Materias: “…Artificial intelligence Government policy…”

Materias: “…Intel·ligència artificial…”

Materias: “…Intel·ligència artificial Informàtica…”

Materias:

Tabla de Contenidos: “…La impronta de la inteligencia artificial en el proceso -- Elemento psicológico de las decisiones judiciales e inteligencia artificial -- El periculum de las medidas cautelares y la inteligencia artificial -- Inteligencia artificial y valoración de la prueba -- Inteligencia artificial y sentencia -- Inteligencia artificial y derechos humanos…”

“…Artificis culturals…”

Materias:

Tabla de Contenidos: “…Introduction -- Technological Progress: Logistic Growth or Singularity -- Artificial Intelligence -- Artificial Happiness -- Issues in Artificial Intelligence -- Artificial Intelligence in Economics: ACE -- Economics of Artificial Intelligence -- State of the Art, Challenges for AGI…”

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Materias: “…Artificial intelligence Periodicals…”

Tabla de Contenidos: “…Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Chapter 1: An Introduction to Artificial Intelligence in Medical Sciences and Psychology -- Context of the Book -- The Book's Central Point -- Artificial Intelligence Subsets Covered in this Book -- Structure of the Book -- Tools Used in This Book -- Python Distribution Package -- Anaconda Distribution Package -- Jupyter Notebook -- Python Libraries -- Encapsulating Artificial Intelligence -- Implementing Algorithms -- Supervised Algorithms -- Unsupervised Algorithms -- Artificial Neural Networks -- Conclusion -- Chapter 2: Realizing Patterns in Diseases with Neural Networks -- Classifying Cardiovascular Disease Diagnosis Outcome Data by Executing a Deep Belief Network -- Preprocessing the Cardiovascular Disease Diagnosis Outcome Data -- Debunking Deep Belief Networks -- Designing the Deep Belief Network -- Relu Activation Function -- Sigmoid Activation Function -- Training the Deep Belief Network -- Outlining the Deep Belief Network's Predictions -- Considering the Deep Neural Network's Performance -- Accuracy Fluctuations Across Epochs in Training and Cross-Validation -- Binary Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation -- Classifying Diabetes Diagnosis Outcome Data by Executing a Deep Belief Network -- Executing a Deep Belief Network to Classify Diabetes Diagnosis Outcome Data -- Outlining the Deep Belief Network's Predictions -- Considering the Deep Neural Network's Performance -- Accuracy Fluctuations Across Epochs in Training and Cross-Validation -- Binary Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation -- Conclusion -- Chapter 3: A Case for COVID-19: Considering the Hidden States and Simulation Results -- Executing the Hidden Markov Model -- Descriptive Analysis…”

Tabla de Contenidos: “…Machine generated contents note: List of Contributors Preface Part One The Contributions of Intelligent Techniques in Multicriteria Decision Aiding 1 Computational Intelligence Techniques for Multicriteria Decision Aiding: An Overview 1.1 Introduction 1.2 The MCDA Paradigm 1.2.1 Modeling Process 1.2.2 Methodological Approaches 1.3 Computational Intelligence in MCDA 1.3.1 Statistical Learning and Data Mining 1.3.2 Fuzzy Modeling 1.3.3 Metaheuristics 1.4 Conclusions References 2 Intelligent Decision Support Systems 2.1 Introduction 2.2 Fundamentals of Human Decision Making 2.3 Decision Support System 2.4 Intelligent Decision Support Systems 2.4.1 Artificial Neural Networks for Intelligent Decision Support 2.4.2 Fuzzy Logic for Intelligent Decision Support 2.4.3 Expert Systems for Intelligent Decision Support 2.4.4 Evolutionary Computing for Intelligent Decision Support 2.4.5 Intelligent Agents for Intelligent Decision Support 2.5 Evaluating Intelligent Decision Support Systems 2.5.1 Determining Evaluation Criteria 2.5.2 Multi-Criteria Model for IDSS Assessment 2.6 Summary and Future Trends References Part Two Intelligent Technologies for Decision Support and Preference Modeling 3 Designing Distributed Multi-Criteria Decision Support Systems for Complex and Uncertain Situations 3.1 Introduction 3.2 Example Applications 3.3 Key Challenges 3.4 Making Trade-offs: Multi-criteria Decision Analysis 3.4.1 Multi-attribute Decision Support 3.4.2 Making Trade-offs Under Uncertainty 3.5 Exploring the Future: Scenario-based Reasoning 3.6 Making Robust Decisions: Combining MCDA and SBR 3.6.1 Decisions Under Uncertainty: The Concept of Robustness 3.6.2 Combining Scenarios and MCDA 3.6.3 Collecting, Sharing and Processing Information: A Distributed Approach 3.6.4 Keeping Track of Future Developments: Constructing Comparable Scenarios 3.6.5 Respecting Constraints and Requirements: Scenario Management 3.6.6 Assisting Evaluation: Assessing Large Numbers of Scenarios 3.7 Discussion 3.8 Conclusion References 4 Preference Representation with Ontologies 4.1 Introduction 4.1.1 Structure of the Chapter 4.2 Ontology-based Preference Models 4.3 Maintaining the User's Profile up to Date 4.4 Decision Making Methods Exploiting the Preference Information Stored in Ontologies 4.4.1 Recommendation Based on Aggregation 4.4.2 Recommendation Based on Similarities 4.4.3 Recommendation Based on Rules 4.5 Discussion and Open Questions References Part Three Decision Models 5 Neural Networks in Multicriteria Decision Support 5.1 Introduction 5.2 Basic Concepts of Neural Networks 5.2.1 Neural Networks for Intelligent Decision Support 5.3 Basics in Multicriteria Decision Aid 5.3.1 MCDM Problems 5.3.2 Solutions of MCDM Problems 5.4 Neural Networks and Multicriteria Decision Support 5.4.1 Review of Neural Network Applications to MCDM Problems 5.4.2 Discussion 5.5 Summary and Conclusions References 6 Rule-Based Approach to Multicriteria Ranking 6.1 Introduction 6.2 Problem Setting 6.3 Pairwise Comparison Table (PCT) 6.4 Rough Approximation of Outranking and Non-outranking Relations 6.5 Induction and Application of Decision Rules 6.6 Exploitation of Preference Graphs 6.7 Illustrative Example 6.8 Summary and Conclusions References 7 About the Application of Evidence Theory in MultiCriteria Decision Aid 7.1 Introduction 7.2 Evidence Theory: Some Concepts 7.2.1 Knowledge Model 7.2.2 Combination 7.2.3 Decision Making 7.3 New Concepts in Evidence Theory for MCDA 7.3.1 First Belief Dominance 7.3.2 RBBD Concept 7.4 Multicriteria Methods modeled by Evidence Theory 7.4.1 Evidential Reasoning Approach 7.4.2 DS/AHP 7.4.3 DISSET 7.4.4 A Choice Model Inspired by ELECTRE I 7.4.5 A Ranking Model Inspired by Xu et al.'…”

Tabla de Contenidos: “…Front Cover -- Artificial Intelligence for Future Generation Robotics -- Copyright Page -- Contents -- List of contributors -- About the editors -- Preface -- 1. …”

Materias:

Materias: “…Intel·ligència artificial…”
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Materias: “…Intel·ligència artificial…”