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.
Artificial Intelligence Techniques for a Scalable Energy Transition : Advanced Methods, Digital Technologies, Decision Support Tools, and Applications / / Moamar Sayed-Mouchaweh, editor
| Artificial Intelligence Techniques for a Scalable Energy Transition : Advanced Methods, Digital Technologies, Decision Support Tools, and Applications / / Moamar Sayed-Mouchaweh, editor |
| Pubbl/distr/stampa | Cham : , : Springer, , [2020] |
| Descrizione fisica | 1 online resource (383 pages) : illustrations |
| Disciplina | 363.70028563 |
| Soggetto topico |
Artificial intelligence - Engineering applications
Power resources - Data processing Electrical engineering Computational intelligence Artificial intelligence Data mining Big data Communications Engineering, Networks Computational Intelligence Artificial Intelligence Data Mining and Knowledge Discovery Big Data/Analytics |
| ISBN |
3-030-42726-9
9783030427269 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Definition, motivation and impact of digitalization in energy transition -- Definition of a general scheme (layers) of a digitalized system in energy transition -- Challenges of digitalization in energy transition -- Artificial Intelligence for energy transition -- General principals and classification of Artificial Intelligence techniques for energy transition -- Artificial Intelligence for Smart Energy Management -- Smart energy management (intrusive and non-intrusive load monitoring) -- Artificial Intelligence for Cyber Security and Privacy -- Artificial Intelligence for Mobility and Electrical Vehicles -- Mobility and electrical vehicles -- Artificial Intelligence for Micro Grid Operations and Dynamic Pricing Revenue Management -- Micro Grid operations and Dynamic Pricing Revenue Management -- Artificial Intelligence for Renewable Energy Penetration and Demand Side Management -- Renewable Energy Penetration and Demand Side Management -- Emerging Trends, Open problems, and Future Challenges -- Conclusion. |
| Record Nr. | UNINA-9910407726003321 |
| Cham : , : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Deep Learning Applications / / M. Arif Wani, Mehmed Kantardzic, Moamar Sayed-Mouchaweh, editors
| Deep Learning Applications / / M. Arif Wani, Mehmed Kantardzic, Moamar Sayed-Mouchaweh, editors |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , [2020] |
| Descrizione fisica | 1 online resource (178 pages) : illustrations |
| Disciplina | 006.31 |
| Collana | Advances in Intelligent Systems and Computing |
| Soggetto topico |
Computational intelligence
Machine learning Big data Control engineering Robotics Mechatronics Computational Intelligence Machine Learning Big Data Control, Robotics, Mechatronics |
| ISBN | 981-15-1816-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Trends in Deep Learning Applications -- Optimization Strategies -- Quasi-Newton Optimization Methods -- Application to Deep Reinforcement Learning -- Medical Image Segmentation using Deep Neural Networks with Pre-trained Encoders -- Enabling Robust and Autonomous Material handling in Logistics through applied Deep Learning Algorithms -- Performance metric -- Dataset creation -- Detecting Work Zones in SHRP2 NDS Videos Using Deep Learning Based Computer Vision -- Deep Learning Framework and Architecture Selection -- Action Recognition in Videos Using Multi-Stream Convolutional Neural Networks -- Ensemble of 3D Densely Connected Convolutional Network for Diagnosis of Mild Cognitive Impairment and Alzheimers disease. |
| Record Nr. | UNINA-9910483362503321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Diagnosability, Security and Safety of Hybrid Dynamic and Cyber-Physical Systems [[electronic resource] /] / edited by Moamar Sayed-Mouchaweh
| Diagnosability, Security and Safety of Hybrid Dynamic and Cyber-Physical Systems [[electronic resource] /] / edited by Moamar Sayed-Mouchaweh |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (x, 327 pages) |
| Disciplina | 004 |
| Soggetto topico |
Electrical engineering
Quality control Reliability Industrial safety Control engineering Computers Communications Engineering, Networks Quality Control, Reliability, Safety and Risk Control and Systems Theory Information Systems and Communication Service |
| ISBN | 3-319-74962-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Prologue.- Wind Turbine Fault Localization: A Practical Application of Model-Based Diagnosis -- Fault detection and localization using Modelica and abductive reasoning -- Robust Data-Driven Fault Detection in Dynamic Process Environments Using Discrete Event Systems -- Critical States Distance Filter Based Approach for Detection and Blockage of Cyberattacks in Industrial Control Systems -- Active diagnosis for switched systems using Mealy machine modeling -- Secure Diagnosability of Hybrid Dynamical Systems -- Diagnosis in Cyber-physical systems with Fault Protection Assemblies -- Passive Diagnosis of Hidden-Mode Switched Affine Models with Detection Guarantees via Model Invalidation -- Diagnosability of Discrete Faults with Uncertain Observations -- Abstractions Refinement for Hybrid Systems Diagnosability Analysis. |
| Record Nr. | UNINA-9910299956203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
ECML PKDD 2018 Workshops [[electronic resource] ] : DMLE 2018 and IoTStream 2018, Dublin, Ireland, September 10-14, 2018, Revised Selected Papers / / edited by Anna Monreale, Carlos Alzate, Michael Kamp, Yamuna Krishnamurthy, Daniel Paurat, Moamar Sayed-Mouchaweh, Albert Bifet, João Gama, Rita P. Ribeiro
| ECML PKDD 2018 Workshops [[electronic resource] ] : DMLE 2018 and IoTStream 2018, Dublin, Ireland, September 10-14, 2018, Revised Selected Papers / / edited by Anna Monreale, Carlos Alzate, Michael Kamp, Yamuna Krishnamurthy, Daniel Paurat, Moamar Sayed-Mouchaweh, Albert Bifet, João Gama, Rita P. Ribeiro |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (IX, 127 p. 43 illus., 27 illus. in color.) |
| Disciplina | 006.31 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Data mining Information storage and retrieval Computer communication systems Artificial Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Computer Communication Networks |
| ISBN | 3-030-14880-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910337569303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Explainable AI within the digital transformation and cyber physical systems : XAI methods and applications / / Moamar Sayed-Mouchaweh, editor
| Explainable AI within the digital transformation and cyber physical systems : XAI methods and applications / / Moamar Sayed-Mouchaweh, editor |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (201 pages) |
| Disciplina | 006.3 |
| Soggetto topico | Artificial intelligence |
| ISBN | 3-030-76409-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Editor -- 1 Prologue: Introduction to Explainable Artificial Intelligence -- 1.1 Explainable Machine Learning -- 1.2 Beyond State-of-the-Art: Contents of the Book -- 1.2.1 Chapter 2: Principles of Explainable Artificial Intelligence -- 1.2.2 Chapter 3: Science of Data: A New Ladder for Causation -- 1.2.3 Chapter 4: Explainable Artificial Intelligence for Predictive Analytics on Customer Turnover -- 1.2.4 Chapter 5: An Efficient Explainable Artificial Intelligence Model of Automatically Generated Summaries Evaluation -- 1.2.5 Chapter 6: On the Transparent Predictive Models for Ecological Momentary Assessment Data -- 1.2.6 Chapter 7: Mitigating the Class Overlap Problem in Discriminative Localization: COVID-19 and Pneumonia Case Study -- 1.2.7 Chapter 8: A Critical Study on the Importance of Feature Selection for Diagnosing Cyber-Attacks in Water Critical Infrastructures -- 1.2.8 Chapter 9: A Study on the Effect of Dimensionality Reduction on Cyber-Attack Identification in Water Storage Tank SCADA Systems -- References -- 2 Principles of Explainable Artificial Intelligence -- 2.1 Introduction -- 2.2 Motivations for XAI -- 2.3 Dimensions of XAI -- 2.4 Explanations and Explanators -- 2.4.1 Single Tree Approximation -- 2.4.2 Rules List and Rules Set -- 2.4.3 Partial Dependency -- 2.4.4 Local Rule-Based Explanation -- 2.4.5 Feature Importance -- 2.4.6 Saliency Maps -- 2.4.7 Prototype-Based Explanations -- 2.4.8 Counterfactual Explanations -- 2.5 Conclusions -- References -- 3 Science of Data: A New Ladder for Causation -- 3.1 Introduction -- 3.2 Related Works -- 3.2.1 Cognitive Architectures -- 3.2.2 Inferential Logic -- 3.2.3 Probabilistic Fuzzy Logic (PFL) -- 3.2.4 Neural Networks (NN) -- 3.2.4.1 Microscopic Neural Network (NN) -- 3.2.4.2 Macroscopic Neural Network (NN) -- 3.2.4.3 Graph Neural Networks.
3.2.4.4 Hybrid Neural Networks for Reasoning -- 3.3 Cognitive Architecture Equipped with PFL and GNNs -- 3.4 Conclusion -- References -- 4 Explainable Artificial Intelligence for Predictive Analytics on Customer Turnover: A User-Friendly Interface for Non-expert Users -- 4.1 Introduction -- 4.2 Background -- 4.2.1 Shapley Values -- 4.2.2 Types of Explanation Techniques -- 4.3 Related Works -- 4.3.1 Shapley Additive Explanations -- 4.3.2 Contrastive Explanations -- 4.3.3 XAI User Interfaces -- 4.4 Our Explainable AI Web Interface -- 4.4.1 Back-End Component -- 4.4.2 Front-End Component -- 4.4.2.1 Home and Expected Loss -- 4.4.2.2 Local Feature Importance -- 4.4.2.3 Global Feature Importance -- 4.4.2.4 Model Recommendation -- 4.5 Evaluation -- 4.6 Conclusions -- References -- 5 An Efficient Explainable Artificial Intelligence Model of Automatically Generated Summaries Evaluation: A Use Case of Bridging Cognitive Psychology and Computational Linguistics -- 5.1 Introduction -- 5.1.1 Automatic Text Summarization -- 5.1.2 Evaluation Protocols of Automatically Generated Text Summaries -- 5.1.3 Cognitive Psychology Models for Text Comprehension -- 5.1.3.1 The Resonance Model -- 5.1.3.2 The Landscape Model -- 5.1.3.3 The Langston and Trabasso Model -- 5.1.3.4 The Construction-Integration Model -- 5.1.3.5 The Predication Model -- 5.1.3.6 The Gestalt Models -- 5.1.3.7 The Golden and Rumelhart Model -- 5.1.3.8 The Distributed Situation Space Model -- 5.1.3.9 The Structure Building Model -- 5.1.4 Originality of Our Work -- 5.2 CATSE: A Cognitive Automatic Text Summarization Evaluation Protocol -- 5.2.1 The Main Idea -- 5.2.2 Levels of Representation -- 5.2.2.1 The Surface Level -- 5.2.2.2 The Intermediate Level: The Textbase -- 5.2.2.3 The Cognitive Level: The Situation Model -- 5.2.3 The CATSE Protocol -- 5.2.3.1 The Construction Phase. 5.2.3.2 The Integration Phase -- 5.3 Experiments and Results -- 5.3.1 Datasets -- 5.3.2 Experimental Results -- 5.4 Conclusion -- References -- 6 On the Transparent Predictive Models for Ecological Momentary Assessment Data -- 6.1 Introduction -- 6.1.1 Ecological Momentary Assessment (EMA) -- 6.1.2 Classification of EMA Data -- 6.1.3 Model Transparency -- 6.2 Dataset -- 6.3 Analysis Methods -- 6.3.1 Classification Settings -- 6.3.2 Tools -- 6.3.2.1 Own Pipeline: Model Training and Testing -- 6.3.3 Experiments -- 6.3.4 Model Interpretation and Analysis -- 6.3.4.1 Analysis of Categorical Features -- 6.3.4.2 Analysis of Continuous Features -- 6.3.4.3 Interpretation of the Resulting Values -- 6.3.4.4 Model Comparison -- 6.4 Results -- 6.4.1 Experiments with KNIME Analytics Platform -- 6.4.2 Experiments with Own Pipeline -- 6.4.3 Model Interpretation and Analysis -- 6.5 Discussion -- 6.5.1 Is Personalization Always Necessary? -- 6.5.2 Model Similarity -- 6.5.3 Model Validation -- 6.5.4 EMA with Other Features -- 6.5.5 Personalization of Class Labels -- 6.5.6 Method Limitations -- 6.6 Conclusion -- References -- 7 Mitigating the Class Overlap Problem in Discriminative Localization: COVID-19 and Pneumonia Case Study -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Discriminative Localization -- 7.3.1 Class Activation Maps -- 7.3.2 Saliency Maps with Backpropagation -- 7.3.3 Amplified Directed Divergence with Ensembles -- 7.3.4 Scaled Directed Divergence (SDD) -- 7.4 Experiments -- 7.4.1 Method -- 7.4.2 COVID-19 and Pneumonia Data -- 7.4.3 COVID-19 AND Pneumonia Classifier -- 7.4.4 Scaled Directed Divergence with Natural Imagery -- 7.4.5 Scaled Directed Divergence Applied to Chest X-rays -- 7.5 Discussion -- 7.6 Conclusion -- References -- 8 A Critical Study on the Importance of Feature Selection for Diagnosing Cyber-Attacks in Water Critical Infrastructures. 8.1 Introduction -- 8.2 Background -- 8.2.1 Infinite Feature Selection -- 8.2.2 Infinite Latent Feature Selection -- 8.2.3 Evolutionary Computation Feature Selection -- 8.2.4 Relief Feature Selection -- 8.2.5 Mutual Information -- 8.2.6 Maximum Relevance and Minimum Redundancy -- 8.2.7 Feature Selection via Concave Minimization -- 8.2.8 Laplacian Score -- 8.2.9 Multi-Cluster Feature Selection -- 8.2.10 Recursive Feature Elimination -- 8.2.11 L0-Norm -- 8.2.12 Fisher Score -- 8.3 Design of Intrusion Detection System -- 8.3.1 Data Collection -- 8.3.2 Decision-Making -- 8.4 Experimental Results -- 8.4.1 Experimental Setting -- 8.4.2 Results Analysis -- 8.4.3 Feature Analysis -- 8.5 Conclusion -- References -- 9 A Study on the Effect of Dimensionality Reduction on Cyber-Attack Identification in Water Storage Tank SCADASystems -- 9.1 Introduction -- 9.2 Background -- 9.2.1 Principal Component Analysis -- 9.2.2 Factor Analysis -- 9.2.3 Confirmatory Factor Analysis -- 9.2.4 Multidimensional Scaling -- 9.2.5 Linear Discriminant Analysis -- 9.2.6 Isomap -- 9.2.7 Semantic Mapping -- 9.2.8 Probabilistic Principal Component Analysis -- 9.2.9 Locally Linear Embedding -- 9.2.10 Laplacian Eigenmaps -- 9.2.11 Landmark Isomap -- 9.2.12 Hessian-based Locally Linear Embedding -- 9.2.13 Local Tangent Space Alignment -- 9.2.14 Kernel Principal Component Analysis -- 9.2.15 Generalized Discriminant Analysis -- 9.2.16 Neighborhood Preserving Embedding -- 9.2.17 Locality Preserving Projections -- 9.2.18 Diffusion Maps -- 9.2.19 Locally Linear Coordination -- 9.2.20 Manifold Charting -- 9.2.21 Large Margin Nearest Neighbor -- 9.2.22 Independent Component Analysis -- 9.3 Design of Intrusion Detection System -- 9.3.1 Data Collection -- 9.3.2 Decision Making -- 9.4 Experimental Results -- 9.4.1 Experiment Setting -- 9.4.2 Results Analysis -- 9.5 Conclusion -- References -- Index. |
| Record Nr. | UNISA-996464448303316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Explainable AI within the digital transformation and cyber physical systems : XAI methods and applications / / Moamar Sayed-Mouchaweh, editor
| Explainable AI within the digital transformation and cyber physical systems : XAI methods and applications / / Moamar Sayed-Mouchaweh, editor |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (201 pages) |
| Disciplina | 006.3 |
| Soggetto topico | Artificial intelligence |
| ISBN | 3-030-76409-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Editor -- 1 Prologue: Introduction to Explainable Artificial Intelligence -- 1.1 Explainable Machine Learning -- 1.2 Beyond State-of-the-Art: Contents of the Book -- 1.2.1 Chapter 2: Principles of Explainable Artificial Intelligence -- 1.2.2 Chapter 3: Science of Data: A New Ladder for Causation -- 1.2.3 Chapter 4: Explainable Artificial Intelligence for Predictive Analytics on Customer Turnover -- 1.2.4 Chapter 5: An Efficient Explainable Artificial Intelligence Model of Automatically Generated Summaries Evaluation -- 1.2.5 Chapter 6: On the Transparent Predictive Models for Ecological Momentary Assessment Data -- 1.2.6 Chapter 7: Mitigating the Class Overlap Problem in Discriminative Localization: COVID-19 and Pneumonia Case Study -- 1.2.7 Chapter 8: A Critical Study on the Importance of Feature Selection for Diagnosing Cyber-Attacks in Water Critical Infrastructures -- 1.2.8 Chapter 9: A Study on the Effect of Dimensionality Reduction on Cyber-Attack Identification in Water Storage Tank SCADA Systems -- References -- 2 Principles of Explainable Artificial Intelligence -- 2.1 Introduction -- 2.2 Motivations for XAI -- 2.3 Dimensions of XAI -- 2.4 Explanations and Explanators -- 2.4.1 Single Tree Approximation -- 2.4.2 Rules List and Rules Set -- 2.4.3 Partial Dependency -- 2.4.4 Local Rule-Based Explanation -- 2.4.5 Feature Importance -- 2.4.6 Saliency Maps -- 2.4.7 Prototype-Based Explanations -- 2.4.8 Counterfactual Explanations -- 2.5 Conclusions -- References -- 3 Science of Data: A New Ladder for Causation -- 3.1 Introduction -- 3.2 Related Works -- 3.2.1 Cognitive Architectures -- 3.2.2 Inferential Logic -- 3.2.3 Probabilistic Fuzzy Logic (PFL) -- 3.2.4 Neural Networks (NN) -- 3.2.4.1 Microscopic Neural Network (NN) -- 3.2.4.2 Macroscopic Neural Network (NN) -- 3.2.4.3 Graph Neural Networks.
3.2.4.4 Hybrid Neural Networks for Reasoning -- 3.3 Cognitive Architecture Equipped with PFL and GNNs -- 3.4 Conclusion -- References -- 4 Explainable Artificial Intelligence for Predictive Analytics on Customer Turnover: A User-Friendly Interface for Non-expert Users -- 4.1 Introduction -- 4.2 Background -- 4.2.1 Shapley Values -- 4.2.2 Types of Explanation Techniques -- 4.3 Related Works -- 4.3.1 Shapley Additive Explanations -- 4.3.2 Contrastive Explanations -- 4.3.3 XAI User Interfaces -- 4.4 Our Explainable AI Web Interface -- 4.4.1 Back-End Component -- 4.4.2 Front-End Component -- 4.4.2.1 Home and Expected Loss -- 4.4.2.2 Local Feature Importance -- 4.4.2.3 Global Feature Importance -- 4.4.2.4 Model Recommendation -- 4.5 Evaluation -- 4.6 Conclusions -- References -- 5 An Efficient Explainable Artificial Intelligence Model of Automatically Generated Summaries Evaluation: A Use Case of Bridging Cognitive Psychology and Computational Linguistics -- 5.1 Introduction -- 5.1.1 Automatic Text Summarization -- 5.1.2 Evaluation Protocols of Automatically Generated Text Summaries -- 5.1.3 Cognitive Psychology Models for Text Comprehension -- 5.1.3.1 The Resonance Model -- 5.1.3.2 The Landscape Model -- 5.1.3.3 The Langston and Trabasso Model -- 5.1.3.4 The Construction-Integration Model -- 5.1.3.5 The Predication Model -- 5.1.3.6 The Gestalt Models -- 5.1.3.7 The Golden and Rumelhart Model -- 5.1.3.8 The Distributed Situation Space Model -- 5.1.3.9 The Structure Building Model -- 5.1.4 Originality of Our Work -- 5.2 CATSE: A Cognitive Automatic Text Summarization Evaluation Protocol -- 5.2.1 The Main Idea -- 5.2.2 Levels of Representation -- 5.2.2.1 The Surface Level -- 5.2.2.2 The Intermediate Level: The Textbase -- 5.2.2.3 The Cognitive Level: The Situation Model -- 5.2.3 The CATSE Protocol -- 5.2.3.1 The Construction Phase. 5.2.3.2 The Integration Phase -- 5.3 Experiments and Results -- 5.3.1 Datasets -- 5.3.2 Experimental Results -- 5.4 Conclusion -- References -- 6 On the Transparent Predictive Models for Ecological Momentary Assessment Data -- 6.1 Introduction -- 6.1.1 Ecological Momentary Assessment (EMA) -- 6.1.2 Classification of EMA Data -- 6.1.3 Model Transparency -- 6.2 Dataset -- 6.3 Analysis Methods -- 6.3.1 Classification Settings -- 6.3.2 Tools -- 6.3.2.1 Own Pipeline: Model Training and Testing -- 6.3.3 Experiments -- 6.3.4 Model Interpretation and Analysis -- 6.3.4.1 Analysis of Categorical Features -- 6.3.4.2 Analysis of Continuous Features -- 6.3.4.3 Interpretation of the Resulting Values -- 6.3.4.4 Model Comparison -- 6.4 Results -- 6.4.1 Experiments with KNIME Analytics Platform -- 6.4.2 Experiments with Own Pipeline -- 6.4.3 Model Interpretation and Analysis -- 6.5 Discussion -- 6.5.1 Is Personalization Always Necessary? -- 6.5.2 Model Similarity -- 6.5.3 Model Validation -- 6.5.4 EMA with Other Features -- 6.5.5 Personalization of Class Labels -- 6.5.6 Method Limitations -- 6.6 Conclusion -- References -- 7 Mitigating the Class Overlap Problem in Discriminative Localization: COVID-19 and Pneumonia Case Study -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Discriminative Localization -- 7.3.1 Class Activation Maps -- 7.3.2 Saliency Maps with Backpropagation -- 7.3.3 Amplified Directed Divergence with Ensembles -- 7.3.4 Scaled Directed Divergence (SDD) -- 7.4 Experiments -- 7.4.1 Method -- 7.4.2 COVID-19 and Pneumonia Data -- 7.4.3 COVID-19 AND Pneumonia Classifier -- 7.4.4 Scaled Directed Divergence with Natural Imagery -- 7.4.5 Scaled Directed Divergence Applied to Chest X-rays -- 7.5 Discussion -- 7.6 Conclusion -- References -- 8 A Critical Study on the Importance of Feature Selection for Diagnosing Cyber-Attacks in Water Critical Infrastructures. 8.1 Introduction -- 8.2 Background -- 8.2.1 Infinite Feature Selection -- 8.2.2 Infinite Latent Feature Selection -- 8.2.3 Evolutionary Computation Feature Selection -- 8.2.4 Relief Feature Selection -- 8.2.5 Mutual Information -- 8.2.6 Maximum Relevance and Minimum Redundancy -- 8.2.7 Feature Selection via Concave Minimization -- 8.2.8 Laplacian Score -- 8.2.9 Multi-Cluster Feature Selection -- 8.2.10 Recursive Feature Elimination -- 8.2.11 L0-Norm -- 8.2.12 Fisher Score -- 8.3 Design of Intrusion Detection System -- 8.3.1 Data Collection -- 8.3.2 Decision-Making -- 8.4 Experimental Results -- 8.4.1 Experimental Setting -- 8.4.2 Results Analysis -- 8.4.3 Feature Analysis -- 8.5 Conclusion -- References -- 9 A Study on the Effect of Dimensionality Reduction on Cyber-Attack Identification in Water Storage Tank SCADASystems -- 9.1 Introduction -- 9.2 Background -- 9.2.1 Principal Component Analysis -- 9.2.2 Factor Analysis -- 9.2.3 Confirmatory Factor Analysis -- 9.2.4 Multidimensional Scaling -- 9.2.5 Linear Discriminant Analysis -- 9.2.6 Isomap -- 9.2.7 Semantic Mapping -- 9.2.8 Probabilistic Principal Component Analysis -- 9.2.9 Locally Linear Embedding -- 9.2.10 Laplacian Eigenmaps -- 9.2.11 Landmark Isomap -- 9.2.12 Hessian-based Locally Linear Embedding -- 9.2.13 Local Tangent Space Alignment -- 9.2.14 Kernel Principal Component Analysis -- 9.2.15 Generalized Discriminant Analysis -- 9.2.16 Neighborhood Preserving Embedding -- 9.2.17 Locality Preserving Projections -- 9.2.18 Diffusion Maps -- 9.2.19 Locally Linear Coordination -- 9.2.20 Manifold Charting -- 9.2.21 Large Margin Nearest Neighbor -- 9.2.22 Independent Component Analysis -- 9.3 Design of Intrusion Detection System -- 9.3.1 Data Collection -- 9.3.2 Decision Making -- 9.4 Experimental Results -- 9.4.1 Experiment Setting -- 9.4.2 Results Analysis -- 9.5 Conclusion -- References -- Index. |
| Record Nr. | UNINA-9910508454303321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Fault Diagnosis of Hybrid Dynamic and Complex Systems [[electronic resource] /] / edited by Moamar Sayed-Mouchaweh
| Fault Diagnosis of Hybrid Dynamic and Complex Systems [[electronic resource] /] / edited by Moamar Sayed-Mouchaweh |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (290 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Electrical engineering
Quality control Reliability Industrial safety Control engineering Computational intelligence Computers Communications Engineering, Networks Quality Control, Reliability, Safety and Risk Control and Systems Theory Computational Intelligence Information Systems and Communication Service |
| ISBN | 3-319-74014-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910299959703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Learning from Data Streams in Evolving Environments : Methods and Applications / / edited by Moamar Sayed-Mouchaweh
| Learning from Data Streams in Evolving Environments : Methods and Applications / / edited by Moamar Sayed-Mouchaweh |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham, : Springer International Publishing, : Imprint : Springer, 2019 |
| Descrizione fisica | 1 online resource (VIII, 317 p. 131 illus., 95 illus. in color.) |
| Altri autori (Persone) | Sayed-MouchawehMoamar |
| Collana | Studies in Big Data |
| Soggetto topico |
Electrical engineering
Quality control Reliability Industrial safety Data mining Control engineering Communications Engineering, Networks Quality Control, Reliability, Safety and Risk Data Mining and Knowledge Discovery Control and Systems Theory |
| ISBN | 3-319-89803-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter1: Transfer Learning in Non-Stationary Environments -- Chapter2: A new combination of diversity techniques in ensemble classifiers for handling complex concept drift -- Chapter3: Analyzing and Clustering Pareto-Optimal Objects in Data Streams -- Chapter4: Error-bounded Approximation of Data Stream: Methods and Theories -- Chapter5: Ensemble Dynamics in Non-stationary Data Stream Classification -- Chapter6: Processing Evolving Social Networks for Change Detection based on Centrality Measures -- Chapter7: Large-scale Learning from Data Streams with Apache SAMOA -- Chapter8: Process Mining for Analyzing Customer Relationship Management Systems A Case Study -- Chapter9: Detecting Smooth Cluster Changes in Evolving Graph Sequences -- Chapter10: Efficient Estimation of Dynamic Density Functions with Applications in Data Streams -- Chapter11: A Survey of Methods of Incremental Support Vector Machine Learning -- Chapter12: On Social Network-based Algorithms for Data Stream Clustering. |
| Record Nr. | UNINA-9910737299903321 |
| Cham, : Springer International Publishing, : Imprint : Springer, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Predictive Maintenance in Dynamic Systems [[electronic resource] ] : Advanced Methods, Decision Support Tools and Real-World Applications / / edited by Edwin Lughofer, Moamar Sayed-Mouchaweh
| Predictive Maintenance in Dynamic Systems [[electronic resource] ] : Advanced Methods, Decision Support Tools and Real-World Applications / / edited by Edwin Lughofer, Moamar Sayed-Mouchaweh |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (564 pages) |
| Disciplina | 658.202 |
| Soggetto topico |
Electrical engineering
Quality control Reliability Industrial safety Control engineering Computational intelligence Computers Communications Engineering, Networks Quality Control, Reliability, Safety and Risk Control and Systems Theory Computational Intelligence Information Systems and Communication Service |
| ISBN | 3-030-05645-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Predictive Maintenance and (Early) FDD in Dynamic Systems -- Beyond State-of-the-Art -- Early Fault Detection and Diagnosis Approaches -- Prognostics and Forecasting -- Self-Reaction and Self-Healing Techniques -- Applications of Predictive Maintenance with emphasize on Industry 4.0 challenges -- Conclusion. |
| Record Nr. | UNINA-9910337638703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||