introduction as well as review of the hybrid computational intelligent paradigm, with supportive case studies. In addition, it aims to provide a gallery of engineering applications where this computing paradigm can be effectively use. Finally, it focuses on the recent quantum inspired hybrid intelligence to develop intelligent solutions for the future. The book also incorporates video demonstrations of each application for better understanding of the subject matter.

Preface xiii
Authors xvii
1 Nature-Inspired Algorithms: A Comprehensive Review
1(26)
Essam H. Houssein
Mina Younan
Aboul Ella Hassanien
1.1 Introduction
2(1)
1.2 Research Trends
2(2)
1.2.1 Based on Algorithm Idea
3(1)
1.2.2 Based on Problem Type
3(1)
1.2.3 Based on Algorithm Applications
3(1)
1.3 Classification of Nature-Inspired Algorithms
4(3)
1.3.1 SI-Based Algorithms
5(1)
1.3.2 BI-not-SI-Based Algorithms
5(1)
1.3.3 Natural Science-Based Algorithms
6(1)
1.3.4 Natural Phenomena-Based Algorithms
7(1)
1.4 Variants of Nature-Inspired Algorithms
7(2)
1.4.1 Binary Algorithms
7(1)
1.4.2 Chaotic Algorithms
7(1)
1.4.3 Multi-objective Algorithms
8(1)
1.4.4 Hybrid Algorithms
9(1)
1.5 A Review of the Most Recent NI Algorithms
9(8)
1.5.1 Artificial Butterfly Optimization Algorithm
9(1)
1.5.2 Grasshopper Optimization Algorithm
10(2)
1.5.3 Salp Swarm Optimization Algorithm
12(2)
1.5.4 Spotted Hyena Optimization Algorithm
14(1)
1.5.5 Chemotherapy Science Optimization Algorithm
15(2)
1.6 Conclusion
17(10)
2 Hybrid Cartesian Genetic Programming Algorithms: A Review
27(36)
Johnathan Melo Neto
Heder S. Bernardino
Helio J.C. Barbosa
2.1 Introduction
28(2)
2.2 Metaheuristics
30(7)
2.2.1 Single-Solution Methods
31(1)
2.2.2 Population-Based Methods
31(1)
2.2.2.1 Evolution strategies
31(1)
2.2.2.2 Differential evolution
32(1)
2.2.2.3 Biogeography-based optimization
32(1)
2.2.2.4 Non-dominated sorting genetic algorithm
33(1)
2.2.2.5 Harmony search
34(1)
2.2.2.6 Estimation of distribution algorithms
35(1)
2.2.2.7 Ant colony optimization
35(1)
2.2.2.8 Particle swarm optimization
36(1)
2.3 Fundamentals of Cartesian Genetic Programming
37(3)
2.3.1 Historical Context
37(1)
2.3.2 Encoding
37(1)
2.3.3 Evolution Scheme
38(1)
2.3.4 Parameters
39(1)
2.3.5 Advantages and Drawbacks
39(1)
2.4 Literature Review on Hybrid Metaheuristics
40(2)
2.5 Hybrid Cartesian Genetic Programming Algorithms
42(10)
2.5.1 Motivation
42(1)
2.5.1.1 CGP combined with ant colony optimization
42(2)
2.5.1.2 CGP combined with biogeography-based optimization and opposition-based learning
44(1)
2.5.1.3 CGP combined with differential evolution
45(2)
2.5.1.4 CGP combined with estimation of distribution algorithm
47(1)
2.5.1.5 CGP combined with NSGA-II
48(1)
2.5.1.6 CGP combined with harmony search
49(2)
2.5.1.7 CGP combined with particle swarm optimization
51(1)
2.6 Discussion on Hybrid CGP Algorithms
52(1)
2.7 Future Directions of Hybrid CGP Algorithms
52(3)
2.8 Concluding Remarks
55(8)
3 Tuberculosis Detection from Conventional Sputum Smear Microscopic Images Using Machine Learning Techniques
63(18)
Rani Oomman Panicker
Biju Soman
M.K. Sabu
3.1 Introduction
63(2)
3.2 Sputum Smear Microscopic Images
65(2)
3.2.1 Disadvantages of Conventional Methods
66(1)
3.3 Machine Learning Techniques for TB Detection
67(9)
3.3.1 Study Design
68(1)
3.3.2 Literature Review
68(8)
3.4 Discussion
76(1)
3.5 Conclusions and Future Scope
77(4)
4 Privacy towards GIS Based Intelligent Tourism Recommender System in Big Data Analytics
81(20)
Abhaya Kumar Sahoo
Chittaranjan Pradhan
Siddhartha Bhattacharyya
4.1 Introduction
82(1)
4.2 Background
83(7)
4.2.1 Intelligent Tourism Recommender System and its Basic Concepts
84(1)
4.2.2 Phases of Tourism Recommender System
84(1)
4.2.3 Collaborative Filtering Technique Used in TRS
85(1)
4.2.3.1 Memory-based collaborative filtering
86(1)
4.2.3.2 Model-based collaborative filtering
87(1)
4.2.3.3 Evaluation of TRS
88(2)
4.3 Geographical Information System Used in TRS
90(1)
4.4 Big Data Analytics in Tourism
90(2)
4.5 Machine Learning Techniques Used in GIS-based TRS
92(2)
4.6 Privacy Preserving Methods Used in GIS-based TRS
94(3)
4.7 Proposed Privacy Preserving TRS Method Using Collaborative Filtering
97(1)
4.7.1 Dataset Description
97(1)
4.7.2 Experimental Result Analysis
97(1)
4.8 Conclusion and Future Work
97(4)
5 Application of Artificial Neural Network: A Case Study of Biomedical Alloy
101(30)
Amit Aherwar
Amar Patnaik
5.1 Introduction
102(3)
5.2 Test Material and Methods
105(5)
5.2.1 Test Materials
105(1)
5.2.2 Manufacturing of Orthopaedic Material
105(2)
5.2.3 Material Characterization
107(1)
5.2.4 Mechanical Studies
107(1)
5.2.5 Wear Measurement of Orthopaedic Material
107(2)
5.2.6 Taguchi Design of the Experiment
109(1)
5.3 Results and Discussions
110(4)
5.3.1 Phase Analysis and Microstructure
110(1)
5.3.2 Mechanical Studies of Manufactured Material
111(1)
5.3.2.1 Micro-hardness
111(2)
5.3.2.2 Compressive strength
113(1)
5.3.3 Taguchi Experimental Design
113(1)
5.4 Simulation Model for Wear Response
114(10)
5.4.1 Data Processing in ANN Model
116(1)
5.4.2 Network Training
117(3)
5.4.3 Neural Network Architecture
120(1)
5.4.4 ANN Prediction and its Factor
120(4)
5.5 Conclusion
124(7)
6 Laws Energy Measure Based on Local Patterns for Texture Classification
131(22)
Sonali Dash
Manas R. Senapati
6.1 Introduction
131(3)
6.2 Related Work
134(6)
6.2.1 Mathematical Background of LBP
134(2)
6.2.2 LBP Minimum
136(1)
6.2.3 LBP Intensity
136(1)
6.2.4 LBP Uniform
136(1)
6.2.5 LBP Number
137(1)
6.2.6 LBP Median
137(1)
6.2.7 LBP Variance
138(1)
6.2.8 CLBP
138(1)
6.2.9 Sobel-LBP
139(1)
6.2.10 Laws' Mask
140(1)
6.3 Local Pattern Laws' Energy Measure
140(2)
6.3.1 Problem Formulation
140(2)
6.4 Implementation and Experiments
142(7)
6.4.1 Results of Brodatz Database
145(1)
6.4.2 Results of ALOT Database
146(2)
6.4.3 Statistical Comparison of the Methods
148(1)
6.5 Conclusion
149(4)
7 Analysis of BSE Sensex Using Statistical and Computational Tools
153(24)
Soumya Chatterjee
Indranil Mukherjee
7.1 Introduction
154(2)
7.2 The Data Analysed
156(2)
7.2.1 Return and Raw Data
156(1)
7.2.1.1 Time series of the return data created from raw data
157(1)
7.2.1.2 Return data created from detrended data
158(1)
7.2.1.3 The role of raw data in analyses
158(1)
7.3 The Data Vectors and Principal Component Analysis
158(7)
7.3.1 Construction of the Data Vectors
159(1)
7.3.2 Principal Component Analysis
160(1)
7.3.3 PCA of Raw Sensex Data
160(1)
7.3.4 PCA of Detrended Sensex Data
161(1)
7.3.5 PCA of Raw Sensex Data with Noise
161(1)
7.3.6 PCA of Return Sensex Data
162(1)
7.3.6.1 PCA of the return data obtained from raw Sensex data
162(1)
7.3.6.2 PCA of the return data from detrended Sensex data
163(2)
7.4 Kernel Principal Component Analysis
165(7)
7.4.1 KPCA of Raw Sensex Data
165(1)
7.4.1.1 Methodology
166(1)
7.4.1.2 Results of KPCA applied to raw Sensex data (with trend)
166(1)
7.4.2 KPCA of Raw Trend-Removed Sensex Values
166(2)
7.4.3 KPCA of Raw Sensex Data with Noise
168(1)
7.4.4 KPCA of Return Sensex Data
168(1)
7.4.4.1 KPCA of return Sensex data
168(2)
7.4.4.2 KPCA of return of detrended Sensex data
170(2)
7.5 Detrended Fluctuation Analysis
172(2)
7.5.1 Detrended Fluctuation Analysis of the Detrended Sensex Data
172(2)
7.6 Conclusion
174(3)
8 Automatic Sheep Age Estimation Based on Active Contours without Edges
177(18)
Aya Abdelhady
Aboul Ella Hassanien
Aly Fahmy
8.1 Introduction
177(1)
8.2 Related Work
178(2)
8.3 Theory and Background
180(2)
8.3.1 Active Contours
180(1)
8.3.2 Blob Detection and Counting
181(1)
8.3.3 Morphological Operations
181(1)
8.3.4 Dentition
181(1)
8.3.5 Image Collection and Camera Setting
181(1)
8.4 The Proposed Automatic Sheep Age Estimation System
182(6)
8.4.1 Pre-processing Phase
183(1)
8.4.2 Segmentation Phase
183(2)
8.4.3 Post-processing Phase
185(1)
8.4.4 Age Estimation Phase
185(3)
8.5 Experimental Results and Discussion
188(4)
8.6 Conclusion and Future Work
192(3)
9 Diversity Matrix Based Performance Improvement for Ensemble Learning Approach
195(22)
Raj deep Chatterjee
Siddhartha Chatterjee
Ankita Datta
Debarshi Kumar Sanyal
9.1 Introduction
196(1)
9.2 Related Work
196(1)
9.3 Theoretical Background
197(5)
9.3.1 Wavelet Based Energy and Entropy
197(2)
9.3.2 Ensemble Classification
199(1)
9.3.2.1 Bagging ensemble learning
199(1)
9.3.2.2 Majority voting
200(1)
9.3.3 Used Diversity Techniques
200(1)
9.3.3.1 Cosine dissimilarity
201(1)
9.3.3.2 Gaussian dissimilarity
202(1)
9.3.3.3 Kullback-Leibler divergence
202(1)
9.3.3.4 Euclidean distance
202(1)
9.4 Proposed Method
202(3)
9.5 Results and Discussion
205(6)
9.5.1 Preparing the Used Datasets
205(2)
9.5.2 Experimental Set-up
207(1)
9.5.3 Results Analysis
208(3)
9.6 Conclusion and Future Work
211(6)
Index 217
Siddhartha Bhattacharyya, Vįclav Snįel, Indrajit Pan, Debashis De
Permanent link: https://www.kriso.lv/db/9780429453427_pe.html
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