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Advances in Machine Learning and Computational Intelligence : Proceedings of ICMLCI 2019 / / edited by Srikanta Patnaik, Xin-She Yang, Ishwar K. Sethi
| Advances in Machine Learning and Computational Intelligence : Proceedings of ICMLCI 2019 / / edited by Srikanta Patnaik, Xin-She Yang, Ishwar K. Sethi |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (XVIII, 878 p. 416 illus., 289 illus. in color.) |
| Disciplina | 006.31 |
| Collana | Algorithms for Intelligent Systems |
| Soggetto topico |
Engineering mathematics
Engineering - Data processing Artificial intelligence Mathematical and Computational Engineering Applications Artificial Intelligence |
| ISBN | 981-15-5243-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1: Modeling & Optimization -- Chapter 1. Intrusion Detection using a Hybrid Sequential Model -- Chapter 2. Simulation and Analysis of the PV Arrays Connected to Buck-Boost converters using MPPT Technique by Implementing Incremental Conductance Algorithm and Integral Controller -- Chapter 3. A New Congestion Control Algorithm for SCTP -- Chapter 4. RGNet: The Novel Framework to Model Linked Research Gate Information into Network using Hierarchical Data Rendering -- Chapter 5. A New Approach for Momentum Particle Swarm Optimization -- Chapter 6. Neural Networks Modeling Based On Recent Global Optimization Techniques -- Part 2: Part 2 - Machine Learning Techniques -- Chapter 7. Network Intrusion Detection Model Using One Class Support Vector Machine -- Chapter 8. Query Performance Analysis Tool for Distributed Systems -- Chapter 9. A Robust Multiple Moving Vehicle Tracking for Intelligent Transportation System -- Chapter 10. Bug Priority Assessment in Cross Project context using Entropy based Measure -- Chapter 11. Internet of Things Security using Machine Learning -- Chapter 12. Churn Prediction and Retention in Banking, Telecom and IT Sector using Machine Learning Techniques. . |
| Record Nr. | UNINA-9910483077103321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence, evolutionary computing and metaheuristics : in the footsteps of Alan Turing / / Xin- She Yang (ed.)
| Artificial intelligence, evolutionary computing and metaheuristics : in the footsteps of Alan Turing / / Xin- She Yang (ed.) |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Berlin ; ; Heidelberg, : Springer, c2013 |
| Descrizione fisica | 1 online resource (XX, 796 p.) |
| Disciplina | 006.3 |
| Altri autori (Persone) | YangXin-She |
| Collana | Studies in computational intelligence |
| Soggetto topico |
Artificial intelligence
Evolutionary computation Computer algorithms |
| ISBN | 3-642-29694-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | From the content: Turing Test as a Defining Feature of AI-Completeness -- Artificial Intelligence Evolved from Random Behaviour -- Turing: Then, Now and Still Key -- Imitation Programming Unorganised Machines -- Towards Machine Equivalent Consciousness -- Multicriteria Models for Learning Ordinal Data: a literature review -- Diophantine and Lattice Cryptanalysis of the RSA Cryptosystem -- Artificial Intelligence Methods in Early Childhood Education. |
| Record Nr. | UNINA-9910437916403321 |
| Berlin ; ; Heidelberg, : Springer, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian inference in the social sciences / / editors, Ivan Jeliazkov, Xin-She Yang
| Bayesian inference in the social sciences / / editors, Ivan Jeliazkov, Xin-She Yang |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley, , [2014] |
| Descrizione fisica | 1 online resource (352 p.) |
| Disciplina | 519.5/42 |
| Soggetto topico |
Social sciences - Statistical methods
Bayesian statistical decision theory |
| ISBN |
1-118-77112-5
1-118-77105-2 1-118-77118-4 |
| Classificazione | MAT029010SOC027000BUS021000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: List of Figures iii 1 Bayesian Analysis of Dynamic Network Regression with Joint Edge/Vertex Dynamics 1 Zack W. Almquist and Carter T. Butts 1.1 Introduction 2 1.2 Statistical Models for Social Network Data 2 1.3 Dynamic Network Logistic Regression with Vertex Dynamics 11 1.4 Empirical Examples and Simulation Analysis 14 1.5 Discussion 29 1.6 Conclusion 30 2 Ethnic Minority Rule and Civil War: A Bayesian Dynamic Multilevel Analysis 39 Xun Pang 2.1 Introduction: Ethnic Minority Rule and Civil War 40 2.2 EMR: Grievance and Opportunities of Rebellion 41 2.3 Bayesian GLMM-AR(p) Model 42 2.4 Variables, Model and Data 47 2.5 Empirical Results and Interpretation 49 2.6 Civil War: Prediction 54 2.7 Robustness Checking: Alternative Measures of EMR 59 2.8 Conclusion 60 References 62 3 Bayesian Analysis of Treatment Effect Models 67 Mingliang Li and Justin L. Tobias 3.1 Introduction 68 3.2 Linear Treatment Response Models Under Normality 69 3.3 Nonlinear Treatment Response Models 73 3.4 Other Issues and Extensions: Non-Normality, Model Selection and Instrument Imperfection 78 3.5 Illustrative Application 84 3.6 Conclusion 89 4 Bayesian Analysis of Sample Selection Models 95 Martijn van Hasselt 4.1 Introduction 95 4.2 Univariate Selection Models 97 4.3 Multivariate Selection Models 101 4.4 Semiparametric Models 111 4.5 Conclusion 114 References 114 5 Modern Bayesian Factor Analysis 117 Hedibert Freitas Lopes 5.1 Introduction 117 5.2 Normal linear factor analysis 119 5.3 Factor stochastic volatility 125 5.4 Spatial factor analysis 128 5.5 Additional developments 133 5.6 Modern non-Bayesian factor analysis 136 5.7 Final remarks 137 6 Estimation of stochastic volatility models with heavy tails and serial dependence 159 Joshua C.C. Chan and Cody Y.L. Hsiao 6.1 Introduction 159 6.2 Stochastic Volatility Model 160 6.3 Moving Average Stochastic Volatility Model 168 6.4 Stochastic Volatility Models with Heavy-Tailed Error Distributions 173 References 178 7 From the Great Depression to the Great Recession: A Modelbased Ranking of U.S. Recessions 181 Rui Liu and Ivan Jeliazkov 7.1 Introduction 181 7.2 Methodology 183 7.3 Results 188 7.4 Conclusions 191 Appendix: Data 192 References 192 8 What Difference Fat Tails Make: A Bayesian MCMC Estimation of Empirical Asset Pricing Models 201 Paskalis Glabadanidis 8.1 Introduction 202 8.2 Methodology 204 8.3 Data 205 8.4 Empirical Results 206 8.5 Concluding Remarks 212 9 Stochastic Search For Price Insensitive Consumers 227 Eric Eisenstat 9.1 Introduction 228 9.2 Random utility models in marketing applications 230 9.3 The censored mixing distribution in detail 234 9.4 Reference price models with price thresholds 240 9.5 Conclusion 244 References 245 10 Hierarchical Modeling of Choice Concentration of US Households 249 Karsten T. Hansen, Romana Khan and Vishal Singh 10.1 Introduction 250 10.2 Data Description 252 10.3 Measures of Choice Concentration 252 10.4 Methodology 254 10.5 Results 256 10.6 Interpreting θ 260 10.7 Decomposing the effects of time, number of decisions and concentration preference 263 10.8 Conclusion 265 References 267 11 Approximate Bayesian inference in models defined through estimating equations 269 11.1 Introduction 269 11.2 Examples 271 11.3 Frequentist estimation 273 11.4 Bayesian estimation 276 11.5 Simulating from the posteriors 281 11.6 Asymptotic theory 283 11.7 Bayesian validity 285 11.8 Application 286 11.9 Conclusions 288 12 Reacting to Surprising Seemingly Inappropriate Results 295 Dale J. Poirier 12.1 Introduction 295 12.2 Statistical Framework 296 12.3 Empirical Illustration 300 12.4 Discussion 301 References 301 13 Identification and MCMC estimation of bivariate probit models w ith partial observability 303 Ashish Rajbhandari 13.1 Introduction 303 13.2 Bivariate Probit Model 305 13.3 Identification in a partially observable model 307 13.4 Monte Carlo Simulations 308 13.5 Bayesian Methodology 309 13.6 Application 312 13.7 Conclusion 315 Chapter Appendix 316 References 317 14 School Choice Effects in Tokyo Metropolitan Area: A Bayesian Spatial Quantile Regression Approach 321 Kazuhiko Kakamu and Hajime Wago 14.1 Introduction 321 14.2 The Model 323 14.3 Posterior Analysis 325 14.4 Empirical Analysis 326 14.5 Conclusions 330. |
| Record Nr. | UNINA-9910132329303321 |
| Hoboken, NJ : , : Wiley, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian inference in the social sciences / / editors, Ivan Jeliazkov, Xin-She Yang
| Bayesian inference in the social sciences / / editors, Ivan Jeliazkov, Xin-She Yang |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley, , [2014] |
| Descrizione fisica | 1 online resource (352 p.) |
| Disciplina | 519.5/42 |
| Soggetto topico |
Social sciences - Statistical methods
Bayesian statistical decision theory |
| ISBN |
1-118-77112-5
1-118-77105-2 1-118-77118-4 |
| Classificazione | MAT029010SOC027000BUS021000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: List of Figures iii 1 Bayesian Analysis of Dynamic Network Regression with Joint Edge/Vertex Dynamics 1 Zack W. Almquist and Carter T. Butts 1.1 Introduction 2 1.2 Statistical Models for Social Network Data 2 1.3 Dynamic Network Logistic Regression with Vertex Dynamics 11 1.4 Empirical Examples and Simulation Analysis 14 1.5 Discussion 29 1.6 Conclusion 30 2 Ethnic Minority Rule and Civil War: A Bayesian Dynamic Multilevel Analysis 39 Xun Pang 2.1 Introduction: Ethnic Minority Rule and Civil War 40 2.2 EMR: Grievance and Opportunities of Rebellion 41 2.3 Bayesian GLMM-AR(p) Model 42 2.4 Variables, Model and Data 47 2.5 Empirical Results and Interpretation 49 2.6 Civil War: Prediction 54 2.7 Robustness Checking: Alternative Measures of EMR 59 2.8 Conclusion 60 References 62 3 Bayesian Analysis of Treatment Effect Models 67 Mingliang Li and Justin L. Tobias 3.1 Introduction 68 3.2 Linear Treatment Response Models Under Normality 69 3.3 Nonlinear Treatment Response Models 73 3.4 Other Issues and Extensions: Non-Normality, Model Selection and Instrument Imperfection 78 3.5 Illustrative Application 84 3.6 Conclusion 89 4 Bayesian Analysis of Sample Selection Models 95 Martijn van Hasselt 4.1 Introduction 95 4.2 Univariate Selection Models 97 4.3 Multivariate Selection Models 101 4.4 Semiparametric Models 111 4.5 Conclusion 114 References 114 5 Modern Bayesian Factor Analysis 117 Hedibert Freitas Lopes 5.1 Introduction 117 5.2 Normal linear factor analysis 119 5.3 Factor stochastic volatility 125 5.4 Spatial factor analysis 128 5.5 Additional developments 133 5.6 Modern non-Bayesian factor analysis 136 5.7 Final remarks 137 6 Estimation of stochastic volatility models with heavy tails and serial dependence 159 Joshua C.C. Chan and Cody Y.L. Hsiao 6.1 Introduction 159 6.2 Stochastic Volatility Model 160 6.3 Moving Average Stochastic Volatility Model 168 6.4 Stochastic Volatility Models with Heavy-Tailed Error Distributions 173 References 178 7 From the Great Depression to the Great Recession: A Modelbased Ranking of U.S. Recessions 181 Rui Liu and Ivan Jeliazkov 7.1 Introduction 181 7.2 Methodology 183 7.3 Results 188 7.4 Conclusions 191 Appendix: Data 192 References 192 8 What Difference Fat Tails Make: A Bayesian MCMC Estimation of Empirical Asset Pricing Models 201 Paskalis Glabadanidis 8.1 Introduction 202 8.2 Methodology 204 8.3 Data 205 8.4 Empirical Results 206 8.5 Concluding Remarks 212 9 Stochastic Search For Price Insensitive Consumers 227 Eric Eisenstat 9.1 Introduction 228 9.2 Random utility models in marketing applications 230 9.3 The censored mixing distribution in detail 234 9.4 Reference price models with price thresholds 240 9.5 Conclusion 244 References 245 10 Hierarchical Modeling of Choice Concentration of US Households 249 Karsten T. Hansen, Romana Khan and Vishal Singh 10.1 Introduction 250 10.2 Data Description 252 10.3 Measures of Choice Concentration 252 10.4 Methodology 254 10.5 Results 256 10.6 Interpreting θ 260 10.7 Decomposing the effects of time, number of decisions and concentration preference 263 10.8 Conclusion 265 References 267 11 Approximate Bayesian inference in models defined through estimating equations 269 11.1 Introduction 269 11.2 Examples 271 11.3 Frequentist estimation 273 11.4 Bayesian estimation 276 11.5 Simulating from the posteriors 281 11.6 Asymptotic theory 283 11.7 Bayesian validity 285 11.8 Application 286 11.9 Conclusions 288 12 Reacting to Surprising Seemingly Inappropriate Results 295 Dale J. Poirier 12.1 Introduction 295 12.2 Statistical Framework 296 12.3 Empirical Illustration 300 12.4 Discussion 301 References 301 13 Identification and MCMC estimation of bivariate probit models w ith partial observability 303 Ashish Rajbhandari 13.1 Introduction 303 13.2 Bivariate Probit Model 305 13.3 Identification in a partially observable model 307 13.4 Monte Carlo Simulations 308 13.5 Bayesian Methodology 309 13.6 Application 312 13.7 Conclusion 315 Chapter Appendix 316 References 317 14 School Choice Effects in Tokyo Metropolitan Area: A Bayesian Spatial Quantile Regression Approach 321 Kazuhiko Kakamu and Hajime Wago 14.1 Introduction 321 14.2 The Model 323 14.3 Posterior Analysis 325 14.4 Empirical Analysis 326 14.5 Conclusions 330. |
| Record Nr. | UNINA-9910825042203321 |
| Hoboken, NJ : , : Wiley, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bio-inspired computation in telecommunications / / edited by Xin-She Yang, Su Fong Chien, Tiew On Ting
| Bio-inspired computation in telecommunications / / edited by Xin-She Yang, Su Fong Chien, Tiew On Ting |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Waltham, Massachusetts : , : Morgan Kaufmann, , 2015 |
| Descrizione fisica | 1 online resource (349 p.) |
| Disciplina | 621.382 |
| Soggetto topico |
Telecommunication
Biologically-inspired computing |
| ISBN |
0-12-801743-0
0-12-801538-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Bio-Inspired Computation in Telecommunications; Copyright ; Contents ; Preface ; List of Contributors ; Chapter 1: Bio-Inspired Computation and Optimization: An Overview; 1.1. Introduction; 1.2. Telecommunications and optimization; 1.3. Key challenges in optimization; 1.3.1. Infinite Monkey Theorem and Heuristicity; 1.3.2. Efficiency of an Algorithm; 1.3.3. How to Choose Algorithms; 1.3.4. Time Constraints; 1.4. Bio-inspired optimization algorithms; 1.4.1. SI-Based Algorithms; 1.4.1.1. Ant and bee algorithms; 1.4.1.2. Bat algorithm; 1.4.1.3. Particle swarm optimization
1.4.1.4. Firefly algorithm1.4.1.5. Cuckoo search; 1.4.2. Non-SI-Based Algorithms; 1.4.2.1. Simulated annealing; 1.4.2.2. Genetic algorithms; 1.4.2.3. Differential evolution; 1.4.2.4. Harmony search; 1.4.3. Other Algorithms; 1.5. Artificial neural networks; 1.5.1. Basic Idea; 1.5.2. Neural Networks; 1.5.3. Back Propagation Algorithm; 1.6. Support vector machine; 1.6.1. Linear SVM; 1.6.2. Kernel Tricks and Nonlinear SVM; 1.7. Conclusions; References; Chapter 2: Bio-Inspired Approaches in Telecommunications; 2.1. Introduction; 2.2. Design problems in telecommunications; 2.3. Green communications 2.3.1. Energy Consumption in Wireless Communications2.3.2. Metrics for Energy Efficiency; 2.3.3. Radio Resource Management; 2.3.4. Strategic Network Deployment; 2.4. Orthogonal frequency division multiplexing; 2.4.1. OFDM Systems; 2.4.2. Three-Step Procedure for Timing and Frequency Synchronization; 2.5. OFDMA model considering energy efficiency and quality-of-service; 2.5.1. Mathematical Formulation; 2.5.2. Results; 2.6. Conclusions; References; Chapter 3: Firefly Algorithm in Telecommunications; 3.1. Introduction; 3.2. Firefly algorithm; 3.2.1. Algorithm Complexity 3.2.2. Variants of Firefly Algorithm3.3. Traffic Characterization; 3.3.1. Network Management Based on Flow Analysis and Traffic Characterization; 3.3.2. Firefly Harmonic Clustering Algorithm; 3.3.3. Results; 3.4. Applications in wireless cooperative networks; 3.4.1. Related Work; 3.4.2. System Model and Problem Statement; 3.4.2.1. Energy and spectral efficiencies; 3.4.2.2. Problem statement; 3.4.3. Dinkelbach Method; 3.4.4. Firefly Algorithm; 3.4.5. Simulations and Numerical Results; 3.5. Concluding remarks; 3.5.1. FA in Traffic Characterization; 3.5.2. FA in Cooperative Networks; References Chapter 4: A Survey of Intrusion Detection Systems Using Evolutionary Computation4.1. Introduction; 4.2. Intrusion detection systems; 4.2.1. IDS Components; 4.2.2. Research Areas and Challenges in Intrusion Detection; 4.3. The method: evolutionary computation; 4.4. Evolutionary computation applications on intrusion detection; 4.4.1. Foundations; 4.4.2. Data Collection; 4.4.3. Detection Techniques and Response; 4.4.3.1. Intrusion detection on conventional networks; 4.4.3.2. Intrusion detection on wireless and resource-constrained networks; 4.4.4. IDS Architecture; 4.4.5. IDS Security 4.4.6. Testing and Evaluation |
| Record Nr. | UNINA-9910787437203321 |
| Waltham, Massachusetts : , : Morgan Kaufmann, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bio-inspired computation in telecommunications / / edited by Xin-She Yang, Su Fong Chien, Tiew On Ting
| Bio-inspired computation in telecommunications / / edited by Xin-She Yang, Su Fong Chien, Tiew On Ting |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Waltham, Massachusetts : , : Morgan Kaufmann, , 2015 |
| Descrizione fisica | 1 online resource (349 p.) |
| Disciplina | 621.382 |
| Soggetto topico |
Telecommunication
Biologically-inspired computing |
| ISBN |
0-12-801743-0
0-12-801538-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Bio-Inspired Computation in Telecommunications; Copyright ; Contents ; Preface ; List of Contributors ; Chapter 1: Bio-Inspired Computation and Optimization: An Overview; 1.1. Introduction; 1.2. Telecommunications and optimization; 1.3. Key challenges in optimization; 1.3.1. Infinite Monkey Theorem and Heuristicity; 1.3.2. Efficiency of an Algorithm; 1.3.3. How to Choose Algorithms; 1.3.4. Time Constraints; 1.4. Bio-inspired optimization algorithms; 1.4.1. SI-Based Algorithms; 1.4.1.1. Ant and bee algorithms; 1.4.1.2. Bat algorithm; 1.4.1.3. Particle swarm optimization
1.4.1.4. Firefly algorithm1.4.1.5. Cuckoo search; 1.4.2. Non-SI-Based Algorithms; 1.4.2.1. Simulated annealing; 1.4.2.2. Genetic algorithms; 1.4.2.3. Differential evolution; 1.4.2.4. Harmony search; 1.4.3. Other Algorithms; 1.5. Artificial neural networks; 1.5.1. Basic Idea; 1.5.2. Neural Networks; 1.5.3. Back Propagation Algorithm; 1.6. Support vector machine; 1.6.1. Linear SVM; 1.6.2. Kernel Tricks and Nonlinear SVM; 1.7. Conclusions; References; Chapter 2: Bio-Inspired Approaches in Telecommunications; 2.1. Introduction; 2.2. Design problems in telecommunications; 2.3. Green communications 2.3.1. Energy Consumption in Wireless Communications2.3.2. Metrics for Energy Efficiency; 2.3.3. Radio Resource Management; 2.3.4. Strategic Network Deployment; 2.4. Orthogonal frequency division multiplexing; 2.4.1. OFDM Systems; 2.4.2. Three-Step Procedure for Timing and Frequency Synchronization; 2.5. OFDMA model considering energy efficiency and quality-of-service; 2.5.1. Mathematical Formulation; 2.5.2. Results; 2.6. Conclusions; References; Chapter 3: Firefly Algorithm in Telecommunications; 3.1. Introduction; 3.2. Firefly algorithm; 3.2.1. Algorithm Complexity 3.2.2. Variants of Firefly Algorithm3.3. Traffic Characterization; 3.3.1. Network Management Based on Flow Analysis and Traffic Characterization; 3.3.2. Firefly Harmonic Clustering Algorithm; 3.3.3. Results; 3.4. Applications in wireless cooperative networks; 3.4.1. Related Work; 3.4.2. System Model and Problem Statement; 3.4.2.1. Energy and spectral efficiencies; 3.4.2.2. Problem statement; 3.4.3. Dinkelbach Method; 3.4.4. Firefly Algorithm; 3.4.5. Simulations and Numerical Results; 3.5. Concluding remarks; 3.5.1. FA in Traffic Characterization; 3.5.2. FA in Cooperative Networks; References Chapter 4: A Survey of Intrusion Detection Systems Using Evolutionary Computation4.1. Introduction; 4.2. Intrusion detection systems; 4.2.1. IDS Components; 4.2.2. Research Areas and Challenges in Intrusion Detection; 4.3. The method: evolutionary computation; 4.4. Evolutionary computation applications on intrusion detection; 4.4.1. Foundations; 4.4.2. Data Collection; 4.4.3. Detection Techniques and Response; 4.4.3.1. Intrusion detection on conventional networks; 4.4.3.2. Intrusion detection on wireless and resource-constrained networks; 4.4.4. IDS Architecture; 4.4.5. IDS Security 4.4.6. Testing and Evaluation |
| Record Nr. | UNINA-9910819902003321 |
| Waltham, Massachusetts : , : Morgan Kaufmann, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering / / edited by Gustavo Mendes Platt, Xin-She Yang, Antônio José Silva Neto
| Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering / / edited by Gustavo Mendes Platt, Xin-She Yang, Antônio José Silva Neto |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (301 pages) |
| Disciplina | 006.3019 |
| Soggetto topico |
Computational intelligence
Mathematical optimization Probabilities Computational Intelligence Discrete Optimization Continuous Optimization Probability Theory and Stochastic Processes |
| ISBN | 3-319-96433-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 01- An Overview of the Use of Metaheuristics in Two Phase Equilibrium Calculation Problems -- Chapter 02- Reliability-based Robust Optimization Applied to Engineering System Design -- Chapter 03- On Initial Populations of Differential Evolution for Practical Optimization Problems -- Chapter 04- Application of Enhanced Particle Swarm Optimization in Euclidean Steiner Tree Problem Solving in RN -- Chapter 05- Rotation-Based Multi-Particle Collision Algorithm with Hooke-Jeeves Approach Applied to the Structural Damage Identification -- Chapter 06- Optimization in Civil Engineering and Metaheuristic Algorithms: a Review of State-of-the-Art Developments -- Chapter 07- A Bioreactor Fault Diagnosis Based on Metaheuristics -- Chapter 08- Optimization of Nuclear Reactors Loading Patterns with Computational Intelligence Methods -- Chapter 09- Inverse Problem of an Anomalous Diffusion Model Employing Lightning Optimization Algorithm -- Chapter 10- Study of the Impact of the Topology of Artificial Neural Networks for the Prediction of Meteorological Data -- Chapter 11- Constructal Design Associated with Genetic Algorithm to Maximize the Performance of H-shaped Isothermal Cavities -- Chapter 12- Co-Design System for Tracking Targets using Template Matching -- Chapter 13- A Hybrid Estimation Scheme Based on the Sequential Importance Resampling Particle Filter and the Particle Swarm Optimization (PSO-SIR) -- Chapter 14- Fault Detection Using Kernel Computational Intelligence Algorithms -- Index. |
| Record Nr. | UNINA-9910484658003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cuckoo Search and Firefly Algorithm : Theory and Applications / / edited by Xin-She Yang
| Cuckoo Search and Firefly Algorithm : Theory and Applications / / edited by Xin-She Yang |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (XI, 360 p. 100 illus., 5 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Optical data processing Computational Intelligence Image Processing and Computer Vision |
| ISBN | 3-319-02141-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | From the Contents: Cuckoo Search and Firefly Algorithm: Overview and Analysis -- On the Randomization Firefly Algorithm -- Cuckoo Search: A Brief Literature Review -- Discrete cuckoo search for travelling salesman problem -- Comparative analysis of the cuckoo search algorithm -- Multilevel Image Processing by Cuckoo Search -- Binary Cuckoo Search -- Training spiking neural models using cuckoo search -- Multi-Objective Optimization of a Real-World Manufacturing Process using Cuckoo Search. |
| Record Nr. | UNINA-9910299485403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data Science and Big Data Analytics : Proceedings of IDBA 2023 / / edited by Durgesh Mishra, Xin She Yang, Aynur Unal, Dharm Singh Jat
| Data Science and Big Data Analytics : Proceedings of IDBA 2023 / / edited by Durgesh Mishra, Xin She Yang, Aynur Unal, Dharm Singh Jat |
| Autore | Mishra Durgesh |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (733 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
YangXin-She
UnalAynur JatDharm Singh |
| Collana | Data-Intensive Research |
| Soggetto topico |
Computational intelligence
Quantitative research Data mining Data protection Computational Intelligence Data Analysis and Big Data Data Mining and Knowledge Discovery Data and Information Security |
| ISBN |
9789819991792
981999179X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Analysing the role of social influence in adoption of technology for student assessment in Indian management institutions -- Plastic and Non-Plastic Waste Classification using Machine Learning Techniques -- Prediction of Customer Satisfaction in E- banking Services through Neural Network Approach -- Role of AI and Machine Learning in Mental Healthcare -- Proposed a mechanism to precise diagnostics in X-ray film using Fuzzy Logic Controller -- Human Gender Classification of Males and Females in a Crowd using Deep Learning Techniques -- Analysis on prediction of crop diseases using TensorFlow with Keras and OpenCV technique of deep learning -- Design of convolutional encoder for multivalued logic -- Improving the efficiency of water quality prediction using the Supertml approach in machine learning -- Probabilistic Data Structure using hashing technique for big data security de- duplication in cloud environment -- Malicious account detection using ANN -- Performance Evaluation of LiCi-2Ultra-lightweight Block Cipher. |
| Record Nr. | UNINA-9910845092903321 |
Mishra Durgesh
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data Science and Big Data Analytics [[electronic resource] ] : ACM-WIR 2018 / / edited by Durgesh Kumar Mishra, Xin-She Yang, Aynur Unal
| Data Science and Big Data Analytics [[electronic resource] ] : ACM-WIR 2018 / / edited by Durgesh Kumar Mishra, Xin-She Yang, Aynur Unal |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (418 pages) |
| Disciplina | 005.7 |
| Collana | Lecture Notes on Data Engineering and Communications Technologies |
| Soggetto topico |
Computational intelligence
Big data Data mining Computer security Computational Intelligence Big Data Data Mining and Knowledge Discovery Systems and Data Security |
| ISBN | 981-10-7641-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Study of the Correlation between Internet Addiction and Aggressive Behavior Among the Namibian University Students -- An efficient model for outlier detection in time series dataset using clustering approach -- Genetic Algorithm Approach for Optimization of Biomass Estimation at LiDAR -- E-ALIVE: An Integrated Platform Based on Machine Learning Techniques to Aware and Educate Common People with the Current Statistics of Maternal and Child Health Care -- An Effective TCP’s Congestion Control Approach for Cross Layer Design in MANET -- A Study on Applying Agile Methodology to Manufacturing Industry Cloud Applications -- Baron-Cohen Model based Personality Classification using Ensemble Learning -- Analysis of Routing Protocols for Large Scale Multihop Multirate MANETs -- Review on Internet Traffic Sharing using Markov Chain Model in Computer Network -- Protein Sequence of Dengue Virus Classification and Secondary Structure Prediction using Random Forest Classifier -- Anomaly detection using Dynamic Sliding Window in Wireless Body Area Networks -- Scalable Privacy preservation in Big Data with Cloud Service Access -- Effective Healthcare Services by IoT based Model of Voluntary Doctors -- Multi Layer Architectures for SQLI Detection and Prevention in Web Application Development -- Emotional State Recognition with EEG signals using Subject Independent Approach -- Development of Early Prediction Model for Epileptic Seizures -- Research Issue in data Anonymization in Electronic Health Service: A survey -- Prediction of Cervical Cancer based on the life style, habits and diseases using Regression Analysis framework -- Novel outlier detection by integration of clustering and classification -- A Study on Benefits of Big Data for Retail Industry -- Protection of User Information by using Modified Data Copy Technique in Data Mining -- Performance Analysis of Traffic at Intersection using Direction Based Clustering in VANET -- Load Balancing using Amazon Cloud Services -- A Review of Wireless Charging Nodes in Wireless Sensor Networks -- Leeway of Lean concept to optimize Bigdata in manufacturing industry: An exploratory review -- NeuroFeedback Guided Learning Style Adaptability Derived from EEG Sensors -- Monitoring Public Participation in Multi-Lateral Initiatives using Social Media Intelligence -- An efficient Context-aware Music Recommendation based on Emotion and Time Context -- Locating and Detecting Nipple for Pornographic Image Identification.-Implementation of Improved Energy Efficient FIR Filter using Reversible Logic -- A Study on benefits of Big data for healthcare sector of India -- Handling Uncertainty in Linguistics using Probability Theory -- Review of Quality of Service based Techniques in Cloud Computing -- Skyline Computation for Big Data -- Available Energy Aware Multipath Routing For Reliable Service Discovery in MANET -- Human Face Detection Enabled Smart Stick for Visually Impaired People -- Web Based Service Recommendation System by Considering User Requirements -- Optimal Energy Conservation for Route Selection to improve in MANET -- Unsupervised Machine Learning for Clustering the Infected Leaves based on the Leaf-colours -- Real Time Big Data Analysis Architecture and Application -- Missing Value Imputation in Medical Records for Remote Healthcare -- Reliable Data Discovery with Two Ray Ground Way on DSR Routing in MANET -- Secure vehicular communication using Road side unit (RSU) trust management scheme -- Recommendation Framework for Diet and Exercise based on Clinical Data: A Systematic Review -- Predictive Models for Recommanding Restaurent System by users own Preference -- Security Assessment of SAODV Protocols in Mobile Adhoc Networks -- Attack Detection and its Analysis in DTN Mobile Ad-hoc network -- Secure Sum Computation using Homomorphic Encryption -- Traffic Analysis in Location Base Routing System in MANET -- Automated Workload management using machine learning -- A survey on Link Recovery in Wireless Mesh Network using resilience scheme -- Multi User Detection in Wireless Networks Using Decision Feedback Signal Cancellation -- ANN Based Predictive State Modelling of Finite State Machines -- Deep dive exploration of mixed reality in the world of Big Data. |
| Record Nr. | UNINA-9910484750103321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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