top

  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.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Advances in Artificial Intelligence : 20th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2024, a Coruña, Spain, June 19-21, 2024, Proceedings
Advances in Artificial Intelligence : 20th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2024, a Coruña, Spain, June 19-21, 2024, Proceedings
Autore Alonso-Betanzos Amparo
Edizione [1st ed.]
Pubbl/distr/stampa Cham : , : Springer International Publishing AG, , 2024
Descrizione fisica 1 online resource (293 pages)
Altri autori (Persone) Guijarro-BerdiñasBertha
Bolón-CanedoVerónica
Hernández-PereiraElena
Fontenla-RomeroOscar
CamachoDavid
RabuñalJuan Ramón
Ojeda-AciegoManuel
MedinaJesús
RiquelmeJosé C
Collana Lecture Notes in Computer Science Series
ISBN 9783031627996
9783031627989
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents -- Taking Advantage of Depth Information for Semantic Segmentation in Field-Measured Vineyards -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Computational Methods -- 3 Results and Discussion -- 4 Conclusions and Further Work -- References -- Advancing Computational Frontiers: Spiking Neural Networks in High-Energy Efficiency Computing Across Diverse Domains -- 1 Introduction -- 2 Related Work -- 3 Comprehensive Exploration of SNNs -- 3.1 Encoding Techniques -- 3.2 Neuron Models: SNNs Architectures for Energy Efficiency -- 3.3 Training Paradigms and Learning Methods in SNNs -- 4 Discussion in SNN Research for Energy Efficiency -- 5 Conclusion and Future Directions in SNNs Research -- References -- Deep Variational Auto-Encoder for Model-Based Water Quality Patrolling with Intelligent Surface Vehicles -- 1 Introduction -- 2 Previous Works -- 3 Statement of the Problem -- 4 Methodology -- 4.1 VAE-UNet Architecture -- 4.2 Multiagent Path Planning -- 5 Results -- 5.1 UNet-VAE Training Results -- 5.2 Patrolling Results -- 6 Conclusions and Future Work -- References -- An Architecture Towards Building a Reliable Suicide Information Chatbot -- 1 Introduction -- 2 Architecture of the Chatbot -- 2.1 Text Classification Filter for Not Suicidal Content -- 2.2 Text Classification Filter for Not Safe Information About Suicide -- 2.3 Retrieval Augmented Generation Module -- 3 Evaluation -- 4 Conclusions and Further Work -- References -- Age Estimation Using Soft Labelling Ordinal Classification Approaches -- 1 Introduction -- 2 Soft Labelling Methodology -- 3 Age Estimation Problems -- 4 Experimental Settings -- 4.1 Model Selection -- 4.2 Compared Methodologies -- 5 Results -- 6 Conclusions -- References.
O-Hydra: A Hybrid Convolutional and Dictionary-Based Approach to Time Series Ordinal Classification -- 1 Introduction -- 2 Methodology -- 2.1 Preliminary Definitions -- 2.2 Dictionary-Based Methods -- 2.3 Convolution-Based Methods -- 2.4 O-Hydra Approach -- 3 Experimental Setup -- 4 Results -- 5 Conclusions -- References -- Predicting Parkinson's Disease Progression: Analyzing Prodromal Stages Through Machine Learning -- 1 Introduction -- 2 Data -- 2.1 Database Description -- 2.2 Demographic and Clinical Data -- 2.3 Structural MRI Data -- 2.4 Pre-processing -- 3 Methodology -- 3.1 Feature Selection -- 3.2 Classification Algorithms -- 3.3 Classification Performance Evaluation -- 3.4 Explanation -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Ground-Level Ozone Forecasting Using Explainable Machine Learning -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Machine Learning -- 3.2 Hyperparameter Optimization -- 3.3 XAI -- 4 Results -- 4.1 Input Data -- 4.2 Evaluation Metrics -- 4.3 ML Optimization -- 4.4 XAI -- 5 Conclusions and Future Works -- References -- Multi-Objective Lagged Feature Selection Based on Dependence Coefficient for Time-Series Forecasting -- 1 Introduction -- 2 Related Works -- 3 Description of the Proposed MOLS Algorithm -- 4 Methodology and Experimentation -- 4.1 Model -- 4.2 Main Phases -- 5 Results and Discussion -- 5.1 Datasets -- 5.2 Result and Analysis -- 6 Conclusions and Future Works -- References -- FuSDG: A Proposal for a Fuzzy Assessment of Sustainable Development Goals Achievement -- 1 Introduction -- 2 Definition of a Fuzzy SDG Index (FuSDG) -- 3 Case Study -- 4 On the Impact of Prioritization of the SDG -- 5 Discussion and Conclusions -- References -- A Surrogate Assisted Approach for Fitness Computation in Robust Optimization over Time -- 1 Introduction -- 2 Proposed Approach.
3 Computational Experiments -- 3.1 Computational Complexity -- 3.2 Experiment Results -- 4 Conclusion and Future Works -- References -- A Path Relinking-Based Approach for the Bi-Objective Double Floor Corridor Allocation Problem -- 1 Introduction -- 2 Problem Description -- 3 Optimization Proposal -- 3.1 Bi-Objective PR -- 3.2 Path Relinking -- 3.3 Algorithmic Description -- 4 Results -- 5 Conclusions and Future Work -- References -- An Experimental Comparison of Qiskit and Pennylane for Hybrid Quantum-Classical Support Vector Machines -- 1 Introduction -- 2 Related Works -- 3 Fundamentals -- 3.1 Quantum Fundamentals -- 3.2 Support Vector Machines -- 4 Methodology -- 5 Results -- 5.1 Quality Parameters -- 5.2 Dataset Description -- 5.3 Discussion -- 6 Conclusions -- References -- Preserving the Essential Features in CNNs: Pruning and Analysis -- 1 Introduction -- 2 Filter Pruning Strategy -- 2.1 Layer Essential Features -- 2.2 Pruning Method -- 3 Experiments and Results -- 3.1 Experimental Setting -- 3.2 Pruning Setting -- 3.3 Comparing Different Filter Selection Criteria -- 3.4 Results -- 4 Discussion on the Importance of Retaining the Essential Features -- 5 Conclusion -- References -- Iterated Local Search for the Facility Location Problem with Limited Choice Rule -- 1 Introduction -- 2 Formal Description of the Problem -- 3 Iterated Local Search -- 4 Computational Experiments -- 5 Conclusions and Future Work -- References -- Driven PCTBagging: Seeking Greater Discriminating Capacity for the Same Level of Interpretability -- 1 Introduction -- 2 Related Work on PCTBagging -- 3 Driven PCTBagging -- 4 Experimental Methodology -- 5 Experimental Results -- 6 Conclusions and Further Work -- References -- Semi-supervised Learning Methods for Semantic Segmentation of Polyps -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset.
2.2 Base Training Procedure -- 2.3 Distillation Methods -- 3 Results -- 4 Conclusions and Further Work -- References -- Community-Based Topic Modeling with Contextual Outlier Handling -- 1 Introduction -- 2 Related Work -- 3 Our Proposal -- 4 Experimental Study -- 4.1 The Datasets -- 4.2 Experimental Setup -- 5 Results -- 5.1 NCM4 and NCM8 Datasets -- 5.2 News and Tweets Datasets -- 6 Conclusions -- References -- Toward Explaining Competitive Success in League of Legends: A Machine Learning Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset -- 3.2 Models -- 3.3 Experimental Settings -- 3.4 Evaluation Metrics -- 3.5 Hyperparameter Optimization -- 4 Results -- 4.1 Best Models -- 4.2 Global Analysis -- 4.3 Role Analysis -- 4.4 Local Analysis -- 5 Conclusions and Future Works -- References -- Reconstruction-Based Anomaly Detection in Wind Turbine Operation Time Series Using Generative Models -- 1 Introduction -- 2 Background -- 2.1 Anomaly Detection in Time Series -- 2.2 Failure Detection in Wind Turbines -- 3 Methodology -- 4 Experimental Setup and Results -- 4.1 Dataset -- 4.2 Experimental Evaluation -- 4.3 Results and Discussion -- 5 Conclusions and Future Work -- References -- Multi-class and Multi-label Classification of an Assembly Task in Manufacturing -- 1 Introduction -- 2 Materials and Methods -- 3 Results -- 4 Conclusions and Further Work -- References -- Image Processing and Deep Learning Methods for the Semantic Segmentation of Blastocyst Structures -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Data Source -- 3.2 Proposal Description -- 3.3 Experimentation Setup -- 4 Results and Discussion -- 5 Conclusions and Further Work -- References -- Multivariate-Autoencoder Flow-Analogue Method for Heat Waves Reconstruction -- 1 Introduction -- 2 Methodology -- 2.1 Data.
2.2 The Multivariate Analogue Method -- 2.3 The MvAE-AM Approach -- 3 Experiments and Results -- 4 Conclusions -- References -- HEX-GNN: Hierarchical EXpanders for Node Classification -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 4 Method -- 5 Experimental Settings -- 5.1 Datasets -- 5.2 Settings -- 6 Results and Analysis -- 7 Conclusion and Future Work -- References -- The Notion of Bond in the Multi-adjoint Concept Lattice Framework -- 1 Introduction -- 2 Preliminaries -- 3 Bonds on a Multi-adjoint Framework -- 4 Conclusions and Future Work -- References -- Exploring the Use of LLMs for Teaching AI and Robotics Concepts at a Master's Degree -- 1 Introduction -- 1.1 Contribution -- 2 State of the Art -- 3 Materials and Methods -- 3.1 Courses -- 3.2 LLMs Impact -- 4 Practical Case -- 4.1 Project Description -- 4.2 llama.cpp -- 4.3 llama_ros -- 4.4 Mini Pupper -- 4.5 ChatBot Application -- 5 Discussion and Conclusions -- References -- Exploring the Capabilities and Limitations of Neural Methods in the Maximum Cut -- 1 Introduction -- 2 Background and Limitations -- 3 Case Study: NCO for the Maximum Cut -- 3.1 Maximum Cut Problem -- 3.2 NCO Model -- 4 Experiments -- 4.1 RQ1-A. Generalization to Different Graph Connectivity Levels -- 4.2 RQ1-B. Generalization to Different Graph Sizes -- 4.3 RQ2. Confidence Level of NCO Models -- 4.4 RQ3. Strategies to Minimize Training Costs -- 4.5 RQ4. NC Vs NI -- 5 Conclusion -- References -- Author Index.
Record Nr. UNINA-9910865239103321
Alonso-Betanzos Amparo  
Cham : , : Springer International Publishing AG, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational Collective Intelligence [[electronic resource] ] : 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II / / edited by Manuel Núñez, Ngoc Thanh Nguyen, David Camacho, Bogdan Trawiński
Computational Collective Intelligence [[electronic resource] ] : 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II / / edited by Manuel Núñez, Ngoc Thanh Nguyen, David Camacho, Bogdan Trawiński
Edizione [1st ed. 2015.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
Descrizione fisica 1 online resource (XXII, 661 p. 220 illus.)
Disciplina 006.3
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Data mining
Application software
Information storage and retrieval
Algorithms
Education—Data processing
Artificial Intelligence
Data Mining and Knowledge Discovery
Information Systems Applications (incl. Internet)
Information Storage and Retrieval
Algorithm Analysis and Problem Complexity
Computers and Education
ISBN 3-319-24306-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Multi-agent systems -- Social networks and NLP -- Sentiment analysis.-Computational intelligence and games -- Ontologies and information extraction.-Formal methods and simulation -- Neural networks, SMT and MIS -- Collective intelligence in Web systems – Web systems analysis -- Computational swarm intelligence -- Cooperative strategies for decision making and optimization.-Advanced networking and security technologies -- IT in biomedicine -- Collective computational intelligence in educational context -- Science intelligence and data analysis -- Computational intelligence in financial markets -- Ensemble learning -- Big data mining and searching.
Record Nr. UNINA-9910483070103321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational Collective Intelligence [[electronic resource] ] : 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II / / edited by Manuel Núñez, Ngoc Thanh Nguyen, David Camacho, Bogdan Trawiński
Computational Collective Intelligence [[electronic resource] ] : 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II / / edited by Manuel Núñez, Ngoc Thanh Nguyen, David Camacho, Bogdan Trawiński
Edizione [1st ed. 2015.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
Descrizione fisica 1 online resource (XXII, 661 p. 220 illus.)
Disciplina 006.3
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Data mining
Application software
Information storage and retrieval
Algorithms
Education—Data processing
Artificial Intelligence
Data Mining and Knowledge Discovery
Information Systems Applications (incl. Internet)
Information Storage and Retrieval
Algorithm Analysis and Problem Complexity
Computers and Education
ISBN 3-319-24306-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Multi-agent systems -- Social networks and NLP -- Sentiment analysis.-Computational intelligence and games -- Ontologies and information extraction.-Formal methods and simulation -- Neural networks, SMT and MIS -- Collective intelligence in Web systems – Web systems analysis -- Computational swarm intelligence -- Cooperative strategies for decision making and optimization.-Advanced networking and security technologies -- IT in biomedicine -- Collective computational intelligence in educational context -- Science intelligence and data analysis -- Computational intelligence in financial markets -- Ensemble learning -- Big data mining and searching.
Record Nr. UNISA-996466319403316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Computational Collective Intelligence [[electronic resource] ] : 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part I / / edited by Manuel Núñez, Ngoc Thanh Nguyen, David Camacho, Bogdan Trawiński
Computational Collective Intelligence [[electronic resource] ] : 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part I / / edited by Manuel Núñez, Ngoc Thanh Nguyen, David Camacho, Bogdan Trawiński
Edizione [1st ed. 2015.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
Descrizione fisica 1 online resource (XXVIII, 515 p. 140 illus. in color.)
Disciplina 006.33
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Data mining
Application software
Information storage and retrieval
Algorithms
Education—Data processing
Artificial Intelligence
Data Mining and Knowledge Discovery
Information Systems Applications (incl. Internet)
Information Storage and Retrieval
Algorithm Analysis and Problem Complexity
Computers and Education
ISBN 3-319-24069-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Multi-agent systems -- Social networks and NLP -- Sentiment analysis -- Computational intelligence and games -- Ontologies and information extraction -- Formal methods and simulation -- Neural networks, SMT and MIS -- Collective intelligence in Web systems – Web systems analysis.-Computational swarm intelligence -- Cooperative strategies for decision making and optimization.-Advanced networking and security technologies -- IT in biomedicine -- Collective computational intelligence in educational context -- Science intelligence and data analysis -- Computational intelligence in financial markets -- Ensemble learning -- Big data mining and searching.
Record Nr. UNINA-9910483333203321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational Collective Intelligence [[electronic resource] ] : 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part I / / edited by Manuel Núñez, Ngoc Thanh Nguyen, David Camacho, Bogdan Trawiński
Computational Collective Intelligence [[electronic resource] ] : 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part I / / edited by Manuel Núñez, Ngoc Thanh Nguyen, David Camacho, Bogdan Trawiński
Edizione [1st ed. 2015.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
Descrizione fisica 1 online resource (XXVIII, 515 p. 140 illus. in color.)
Disciplina 006.33
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Data mining
Application software
Information storage and retrieval
Algorithms
Education—Data processing
Artificial Intelligence
Data Mining and Knowledge Discovery
Information Systems Applications (incl. Internet)
Information Storage and Retrieval
Algorithm Analysis and Problem Complexity
Computers and Education
ISBN 3-319-24069-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Multi-agent systems -- Social networks and NLP -- Sentiment analysis -- Computational intelligence and games -- Ontologies and information extraction -- Formal methods and simulation -- Neural networks, SMT and MIS -- Collective intelligence in Web systems – Web systems analysis.-Computational swarm intelligence -- Cooperative strategies for decision making and optimization.-Advanced networking and security technologies -- IT in biomedicine -- Collective computational intelligence in educational context -- Science intelligence and data analysis -- Computational intelligence in financial markets -- Ensemble learning -- Big data mining and searching.
Record Nr. UNISA-996466468603316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Intelligent data engineering and automated learning - IDEAL 2022 : 23rd International Conference, Manchester, UK, November 24-26, 2022, proceedings / / Hujun Yin, David Camacho, Peter Tino, editors
Intelligent data engineering and automated learning - IDEAL 2022 : 23rd International Conference, Manchester, UK, November 24-26, 2022, proceedings / / Hujun Yin, David Camacho, Peter Tino, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (564 pages)
Disciplina 006.312
Collana Lecture Notes in Computer Science
Soggetto topico Data mining
Database management
ISBN 3-031-21753-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents -- Main Track -- Ensemble Stack Architecture for Lungs Segmentation from X-ray Images -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 4 Experiment -- 4.1 Evaluation Protocols -- 4.2 Dataset -- 4.3 Training Regime -- 4.4 Results -- 5 Comparison with State-of-the-Arts -- 6 Conclusion -- References -- Synonym-Based Essay Generation and Augmentation for Robust Automatic Essay Scoring -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Synonym Replacement and Essay Generation -- 3.2 Data Augmentation -- 4 Scoring Models -- 5 Experiment -- 5.1 Data Sets -- 5.2 Essay Pre-processing -- 5.3 Evaluation Methodology -- 6 Results and Discussion -- 6.1 Improving Robustness with Adversarial Data Augmentation and Training -- 7 Conclusions -- References -- Characterizing Cardiovascular Risk Through Unsupervised and Interpretable Techniques -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset Description -- 2.2 Unsupervised and Interpretable Methods -- 3 Experimental Results -- 3.1 Characterization of Clusters and CVD Risk Analysis -- 4 Conclusions -- References -- Identification of Sedimentary Strata by Segmentation Neural Networks of Oblique Photogrammetry of UAVs -- 1 Introduction -- 2 Theoretical Foundations and Related Works -- 3 Data and Methods -- 3.1 Segmentation Architecture -- 3.2 Dataset -- 4 Experiment and Discussion -- 4.1 Experiment -- 4.2 Discussion -- 5 Conclusion -- References -- Detection of False Information in Spanish Using Machine Learning Techniques -- 1 Introduction -- 2 Background and Related Work -- 3 Data and Resources -- 4 Methodology -- 4.1 Linguistic Features -- 4.2 The Conceptual Architecture of the Fine-Tuned Model -- 4.3 The Technological Implementation -- 4.4 Evaluation Metrics -- 5 Results -- 6 Conclusions and Future Work -- References.
An Approach to Authenticity Speech Validation Through Facial Recognition and Artificial Intelligence Techniques -- 1 Introduction -- 2 State of Data -- 2.1 Deception Detection Techniques -- 2.2 Face Recognition and Face Features Extraction -- 3 Experiment -- 3.1 Framework -- 3.2 Dataset -- 3.3 Concept Proof -- 3.4 Training Details -- 4 Results and Discussion -- 4.1 Dataset Analysis -- 4.2 RNN Model -- 5 Conclusion -- References -- Federating Unlabeled Samples: A Semi-supervised Collaborative Framework for Whole Slide Image Analysis -- 1 Introduction -- 2 Methodology -- 2.1 Problem Formulation -- 2.2 Federated Model -- 2.3 Self-trained Student Model -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Validation of the Framework -- 3.4 Results of the Proposed Framework -- 4 Conclusion -- References -- Automatic Exploration of Domain Knowledge in Healthcare -- 1 Introduction -- 2 Background -- 3 DANKFE - DomAiN Knowledge Based Feature Engineering -- 4 Case Study: Prediction During COVID-19 Pandemic -- 4.1 Experimental Results -- 5 Conclusion -- References -- On Studying the Effect of Data Quality on Classification Performances -- 1 Introduction -- 2 C1: The Perceived Difficulty of Using a Method According to Experts -- 3 How Good Is a Repairing (Study of C2 to C5) -- 3.1 Empirical Setup -- 3.2 C2: Impact of the Degradation of the Data on Repairing Effectiveness -- 3.3 C3: Effectiveness of the Repairing Tools -- 3.4 C4 and C5: Impact of the Type of Error and Impact of the Classification Model -- 4 Discussion -- 4.1 Is It Always Better to Repair Data? -- 4.2 Threats to Validity -- 5 Conclusion -- References -- A Binary Water Flow Optimizer Applied to Feature Selection -- 1 Introduction -- 2 Water Flow Optimizer -- 2.1 Laminar Operator -- 2.2 Turbulent Operator -- 2.3 Algorithm -- 3 Proposal: Binary Water Flow Optimizer (BWFO).
3.1 Binary Laminar Flow Operator -- 3.2 Binary Turbulent Flow -- 3.3 Framework BWFO -- 4 Simulations and Discussions -- 5 Conclusion -- References -- Benchmarking Data Augmentation Techniques for Tabular Data -- 1 Introduction -- 2 State of Art -- 3 Experiments -- 3.1 Data -- 3.2 Assessment Metrics -- 3.3 Experimental Results -- 4 Conclusion -- References -- Deep Learning Based Predictive Analytics for Decentralized Content Caching in Hierarchical Edge Networks -- 1 Introduction -- 2 Literature Review -- 3 Related Works -- 4 Methodology -- 4.1 System Architecture -- 4.2 Dataset Preprocessing -- 4.3 Model Specification -- 5 Implementation -- 5.1 Constructing the Model -- 5.2 Content Caching and Replacing -- 6 Result Analysis -- 7 Conclusion -- References -- Explanations of Performance Differences in Segment Lining for Tunnel Boring Machines -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Performance Classification -- 3.2 Model Evaluation -- 4 Results -- 4.1 Model Performance Comparison -- 4.2 Feature Representation Extraction -- 5 Discussion -- 6 Conclusion -- References -- On Autonomous Drone Navigation Using Deep Learning and an Intelligent Rainbow DQN Agent -- 1 Introduction -- 2 Preliminaries -- 2.1 Value Function -- 2.2 Multilayer Perceptron Neural Networks -- 3 Methodology -- 3.1 Deep Q Networks -- 3.2 Double Deep Q Networks -- 3.3 Learning with Multiple Training Cycles -- 3.4 Rainbow Agent -- 3.5 Problem Formulation -- 4 Experimental Results -- 5 Conclusions and Future Work -- References -- An Intelligent Decision Support System for Road Freight Transport -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Problem Formulation -- 3.2 Proposed IDSS -- 3.3 Evaluation Methodology -- 4 Results -- 4.1 Developed IDSS Prototype -- 4.2 Evaluation -- 5 Conclusions -- References.
Endowing Intelligent Vehicles with the Ability to Learn User's Habits and Preferences with Machine Learning Methods -- 1 Introduction -- 2 Overview of Applied Techniques -- 2.1 Clustering Approaches for Point of Interest (POI) Extraction -- 2.2 Artificial Neural Networks -- 2.3 Regressions -- 3 Methodology -- 3.1 Predicting the Next Vehicle Trip State -- 3.2 Predicting the Comfort Setting -- 4 Results -- 4.1 Datasets -- 4.2 Next Trip State of a Vehicle -- 4.3 Next Trip's Comfort Setting -- 5 Conclusion -- References -- Duplication Scheduling with Bottom-Up Top-Down Recursive Neural Network -- 1 Introduction -- 2 Preliminaries -- 2.1 Recursive Neural Network -- 2.2 Bottom-Up Top-Down Recursive Neural Network -- 3 Experiments -- 4 Results and Performance Comparison -- 5 Conclusion -- References -- Towards a Low-Cost Companion Robot for Helping Elderly Well-Being -- 1 Introduction -- 2 System Description -- 2.1 Hardware Description -- 2.2 Software Description -- 3 Conclusions and Future Works -- References -- Zero-Shot Knowledge Graph Completion for Recommendation System -- 1 Introduction -- 2 Related Work -- 3 Our Approach -- 3.1 Framework -- 3.2 Problem Formulation -- 3.3 Zero-Shot KGC -- 4 Experiments -- 4.1 Dataset -- 4.2 Data Pre-processing -- 4.3 Experimental Setup -- 4.4 Experiments Result and Comparisons -- 5 Conclusion and Future Work -- References -- The Covid-19 Influence on the Desire to Stay at Home: A Big Data Architecture -- 1 Introduction -- 2 State of the Art -- 3 Materials and Methods -- 3.1 Sources and Data Collection -- 3.2 Storage -- 3.3 ETL Process -- 3.4 Data Visualization -- 4 Results and Discussion -- 4.1 Use Case 1: United States of America -- 4.2 Use Case 2: India -- 4.3 Use Case 3: Brazil -- 5 Conclusions -- References -- Distance-Based Delays in Echo State Networks -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Conclusion.
5 Future Work -- References -- EduBot: A Proof-of-Concept for a High School Motivational Agent -- 1 Introduction -- 2 State of the Art -- 3 Dataset Presentation -- 4 An Active Motivational Digital Assistant -- 4.1 Education Data Mining -- 4.2 Education Intelligence Module -- 4.3 Digital Assistant Motivational Module -- 5 Conclusion -- References -- A Simulation Model for Predicting the Spread of COVID-19 Virus -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Conclusions -- References -- ICU Mortality Prediction Using Long Short-Term Memory Networks -- 1 Introduction -- 2 Related Works -- 3 Dataset -- 3.1 Feature Engineering -- 3.2 Feature Preprocessing -- 4 Methodology -- 4.1 Model Configuration -- 4.2 Model Implementation -- 5 Experimental Results -- 6 Conclusion and Future Works -- References -- Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Search Space -- 3.2 Combining Learning Rate Distributions -- 4 Experimental Approach -- 4.1 Datasets -- 4.2 Types of Data Shift -- 4.3 Baselines and Implementation Details -- 5 Results -- 5.1 Dataset Shift -- 5.2 Distribution Shift -- 6 Conclusion -- References -- How Image Retrieval and Matching Can Improve Object Localisation on Offshore Platforms -- 1 Introduction -- 2 Related Work -- 3 ARRANGE: ImAge RetRieval mAtchiNG ObjEct -- 3.1 Principle -- 3.2 Image Retrieval -- 3.3 Image Matching -- 4 Performance Evaluation -- 4.1 ARRANGE`s Analysis -- 4.2 ARRANGE Vs. State-of.the-art Object Localisation Algorithms -- 5 Conclusion -- References -- Ethereum Investment Based on LSTM and GRU Forecast -- 1 Introduction -- 2 Materials: Data and Pre-processing -- 2.1 Feature Selection -- 3 Methods: Neural Networks Application -- 3.1 Network Architecture and Parametrization -- 3.2 Metrics.
3.3 Training and Testing Data.
Record Nr. UNINA-9910631095403321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Intelligent data engineering and automated learning - IDEAL 2022 : 23rd International Conference, Manchester, UK, November 24-26, 2022, proceedings / / Hujun Yin, David Camacho, Peter Tino, editors
Intelligent data engineering and automated learning - IDEAL 2022 : 23rd International Conference, Manchester, UK, November 24-26, 2022, proceedings / / Hujun Yin, David Camacho, Peter Tino, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (564 pages)
Disciplina 006.312
Collana Lecture Notes in Computer Science
Soggetto topico Data mining
Database management
ISBN 3-031-21753-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents -- Main Track -- Ensemble Stack Architecture for Lungs Segmentation from X-ray Images -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 4 Experiment -- 4.1 Evaluation Protocols -- 4.2 Dataset -- 4.3 Training Regime -- 4.4 Results -- 5 Comparison with State-of-the-Arts -- 6 Conclusion -- References -- Synonym-Based Essay Generation and Augmentation for Robust Automatic Essay Scoring -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Synonym Replacement and Essay Generation -- 3.2 Data Augmentation -- 4 Scoring Models -- 5 Experiment -- 5.1 Data Sets -- 5.2 Essay Pre-processing -- 5.3 Evaluation Methodology -- 6 Results and Discussion -- 6.1 Improving Robustness with Adversarial Data Augmentation and Training -- 7 Conclusions -- References -- Characterizing Cardiovascular Risk Through Unsupervised and Interpretable Techniques -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset Description -- 2.2 Unsupervised and Interpretable Methods -- 3 Experimental Results -- 3.1 Characterization of Clusters and CVD Risk Analysis -- 4 Conclusions -- References -- Identification of Sedimentary Strata by Segmentation Neural Networks of Oblique Photogrammetry of UAVs -- 1 Introduction -- 2 Theoretical Foundations and Related Works -- 3 Data and Methods -- 3.1 Segmentation Architecture -- 3.2 Dataset -- 4 Experiment and Discussion -- 4.1 Experiment -- 4.2 Discussion -- 5 Conclusion -- References -- Detection of False Information in Spanish Using Machine Learning Techniques -- 1 Introduction -- 2 Background and Related Work -- 3 Data and Resources -- 4 Methodology -- 4.1 Linguistic Features -- 4.2 The Conceptual Architecture of the Fine-Tuned Model -- 4.3 The Technological Implementation -- 4.4 Evaluation Metrics -- 5 Results -- 6 Conclusions and Future Work -- References.
An Approach to Authenticity Speech Validation Through Facial Recognition and Artificial Intelligence Techniques -- 1 Introduction -- 2 State of Data -- 2.1 Deception Detection Techniques -- 2.2 Face Recognition and Face Features Extraction -- 3 Experiment -- 3.1 Framework -- 3.2 Dataset -- 3.3 Concept Proof -- 3.4 Training Details -- 4 Results and Discussion -- 4.1 Dataset Analysis -- 4.2 RNN Model -- 5 Conclusion -- References -- Federating Unlabeled Samples: A Semi-supervised Collaborative Framework for Whole Slide Image Analysis -- 1 Introduction -- 2 Methodology -- 2.1 Problem Formulation -- 2.2 Federated Model -- 2.3 Self-trained Student Model -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Validation of the Framework -- 3.4 Results of the Proposed Framework -- 4 Conclusion -- References -- Automatic Exploration of Domain Knowledge in Healthcare -- 1 Introduction -- 2 Background -- 3 DANKFE - DomAiN Knowledge Based Feature Engineering -- 4 Case Study: Prediction During COVID-19 Pandemic -- 4.1 Experimental Results -- 5 Conclusion -- References -- On Studying the Effect of Data Quality on Classification Performances -- 1 Introduction -- 2 C1: The Perceived Difficulty of Using a Method According to Experts -- 3 How Good Is a Repairing (Study of C2 to C5) -- 3.1 Empirical Setup -- 3.2 C2: Impact of the Degradation of the Data on Repairing Effectiveness -- 3.3 C3: Effectiveness of the Repairing Tools -- 3.4 C4 and C5: Impact of the Type of Error and Impact of the Classification Model -- 4 Discussion -- 4.1 Is It Always Better to Repair Data? -- 4.2 Threats to Validity -- 5 Conclusion -- References -- A Binary Water Flow Optimizer Applied to Feature Selection -- 1 Introduction -- 2 Water Flow Optimizer -- 2.1 Laminar Operator -- 2.2 Turbulent Operator -- 2.3 Algorithm -- 3 Proposal: Binary Water Flow Optimizer (BWFO).
3.1 Binary Laminar Flow Operator -- 3.2 Binary Turbulent Flow -- 3.3 Framework BWFO -- 4 Simulations and Discussions -- 5 Conclusion -- References -- Benchmarking Data Augmentation Techniques for Tabular Data -- 1 Introduction -- 2 State of Art -- 3 Experiments -- 3.1 Data -- 3.2 Assessment Metrics -- 3.3 Experimental Results -- 4 Conclusion -- References -- Deep Learning Based Predictive Analytics for Decentralized Content Caching in Hierarchical Edge Networks -- 1 Introduction -- 2 Literature Review -- 3 Related Works -- 4 Methodology -- 4.1 System Architecture -- 4.2 Dataset Preprocessing -- 4.3 Model Specification -- 5 Implementation -- 5.1 Constructing the Model -- 5.2 Content Caching and Replacing -- 6 Result Analysis -- 7 Conclusion -- References -- Explanations of Performance Differences in Segment Lining for Tunnel Boring Machines -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Performance Classification -- 3.2 Model Evaluation -- 4 Results -- 4.1 Model Performance Comparison -- 4.2 Feature Representation Extraction -- 5 Discussion -- 6 Conclusion -- References -- On Autonomous Drone Navigation Using Deep Learning and an Intelligent Rainbow DQN Agent -- 1 Introduction -- 2 Preliminaries -- 2.1 Value Function -- 2.2 Multilayer Perceptron Neural Networks -- 3 Methodology -- 3.1 Deep Q Networks -- 3.2 Double Deep Q Networks -- 3.3 Learning with Multiple Training Cycles -- 3.4 Rainbow Agent -- 3.5 Problem Formulation -- 4 Experimental Results -- 5 Conclusions and Future Work -- References -- An Intelligent Decision Support System for Road Freight Transport -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Problem Formulation -- 3.2 Proposed IDSS -- 3.3 Evaluation Methodology -- 4 Results -- 4.1 Developed IDSS Prototype -- 4.2 Evaluation -- 5 Conclusions -- References.
Endowing Intelligent Vehicles with the Ability to Learn User's Habits and Preferences with Machine Learning Methods -- 1 Introduction -- 2 Overview of Applied Techniques -- 2.1 Clustering Approaches for Point of Interest (POI) Extraction -- 2.2 Artificial Neural Networks -- 2.3 Regressions -- 3 Methodology -- 3.1 Predicting the Next Vehicle Trip State -- 3.2 Predicting the Comfort Setting -- 4 Results -- 4.1 Datasets -- 4.2 Next Trip State of a Vehicle -- 4.3 Next Trip's Comfort Setting -- 5 Conclusion -- References -- Duplication Scheduling with Bottom-Up Top-Down Recursive Neural Network -- 1 Introduction -- 2 Preliminaries -- 2.1 Recursive Neural Network -- 2.2 Bottom-Up Top-Down Recursive Neural Network -- 3 Experiments -- 4 Results and Performance Comparison -- 5 Conclusion -- References -- Towards a Low-Cost Companion Robot for Helping Elderly Well-Being -- 1 Introduction -- 2 System Description -- 2.1 Hardware Description -- 2.2 Software Description -- 3 Conclusions and Future Works -- References -- Zero-Shot Knowledge Graph Completion for Recommendation System -- 1 Introduction -- 2 Related Work -- 3 Our Approach -- 3.1 Framework -- 3.2 Problem Formulation -- 3.3 Zero-Shot KGC -- 4 Experiments -- 4.1 Dataset -- 4.2 Data Pre-processing -- 4.3 Experimental Setup -- 4.4 Experiments Result and Comparisons -- 5 Conclusion and Future Work -- References -- The Covid-19 Influence on the Desire to Stay at Home: A Big Data Architecture -- 1 Introduction -- 2 State of the Art -- 3 Materials and Methods -- 3.1 Sources and Data Collection -- 3.2 Storage -- 3.3 ETL Process -- 3.4 Data Visualization -- 4 Results and Discussion -- 4.1 Use Case 1: United States of America -- 4.2 Use Case 2: India -- 4.3 Use Case 3: Brazil -- 5 Conclusions -- References -- Distance-Based Delays in Echo State Networks -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Conclusion.
5 Future Work -- References -- EduBot: A Proof-of-Concept for a High School Motivational Agent -- 1 Introduction -- 2 State of the Art -- 3 Dataset Presentation -- 4 An Active Motivational Digital Assistant -- 4.1 Education Data Mining -- 4.2 Education Intelligence Module -- 4.3 Digital Assistant Motivational Module -- 5 Conclusion -- References -- A Simulation Model for Predicting the Spread of COVID-19 Virus -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Conclusions -- References -- ICU Mortality Prediction Using Long Short-Term Memory Networks -- 1 Introduction -- 2 Related Works -- 3 Dataset -- 3.1 Feature Engineering -- 3.2 Feature Preprocessing -- 4 Methodology -- 4.1 Model Configuration -- 4.2 Model Implementation -- 5 Experimental Results -- 6 Conclusion and Future Works -- References -- Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Search Space -- 3.2 Combining Learning Rate Distributions -- 4 Experimental Approach -- 4.1 Datasets -- 4.2 Types of Data Shift -- 4.3 Baselines and Implementation Details -- 5 Results -- 5.1 Dataset Shift -- 5.2 Distribution Shift -- 6 Conclusion -- References -- How Image Retrieval and Matching Can Improve Object Localisation on Offshore Platforms -- 1 Introduction -- 2 Related Work -- 3 ARRANGE: ImAge RetRieval mAtchiNG ObjEct -- 3.1 Principle -- 3.2 Image Retrieval -- 3.3 Image Matching -- 4 Performance Evaluation -- 4.1 ARRANGE`s Analysis -- 4.2 ARRANGE Vs. State-of.the-art Object Localisation Algorithms -- 5 Conclusion -- References -- Ethereum Investment Based on LSTM and GRU Forecast -- 1 Introduction -- 2 Materials: Data and Pre-processing -- 2.1 Feature Selection -- 3 Methods: Neural Networks Application -- 3.1 Network Architecture and Parametrization -- 3.2 Metrics.
3.3 Training and Testing Data.
Record Nr. UNISA-996500062703316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Intelligent Data Engineering and Automated Learning – IDEAL 2018 [[electronic resource] ] : 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part II / / edited by Hujun Yin, David Camacho, Paulo Novais, Antonio J. Tallón-Ballesteros
Intelligent Data Engineering and Automated Learning – IDEAL 2018 [[electronic resource] ] : 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part II / / edited by Hujun Yin, David Camacho, Paulo Novais, Antonio J. Tallón-Ballesteros
Edizione [1st ed. 2018.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Descrizione fisica 1 online resource (XXVI, 349 p. 96 illus., 62 illus. in color.)
Disciplina 006.3
Collana Information Systems and Applications, incl. Internet/Web, and HCI
Soggetto topico Data mining
Artificial intelligence
Natural language processing (Computer science)
Application software
Computer organization
Data Mining and Knowledge Discovery
Artificial Intelligence
Natural Language Processing (NLP)
Computer Applications
Computer Systems Organization and Communication Networks
ISBN 3-030-03496-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910349393603321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Intelligent Data Engineering and Automated Learning – IDEAL 2018 [[electronic resource] ] : 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part I / / edited by Hujun Yin, David Camacho, Paulo Novais, Antonio J. Tallón-Ballesteros
Intelligent Data Engineering and Automated Learning – IDEAL 2018 [[electronic resource] ] : 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part I / / edited by Hujun Yin, David Camacho, Paulo Novais, Antonio J. Tallón-Ballesteros
Edizione [1st ed. 2018.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Descrizione fisica 1 online resource (XXVI, 865 p. 285 illus., 197 illus. in color.)
Disciplina 006.3
Collana Information Systems and Applications, incl. Internet/Web, and HCI
Soggetto topico Data mining
Artificial intelligence
Optical data processing
Computers
Data Mining and Knowledge Discovery
Artificial Intelligence
Computer Imaging, Vision, Pattern Recognition and Graphics
Theory of Computation
ISBN 3-030-03493-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intelligent data analysis -- data mining and their associated learning systems and paradigms -- big data challenges -- machine learning, data mining, information retrieval and management -- bio- and neuro-informatics -- bio-inspired models including neural networks, evolutionary computation and swarm intelligence -- agents and hybrid intelligent systems, and real-world applications of intelligent techniques -- evolutionary algorithms -- deep learning neural networks -- probabilistic modeling -- particle swarm intelligence -- big data analytics and applications in image recognition -- regression, classification, clustering, medical and biological modelling and prediction -- text processing and social media analysis.
Record Nr. UNINA-9910349393703321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Intelligent Data Engineering and Automated Learning – IDEAL 2018 [[electronic resource] ] : 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part I / / edited by Hujun Yin, David Camacho, Paulo Novais, Antonio J. Tallón-Ballesteros
Intelligent Data Engineering and Automated Learning – IDEAL 2018 [[electronic resource] ] : 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part I / / edited by Hujun Yin, David Camacho, Paulo Novais, Antonio J. Tallón-Ballesteros
Edizione [1st ed. 2018.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Descrizione fisica 1 online resource (XXVI, 865 p. 285 illus., 197 illus. in color.)
Disciplina 006.3
Collana Information Systems and Applications, incl. Internet/Web, and HCI
Soggetto topico Data mining
Artificial intelligence
Optical data processing
Computers
Data Mining and Knowledge Discovery
Artificial Intelligence
Computer Imaging, Vision, Pattern Recognition and Graphics
Theory of Computation
ISBN 3-030-03493-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intelligent data analysis -- data mining and their associated learning systems and paradigms -- big data challenges -- machine learning, data mining, information retrieval and management -- bio- and neuro-informatics -- bio-inspired models including neural networks, evolutionary computation and swarm intelligence -- agents and hybrid intelligent systems, and real-world applications of intelligent techniques -- evolutionary algorithms -- deep learning neural networks -- probabilistic modeling -- particle swarm intelligence -- big data analytics and applications in image recognition -- regression, classification, clustering, medical and biological modelling and prediction -- text processing and social media analysis.
Record Nr. UNISA-996466314103316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui