Anmerkung: Intro -- Preface -- Organization -- Abstracts of Invited Talks -- Consistency of Fuzzy Preference Relations -- Towards Distorted Statistics Based on Choquet Calculus -- Assessing the Risk of Default Propagation in Interconnected Sectorial Financial Networks -- Decision Making Tools with Semantic Data to Improve Tourists' Experiences -- Improving Spatial Reasoning in Intelligent Systems: Challenges -- Contents -- Invited Paper -- Graded Logic Aggregation -- Abstract -- 1 A Soft Computing Generalization of Boolean Logic -- 2 Graded Logic Conjecture and Graded Conjunction/Disjunction -- 3 Partitioning of Unit Hypercube -- 4 Conclusions -- References -- Aggregation Operators, Fuzzy Measures and Integrals -- Coherent Risk Measures Derived from Utility Functions -- 1 Introduction -- 2 Value-at-Risks and Coherent Risk Measures -- 3 Weighted Average Value-at-risks with Risk Spectra -- 4 An Optimal Risk Spectrum Derived from Risk Averse Utility Functions -- 5 Examples -- References -- On k-additive Aggregation Functions -- 1 Introduction -- 2 k-maxitive and k-additive Aggregation Functions -- 3 Pseudo-additions -- 4 K-additive Aggregation Functions -- 4.1 Archimedean Pseudo-additions -- 4.2 Archimedean t-conorm-based Pseudo-additions -- 5 Concluding Remarks -- References -- Constructing an Outranking Relation with Weighted OWA for Multi-criteria Decision Analysis -- Abstract -- 1 Introduction -- 2 Weighted OWA Operators -- 2.1 OWAWA -- 2.2 WOWA -- 2.3 IOWA -- 3 Outranking Relations in the ELECTRE Methodology -- 4 Using Weighted OWA in the Overall Concordance Calculation -- 5 Experiments -- 5.1 Finding a Hotel -- 5.2 Generating a Ranking of Universities -- 6 Conclusions and Future Work -- Acknowledgements -- References -- Sugeno Integrals and the Commutation Problem -- 1 Introduction -- 2 A Refresher on 1D Sugeno Integral. , 3 The Commutation of Sugeno Integrals -- 4 Commuting Capacities -- 5 Conclusion -- References -- Characterization of k-Choquet Integrals -- 1 Introduction -- 2 Preliminaries -- 3 Characterization of k-Choquet Integrals -- 3.1 k-Choquet Integrals -- 3.2 The Class Chk,1 -- 3.3 The Class Ch2,2 -- 3.4 The Class Ch2,n -- 3.5 The Class Chk,n -- 4 Conclusion -- References -- Event-Based Transformations of Set Functions and the Consensus Requirement -- 1 Introduction -- 2 Preliminaries -- 3 Linear Operators and Consensus Requirement -- 4 Linear Operators and Event-Based Transformations -- 5 Conclusion -- References -- Association Analysis on Interval-Valued Fuzzy Sets -- 1 Introduction -- 2 Operations on Interval-Valued Fuzzy Sets -- 2.1 Basic Logical Operations -- 3 Introduction to Association Rules -- 4 Association Rules on Interval-Valued Fuzzy Sets -- 4.1 Motivational Example -- 4.2 Computing Support and Confidence on Interval-Valued Data -- 5 Conclusion and Future Work -- References -- Fuzzy Hit-or-Miss Transform Using Uninorms -- 1 Introduction -- 2 Preliminaries -- 3 From Binary to Fuzzy Hit-or-Miss Transform -- 4 Fuzzy Hit or Miss Transform Using Uninorms: Definition and Results -- 5 Experimental Results -- 6 Conclusions and Future Work -- References -- Learning Fuzzy Measures for Aggregation in Fuzzy Rule-Based Models -- 1 Introduction -- 2 Preliminaries -- 3 Methodology -- 3.1 Fuzzy Rule-Based Models Construction -- 3.2 Fuzzy Measure Based on the Rule Confidence -- 3.3 Classification Using the Fuzzy Rules -- 4 Experimental Results -- 4.1 The Diabetic Retinopathy Problem and Dataset -- 4.2 Tests, Results and Discussion -- 5 Conclusion and Future Work -- References -- Decision Making -- Extraction of Patterns to Support Dairy Culling Management -- 1 Introduction -- 2 The JADE Method -- 3 The Anti-unification Concept -- 4 The Data Base. , 5 Modelling the Culling Task -- 6 Experiments -- 6.1 The Model -- 6.2 The Class Production/DIM = VL -- 7 Conclusions -- References -- An Axiomatisation of the Banzhaf Value and Interaction Index for Multichoice Games -- 1 Introduction -- 2 Preliminary Definitions -- 3 Banzhaf Value and Interaction Indices -- 4 Axiomatisation of the Banzhaf Value for Multichoice Games -- 5 Axiomatisation of the Banzhaf Interaction Index -- References -- Fuzzy Positive Primitive Formulas -- 1 Introduction -- 2 Preliminaries -- 3 Fuzzy Positive-Primitive Formulas -- 4 Fuzzy Positive-Primitive Sets of Axioms -- 5 Discussion and Future Work -- References -- Basic Level Concepts as a Means to Better Interpretability of Boolean Matrix Factors and Their Application to Clustering -- 1 Introduction -- 2 Basic Notions -- 2.1 Formal Concept Analysis -- 2.2 Basic Level of Concepts -- 2.3 Boolean Matrix Decomposition -- 3 Boolean Matrix Decomposition: Algorithms -- 3.1 Design of New Algorithms and Experimental Evaluation -- 3.2 Algorithms Utilizing Coverage or Basic Level only -- 3.3 Combining Coverage and Basic Level -- 4 Clustering Algorithm -- 4.1 Algorithm Evaluation -- 5 Conclusion, Related Works, and Future Research -- References -- Fuzzy Type Powerset Operators and F-Transforms -- 1 Introduction -- 2 Preliminary Notions -- 3 L-Fuzzy Type Powerset Theories -- 4 Examples -- 5 Conclusions -- References -- Implicative Weights as Importance Quantifiers in Evaluation Criteria -- Abstract -- 1 Introduction -- 2 Implicative Weights in Conjunctive Aggregators -- 3 Implicative Weight Functions -- 4 GCD and Monotonicity with Respect to Andness and Orness -- 5 Andness-Domination Versus Weight-Domination -- 6 Experimental Comparison of Implicative Weight Models -- 7 Conclusions -- References -- Balancing Assembly Lines and Matching Demand Through Worker Reallocations -- 1 Introduction. , 2 Mathematical Formulation -- 2.1 Input Description -- 2.2 Incorporating Learning and Forgetting Effects -- 2.3 Objective Functions -- 2.4 Restrictions -- 3 Numerical Experiments -- 3.1 Experimental Design -- 3.2 Results -- 3.3 Further Discussion -- 4 Conclusions -- References -- Clustering and Classification -- Optimal Clustering with Twofold Memberships -- 1 Introduction -- 2 K-Means and Rough K-Means -- 3 Algorithms with Twofold Memberships -- 3.1 Second Method with Core Regions of Circles -- 3.2 Categorical Data -- 4 Examples -- 4.1 Illustrative Examples -- 4.2 Real Data -- 5 Conclusion -- References -- Privacy Preserving Collaborative Fuzzy Co-clustering of Three-Mode Cooccurrence Data -- 1 Introduction -- 2 FCM Clustering and FCM-Type Fuzzy Co-clustering -- 2.1 Fuzzy c-Means (FCM) -- 2.2 Fuzzy Clustering for Categorical Multivariate Data (FCCM) -- 2.3 Three-Mode Fuzzy Clustering for Categorical Multivariate Data (3-Mode FCCM) -- 3 Extension of 3-Mode FCCM for Collaborative 3-Mode FCCM -- 4 Experimental Result -- 5 Conclusion -- References -- Generalized Fuzzy c-Means Clustering and Its Theoretical Properties -- 1 Introduction -- 2 Preliminaries -- 3 Generalized FCM -- 3.1 Optimization Problem -- 3.2 Algorithm, FCF, and Its Property -- 4 Numerical Examples -- 5 Conclusion -- References -- A Self-tuning Possibilistic c-Means Clustering Algorithm -- 1 Introduction -- 2 Background -- 3 Methods -- 4 Results and Discussion -- 4.1 Evaluation Criteria -- 4.2 Tests with Two Clusters in Various Scenarios -- 4.3 Tests with the IRIS Data Set -- 5 Conclusions -- References -- k-CCM: A Center-Based Algorithm for Clustering Categorical Data with Missing Values -- 1 Introduction -- 2 Related Work -- 2.1 Partitional Clustering for Categorical Data -- 2.2 Imputation Methods for Categorical Data with Missing Values -- 3 Preliminaries and Problem Statement. , 4 The Proposed k-CCM Algorithm -- 5 Comparative Experiment -- 6 Summary and Future Work -- References -- Data Privacy and Security -- WEDL-NIDS: Improving Network Intrusion Detection Using Word Embedding-Based Deep Learning Method -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Preprocessing -- 3.2 Dimension Reduction -- 3.3 Deep Learning Architecture -- 4 Experiment Results and Discussion -- 4.1 Experimental Methodology -- 4.2 Dataset -- 4.3 Multi-classification Using WEDL-NIDS -- 4.4 Comparison with Other Methods -- 5 Conclusions and Future Work -- References -- Anonymization of Unstructured Data via Named-Entity Recognition -- 1 Introduction -- 2 Background -- 2.1 Named-Entity Recognition -- 2.2 Conditional Random Fields -- 3 Related Work -- 4 Methodology -- 4.1 General Approach -- 4.2 Proof of Concept -- 5 Experimental Results -- 5.1 Data Set -- 5.2 Evaluation Metrics -- 5.3 Results and Discussion -- 6 Conclusions and Future Work -- References -- On the Application of SDC Stream Methods to Card Payments Analytics -- 1 Introduction -- 2 Preliminaries -- 2.1 Statistical Disclosure Control -- 2.2 Differential Privacy -- 3 Adapting SDC Methods to the Streaming Setting -- 3.1 Noise Addition -- 3.2 Microaggregation -- 3.3 Rank Swapping -- 3.4 Differential Privacy -- 4 Experiments -- 4.1 Data Description -- 4.2 Metrics for IL and DR -- 4.3 Results -- 4.4 Discussion -- 5 Conclusion -- References -- Correction to: Modeling Decisions for Artificial Intelligence -- Correction to: V. Torra et al. (Eds.): Modeling Decisions for Artificial Intelligence, LNAI 11144, https://doi.org/10.1007/978-3-030-00202-2 -- Author Index.

Anmerkung: Intro -- Preface -- Organization -- Contents -- Medical Applications -- Predicting the Use of Invasive Mechanical Ventilation in ICU COVID-19 Patients -- 1 Introduction -- 2 State of the Art -- 3 Data Set -- 4 Methods -- 4.1 Data Preparation -- 4.2 Data Pre-processing -- 4.3 Modelling -- 5 Results -- 6 Conclusions -- References -- A Coarse to Fine Corneal Ulcer Segmentation Approach Using U-net and DexiNed in Chain -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 Evaluated CNN Architectures -- 3.2 Image Dataset -- 3.3 Evaluation Metrics -- 4 Proposed Method -- 5 Results and Discussion -- 6 Conclusion -- References -- Replacing Data Augmentation with Rotation-Equivariant CNNs in Image-Based Classification of Oral Cancer -- 1 Introduction -- 2 Methodology -- 3 Oral Dataset -- 4 Experiments -- 5 Results -- 6 Conclusions and Future Work -- References -- A Multitasking Learning Framework for Dermoscopic Image Analysis -- 1 Introduction -- 2 Network Architecture and Learning Details -- 2.1 Learning Details -- 3 Experimental Design and Results -- 3.1 Dataset and Implementation Details -- 3.2 Experiments and Analysis -- 4 Conclusions -- References -- An Evaluation of Segmentation Techniques for Covid-19 Identification in Chest X-Ray -- 1 Introduction -- 2 Proposed Method -- 3 Experimental Setup -- 3.1 Parameters -- 3.2 Data Augmentation -- 3.3 Evaluation Metrics -- 4 Results and Discussion -- 4.1 Segmentation Performance -- 4.2 COVID-19 Identification Scores -- 4.3 Models Interpretability with LIME -- 5 Conclusion -- References -- A Study on Annotation Efficient Learning Methods for Segmentation in Prostate Histopathological Images -- 1 Introduction -- 2 Related Work -- 2.1 Segmentation -- 2.2 Cancer Detection in WSIs -- 2.3 Unsupervised Representation Learning -- 3 Methodology -- 3.1 U-Net -- 3.2 Transfer Learning. , 3.3 Self-supervised Learning -- 4 Evaluation -- 4.1 Dataset -- 4.2 Segmentation Results -- 5 Conclusion -- References -- Natural Language Processing -- Data-Augmented Emoji Approach to Sentiment Classification of Tweets -- 1 Introduction and Background -- 1.1 Bidirectional Encoder Representations from Transformers (BERT) -- 2 Methodology -- 2.1 Datasets -- 2.2 Additional Pre-training -- 2.3 Data Augmentation -- 2.4 Emoji Extraction -- 2.5 Model Architecture -- 2.6 Training Protocol -- 3 Results -- 3.1 Evaluation Metrics -- 3.2 Pre-training Results -- 3.3 Data Augmentation Results -- 3.4 Fine-Tuning Results -- 4 Conclusions -- References -- Detecting Hate Speech in Cross-Lingual and Multi-lingual Settings Using Language Agnostic Representations -- 1 Introduction -- 2 Related Works -- 3 Proposal -- 4 Experiments -- 4.1 Dataset -- 4.2 Models and Evaluation Metrics -- 5 Results -- 5.1 Mono-lingual -- 5.2 Multi-lingual -- 5.3 Cross-Lingual -- 6 Conclusions -- References -- Prediction of Perception of Security Using Social Media Content -- 1 Introduction -- 2 Materials and Methods -- 2.1 Proposed Model -- 2.2 Estimating Model Parameters -- 2.3 Predicting Future Tweets -- 2.4 Experimental Settings -- 3 Results -- 4 Conclusions -- References -- Metaheuristics -- Fine-Tuning Dropout Regularization in Energy-Based Deep Learning -- 1 Introduction -- 2 Related Works -- 3 Theoretical Background -- 3.1 Restricted Boltzmann Machines -- 3.2 Dropout-Based Restricted Boltzmann Machines -- 4 Methodology -- 4.1 Proposed Approach -- 4.2 Experimental Setup -- 4.3 Datasets -- 5 Experimental Results -- 5.1 Restricted Boltzmann Machines -- 5.2 Deep Belief Networks -- 6 Conclusion -- References -- Enhancing Hyper-to-Real Space Projections Through Euclidean Norm Meta-heuristic Optimization -- 1 Introduction -- 2 Hypercomplex Representation -- 2.1 Minkowski p-norm. , 3 Meta-heuristic Optimization -- 4 Methodology -- 4.1 Hypercomplex Optimization -- 4.2 Last Iteration Optimization -- 4.3 Benchmarking Functions -- 4.4 Experimental Setup -- 5 Experimental Results -- 5.1 Overall Discussion -- 5.2 Computational Burden -- 5.3 How Does p Influence Projections? -- 6 Conclusion -- References -- Using Particle Swarm Optimization with Gradient Descent for Parameter Learning in Convolutional Neural Networks -- 1 Introduction -- 2 Gradient-Based Learning in Neural Networks -- 3 Particle Swarm Optimization -- 4 Literature Review -- 5 Experimental Setup -- 5.1 MNIST Database -- 5.2 Evaluation -- 5.3 Hyperparameters -- 6 Model -- 7 Results -- 8 Conclusions and Future Work -- References -- Image Segmentation -- Object Delineation by Iterative Dynamic Trees -- 1 Introduction -- 2 Iterative Dynamic Trees -- 2.1 Object Delineation by Image Foresting Transform -- 2.2 The IDT Algorithm -- 3 Experimental Results -- 4 Conclusion -- References -- Low-Cost Domain Adaptation for Crop and Weed Segmentation -- 1 Introduction -- 2 Background -- 3 Methodology -- 4 Results -- 5 Conclusions and Future Work -- References -- Databases -- MIGMA: The Facial Emotion Image Dataset for Human Expression Recognition -- 1 Introduction -- 2 Related Works -- 3 Methodology: Proposed Dataset Environmental Protocol -- 4 Results -- 4.1 Dataset Properties -- 4.2 Dataset Statistical Analysis -- 4.3 Case-Study: Dataset Performance in a Convolutional Neural Network Framework -- 5 Conclusion and Discussions -- References -- Construction of Brazilian Regulatory Traffic Sign Recognition Dataset -- 1 Introduction -- 2 Related Works -- 3 Proposed Architecture -- 3.1 Image Pre-processing -- 3.2 The Dataset -- 3.3 CNN Architecture -- 4 Results and Discussion -- 5 Conclusion and Future Work -- References -- Japanese Kana and Brazilian Portuguese Manuscript Database. , 1 Introduction -- 2 The Dataset -- 3 Related Databases -- 4 Experimental Settings -- 4.1 Feature Extraction -- 4.2 Classifiers -- 5 Experimental Results and Discussion -- 5.1 Writer Identification -- 5.2 Syllabary Identification -- 6 Concluding Remarks -- References -- Skelibras: A Large 2D Skeleton Dataset of Dynamic Brazilian Signs -- 1 Introduction -- 2 Related Work -- 3 Corpus de Libras Dataset -- 4 Skelibras Dataset -- 5 Baseline Classifiers -- 6 Experiments -- 7 Conclusion -- References -- Deep Learning -- Cricket Scene Analysis Using the RetinaNet Architecture -- 1 Introduction -- 2 Problem Background -- 2.1 Related Works -- 3 Dataset and Experiment Setup -- 4 RetinaNet Architecture -- 5 Results -- 6 Discussion and Critique -- 7 Conclusion -- References -- Texture-Based Image Transformations for Improved Deep Learning Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Datasets -- 5 Evaluation and Results -- 5.1 Results for KTH-TIPS2-b Dataset -- 5.2 Results for Virus Dataset -- 6 Conclusion -- References -- Towards Precise Recognition of Pollen Bearing Bees by Convolutional Neural Networks -- 1 Introduction -- 2 Related Works -- 3 Convolutional Neural Networks Architectures -- 3.1 Transfer Learning -- 4 Pollen Bearing Bees Dataset -- 5 Experimental Setup -- 5.1 Colour Preprocessing Techniques -- 6 Results and Discussion -- 7 Conclusion -- References -- Web Application Attacks Detection Using Deep Learning -- 1 Introduction -- 2 Background and Related Work -- 3 A Two-Step Learning Approach for Anomaly Detection -- 3.1 Pre-training a HTTP Language Model -- 3.2 One-Class Classification -- 4 Results -- 5 Conclusion and Further Work -- References -- Less Is More: Accelerating Faster Neural Networks Straight from JPEG -- 1 Introduction -- 2 JPEG Compression -- 3 Related Work. , 4 Speeding up CNN Models Designed for DCT Input -- 4.1 Reducing the Number of Channels -- 4.2 Reducing the Number of Layers -- 5 Experiments and Results -- 5.1 Effects of Reducing the Number of Channels -- 5.2 Effects of Reducing the Number of Layers -- 6 Conclusion -- References -- Optimizing Person Re-Identification Using Generated Attention Masks -- 1 Introduction -- 2 Proposed Methodology -- 2.1 Network Architecture -- 2.2 Loss -- 3 Experimental Settings -- 3.1 Data -- 3.2 Data Augmentation -- 3.3 Training -- 4 Results and Discussion -- 5 Conclusion -- References -- Self-supervised Bernoulli Autoencoders for Semi-supervised Hashing*-8pt -- 1 Introduction -- 2 Related Works -- 3 Methods -- 3.1 Generative Model and Bernoulli Autoencoders -- 3.2 Parametrization by Neural Nets -- 3.3 Unsupervised Training -- 3.4 Semi-supervised Training -- 3.5 Efficient Implementation -- 4 Experiments -- 5 Conclusions -- References -- Explainable Artificial Intelligence -- Interpretable Concept Drift -- 1 Introduction -- 2 Related Works -- 3 Visualizing Drift in Decision Trees -- 3.1 Node Frequency Analysis -- 3.2 Node Accuracy Analysis -- 4 Interpretable Drift Detector -- 5 Experiments -- 6 Conclusion -- References -- Interpreting a Conditional Generative Adversarial Network Model for Crime Prediction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset -- 3.2 Conditional Generative Adversarial Network -- 3.3 Training and Evaluation Metrics -- 3.4 SHAP Values and Model Interpretation -- 4 Results -- 4.1 cGAN -- 4.2 Analysis of SHAP Values -- 5 Conclusions and Future Work -- References -- Interpreting Decision Patterns in Financial Applications -- 1 Introduction -- 2 Background - Interpretable AI in Finance -- 2.1 Interpretability Approaches -- 2.2 Interpretability Models -- 3 Proposed Approach -- 4 Experimental Setup -- 4.1 Dataset Description. , 4.2 Evaluation Metrics.