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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 |
9783642296949
3642296947 |
| 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 |
9783319021416
3319021419 |
| 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 Mining and Information Security : Proceedings of ICDMIS 2024, Volume 1 / / edited by Abhishek Bhattacharya, Soumi Dutta, Sheng-Lung Peng, Xin She Yang
| Data Mining and Information Security : Proceedings of ICDMIS 2024, Volume 1 / / edited by Abhishek Bhattacharya, Soumi Dutta, Sheng-Lung Peng, Xin She Yang |
| Edizione | [1st ed. 2026.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2026 |
| Descrizione fisica | 1 online resource (XVIII, 696 p. 278 illus., 241 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Computational intelligence
Artificial intelligence Data mining Data protection Computational Intelligence Artificial Intelligence Data Mining and Knowledge Discovery Data and Information Security |
| ISBN |
981-9660-46-7
9789819660469 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | GPU-Accelerated Monte Carlo Simulations for Real-time Financial Risk Analysis -- Prevention of cyberattacks on Personal Identifiable Information in Large Language Models using Data Anonymization Techniques along with Encryption -- Insights in the Data: Ensuring Quality in Healthcare Big Data -- Multimodal Biometric Homomorphic Encryption in Wavelet Domain -- Ethical AI in Smart Agriculture 4.0: A Consortium of IoT and AI for Sustainable Farmin. |
| Record Nr. | UNINA-9911114534603321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data Science and Big Data Analytics : Proceedings of IDBA 2025, Volume 2
| Data Science and Big Data Analytics : Proceedings of IDBA 2025, Volume 2 |
| Autore | Mishra Durgesh |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2025 |
| Descrizione fisica | 1 online resource (741 pages) |
| Disciplina | 006.312 |
| Altri autori (Persone) |
YangXin-She
UnalAynur JatDharm Singh |
| Collana | Learning and Analytics in Intelligent Systems Series |
| Soggetto topico |
COMPUTERS / Artificial Intelligence / General
COMPUTERS / Database Administration & Management MATHEMATICS / Probability & Statistics / General |
| ISBN | 3-032-05373-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Learning and Analytics in Intelligent Systems -- Data Science and Big Data Analytics -- Preface -- Contents -- Congestion Control Approach for Bandwidth Utilization and Delay Minimization in VANETs -- Oracle APEX for Healthcare: Enhancing Electronic Health Record Integration and Patient Data Management -- Self-Healing Cloud Infrastructure: Leveraging AI for Fault Detection and Recovery -- Predictive Analytics for Cataract Surgical Outcomes: A Machine Learning Approach -- Care Tracker: Digital Management for Healthcare Using Google Cloud -- Counter Hijacking Simulation System for Sky Marshalling Operations -- Machine Learning for Decision Tree Malware Detection System in Mobile Ad-Hoc Network -- Analysis of Water Quality Using Satellite Imagery and GIS Technology Using Cloud Computing -- Load Optimization Using Dynamic Workload Distribution of Cloud Storage -- Lung Cancer Prediction Through Using CNN and Xception Based Transfer Learning -- Destinai: An AI-Powered Personalized Travel Planning System -- EcoTrack: Smart Waste Management and Community Engagement for a Sustainable Future -- Enhancing Railway Safety Using Zigbee and IoT-Enabled Energy-Efficient Wireless Sensor Network Approach -- Implementation of Memristor Based 8T SRAM Cells for Optimizing Noise Voltage Reliability -- Hierarchical Temporal Memory and Its Comparative Study with Neural Networks -- Automated Subparametric Mesh-Based Optical Flow Estimation and Video Quality Enhancement -- Building Resilient Cryptocurrency Ecosystems: Lessons from Cyberattacks and the Role of Blockchain and AI -- Eco-Engine: The Environmental Impact and Carbon Sequestration Estimation System using NLP -- Enhancing Student Learning Through a Web Based Peer Tutoring Platform: A Technological Approach to Academic Support (StutorPal) -- An Intelligent and Fast YouTube Video Content Summarizer.
Optimizing Candidate Selection: Machine Learning for Predicting Election Ticket Allocation and Damage Control -- Predicting System Failures Using Machine Learning: An Analytical Comparison -- Decentralised Employee Training Recommendation Systems with Federated Learning -- Decentralized Blockchain Wallet for Secure Transaction Using Internet Control Protocol -- Revolutionizing Retail: Automated Grocery Billing and Verification via YOLO-V7 -- Interactive Gloves for Real-Time Gesture Translation -- One-Stop Crime Solution: AI-Based Platform for Predictions of Potential Crime Occurrence -- Dual Trust for Probe Attack Detection and Security Technique to Wedge Malicious Continuous Prevalence in MANET -- SprechX: A Decentralized Social Media Platform for Transparent and Empowered Public Discourse -- Exploring Context-Aware Financial Advisory Systems: Insights into Sentiment Analysis and Personalized Investment Strategies -- A Comprehensive Review on Adaptive Ensemble Serial Cascade Convolution Networks for Early-Stage Heart Disease Prediction -- Gen AI-Driven AI Threats: Enhancing Cyber Security in Military Cyber-Physical Systems -- Performances of Progressive Ensemble Machine Learning Models for Alzheimer Disease Prediction -- Blood Cancer Detection Using LSTM from Microscopic Blood Images -- Leveraging Ensemble Technique for Optimal Asset Liability Management: Evidence from an Indian Bank -- Evaluating Machine Learning Approaches for Predicting Mental Health Among Working Professionals -- Enhancing Telemedicine Services Through AI, Blockchain, and Cloud Computing Integration -- RETRACTED CHAPTER: AI-Enabled Intelligent Transportation Systems for Optimizing Internet of Vehicles (IoV) Performance -- Disruptive Technology: Thriving Strategies and Dynamics of Breeze Changes. Innovative Approach to Enhancing Through Heterogeneous Context-Aware Graph Convolutional Networks for Accurate Ripeness Detection and Yield Estimation with Comprehensive Performance Metrics Evaluation -- Cyber Resilience Strategies Against Ransomware in SCADA Systems for Oil and Gas Operations -- AI-Driven Continuous Authentication: Integrating Deep Learning with Multimodal Biometrics for Enhanced Identity Verification -- Autonomous AI Agents for Identity Governance: Enhancing Financial Security Through Intelligent Insider Threat Detection and Compliance Enforcement -- A Comparative Analysis of Generative AI Models Based on Empirical Evaluation for Video Generation from Structured Scripts -- Is Indian HRM Landscape Ready to Implement AI in Recruiting Leadership Positions? A Quantitative Analysis -- Retraction Note to: AI-Enabled Intelligent Transportation Systems for Optimizing Internet of Vehicles (IoV) Performance -- Author Index. |
| Record Nr. | UNINA-9911028742603321 |
Mishra Durgesh
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| Cham : , : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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