Note: Intro -- Preface -- Organization -- Contents -- Bioinformatics -- A Comparison of Machine Learning Techniques for the Detection of Type-4 PhotoParoxysmal Responses in Electroencephalographic Signals -- 1 Introduction -- 2 Preliminaries and Related Work -- 3 Type-4 PPR Detection Using ML -- 3.1 Dimensional Reduction -- 3.2 Clustering and Classification -- 4 Materials and Methods -- 4.1 Data Set Description -- 4.2 Experimentation Design -- 5 Results and Discussion -- 6 Conclusions and Future Work -- References -- Smartwatch Sleep-Tracking Services Precision Evaluation Using Supervised Domain Adaptation -- 1 Introduction -- 2 The Proposal -- 2.1 Step1: RAW Signals Preprocessing -- 2.2 Step2: Features Computing -- 2.3 Steps 3 and 4: Models Training and Domain Adaptation -- 3 Numerical Results -- 3.1 Materials and Methods -- 3.2 Experimentation Set up -- 3.3 Numerical Results -- 4 Conclusions and Future Work -- References -- Tracking and Classification of Features in the Bio-Inspired Layered Networks -- 1 Introduction -- 2 Bio-Inspired Neural Networks -- 2.1 Background of Asymmetric Neural Networks Based on the Bio-Inspired Network -- 2.2 Model of Asymmetric Networks -- 2.3 Tracking in the Asymmetric Networks -- 2.4 Orthogonality in the Asymmetric Layered Networks -- 3 Sparse Coding for Classification in the Extended Asymmetric Networks -- 3.1 Independence and Sparse Coding on the Orthogonal Subnetworks -- 3.2 Generation of Independent Basis Set via Sparse Coding Realization -- 4 Application to Data Classification via Sparse Coding Realization in the Asymmetric Networks -- 5 Conclusion -- References -- Frailty Related Survival Risks at Short and Middle Term of Older Adults Admitted to Hospital -- 1 Introduction -- 2 Materials and Methods -- 2.1 Study Design and Subjects -- 2.2 Statistical Methods -- 3 Results -- 4 Discussion. , 5 Conclusions and Future Work -- References -- On the Analysis of a Real Dataset of COVID-19 Patients in Alava -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Study Design -- 3.2 Ethical Approval and Patient Consent -- 3.3 Data Collection and Description -- 3.4 Attribute Analysis -- 3.5 Principal Component Analysis -- 3.6 Logistic Regression Feature Importance -- 4 Discussion -- 5 Conclusion -- References -- Indoor Access Control System Through Symptomatic Examination Using IoT Technology, Fog Computing and Cloud Computing -- 1 Introduction -- 2 Related Works -- 3 Operation of the Control System -- 3.1 Facial Recognition Module -- 3.2 Steps of the Detection System -- 3.3 Medical Sensors -- 3.4 Fog Computing -- 3.5 Statistics Management and User Registration Module -- 3.6 Accessibility Improvements -- 4 Conclusions and Future Work Lines -- References -- Data Mining and Decision Support Systems -- Measuring the Quality Information of Sources of Cybersecurity by Multi-Criteria Decision Making Techniques -- 1 Introduction -- 2 Background -- 2.1 Related Work -- 2.2 DQ Model -- 3 Ranking of Sources by MCDM -- 3.1 Weighted Sum Model (WSM) -- 3.2 Analytic Hierarchy Process (AHP) -- 3.3 Concordance Between Rankings -- 4 Experimental Section -- 5 Results and Discussion -- 6 Conclusions and Future Work -- References -- A Case of Study with the Clustering R Library to Measure the Quality of Cluster Algorithms -- 1 Introduction -- 2 The Clustering Package -- 3 A Case Study Using the Clustering Library on the Dataset of Deaths -- 4 Graphical Distribution of Results -- 5 Conclusions -- References -- Comparing Clustering Techniques on Brazilian Legal Document Datasets -- 1 Introduction -- 2 Related Work -- 3 Theoretical Basis -- 3.1 Clustering Algorithms -- 3.2 Natural Language Processing Techniques -- 4 Methodology -- 4.1 Models Description. , 4.2 Databases, Preprocessing and Embedding -- 4.3 Clustering Evaluation Framework -- 4.4 Clustering Human Evaluation -- 5 Results and Discussion -- 6 Conclusion -- 7 Future Work -- References -- Improving Short Query Representation in LDA Based Information Retrieval Systems -- 1 Introduction -- 2 Material and Methods -- 2.1 Information Retrieval Systems -- 2.2 Latent Dirichlet Allocation -- 2.3 Relevance Estimation -- 3 Proposed Query Representation Method: LDAW -- 3.1 LDAW Calculation Method -- 3.2 Relevance of the Word in Each LDA Topic -- 3.3 Relevance of the Word in the Corpus Vocabulary -- 3.4 Relevance of the Word in the Query -- 3.5 Word Vector Calculation -- 4 Evaluation -- 4.1 Data Sets -- 4.2 Evaluation Measures -- 4.3 Text Pre-processing -- 4.4 Experiments Description -- 5 Results and Discussion -- 6 Conclusions -- References -- A New Game Theoretic Based Random Forest for Binary Classification -- 1 Introduction -- 2 Decision Trees and Random Forests -- 2.1 FROG -- 2.2 RF-FROG -- 3 Numerical Experiments -- 4 Conclusions -- References -- Concept Drift Detection to Improve Time Series Forecasting of Wind Energy Generation -- 1 Introduction -- 2 Materials and Method -- 2.1 Dataset -- 2.2 Concept Drifts Detection Techniques -- 2.3 Comparison Procedure -- 3 Results -- 4 Conclusions -- References -- A Decision Support Tool for the Static Allocation of Emergency Vehicles to Stations -- 1 Introduction -- 2 Background -- 3 Architecture -- 4 Static Ambulance Allocation Model -- 4.1 Problem Description -- 4.2 Mathematical Model -- 5 Evaluation -- 5.1 Computational Evaluation -- 5.2 Model Evaluation -- 6 Conclusions -- References -- Adapting K-Means Algorithm for Pair-Wise Constrained Clustering of Imbalanced Data Streams -- 1 Introduction -- 2 Algorithm -- 3 Experiments -- 3.1 Research Protocol -- 3.2 Experimental Setup -- 3.3 Results. , 4 Conclusions -- References -- Small Wind Turbine Power Forecasting Using Long Short-Term Memory Networks for Energy Management Systems -- 1 Introduction -- 2 Case Study -- 2.1 Sotavento Galicia Building -- 2.2 Dataset Description -- 3 Energy Management System -- 4 Experiments and Results -- 4.1 Experiments Setup -- 4.2 Results -- 5 Conclusions and Future Work -- References -- CORE-BCD-mAI: A Composite Framework for Representing, Querying, and Analyzing Big Clinical Data by Means of Multidimensional AI Tools -- 1 Introduction -- 2 Motivations: Combining Multidimensional AI Tools and Big Clinical Data -- 3 CORE-BCD-mAI: Methodologies and Anatomy -- 4 CORE-BCD-mAI: Research Challenges -- 5 Conclusions and Future Work -- References -- Generalized Fisher Kernel with Bregman Divergence -- 1 Introduction -- 2 Statement of the Problem -- 2.1 Non-parametric Approach -- 3 Non Parametric General Solutions -- 4 Examples -- 5 Conclusion -- References -- A HAIS Approach to Predict the Energy Produced by a Solar Panel -- 1 Introduction -- 2 Case of Study -- 2.1 Sotavento Bioclimatic House -- 2.2 Bioclimatic House Facilities -- 2.3 Solar Thermal System -- 3 Techniques Applied -- 3.1 Statistical Regression Techniques -- 3.2 Artificial Neural Networks -- 3.3 Clustering Technique -- 4 Results and Discussion -- 5 Conclusions and Future Work -- References -- Deep Learning -- Companion Losses for Ordinal Regression -- 1 Introduction -- 2 OR Overview -- 3 Companion Losses for OR -- 4 Experimental Results -- 4.1 Companion Loss Models -- 4.2 Comparison with Classical or Models -- 5 Discussion and Conclusions -- References -- Convex Multi-Task Learning with Neural Networks -- 1 Introduction -- 2 Multi-Task Learning Approaches -- 2.1 Multi-Task Learning with a Feature-Learning Approach -- 2.2 Multi-Task Learning with a Regularization-Based Approach. , 2.3 Multi-Task Learning with a Combination Approach -- 3 Convex MTL Neural Networks -- 3.1 Definition -- 3.2 Training Procedure -- 3.3 Implementation Details -- 4 Experimental Results -- 4.1 Problems Description -- 4.2 Experimental Procedure -- 4.3 Analysis of the Results -- 5 Conclusions and Further Work -- References -- Smash: A Compression Benchmark with AI Datasets from Remote GPU Virtualization Systems -- 1 Introduction -- 2 Related Work -- 2.1 Remote GPU Virtualization -- 2.2 Compression Libraries -- 2.3 Datasets Used with Compression Libraries -- 3 The Smash Compression Benchmark for AI -- 3.1 A New Dataset for AI Applications -- 3.2 The Smash Compression Benchmark -- 4 Experiments -- 5 Conclusion -- References -- Time Series Forecasting Using Artificial Neural Networks -- 1 Introduction -- 2 Background -- 3 Materials and Methods -- 4 ANN Architectures -- 4.1 Multi-layer Neural Network -- 4.2 Recurrent Neural Networks -- 5 Results and Discussion -- 5.1 Recurrent Neural Network Performance -- 5.2 Results Comparison -- 6 Conclusions -- References -- A Fine-Grained Study of Interpretability of Convolutional Neural Networks for Text Classification -- 1 Introduction -- 2 Related Work -- 2.1 Network Interpretability -- 3 Methodology -- 4 Evaluation -- 4.1 Corpora -- 4.2 Model Studied -- 4.3 Experimental Phase -- 4.4 Study of the Interpretability of the Convolutional Layers -- 5 Conclusions and Future Work -- References -- Olive Phenology Forecasting Using Information Fusion-Based Imbalanced Preprocessing and Automated Deep Learning -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Data Preparation -- 3.2 Imbalanced Techniques -- 3.3 Automated Deep Learning Proposal -- 3.4 Benchmark Algorithms -- 4 Experimentation and Results -- 4.1 Dataset -- 4.2 Evaluation Metrics -- 4.3 Experimental Settings -- 4.4 Results and Discussion. , 5 Conclusions.