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Biologically Inspired Techniques in Many-Criteria Decision Making [[electronic resource] ] : International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM-2019) / / edited by Satchidananda Dehuri, Bhabani Shankar Prasad Mishra, Pradeep Kumar Mallick, Sung-Bae Cho, Margarita N. Favorskaya
| Biologically Inspired Techniques in Many-Criteria Decision Making [[electronic resource] ] : International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM-2019) / / edited by Satchidananda Dehuri, Bhabani Shankar Prasad Mishra, Pradeep Kumar Mallick, Sung-Bae Cho, Margarita N. Favorskaya |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xv, 258 pages) |
| Disciplina | 658.403 |
| Collana | Learning and Analytics in Intelligent Systems |
| Soggetto topico |
Computational intelligence
Engineering—Data processing Artificial intelligence Computational Intelligence Data Engineering Artificial Intelligence |
| ISBN | 3-030-39033-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Classification of Arrhythmia Using Artificial Neural Network with Grey Wolf Optimization -- Chapter 2: Multi-objective Biogeography-Based Optimization for Influence Maximization-Cost Minimization in Social Networks -- Chapter 3: Classification of Credit Dataset Using Improved Particle Swarm Optimization Tuned Radial Basis Function Neural Networks -- Chapter 4: Multi-verse Optimization of Multilayer Perceptrons (MV-MLPs) for Efficient Modeling and Forecasting of Crude Oil Prices Data -- Chapter 5: Application of machine learning to predict diseases based on symptoms in rural India -- Chapter 6: Classıfıcatıon of Real Tıme Noısy Fıngerprınt Images Usıng FLANN -- Chapter 7: Software Reliability Prediction with Ensemble Method and Virtual Data Point Incorporation -- Chapter 8: Hyperspectral Image Classification using Stochastic Gradient Descent based Support Vector Machine -- Chapter 9: A Survey on Ant Colony Optimization for Solving Some of the Selected NP-Hard Problem -- Chapter 10: Machine Learning Models for Stock Prediction using Real-Time Streaming Data -- Chapter 11: Epidemiology of Breast Cancer (BC) and its Early Identification via Evolving Machine Learning Classification Tools (MLCT)–A Study -- Chapter 12: Ensemble Classification Approach for Cancer Prognosis and Prediction -- Chapter 13: Extractive Odia Text Summarization System: An OCR based Approach -- Chapter 14: Predicting sensitivity of local news articles from Odia dailies -- Chapter 15: A systematic frame work using machine learning approaches in supply chain forecasting -- Chapter 16: An Intelligent system on computer-aided diagnosis for Parkinson’s disease with MRI using Machine Learning -- Chapter 17: Operations on Picture Fuzzy Numbers and their Application in Multi-Criteria Group Decision Making Problems -- Chapter 18: Some Generalized Results on Multi-Criteria Decision Making Model using Fuzzy TOPSIS Technique -- Chapter 19: A Survey on FP-Tree Based Incremental Frequent Pattern Mining -- Chapter 20: Improving Co-expressed Gene Pattern Finding Using Gene Ontology -- Chapter 21: Survey of Methods Used for Differential Expression Analysis on RNA Seq Data -- Chapter 22: Adaptive Antenna Tilt for Cellular Coverage Optimization in Suburban Scenario -- Chapter 23: A survey of the different itemset representation for candidate. |
| Record Nr. | UNINA-9910484374803321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Hybrid Artificial Intelligent Systems [[electronic resource] ] : 7th International Conference, HAIS 2012, Salamanca, Spain, March 28-30th, 2012, Proceedings, Part I / / edited by Emilio S. Corchado Rodriguez, Vaclav Snasel, Ajit Abraham, Michal Wozniak, Manuel Grana, Sung-Bae Cho
| Hybrid Artificial Intelligent Systems [[electronic resource] ] : 7th International Conference, HAIS 2012, Salamanca, Spain, March 28-30th, 2012, Proceedings, Part I / / edited by Emilio S. Corchado Rodriguez, Vaclav Snasel, Ajit Abraham, Michal Wozniak, Manuel Grana, Sung-Bae Cho |
| Edizione | [1st ed. 2012.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012 |
| Descrizione fisica | 1 online resource (XXXII, 708 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Application software Computers Database management Information storage and retrieval Artificial Intelligence Algorithm Analysis and Problem Complexity Information Systems Applications (incl. Internet) Computation by Abstract Devices Database Management Information Storage and Retrieval |
| ISBN | 3-642-28942-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. 1. [pages 1-708] |
| Record Nr. | UNISA-996466254803316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Hybrid Artificial Intelligent Systems [[electronic resource] ] : 7th International Conference, HAIS 2012, Salamanca, Spain, March 28-30th, 2012, Proceedings, Part II / / edited by Emilio S. Corchado Rodriguez, Vaclav Snasel, Ajith Abraham, Michal Wozniak, Manuel Grana, Sung-Bae Cho
| Hybrid Artificial Intelligent Systems [[electronic resource] ] : 7th International Conference, HAIS 2012, Salamanca, Spain, March 28-30th, 2012, Proceedings, Part II / / edited by Emilio S. Corchado Rodriguez, Vaclav Snasel, Ajith Abraham, Michal Wozniak, Manuel Grana, Sung-Bae Cho |
| Edizione | [1st ed. 2012.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012 |
| Descrizione fisica | 1 online resource (XXXII, 606 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Algorithms Application software Computers Database management Information storage and retrieval Artificial Intelligence Algorithm Analysis and Problem Complexity Information Systems Applications (incl. Internet) Computation by Abstract Devices Database Management Information Storage and Retrieval |
| ISBN | 3-642-28931-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466254203316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Integration of swarm intelligence and artificial neutral network [[electronic resource] /] / Satchidananda Dehuri, Susmita Ghosh, Sung-bae Cho, editors
| Integration of swarm intelligence and artificial neutral network [[electronic resource] /] / Satchidananda Dehuri, Susmita Ghosh, Sung-bae Cho, editors |
| Pubbl/distr/stampa | Hackensack, N.J. ; ; London, : World Scientific, 2011 |
| Descrizione fisica | 1 online resource (352 p.) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
DehuriSatchidananda
GhoshSusmita ChoSung-Bae |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Swarm intelligence
Neural networks (Computer science) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-43330-3
9786613433305 981-4280-15-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; Chapter 1 Swarm Intelligence and Neural Networks; 1.1. Introduction; 1.2. Swarm Intelligence; 1.2.1. Particle Swarm Optimization; 1.2.2. Ant Colony Optimization; 1.2.3. Bee Colony Optimization; 1.3. Neural Networks; 1.3.1. Evolvable Neural Network; 1.3.2. Higher Order Neural Network; 1.3.3. Pi (Π)-Sigma (Σ) Neural Networks; 1.3.4. Functional Link Artificial Neural Network; 1.3.5. Ridge Polynomial Neural Networks (RPNNs); 1.4. Summary and Discussion; References; Chapter 2 Neural Network and Swarm Intelligence for Data Mining; 2.1. Introduction; 2.2. Testbeds for Data Mining
2.2.1. Fisher Iris Data2.2.2. Pima - Diabetes Data; 2.2.3. Shuttle Data; 2.2.4. Classification Efficiency; 2.3. Neural Network for Data Mining; 2.3.1. Multi-Layer Perceptron (MLP); 2.3.2. Radial Basis Function Network; 2.4. Swarm Intelligence for Data Mining; 2.4.1. Ant Miner; 2.4.2. Artificial Bee Colony; 2.4.3. Particle Swarm Optimization; 2.5. Comparative Study; 2.6. Conclusions and Outlook; Acknowledgments; References; Chapter 3 Multi-Objective Ant Colony Optimization: A Taxonomy and Review of Approaches; 3.1. Introduction; 3.2. Ant Colony Optimization 3.3. Basic Concepts of Multi-Objective Optimization3.4. The ACO Metaheuristic for MOOPs in the Literature; 3.5. ACO Variants for MOOP: A Refined Taxonomy; 3.6. Promising Research Areas; 3.7. Conclusions; Acknowledgments; References; Chapter 4 Recurrent Neural Networks with Discontinuous Activation Functions for Convex Optimization; 4.1. Introduction; 4.2. Related Definitions and Lemmas; 4.3. For Linear Programming; 4.3.1. Model Description and Convergence Results; 4.3.2. Simulation Results; 4.4. For Quadratic Programming; 4.4.1. Model Description; 4.4.2. Convergence Results 4.4.3. Simulation Results4.5. For Non-Smooth Convex Optimization Subject to Linear Equality Constraints; 4.5.1. Model Description and Convergence Results; 4.5.2. Constrained Least Absolute Deviation; 4.6. Application to k-Winners-Take-All; 4.6.1. LP-Based Model; 4.6.2. QP-Based Model; 4.6.3. Simulation Results; 4.7. Concluding Remarks; Acknowledgments; References; Chapter 5 Automated Power Quality Disturbance Classification Using Evolvable Neural Network; 5.1. Introduction; 5.2. Wavelet Transform (WT); 5.3. Brief Overview of Neural Network Classifiers 5.4. Overview of Particle Swarm Optimization5.5. Signal Generation, Feature Extraction and Classification; 5.6. Results and Discussion; 5.7. Conclusions; References; Chapter 6 Condition Monitoring and Fault Diagnosis Using Intelligent Techniques; 6.1. Introduction; 6.2. Methodology; 6.2.1. Hardware Specification, System Setup and Audio Data Generation; 6.2.2. Data Pre-Processing; 6.2.3. Data Classification Techniques; 6.2.4. Signal Segregation using Independent Component Analysis; 6.3. Experimental Details; 6.3.1. Pre-Processing 6.3.2. Method 1: Artificial Neural Network Setup for Engine Classification |
| Record Nr. | UNINA-9910464493103321 |
| Hackensack, N.J. ; ; London, : World Scientific, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Integration of swarm intelligence and artificial neutral network [[electronic resource] /] / Satchidananda Dehuri, Susmita Ghosh, Sung-bae Cho, editors
| Integration of swarm intelligence and artificial neutral network [[electronic resource] /] / Satchidananda Dehuri, Susmita Ghosh, Sung-bae Cho, editors |
| Pubbl/distr/stampa | Hackensack, N.J. ; ; London, : World Scientific, 2011 |
| Descrizione fisica | 1 online resource (352 p.) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
DehuriSatchidananda
GhoshSusmita ChoSung-Bae |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Swarm intelligence
Neural networks (Computer science) |
| ISBN |
1-283-43330-3
9786613433305 981-4280-15-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; Chapter 1 Swarm Intelligence and Neural Networks; 1.1. Introduction; 1.2. Swarm Intelligence; 1.2.1. Particle Swarm Optimization; 1.2.2. Ant Colony Optimization; 1.2.3. Bee Colony Optimization; 1.3. Neural Networks; 1.3.1. Evolvable Neural Network; 1.3.2. Higher Order Neural Network; 1.3.3. Pi (Π)-Sigma (Σ) Neural Networks; 1.3.4. Functional Link Artificial Neural Network; 1.3.5. Ridge Polynomial Neural Networks (RPNNs); 1.4. Summary and Discussion; References; Chapter 2 Neural Network and Swarm Intelligence for Data Mining; 2.1. Introduction; 2.2. Testbeds for Data Mining
2.2.1. Fisher Iris Data2.2.2. Pima - Diabetes Data; 2.2.3. Shuttle Data; 2.2.4. Classification Efficiency; 2.3. Neural Network for Data Mining; 2.3.1. Multi-Layer Perceptron (MLP); 2.3.2. Radial Basis Function Network; 2.4. Swarm Intelligence for Data Mining; 2.4.1. Ant Miner; 2.4.2. Artificial Bee Colony; 2.4.3. Particle Swarm Optimization; 2.5. Comparative Study; 2.6. Conclusions and Outlook; Acknowledgments; References; Chapter 3 Multi-Objective Ant Colony Optimization: A Taxonomy and Review of Approaches; 3.1. Introduction; 3.2. Ant Colony Optimization 3.3. Basic Concepts of Multi-Objective Optimization3.4. The ACO Metaheuristic for MOOPs in the Literature; 3.5. ACO Variants for MOOP: A Refined Taxonomy; 3.6. Promising Research Areas; 3.7. Conclusions; Acknowledgments; References; Chapter 4 Recurrent Neural Networks with Discontinuous Activation Functions for Convex Optimization; 4.1. Introduction; 4.2. Related Definitions and Lemmas; 4.3. For Linear Programming; 4.3.1. Model Description and Convergence Results; 4.3.2. Simulation Results; 4.4. For Quadratic Programming; 4.4.1. Model Description; 4.4.2. Convergence Results 4.4.3. Simulation Results4.5. For Non-Smooth Convex Optimization Subject to Linear Equality Constraints; 4.5.1. Model Description and Convergence Results; 4.5.2. Constrained Least Absolute Deviation; 4.6. Application to k-Winners-Take-All; 4.6.1. LP-Based Model; 4.6.2. QP-Based Model; 4.6.3. Simulation Results; 4.7. Concluding Remarks; Acknowledgments; References; Chapter 5 Automated Power Quality Disturbance Classification Using Evolvable Neural Network; 5.1. Introduction; 5.2. Wavelet Transform (WT); 5.3. Brief Overview of Neural Network Classifiers 5.4. Overview of Particle Swarm Optimization5.5. Signal Generation, Feature Extraction and Classification; 5.6. Results and Discussion; 5.7. Conclusions; References; Chapter 6 Condition Monitoring and Fault Diagnosis Using Intelligent Techniques; 6.1. Introduction; 6.2. Methodology; 6.2.1. Hardware Specification, System Setup and Audio Data Generation; 6.2.2. Data Pre-Processing; 6.2.3. Data Classification Techniques; 6.2.4. Signal Segregation using Independent Component Analysis; 6.3. Experimental Details; 6.3.1. Pre-Processing 6.3.2. Method 1: Artificial Neural Network Setup for Engine Classification |
| Record Nr. | UNINA-9910788961703321 |
| Hackensack, N.J. ; ; London, : World Scientific, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Integration of swarm intelligence and artificial neutral network [[electronic resource] /] / Satchidananda Dehuri, Susmita Ghosh, Sung-bae Cho, editors
| Integration of swarm intelligence and artificial neutral network [[electronic resource] /] / Satchidananda Dehuri, Susmita Ghosh, Sung-bae Cho, editors |
| Pubbl/distr/stampa | Hackensack, N.J. ; ; London, : World Scientific, 2011 |
| Descrizione fisica | 1 online resource (352 p.) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
DehuriSatchidananda
GhoshSusmita ChoSung-Bae |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Swarm intelligence
Neural networks (Computer science) |
| ISBN |
1-283-43330-3
9786613433305 981-4280-15-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; Chapter 1 Swarm Intelligence and Neural Networks; 1.1. Introduction; 1.2. Swarm Intelligence; 1.2.1. Particle Swarm Optimization; 1.2.2. Ant Colony Optimization; 1.2.3. Bee Colony Optimization; 1.3. Neural Networks; 1.3.1. Evolvable Neural Network; 1.3.2. Higher Order Neural Network; 1.3.3. Pi (Π)-Sigma (Σ) Neural Networks; 1.3.4. Functional Link Artificial Neural Network; 1.3.5. Ridge Polynomial Neural Networks (RPNNs); 1.4. Summary and Discussion; References; Chapter 2 Neural Network and Swarm Intelligence for Data Mining; 2.1. Introduction; 2.2. Testbeds for Data Mining
2.2.1. Fisher Iris Data2.2.2. Pima - Diabetes Data; 2.2.3. Shuttle Data; 2.2.4. Classification Efficiency; 2.3. Neural Network for Data Mining; 2.3.1. Multi-Layer Perceptron (MLP); 2.3.2. Radial Basis Function Network; 2.4. Swarm Intelligence for Data Mining; 2.4.1. Ant Miner; 2.4.2. Artificial Bee Colony; 2.4.3. Particle Swarm Optimization; 2.5. Comparative Study; 2.6. Conclusions and Outlook; Acknowledgments; References; Chapter 3 Multi-Objective Ant Colony Optimization: A Taxonomy and Review of Approaches; 3.1. Introduction; 3.2. Ant Colony Optimization 3.3. Basic Concepts of Multi-Objective Optimization3.4. The ACO Metaheuristic for MOOPs in the Literature; 3.5. ACO Variants for MOOP: A Refined Taxonomy; 3.6. Promising Research Areas; 3.7. Conclusions; Acknowledgments; References; Chapter 4 Recurrent Neural Networks with Discontinuous Activation Functions for Convex Optimization; 4.1. Introduction; 4.2. Related Definitions and Lemmas; 4.3. For Linear Programming; 4.3.1. Model Description and Convergence Results; 4.3.2. Simulation Results; 4.4. For Quadratic Programming; 4.4.1. Model Description; 4.4.2. Convergence Results 4.4.3. Simulation Results4.5. For Non-Smooth Convex Optimization Subject to Linear Equality Constraints; 4.5.1. Model Description and Convergence Results; 4.5.2. Constrained Least Absolute Deviation; 4.6. Application to k-Winners-Take-All; 4.6.1. LP-Based Model; 4.6.2. QP-Based Model; 4.6.3. Simulation Results; 4.7. Concluding Remarks; Acknowledgments; References; Chapter 5 Automated Power Quality Disturbance Classification Using Evolvable Neural Network; 5.1. Introduction; 5.2. Wavelet Transform (WT); 5.3. Brief Overview of Neural Network Classifiers 5.4. Overview of Particle Swarm Optimization5.5. Signal Generation, Feature Extraction and Classification; 5.6. Results and Discussion; 5.7. Conclusions; References; Chapter 6 Condition Monitoring and Fault Diagnosis Using Intelligent Techniques; 6.1. Introduction; 6.2. Methodology; 6.2.1. Hardware Specification, System Setup and Audio Data Generation; 6.2.2. Data Pre-Processing; 6.2.3. Data Classification Techniques; 6.2.4. Signal Segregation using Independent Component Analysis; 6.3. Experimental Details; 6.3.1. Pre-Processing 6.3.2. Method 1: Artificial Neural Network Setup for Engine Classification |
| Record Nr. | UNINA-9910817653803321 |
| Hackensack, N.J. ; ; London, : World Scientific, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Knowledge mining using intelligent agents [[electronic resource] /] / editors, Satchidananda Dehuri, Sung-Bae Cho
| Knowledge mining using intelligent agents [[electronic resource] /] / editors, Satchidananda Dehuri, Sung-Bae Cho |
| Pubbl/distr/stampa | London, : Imperial College Press, 2011 |
| Descrizione fisica | 1 online resource (400 p.) |
| Disciplina | 006.312 |
| Altri autori (Persone) |
DehuriSatchidananda
ChoSung-Bae |
| Collana | Advances in computer science and engineering: Texts |
| Soggetto topico |
Intelligent agents (Computer software)
Data mining |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-14333-X
9786613143334 1-84816-387-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
PREFACE; CONTENTS; Chapter 1THEORETICAL FOUNDATIONS OF KNOWLEDGE MINING AND INTELLIGENT AGENT; 1.1. Knowledge and Agent; 1.2. Knowledge Mining from Databases; 1.2.1. KMD tasks; 1.2.1.1. Mining Association Rules; 1.2.1.2. Classification; 1.2.1.3. Clustering; 1.2.1.4. Dependency Modeling; 1.2.1.5. Change and Deviation Detection; 1.2.1.6. Regression; 1.2.1.7. Summarization; 1.2.1.8. Causation Modeling; 1.3. Intelligent Agents; 1.3.1. Evolutionary computing; 1.3.2. Swarm intelligence; 1.3.2.1. Particle Swarm Optimization; 1.3.2.2. Ant Colony Optimization (ACO)
1.3.2.3. Artificial Bee Colony (ABC)1.3.2.4. Artificial Wasp Colony (AWC); 1.3.2.5. Artificial Termite Colony (ATC); 1.4. Summary; References; Chapter 2 THE USE OF EVOLUTIONARY COMPUTATION IN KNOWLEDGE DISCOVERY: THE EXAMPLE OF INTRUSION DETECTION SYSTEMS; 2.1. Introduction; 2.2. Background; 2.2.1. Knowledge discovery and data mining; 2.2.2. Evolutionary computation; 2.2.3. Intrusion detection systems; 2.3. The Role of Evolutionary Computation in KDD; 2.3.1. Feature selection; 2.3.2. Classification; 2.3.2.1. Representation; 2.3.2.2. Learning approaches; 2.3.2.3. Rule discovery 2.3.3. Regression2.3.4. Clustering; 2.3.5. Comparison between classification and regression; 2.4. Evolutionary Operators and Niching; 2.4.1. Evolutionary operators; 2.4.2. Niching; 2.5. Fitness Function; 2.6. Conclusions and Future Directions; Acknowledgment; References; Chapter 3 EVOLUTION OF NEURAL NETWORK AND POLYNOMIAL NETWORK; 3.1. Introduction; 3.2. Evolving Neural Network; 3.2.1. The evolution of connection weights; 3.2.2. The evolution of architecture; 3.2.3. The evolution of node transfer function; 3.2.4. Evolution of learning rules; 3.2.5. Evolution of algorithmic parameters 3.3. Evolving Neural Network using Swarm Intelligence3.3.1. Particle swarm optimization; 3.3.2. Swarm intelligence for evolution of neural network architecture; 3.3.2.1. Particle representation; 3.3.2.2. Fitness evaluation; 3.3.3. Simulation and results; 3.4. Evolving Polynomial Network (EPN) using Swarm Intelligence; 3.4.1. GMDH-type polynomial neural network model; 3.4.2. Evolving polynomial network (EPN) using PSO; 3.4.3. Parameters of evolving polynomial network (EPN); 3.4.3.1. Highest degree of the polynomials; 3.4.3.2. Number of terms in the polynomials 3.4.3.3. Maximum unique features in each term of the polynomials3.4.4. Experimental studies for EPN; 3.5. Summary and Conclusions; References; Chapter 4 DESIGN OF ALLOY STEELS USING MULTI-OBJECTIVE OPTIMIZATION; 4.1. Introduction; 4.2. The Alloy Optimal Design Problem; 4.3. Neurofuzzy Modeling for Mechanical Property Prediction; 4.3.1. General scheme of neurofuzzy models; 4.3.2. Incorporating knowledge into neurofuzzy models; 4.3.3. Property prediction of alloy steels using neurofuzzy models; 4.3.3.1. Tensile strength prediction for heat-treated alloy steels 4.3.3.2. Impact toughness prediction for heat-treated alloy steels |
| Record Nr. | UNINA-9910461625603321 |
| London, : Imperial College Press, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Knowledge mining using intelligent agents [[electronic resource] /] / editors, Satchidananda Dehuri, Sung-Bae Cho
| Knowledge mining using intelligent agents [[electronic resource] /] / editors, Satchidananda Dehuri, Sung-Bae Cho |
| Pubbl/distr/stampa | London, : Imperial College Press, 2011 |
| Descrizione fisica | 1 online resource (400 p.) |
| Disciplina | 006.312 |
| Altri autori (Persone) |
DehuriSatchidananda
ChoSung-Bae |
| Collana | Advances in computer science and engineering: Texts |
| Soggetto topico |
Intelligent agents (Computer software)
Data mining |
| ISBN |
1-283-14333-X
9786613143334 1-84816-387-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
PREFACE; CONTENTS; Chapter 1THEORETICAL FOUNDATIONS OF KNOWLEDGE MINING AND INTELLIGENT AGENT; 1.1. Knowledge and Agent; 1.2. Knowledge Mining from Databases; 1.2.1. KMD tasks; 1.2.1.1. Mining Association Rules; 1.2.1.2. Classification; 1.2.1.3. Clustering; 1.2.1.4. Dependency Modeling; 1.2.1.5. Change and Deviation Detection; 1.2.1.6. Regression; 1.2.1.7. Summarization; 1.2.1.8. Causation Modeling; 1.3. Intelligent Agents; 1.3.1. Evolutionary computing; 1.3.2. Swarm intelligence; 1.3.2.1. Particle Swarm Optimization; 1.3.2.2. Ant Colony Optimization (ACO)
1.3.2.3. Artificial Bee Colony (ABC)1.3.2.4. Artificial Wasp Colony (AWC); 1.3.2.5. Artificial Termite Colony (ATC); 1.4. Summary; References; Chapter 2 THE USE OF EVOLUTIONARY COMPUTATION IN KNOWLEDGE DISCOVERY: THE EXAMPLE OF INTRUSION DETECTION SYSTEMS; 2.1. Introduction; 2.2. Background; 2.2.1. Knowledge discovery and data mining; 2.2.2. Evolutionary computation; 2.2.3. Intrusion detection systems; 2.3. The Role of Evolutionary Computation in KDD; 2.3.1. Feature selection; 2.3.2. Classification; 2.3.2.1. Representation; 2.3.2.2. Learning approaches; 2.3.2.3. Rule discovery 2.3.3. Regression2.3.4. Clustering; 2.3.5. Comparison between classification and regression; 2.4. Evolutionary Operators and Niching; 2.4.1. Evolutionary operators; 2.4.2. Niching; 2.5. Fitness Function; 2.6. Conclusions and Future Directions; Acknowledgment; References; Chapter 3 EVOLUTION OF NEURAL NETWORK AND POLYNOMIAL NETWORK; 3.1. Introduction; 3.2. Evolving Neural Network; 3.2.1. The evolution of connection weights; 3.2.2. The evolution of architecture; 3.2.3. The evolution of node transfer function; 3.2.4. Evolution of learning rules; 3.2.5. Evolution of algorithmic parameters 3.3. Evolving Neural Network using Swarm Intelligence3.3.1. Particle swarm optimization; 3.3.2. Swarm intelligence for evolution of neural network architecture; 3.3.2.1. Particle representation; 3.3.2.2. Fitness evaluation; 3.3.3. Simulation and results; 3.4. Evolving Polynomial Network (EPN) using Swarm Intelligence; 3.4.1. GMDH-type polynomial neural network model; 3.4.2. Evolving polynomial network (EPN) using PSO; 3.4.3. Parameters of evolving polynomial network (EPN); 3.4.3.1. Highest degree of the polynomials; 3.4.3.2. Number of terms in the polynomials 3.4.3.3. Maximum unique features in each term of the polynomials3.4.4. Experimental studies for EPN; 3.5. Summary and Conclusions; References; Chapter 4 DESIGN OF ALLOY STEELS USING MULTI-OBJECTIVE OPTIMIZATION; 4.1. Introduction; 4.2. The Alloy Optimal Design Problem; 4.3. Neurofuzzy Modeling for Mechanical Property Prediction; 4.3.1. General scheme of neurofuzzy models; 4.3.2. Incorporating knowledge into neurofuzzy models; 4.3.3. Property prediction of alloy steels using neurofuzzy models; 4.3.3.1. Tensile strength prediction for heat-treated alloy steels 4.3.3.2. Impact toughness prediction for heat-treated alloy steels |
| Record Nr. | UNINA-9910789404803321 |
| London, : Imperial College Press, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Knowledge mining using intelligent agents [[electronic resource] /] / editors, Satchidananda Dehuri, Sung-Bae Cho
| Knowledge mining using intelligent agents [[electronic resource] /] / editors, Satchidananda Dehuri, Sung-Bae Cho |
| Pubbl/distr/stampa | London, : Imperial College Press, 2011 |
| Descrizione fisica | 1 online resource (400 p.) |
| Disciplina | 006.312 |
| Altri autori (Persone) |
DehuriSatchidananda
ChoSung-Bae |
| Collana | Advances in computer science and engineering: Texts |
| Soggetto topico |
Intelligent agents (Computer software)
Data mining |
| ISBN |
1-283-14333-X
9786613143334 1-84816-387-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
PREFACE; CONTENTS; Chapter 1THEORETICAL FOUNDATIONS OF KNOWLEDGE MINING AND INTELLIGENT AGENT; 1.1. Knowledge and Agent; 1.2. Knowledge Mining from Databases; 1.2.1. KMD tasks; 1.2.1.1. Mining Association Rules; 1.2.1.2. Classification; 1.2.1.3. Clustering; 1.2.1.4. Dependency Modeling; 1.2.1.5. Change and Deviation Detection; 1.2.1.6. Regression; 1.2.1.7. Summarization; 1.2.1.8. Causation Modeling; 1.3. Intelligent Agents; 1.3.1. Evolutionary computing; 1.3.2. Swarm intelligence; 1.3.2.1. Particle Swarm Optimization; 1.3.2.2. Ant Colony Optimization (ACO)
1.3.2.3. Artificial Bee Colony (ABC)1.3.2.4. Artificial Wasp Colony (AWC); 1.3.2.5. Artificial Termite Colony (ATC); 1.4. Summary; References; Chapter 2 THE USE OF EVOLUTIONARY COMPUTATION IN KNOWLEDGE DISCOVERY: THE EXAMPLE OF INTRUSION DETECTION SYSTEMS; 2.1. Introduction; 2.2. Background; 2.2.1. Knowledge discovery and data mining; 2.2.2. Evolutionary computation; 2.2.3. Intrusion detection systems; 2.3. The Role of Evolutionary Computation in KDD; 2.3.1. Feature selection; 2.3.2. Classification; 2.3.2.1. Representation; 2.3.2.2. Learning approaches; 2.3.2.3. Rule discovery 2.3.3. Regression2.3.4. Clustering; 2.3.5. Comparison between classification and regression; 2.4. Evolutionary Operators and Niching; 2.4.1. Evolutionary operators; 2.4.2. Niching; 2.5. Fitness Function; 2.6. Conclusions and Future Directions; Acknowledgment; References; Chapter 3 EVOLUTION OF NEURAL NETWORK AND POLYNOMIAL NETWORK; 3.1. Introduction; 3.2. Evolving Neural Network; 3.2.1. The evolution of connection weights; 3.2.2. The evolution of architecture; 3.2.3. The evolution of node transfer function; 3.2.4. Evolution of learning rules; 3.2.5. Evolution of algorithmic parameters 3.3. Evolving Neural Network using Swarm Intelligence3.3.1. Particle swarm optimization; 3.3.2. Swarm intelligence for evolution of neural network architecture; 3.3.2.1. Particle representation; 3.3.2.2. Fitness evaluation; 3.3.3. Simulation and results; 3.4. Evolving Polynomial Network (EPN) using Swarm Intelligence; 3.4.1. GMDH-type polynomial neural network model; 3.4.2. Evolving polynomial network (EPN) using PSO; 3.4.3. Parameters of evolving polynomial network (EPN); 3.4.3.1. Highest degree of the polynomials; 3.4.3.2. Number of terms in the polynomials 3.4.3.3. Maximum unique features in each term of the polynomials3.4.4. Experimental studies for EPN; 3.5. Summary and Conclusions; References; Chapter 4 DESIGN OF ALLOY STEELS USING MULTI-OBJECTIVE OPTIMIZATION; 4.1. Introduction; 4.2. The Alloy Optimal Design Problem; 4.3. Neurofuzzy Modeling for Mechanical Property Prediction; 4.3.1. General scheme of neurofuzzy models; 4.3.2. Incorporating knowledge into neurofuzzy models; 4.3.3. Property prediction of alloy steels using neurofuzzy models; 4.3.3.1. Tensile strength prediction for heat-treated alloy steels 4.3.3.2. Impact toughness prediction for heat-treated alloy steels |
| Record Nr. | UNINA-9910808832503321 |
| London, : Imperial College Press, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Practical applications of soft computing in engineering [[electronic resource] /] / editor, Sung-Bae Cho
| Practical applications of soft computing in engineering [[electronic resource] /] / editor, Sung-Bae Cho |
| Pubbl/distr/stampa | Singapore ; ; River Edge, NJ, : World Scientific, c2001 |
| Descrizione fisica | 1 online resource (439 p.) |
| Disciplina | 620.0042028563 |
| Altri autori (Persone) | ChoSung-Bae |
| Collana | FLSI soft computing series |
| Soggetto topico |
Soft computing
Computer-aided engineering |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-95179-X
9786611951795 981-281-028-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents ; Series Editor's Preface (To Yoon-Bin and Joon-Hee) ; Volume Editor's Preface ; Chapter 1 Automatic Detection of Microcalcifications in Mammograms Using a Fuzzy Classifier ; 1.1 Introduction ; 1.2 Background Study ; 1.3 Object classification
1.4 Uncertainty in medical imaging and fuzzy set theory 1.5 Applications of fuzzy theory in image understanding ; 1.6 Fuzzy microcalcification detector ; 1.7 Overview of the fuzzy method ; 1.8 Fuzzy detection ; 1.9 Validation of the fuzzy microcalcification detector 1.10 Experimental results 1.11 Conclusions ; Chapter 2 Software Deployability Control System: Application of Choquet Integral and Rough Sets ; 2.1 Introduction ; 2.2 Basic Approach to Controlling Software Cost ; 2.3 Fuzzy Sets: Basic Concepts ; 2.4 Rough Sets: Basic Concepts 2.5 Petri Net Model of Cost Estimation Process 2.6 Example Software Cost Estimation ; 2.7 Concluding Remarks ; Chapter 3 Predictive Fuzzy Model for Control of an Artificial Muscle ; 3.1 Introduction ; 3.2 APM Analytical Model and Control Law synthesis ; 3.3 APM Predictive Fuzzy Model 3.4 Computer Simulation 3.5 Conclusions ; References ; Chapter 4 Fuzzy Supervisory Control with Fuzzy-PID Controller and its Application to Petroleum Plants ; 1.1 Introduction ; 1.2 Process Description and Control Problems ; 1.3 Proposed Hierarchical Control System 1.4 Control Result of Fuzzy-PID Controller with Supervisor in Actual Plant |
| Record Nr. | UNINA-9910454400103321 |
| Singapore ; ; River Edge, NJ, : World Scientific, c2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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