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E-raamat: Nature-Inspired Algorithms and Applications

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  • Ilmumisaeg: 18-Nov-2021
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119681663
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Nature-Inspired Algorithms and ApplicationsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 18-Nov-2021
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119681663
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NATURE-INSPIRED ALGORITHMS AND APPLICATIONS The books unified approach of balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work.

Inspired by the world around them, researchers are gathering information that can be developed for use in areas where certain practical applications of nature-inspired computation and machine learning can be applied. This book is designed to enhance the readers understanding of this process by portraying certain practical applications of nature-inspired algorithms (NIAs) specifically designed to solve complex real-world problems in data analytics and pattern recognition by means of domain-specific solutions. Since various NIAs and their multidisciplinary applications in the mechanical engineering and electrical engineering sectors; and in machine learning, image processing, data mining, and wireless networks are dealt with in detail in this book, it can act as a handy reference guide.

Among the subjects of the 12 chapters are:





A novel method based on TRIZ to map real-world problems to nature problems Applications of cuckoo search algorithm for optimization problems Performance analysis of nature-inspired algorithms in breast cancer diagnosis Nature-inspired computation in data mining Hybrid bat-genetic algorithmbased novel optimal wavelet filter for compression of image data Efficiency of finding best solutions through ant colony optimization techniques Applications of hybridized algorithms and novel algorithms in the field of machine learning.

Audience: Researchers and graduate students in mechanical engineering, electrical engineering, machine learning, image processing, data mining, and wireless networks will find this book very useful.
Preface xv
1 Introduction to Nature-Inspired Computing 1(32)
N.M. Saravana Kumar
K. Hariprasath
N. Kaviyavarshini
A. Kavinya
1.1 Introduction
1(1)
1.2 Aspiration From Nature
2(1)
1.3 Working of Nature
3(1)
1.4 Nature-Inspired Computing
4(2)
1.4.1 Autonomous Entity
5(1)
1.5 General Stochastic Process of Nature-Inspired Computation
6(24)
1.5.1 NIC Categorization
8(25)
1.5.1.1 Bioinspired Algorithm
9(1)
1.5.1.2 Swarm Intelligence
10(1)
1.5.1.3 Physical Algorithms
11(1)
1.5.1.4 Familiar NIC Algorithms
12(18)
References
30(3)
2 Applications of Hybridized Algorithms and Novel Algorithms in the Field of Machine Learning 33(34)
P. Mary Jeyanthi
A. Mansurali
2.1 Introduction of Genetic Algorithm
33(17)
2.1.1 Background of GA
35(1)
2.1.2 Why Natural Selection Theory Compared With the Search Heuristic Algorithm?
35(1)
2.1.3 Working Sequence of Genetic Algorithm
35(3)
2.1.3.1 Population
35(1)
2.1.3.2 Fitness Among the Individuals
36(1)
2.1.3.3 Selection of Fitted Individuals
36(1)
2.1.3.4 Crossover Point
37(1)
2.1.3.5 Mutation
37(1)
2.1.4 Application of Machine Learning in GA
38(6)
2.1.4.1 Genetic Algorithm Role in Feature Selection for ML Problem
38(1)
2.1.4.2 Traveling Salesman Problem
39(1)
2.1.4.3 Blackjack-A Casino Game
40(1)
2.1.4.4 Pong Against AI-Evolving Agents (Reinforcement Learning) Using GA
41(1)
2.1.4.5 SNAKE AI-Game
41(1)
2.1.4.6 Genetic Algorithm's Role in Neural Network
42(1)
2.1.4.7 Solving a Battleship Board Game as an Optimization Problem Which Was Initially Released by Milton Bradley in 1967
43(1)
2.1.4.8 Frozen Lake Problem From OpenAl Gym
43(1)
2.1.4.9 N-Queen Problem
44(1)
2.1.5 Application of Data Mining in GA
44(3)
2.1.5.1 Association Rules Generation
44(1)
2.1.5.2 Pattern Classification With Genetic Algorithm
45(1)
2.1.5.3 Genetic Algorithms in Stock Market Data Mining Optimization
46(1)
2.1.5.4 Market Basket Analysis
46(1)
2.1.5.5 Job Scheduling
46(1)
2.1.5.6 Classification Problem
47(1)
2.1.5.7 Hybrid Decision Tree-Genetic Algorithm to Data Mining
47(1)
2.1.5.8 Genetic Algorithm-Optimization of Data Mining in Education
47(1)
2.1.6 Advantages of Genetic Algorithms
47(1)
2.1.7 Genetic Algorithms Demerits in the Current Era
48(2)
2.2 Introduction to Artificial Bear Optimization (ABO)
50(11)
2.2.1 Bear's Nasal Cavity
52(2)
2.2.2 Artificial Bear ABO Gist
54(4)
2.2.3 Implementation Based on Requirement
58(2)
2.2.3.1 Market Place
58(1)
2.2.3.2 Industry-Specific
58(1)
2.2.3.3 Semi-Structured or Unstructured Data
59(1)
2.2.4 Merits of ABO
60(1)
2.3 Performance Evaluation
61(1)
2.4 What is Next?
62(1)
References
63(4)
3 Efficiency of Finding Best Solutions Through Ant Colony Optimization (ACO) Technique 67(22)
K. Sasi Kala Rani
N. Pooranam
3.1 Introduction
68(9)
3.1.1 Example of Optimization Process
69(1)
3.1.2 Components of Optimization Algorithms
70(1)
3.1.3 Optimization Techniques Based on Solutions
70(3)
3.1.3.1 Optimization Techniques Based on Algorithms
72(1)
3.1.4 Characteristics
73(1)
3.1.5 Classes of Heuristic Algorithms
74(1)
3.1.6 Metaheuristic Algorithms
75(1)
3.1.6.1 Classification of Metaheuristic Algorithms: Nature-Inspired vs. Non-Nature-Inspired
75(1)
3.1.6.2 Population-Based vs. Single-Point Search (Trajectory)
75(1)
3.1.7 Data Processing Flow of ACO
76(1)
3.2 A Case Study on Surgical Treatment in Operation Room
77(3)
3.3 Case Study on Waste Management System
80(1)
3.4 Working Process of the System
81(1)
3.5 Background Knowledge to be Considered for Estimation
82(3)
3.5.1 Heuristic Function
83(2)
3.5.2 Functional Approach
85(1)
3.6 Case Study on Traveling System
85(2)
3.7 Future Trends and Conclusion
87(1)
References
88(1)
4 A Hybrid Bat-Genetic Algorithm-Based Novel Optimal Wavelet Filter for Compression of Image Data 89(48)
Renjith V. Ravi
Kamalraj Subramaniam
4.1 Introduction
90(1)
4.2 Review of Related Works
91(2)
4.3 Existing Technique for Secure Image Transmission
93(1)
4.4 Proposed Design of Optimal Wavelet Coefficients for Image Compression
93(11)
4.4.1 Optimized Transformation Module
94(6)
4.4.1.1 DWT Analysis and Synthesis Filter Bank
94(6)
4.4.2 Compression and Encryption Module
100(4)
4.4.2.1 SPIHT
100(2)
4.4.2.2 Chaos-Based Encryption
102(2)
4.5 Results and Discussion
104(30)
4.5.1 Experimental Setup and Evaluation Metrics
104(3)
4.5.2 Simulation Results
107(1)
4.5.2.1 Performance Analysis of the Novel Filter KARELET
107(1)
4.5.3 Result Analysis Proposed System
108(26)
4.6 Conclusion
134(1)
References
135(2)
5 A Swarm Robot for Harvesting a Paddy Field 137(20)
N. Pooranam
T. Vignesh
5.1 Introduction
137(5)
5.1.1 Working Principle of Particle Swarm Optimization
138(1)
5.1.2 First Case Study on Birds Fly
138(1)
5.1.3 Operational Moves on Birds Dataset
138(3)
5.1.4 Working Process of the Proposed Model
141(1)
5.2 Second Case Study on Recommendation Systems
142(3)
5.3 Third Case Study on Weight Lifting Robot
145(4)
5.4 Background Knowledge of Harvesting Process
149(6)
5.4.1 Data Flow of PSO Process
150(1)
5.4.2 Working Flow of Harvesting Process
151(1)
5.4.3 The First Phase of Harvesting Process
151(1)
5.4.4 Separation Process in Harvesting
152(1)
5.4.5 Cleaning Process in the Field
152(3)
5.5 Future Trend and Conclusion
155(1)
References
155(2)
6 Firefly Algorithm 157(24)
Anupriya Jain
Seema Sharma
Sachin Sharma
6.1 Introduction
158(2)
6.2 Firefly Algorithm
160(10)
6.2.1 Firefly Behavior
160(1)
6.2.2 Standard Firefly Algorithm
161(2)
6.2.3 Variations in Light Intensity and Attractiveness
163(1)
6.2.4 Distance and Movement
164(1)
6.2.5 Implementation of FA
165(1)
6.2.6 Special Cases of Firefly Algorithm
166(2)
6.2.7 Variants of FA
168(2)
6.3 Applications of Firefly Algorithm
170(4)
6.3.1 Job Shop Scheduling
170(1)
6.3.2 Image Segmentation
171(1)
6.3.3 Stroke Patient Rehabilitation
172(1)
6.3.4 Economic Emission Load Dispatch
172(1)
6.3.5 Structural Design
173(1)
6.4 Why Firefly Algorithm is Efficient
174(2)
6.4.1 FA is Not PSO
176(1)
6.5 Discussion and Conclusion
176(1)
References
177(4)
7 The Comprehensive Review for Biobased FPA Algorithm 181(28)
Meenakshi Rana
7.1 Introduction
182(3)
7.1.1 Stochastic Optimization
183(1)
7.1.2 Robust Optimization
183(1)
7.1.3 Dynamic Optimization
184(1)
7.1.4 Alogrithm
184(1)
7.1.5 Swarm Intelligence
185(1)
7.2 Related Work to FPA
185(17)
7.2.1 Flower Pollination Algorithm
187(3)
7.2.2 Versions of FPA
190(1)
7.2.3 Methods and Description
190(21)
7.2.3.1 Reproduction Factor
193(1)
7.2.3.2 Levy Flights
193(2)
7.2.3.3 User-Defined Parameters
195(1)
7.2.3.4 Psuedo Code for FPA
195(1)
7.2.3.5 Comparative Studies for FPA
196(1)
7.2.3.6 Working Environment
197(1)
7.2.3.7 Improved Versions of FPA
197(5)
7.3 Limitations
202(1)
7.4 Future Research
202(2)
7.5 Conclusion
204(1)
References
204(5)
8 Nature-Inspired Computation in Data Mining 209(34)
Aditi Sharma
8.1 Introduction
209(2)
8.2 Classification of NIC
211(16)
8.2.1 Swarm Intelligence for Data Mining
211(16)
8.2.1.1 Swarm Intelligence Algorithm
212(2)
8.2.1.2 Applications of Swarm Intelligence in Data Mining
214(1)
8.2.1.3 Swarm-Based Intelligence Techniques
214(13)
8.3 Evolutionary Computation
227(5)
8.3.1 Genetic Algorithms
227(1)
8.3.1.1 Applications of Genetic Algorithms in Data Mining
228(1)
8.3.2 Evolutionary Programming
228(1)
8.3.2.1 Applications of Evolutionary Programming in Data Mining
229(1)
8.3.3 Genetic Programming
229(1)
8.3.3.1 Applications of Genetic Programming in Data Mining
229(1)
8.3.4 Evolution Strategies
230(1)
8.3.4.1 Applications of Evolution Strategies in Data Mining
231(1)
8.3.5 Differential Evolutions
231(1)
8.3.5.1 Applications of Differential Evolution in Data Mining
231(1)
8.4 Biological Neural Network
232(1)
8.4.1 Artificial Neural Computation
232(1)
8.4.1.1 Neural Network Models
232(1)
8.4.1.2 Challenges of Artificial Neural Network in Data Mining
233(1)
8.4.1.3 Applications of Artificial Neural Network in Data Mining
233(1)
8.5 Molecular Biology
233(2)
8.5.1 Membrane Computing
233(1)
8.5.2 Algorithm Basis
234(1)
8.5.3 Challenges of Membrane Computing in Data Mining
234(1)
8.5.4 Applications of Membrane Computing in Data Mining
234(1)
8.6 Immune System
235(2)
8.6.1 Artificial Immune System
235(10)
8.6.1.1 Artificial Immune System Algorithm (Enhanced)
236(1)
8.6.1.2 Challenges of Artificial Immune System in Data Mining
236(1)
8.6.1.3 Applications of Artificial Immune System in Data Mining
237(1)
8.7 Applications of NIC in Data Mining
237(1)
8.8 Conclusion
238(1)
References
238(5)
9 Optimization Techniques for Removing Noise in Digital Medical Images 243(24)
D. Devasena
M. Jagadeeswari
B. Sharmila
K. Srinivasan
9.1 Introduction
244(1)
9.2 Medical Imaging Techniques
245(2)
9.2.1 X-Ray Images
245(1)
9.2.2 Computer Tomography Imaging
245(1)
9.2.3 Magnetic Resonance Images
246(1)
9.2.4 Positron Emission Tomography
246(1)
9.2.5 Ultrasound Imaging Techniques
246(1)
9.3 Image Denoising
247(2)
9.3.1 Impulse Noise and Speckle Noise Denoising
247(2)
9.4 Optimization in Image Denoising
249(8)
9.4.1 Particle Swarm Optimization
250(1)
9.4.2 Adaptive Center Pixel Weighted Median Exponential Filter
250(1)
9.4.3 Hybrid Wiener Filter
251(1)
9.4.4 Removal of Noise in Medical Images Using Particle Swarm Optimization
252(3)
9.4.4.1 Curvelet Transform
252(1)
9.4.4.2 PSO With Curvelet Transform and Hybrid Wiener Filter
253(2)
9.4.5 DFOA-Based Curvelet Transform and Hybrid Wiener Filter
255(2)
9.4.5.1 Dragon Fly Optimization Algorithm
255(1)
9.4.5.2 DFOA-Based HWACWMF
256(1)
9.5 Results and Discussions
257(7)
9.5.1 Simulation Results
257(1)
9.5.2 Performance Metric Analysis
257(6)
9.5.3 Summary
263(1)
9.6 Conclusion and Future Scope
264(1)
References
265(2)
10 Performance Analysis of Nature-Inspired Algorithms in Breast Cancer Diagnosis 267(28)
K. Hariprasath
S. Tamilselvi
N.M. Saravana Kumar
N. Kaviyavarshini
S. Balamurugan
10.1 Introduction
268(2)
10.1.1 NIC Algorithms
268(2)
10.2 Related Works
270(4)
10.3 Dataset: Wisconsin Breast Cancer Dataset (WBCD)
274(1)
10.4 Ten-Fold Cross-Validation
275(1)
10.4.1 Training Data
275(1)
10.4.2 Validation Data
275(1)
10.4.3 Test Data
276(1)
10.4.4 Pseudocode
276(1)
10.4.5 Advantages of K-Fold or 10-Fold Cross-Validation
276(1)
10.5 Naive Bayesian Classifier
276(3)
10.5.1 Pseudocode of Naive Bayesian Classifier
278(1)
10.5.2 Advantages of Naive Bayesian Classifier
278(1)
10.6 K-Means Clustering
279(1)
10.7 Support Vector Machine (SVM)
280(2)
10.8 Swarm Intelligence Algorithms
282(6)
10.8.1 Particle Swarm Optimization
283(2)
10.8.2 Firefly Algorithm
285(2)
10.8.3 Ant Colony Optimization
287(1)
10.9 Evaluation Metrics
288(1)
10.10 Results and Discussion
289(2)
10.11 Conclusion
291(1)
References
292(3)
11 Applications of Cuckoo Search Algorithm for Optimization Problems 295(22)
Akanksha Deep
Prasant Kumar Dash
11.1 Introduction
296(2)
11.2 Related Works
298(1)
11.3 Cuckoo Search Algorithm
299(5)
11.3.1 Biological Description
300(1)
11.3.2 Algorithm
300(4)
11.4 Applications of Cuckoo Search
304(10)
11.4.1 In Engineering
305(3)
11.4.1.1 Applications in Mechanical Engineering
305(3)
11.4.2 In Structural Optimization
308(1)
11.4.2.1 Test Problems
308(1)
11.4.3 Application CSA in Electrical Engineering, Power, and Energy
308(2)
11.4.3.1 Embedded System
308(1)
11.4.3.2 PCB
309(1)
11.4.3.3 Power and Energy
309(1)
11.4.4 Applications of CS in Field of Machine Learning and Computation
310(1)
11.4.5 Applications of CS in Image Processing
311(1)
11.4.6 Application of CSA in Data Processing
311(1)
11.4.7 Applications of CSA in Computation and Neural Network
312(1)
11.4.8 Application in Wireless Sensor Network
313(1)
11.5 Conclusion and Future Work
314(1)
References
315(2)
12 Mapping of Real-World Problems to Nature-Inspired Algorithm Using Goal-Based Classification and TRIZ 317(24)
Polak Sukharamwala
Manojkumar Parmar
12.1 Introduction and Background
318(1)
12.2 Motivations Behind NIA Exploration
319(3)
12.2.1 Prevailing Issues With Technology
319(2)
12.2.1.1 Data Dependencies
319(1)
12.2.1.2 Demand for Higher Software Complexity
320(1)
12.2.1.3 NP-Hard Problems
320(1)
12.2.1.4 Energy Consumption
321(1)
12.2.2 Nature-Inspired Algorithm at a Rescue
321(1)
12.3 Novel TRIZ + NIA Approach
322(5)
12.3.1 Traditional Classification
322(2)
12.3.1.1 Swarm Intelligence
322(1)
12.3.1.2 Evolution Algorithm
323(1)
12.3.1.3 Bio-Inspired Algorithms
324(1)
12.3.1.4 Physics-Based Algorithm
324(1)
12.3.1.5 Other Nature-Inspired Algorithms
324(1)
12.3.2 Limitation of Traditional Classification
324(1)
12.3.3 Combined Approach NIA + TRIZ
325(1)
12.3.3.1 TRIZ
325(1)
12.3.3.2 NIA + TRIZ
325(1)
12.3.4 End Goal-Based Classification
326(1)
12.4 Examples to Support the TRIZ + NIA Approach
327(8)
12.4.1 Fruit Optimization Algorithm to Predict Monthly Electricity Consumption
327(5)
12.4.2 Bat Algorithm to Model River Dissolved Oxygen Concentration
332(1)
12.4.3 Genetic Algorithm to Tune the Structure and Parameters of a Neural Network
333(2)
12.5 A Solution of NP-H Using NIA
335(3)
12.5.1 The 0-1 Knapsack Problem
335(2)
12.5.2 Traveling Salesman Problem
337(1)
12.6 Conclusion
338(1)
References
338(3)
Index 341
S. Balamurugan, PhD is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.

Anupriya Jain, PhD is an associate professor at the Manav Rachna International Institute of Research and Studies, Faridabad, Haryana.

Sachin Sharma, PhD is an assistant professor in computer applications at the Manav Rachna International Institute of Research and Studies, Faridabad, India. He has published more than 30 research papers in different areas of technology and has been a part of two patents as well.

Dinesh Goyal, PhD is the Director at the Poornima Institute of Engineering and Technology, Jaipur, India. His research interests are related to information & network security, image processing, data analytics, and cloud computing, and has published more than 60 research articles.

Sonia Duggal, PhD is an associate professor at the Manav Rachna International Institute of Research and Studies, Faridabad, Haryana.

Seema Sharma is an assistant professor at the Manav Rachna International Institute of Research and Studies, Faridabad, India.
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