Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Advances in machine learning for big data analysis / / Satchidananda Dehuri, Yen-Wei Chen, editors
| Advances in machine learning for big data analysis / / Satchidananda Dehuri, Yen-Wei Chen, editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (xix, 239 pages) : illustrations (some color), charts |
| Disciplina | 780 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico |
Computational intelligence
Machine learning Artificial intelligence - Data processing Big data |
| ISBN |
981-16-8929-6
981-16-8930-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Multi-objective ant colony optimization : an updated review of approaches and applications -- 2. Cost-effective detection of cyber physical system attacks -- 3. A prognostic approach to crime analysis -- 4. A counter-based profiling scheme for improving locality through data and reducer placement -- 5. Hybridization of the higher order neural networks with the evolutionary optimization algorithms--an application to financial time series forecasting -- 6. Supply chain management (SCM) : employing various big data and metaheuristic strategies -- 7. Value of random vector functional link neural networks in software development effort estimation -- 8. Hybrid approach to prevent accidents at railway : an assimilation of big data, IoT and cloud -- 9. Hybrid decision tree for machine learning : a big data perspective. |
| Record Nr. | UNINA-9910743352903321 |
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Biologically inspired techniques in many criteria decision making : proceedings of BITMDM 2021 / / edited by Satchidananda Dehuri [and three others]
| Biologically inspired techniques in many criteria decision making : proceedings of BITMDM 2021 / / edited by Satchidananda Dehuri [and three others] |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (718 pages) |
| Disciplina | 658.403 |
| Collana | Smart Innovation, Systems and Technologies |
| Soggetto topico |
Bioinformatics
Artificial intelligence Data mining |
| ISBN |
981-16-8738-2
981-16-8739-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910743225103321 |
| Gateway East, Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Biologically Inspired Techniques in Many-Criteria Decision Making : 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 : 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 | ||
| ||
Cloud Computing for Optimization: Foundations, Applications, and Challenges / / edited by Bhabani Shankar Prasad Mishra, Himansu Das, Satchidananda Dehuri, Alok Kumar Jagadev
| Cloud Computing for Optimization: Foundations, Applications, and Challenges / / edited by Bhabani Shankar Prasad Mishra, Himansu Das, Satchidananda Dehuri, Alok Kumar Jagadev |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (467 pages) |
| Disciplina | 004.6782 |
| Collana | Studies in Big Data |
| Soggetto topico |
Computational intelligence
Artificial intelligence Big data Computational Intelligence Artificial Intelligence Big Data |
| ISBN | 3-319-73676-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Nature Inspired Optimizations in Cloud Computing: Applications and Challenges.- Resource Allocation in Cloud Computing Using OptimizationTechniques.- Energy Aware Resource Allocation Model for IaaS Optimization.- A Game Theoretic Model for Cloud Federation -- Resource Provisioning Strategy for Scientific Workflows in Cloud Computing Environment -- Consolidation in Cloud Environment Using Optimization Techniques. |
| Record Nr. | UNINA-9910739444303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational Intelligence for Big Data Analysis : Frontier Advances and Applications / / edited by D.P. Acharjya, Satchidananda Dehuri, Sugata Sanyal
| Computational Intelligence for Big Data Analysis : Frontier Advances and Applications / / edited by D.P. Acharjya, Satchidananda Dehuri, Sugata Sanyal |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (276 p.) |
| Disciplina | 620.00151 |
| Collana | Adaptation, Learning, and Optimization |
| Soggetto topico |
Computational intelligence
Data mining Artificial intelligence Computational Intelligence Data Mining and Knowledge Discovery Artificial Intelligence |
| ISBN | 3-319-16598-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | “Atrain Distributed System” (ADS) : An Infinitely Scalable Architecture for Processing Big Data of any 4Vs -- “Atrain Distributed System” (ADS) : An Infinitely Scalable Architecture for Processing Big Data of any 4Vs -- Learning Using Hybrid Intelligence Techniques -- Neutrosophic Sets and its Applications to Decision Making -- An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis -- Self Organizing Migrating Algorithm with Nelder Mead Crossover and Log-Logisti Mutation for Large Scale Optimization -- A Spectrum of Big Data Applications for Data Analytics -- Fundamentals of Brain Signals and its Medical Application Using Data Analysis Techniques -- BigData: Processing of Data Intensive Applications on Cloud -- Framework for Supporting Heterogenous Clouds using Model Driven Approach -- Cloud based Big Data Analytics:WAN Optimization Techniques and Solutions -- Cloud Based E-Governance Solution: A Case Study. |
| Record Nr. | UNINA-9910299836203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| 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) |
| 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 / / Satchidananda Dehuri, Susmita Ghosh, Sung-bae Cho, editors
| Integration of swarm intelligence and artificial neutral network / / Satchidananda Dehuri, Susmita Ghosh, Sung-bae Cho, editors |
| Edizione | [1st ed.] |
| 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 | ||
| ||
A Journey Towards Bio-inspired Techniques in Software Engineering / / edited by Jagannath Singh, Saurabh Bilgaiyan, Bhabani Shankar Prasad Mishra, Satchidananda Dehuri
| A Journey Towards Bio-inspired Techniques in Software Engineering / / edited by Jagannath Singh, Saurabh Bilgaiyan, Bhabani Shankar Prasad Mishra, Satchidananda Dehuri |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (viii, 210 pages) : illustrations |
| Disciplina | 660.6 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico |
Computational intelligence
Software engineering Computational Intelligence Software Engineering |
| ISBN | 3-030-40928-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910483671703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| 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 | ||
| ||
