Nature inspired algorithms and their applications / / editors, S. Balamurugan [et al.]

• Record Nr.: UNINA9910830496003321
• Pubbl/distr/stampa: Hoboken, NJ : , : John Wiley & Sons, Inc. : , : Scrivener Publishing, , [2022]; ©2022
• ISBN: 1-119-68166-9; 1-119-68198-7; 1-119-68199-5
• Descrizione fisica: 1 online resource (384 pages)
• Disciplina: 571.0284
• Soggetti: Nature-inspired algorithms
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Introduction to Nature-Inspired Computing -- 1.1 Introduction -- 1.2 Aspiration From Nature -- 1.3 Working of Nature -- 1.4 Nature-Inspired Computing -- 1.4.1 Autonomous Entity -- 1.5 General Stochastic Process of Nature-Inspired Computation -- 1.5.1 NIC Categorization -- 1.5.1.1 Bioinspired Algorithm -- 1.5.1.2 Swarm Intelligence -- 1.5.1.3 Physical Algorithms -- 1.5.1.4 Familiar NIC Algorithms -- References -- 2 Applications of Hybridized Algorithms and Novel Algorithms in the Field of Machine Learning -- 2.1 Introduction of Genetic Algorithm -- 2.1.1 Background of GA -- 2.1.2 Why Natural Selection Theory Compared With the Search Heuristic Algorithm? -- 2.1.3 Working Sequence of Genetic Algorithm -- 2.1.3.1 Population -- 2.1.3.2 Fitness Among the Individuals -- 2.1.3.3 Selection of Fitted Individuals -- 2.1.3.4 Crossover Point -- 2.1.3.5 Mutation -- 2.1.4 Application of Machine Learning in GA -- 2.1.4.1 Genetic Algorithm Role in Feature Selection for ML Problem -- 2.1.4.2 Traveling Salesman Problem -- 2.1.4.3 Blackjack-A Casino Game -- 2.1.4.4 Pong Against AI-Evolving Agents (Reinforcement Learning) Using GA -- 2.1.4.5 SNAKE AI-Game -- 2.1.4.6 Genetic Algorithm's Role in Neural Network -- 2.1.4.7 Solving a Battleship Board Game as ; an Optimization Problem Which Was Initially Released by Milton Bradley in 1967 -- 2.1.4.8 Frozen Lake Problem From OpenAI Gym -- 2.1.4.9 N-Queen Problem -- 2.1.5 Application of Data Mining in GA -- 2.1.5.1 Association Rules Generation -- 2.1.5.2 Pattern Classification With Genetic Algorithm -- 2.1.5.3 Genetic Algorithms in Stock Market Data Mining Optimization -- 2.1.5.4 Market Basket Analysis -- 2.1.5.5 Job Scheduling -- 2.1.5.6 Classification Problem -- 2.1.5.7 Hybrid Decision Tree-Genetic Algorithm to Data Mining.; 2.1.5.8 Genetic Algorithm-Optimization of Data Mining in Education -- 2.1.6 Advantages of Genetic Algorithms -- 2.1.7 Genetic Algorithms Demerits in the Current Era -- 2.2 Introduction to Artificial Bear Optimization (ABO) -- 2.2.1 Bear's Nasal Cavity -- 2.2.2 Artificial Bear ABO Gist Algorithm: -- Pseudo Algorithm: -- Implementation: -- 2.2.3 Implementation Based on Requirement -- 2.2.3.1 Market Place -- 2.2.3.2 Industry-Specific -- 2.2.3.3 Semi-Structured or Unstructured Data -- 2.2.4 Merits of ABO -- 2.3 Performance Evaluation -- 2.4 What is Next? -- References -- 3 Efficiency of Finding Best Solutions Through Ant Colony Optimization (ACO) Technique -- 3.1 Introduction -- 3.1.1 Example of Optimization Process -- 3.1.2 Components of Optimization Algorithms -- 3.1.3 Optimization Techniques Based on Solutions -- 3.1.3.1 Optimization Techniques Based on Algorithms -- 3.1.4 Characteristics -- 3.1.5 Classes of Heuristic Algorithms -- 3.1.6 Metaheuristic Algorithms -- 3.1.6.1 Classification of Metaheuristic Algorithms: Nature-Inspired vs. Non-Nature-Inspired -- 3.1.6.2 Population-Based vs. Single-Point Search (Trajectory) -- 3.1.7 Data Processing Flow of ACO -- 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 -- 3.5.1 Heuristic Function -- 3.5.2 Functional Approach -- 3.6 Case Study on Traveling System -- 3.7 Future Trends and Conclusion -- References -- 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 -- 4.4.1 Optimized Transformation Module.; 4.4.1.1 DWT Analysis and Synthesis Filter Bank -- 4.4.2 Compression and Encryption Module -- 4.4.2.1 SPIHT -- 4.4.2.2 Chaos-Based Encryption -- 4.5 Results and Discussion -- 4.5.1 Experimental Setup and Evaluation Metrics -- 4.5.2 Simulation Results -- 4.5.2.1 Performance Analysis of the Novel Filter KARELET -- 4.5.3 Result Analysis Proposed System -- 4.6 Conclusion -- References -- 5 A Swarm Robot for Harvesting a Paddy Field -- 5.1 Introduction -- 5.1.1 Working Principle of Particle Swarm Optimization -- 5.1.2 First Case Study on Birds Fly -- 5.1.3 Operational Moves on Birds Dataset -- 5.1.4 Working Process of the Proposed Model -- 5.2 Second Case Study on Recommendation Systems -- 5.3 Third Case Study on Weight Lifting Robot -- 5.4 Background Knowledge of Harvesting Process -- 5.4.1 Data Flow of PSO Process -- 5.4.2 Working Flow of Harvesting Process -- 5.4.3 The First Phase of Harvesting Process -- 5.4.4 Separation Process in Harvesting -- 5.4.5 Cleaning Process in the Field -- 5.5 Future Trend and Conclusion -- References -- 6 Firefly Algorithm -- 6.1 Introduction -- 6.2 Firefly Algorithm -- 6.2.1 Firefly Behavior -- 6.2.2 Standard Firefly Algorithm -- 6.2.3 Variations in Light Intensity and Attractiveness -- 6.2.4 Distance and Movement -- 6.2.5 Implementation of FA -- 6.2.6 Special Cases of Firefly Algorithm -- 6.2.7 Variants of FA -- 6.3 Applications of Firefly Algorithm -- 6.3.1 Job Shop Scheduling -- 6.3.2 Image Segmentation -- 6.3.3 Stroke ; Patient Rehabilitation -- 6.3.4 Economic Emission Load Dispatch -- 6.3.5 Structural Design -- 6.4 Why Firefly Algorithm is Efficient -- 6.4.1 FA is Not PSO -- 6.5 Discussion and Conclusion -- References -- 7 The Comprehensive Review for Biobased FPA Algorithm -- 7.1 Introduction -- 7.1.1 Stochastic Optimization -- 7.1.2 Robust Optimization -- 7.1.3 Dynamic Optimization -- 7.1.4 Alogrithm.; 7.1.5 Swarm Intelligence -- 7.2 Related Work to FPA -- 7.2.1 Flower Pollination Algorithm -- 7.2.2 Versions of FPA -- 7.2.3 Methods and Description -- 7.3 Limitations -- 7.4 Future Research -- 7.5 Conclusion -- References -- 8 Nature-Inspired Computation in Data Mining -- 8.1 Introduction -- 8.2 Classification of NIC -- 8.2.1 Swarm Intelligence for Data Mining -- 8.2.1.1 Swarm Intelligence Algorithm -- 8.2.1.2 Applications of Swarm Intelligence in Data Mining -- 8.2.1.3 Swarm-Based Intelligence Techniques -- 8.3 Evolutionary Computation -- 8.3.1 Genetic Algorithms -- 8.3.1.1 Applications of Genetic Algorithms in Data Mining -- 8.3.2 Evolutionary Programming -- 8.3.2.1 Applications of Evolutionary Programming in Data Mining -- 8.3.3 Genetic Programming -- 8.3.3.1 Applications of Genetic Programming in Data Mining -- 8.3.4 Evolution Strategies -- 8.3.4.1 Applications of Evolution Strategies in Data Mining -- 8.3.5 Differential Evolutions -- 8.3.5.1 Applications of Differential Evolution in Data Mining -- 8.4 Biological Neural Network -- 8.4.1 Artificial Neural Computation -- 8.4.1.1 Neural Network Models -- 8.4.1.2 Challenges of Artificial Neural Network in Data Mining -- 8.4.1.3 Applications of Artificial Neural Network in Data Mining -- 8.5 Molecular Biology -- 8.5.1 Membrane Computing -- 8.5.2 Algorithm Basis -- 8.5.3 Challenges of Membrane Computing in Data Mining -- 8.5.4 Applications of Membrane Computing in Data Mining -- 8.6 Immune System -- 8.6.1 Artificial Immune System -- 8.6.1.1 Artificial Immune System Algorithm (Enhanced) -- 8.6.1.2 Challenges of Artificial Immune System in Data Mining -- 8.6.1.3 Applications of Artificial Immune System in Data Mining -- 8.7 Applications of NIC in Data Mining -- 8.8 Conclusion -- References -- 9 Optimization Techniques for Removing Noise in Digital Medical Images -- 9.1 Introduction.; 9.2 Medical Imaging Techniques -- 9.2.1 X-Ray Images -- 9.2.2 Computer Tomography Imaging -- 9.2.3 Magnetic Resonance Images -- 9.2.4 Positron Emission Tomography -- 9.2.5 Ultrasound Imaging Techniques -- 9.3 Image Denoising -- 9.3.1 Impulse Noise and Speckle Noise Denoising -- 9.4 Optimization in Image Denoising -- 9.4.1 Particle Swarm Optimization -- 9.4.2 Adaptive Center Pixel Weighted Median Exponential Filter -- 9.4.3 Hybrid Wiener Filter -- 9.4.4 Removal of Noise in Medical Images Using Particle Swarm Optimization -- 9.4.4.1 Curvelet Transform -- 9.4.4.2 PSO With Curvelet Transform and Hybrid Wiener Filter -- 9.4.5 DFOA-Based Curvelet Transform and Hybrid Wiener Filter -- 9.4.5.1 Dragon Fly Optimization Algorithm -- 9.4.5.2 DFOA-Based HWACWMF -- 9.5 Results and Discussions -- 9.5.1 Simulation Results -- 9.5.2 Performance Metric Analysis -- 9.5.3 Summary -- 9.6 Conclusion and Future Scope -- References -- 10 Performance Analysis of Nature-Inspired Algorithms in Breast Cancer Diagnosis -- 10.1 Introduction -- 10.1.1 NIC Algorithms -- 10.2 Related Works -- 10.3 Dataset: Wisconsin Breast Cancer Dataset (WBCD) -- 10.4 Ten-Fold Cross-Validation -- 10.4.1 Training Data -- 10.4.2 Validation Data -- 10.4.3 Test Data -- 10.4.4 Pseudocode -- 10.4.5 Advantages of K-Fold or 10-Fold Cross-Validation -- 10.5 Naive Bayesian Classifier -- 10.5.1 Pseudocode of Naive Bayesian Classifier -- 10.5.2 Advantages of Naive Bayesian Classifier -- 10.6 K-Means Clustering -- 10.7 Support Vector Machine (SVM) -- 10.8 Swarm Intelligence Algorithms -- 10.8.1 Particle Swarm Optimization -- ; 10.8.2 Firefly Algorithm -- 10.8.3 Ant Colony Optimization -- 10.9 Evaluation Metrics -- 10.10 Results and Discussion -- 10.11 Conclusion -- References -- 11 Applications of Cuckoo Search Algorithm for Optimization Problems -- 11.1 Introduction -- 11.2 Related Works.; 11.3 Cuckoo Search Algorithm.
• Sommario/riassunto: The purpose of designing this book is to portray certain practical applications of nature-inspired computation in machine learning for the better understanding of the world around us. The focus is to portray and present recent developments in the areas where nature- inspired algorithms are specifically designed and applied to solve complex real-world problems in data analytics and pattern recognition, by means of domain-specific solutions. Various nature-inspired algorithms and their multidisciplinary applications (in mechanical engineering, electrical engineering, machine learning, image processing, data mining and wireless network domains are detailed, which will make this book a handy reference guide.