Handbook on decision making, Volume 3: trends and challenges in intelligent decision support systems
Cham, Switzerland: Springer, [2023]
Online
Sammelwerk, Teil eines Werkes, Elektronische Ressource
- 1 online resource (466 pages)
Ermittle Ausleihstatus...
Intro -- Preface -- Acknowledgements -- Contents -- Contributors -- Part I Methods and Techniques -- 1 A Vertical Fragmentation Method for Multimedia Databases Considering Content-Based Queries -- 1.1 Introduction -- 1.2 Background -- 1.3 State of the Art -- 1.4 Design of CBRVF -- 1.4.1 CBRVF -- 1.4.2 Web Application Design -- 1.5 Results and Discussion -- 1.6 Conclusion and Future Work -- References -- 2 An Approach Based on Process Mining Techniques to Support Software Development -- 2.1 Introduction -- 2.2 Background -- 2.3 Related Work -- 2.4 Framework -- 2.4.1 Phase 1: Event Log Management -- 2.4.2 Phase 2: Process Model Discovery -- 2.4.3 Phase 3: Statistics -- 2.5 Results -- 2.5.1 Case of a Purchase Order Process -- 2.5.2 Case of an Air Quality Monitoring System Process -- 2.6 Conclusions -- References -- 3 Analysis of Canonical Heuristic Methods for the Optimization of an Investment Portfolio -- 3.1 Introduction -- 3.2 Evolutionary Algorithms -- 3.3 Investment Portfolio -- 3.4 Theoretical Scaffolding -- 3.5 Genetic Algorithm -- 3.6 Differential Evolution -- 3.7 Artificial Immunological System -- 3.8 Methodology -- 3.9 Results -- 3.10 Conclusions -- References -- 4 Design of a Dynamic Horizontal Fragmentation Method for Multimedia Databases -- 4.1 Introduction -- 4.2 Background -- 4.3 Related Works -- 4.4 Design of a Dynamic Horizontal Fragmentation Method for Multimedia Databases -- 4.5 Results and Discussion -- 4.6 Conclusion -- References -- 5 Efficient Archiving Method for Handling Preferences in Constrained Multi-objective Evolutionary Optimization -- 5.1 Introduction -- 5.2 Problem Statement -- 5.3 Multi-objective Evolutionary Algorithms -- 5.3.1 Algorithms of Multi-Objective Evolutionary Optimization -- 5.3.2 Preference-Based MOEAs -- 5.3.3 Assessing Performance -- 5.4 Proposal -- 5.4.1 Archiving Regions of Interest.
5.5 Experimental Step -- 5.5.1 Problems to Be Solved -- 5.5.2 Algorithms for Comparison -- 5.5.3 Parameter Settings -- 5.6 Results and Discussion -- 5.6.1 Results on Unconstrained Problems (DTLZ) -- 5.6.2 Results on Constrained Problems (C-DTLZ) -- 5.6.3 Results on Real-World Multi-Objective Problems -- 5.7 Conclusions and Future Work -- References -- 6 Evaluation of Machine Learning Techniques for Malware Detection -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Background -- 6.3.1 Machine Learning Techniques -- 6.3.2 Measurement -- 6.4 Methodology -- 6.4.1 Data Preprocessing -- 6.4.2 Data Representation -- 6.4.3 Model Training/Testing -- 6.5 Results -- 6.5.1 Data Sets -- 6.5.2 Performance -- 6.6 Conclusions -- References -- 7 Implementation of Reinforcement-Learning Algorithms in Autonomous Robot Navigation -- 7.1 Introduction -- 7.2 Systematic Review of the Literature -- 7.2.1 Heuristic Algorithms -- 7.2.2 Applications of Reinforcement Learning -- 7.2.3 Synthesis and Considerations -- 7.3 Characteristics of Reinforcement Learning Algorithms -- 7.4 Methodology -- 7.4.1 Reinforcement Learning Algorithms -- 7.4.2 System Structure -- 7.4.3 Experiment Description -- 7.5 Results -- 7.6 Conclusions -- References -- 8 Trends on Decision Support Systems: A Bibliometric Review -- 8.1 Introduction -- 8.2 Methodology -- 8.2.1 PRISMA Method -- 8.2.2 Analysis with VOSviewer -- 8.3 Results -- 8.3.1 General Data of the DSS Applied -- 8.3.2 Authors, Organizations, and Countries that Publish the Most -- 8.3.3 Most Used Keywords -- 8.3.4 Most Cited Papers, Journals, Authors, Organizations, and Countries -- 8.3.5 Evolutions and Trends -- 8.4 Conclusions -- References -- 9 Use of Special Cases of Ontologies for Big Data Analysis in Decision Making Systems -- 9.1 Introduction -- 9.2 Ontological Representation of Knowledge.
9.3 Ontologies in Knowledge Organization Systems -- 9.4 Decision Making Models and External Knowledge -- 9.5 Semantization of Big Data Technology -- 9.6 Use of Big Data Analysis in DMS -- 9.7 Semantic Processing of Metadata for Big Data -- 9.8 Generation of Ontologies for DM -- 9.8.1 Wiki Ontologies -- 9.8.2 Task Thesauri -- 9.9 Practical Use of Proposed Approach -- 9.10 Conclusion -- References -- 10 Multicriteria Decision Making Methods-A Review and Case of Study -- 10.1 Introduction -- 10.2 Bibliometric Analysis of MCDM -- 10.2.1 The Timeline of Multicriteria Decision Models -- 10.2.2 Journals and Authors in MCDM -- 10.2.3 The Most Cited MCDM Documents and Their Keywords -- 10.2.4 The Application Areas of MCDM -- 10.2.5 Institutions and Countries that Publish the Most on MCDM -- 10.2.6 The Funding Sources in MCDM Research -- 10.3 Case Study -- 10.3.1 The Research Problem -- 10.3.2 Methodology -- 10.4 Results from Case Study -- 10.4.1 Obtaining the Subjective Attribute Values -- 10.4.2 The Final Decision Matrix (FDM) -- 10.4.3 Normalizing the Alternatives -- 10.4.4 Obtaining the Weights for Attributes -- 10.4.5 Weighting the Normalized Matrix -- 10.4.6 Distance to Ideal Positive and Ideal Negative -- 10.4.7 Proximity Indexes -- 10.5 Conclusions -- References -- Part II Cases of Study -- 11 Bitcoin Price Forecasting Through Crypto Market Variables: Quantile Regression and Machine Learning Approaches -- 11.1 Introduction and Related Literature -- 11.2 Methodology -- 11.2.1 Quantile Regression Model -- 11.2.2 Machine Learning Approach -- 11.3 Data -- 11.3.1 Determining Data Set for Quantile Regression Model and Machine Learning -- 11.4 Empirical Results and Discussion -- 11.4.1 Quantile Regression Results -- 11.4.2 Machine Learning Results -- 11.5 Conclusions -- References.
12 Crops Classification in Small Areas Using Unmanned Aerial Vehicles (UAV) and Deep Learning Pre-trained Models from Detectron2 -- 12.1 Introduction -- 12.1.1 Technologies 4.0 for Crop Classification -- 12.1.2 Types of Images Obtained by UAVs -- 12.1.3 Artificial Intelligence Methods Applied in Agriculture -- 12.1.4 Methods for Object Detection with Deep Learning -- 12.1.5 Transfer Learning -- 12.2 Materials and Method -- 12.2.1 Study Area -- 12.2.2 Data Collection -- 12.2.3 Data Labeling -- 12.2.4 Data Description -- 12.2.5 Detectron2 -- 12.2.6 Common Settings for COCO Models -- 12.2.7 ImageNet Pretrained Models -- 12.3 Results and Analysis -- 12.4 Conclusions -- 12.5 Future Work -- References -- 13 Design and Evaluation of Strategies to Mitigate the Impact of Dengue in Healthcare Institutions Through Dynamic Simulation -- 13.1 Introduction -- 13.2 State of the Art -- 13.3 Methodology -- 13.3.1 Conceptualization -- 13.3.2 Formulation -- 13.4 Results and Discussion -- 13.4.1 Test -- 13.4.2 Implementation -- 13.4.3 Sensitivity Analysis -- 13.5 Conclusion and Future Directions -- References -- 14 Detecting Arrhythmia Using the IoT Paradigm -- 14.1 Introduction -- 14.2 Related Work -- 14.3 Wearables for CVD Detection -- 14.4 A Web Application for AF Detection: Architecture and Functionality -- 14.5 Case Study: People Monitoring for Arrhythmia Detection -- 14.5.1 Application Features -- 14.5.2 Parameters and Rules for Arrhythmia Detection -- 14.5.3 Patient Monitoring -- 14.6 Conclusion and Future Directions -- References -- 15 Emotion Detection in Learning Environments Using Facial Expressions: A Brief Review -- 15.1 Introduction -- 15.2 State of the Art -- 15.3 API Analysis of Emotion Detection from Facial Expressions -- 15.4 Case Study: Emotions Recognition in a Learning Environment -- 15.5 Conclusion and Future Directions -- References.
16 Face Recognition-Eigenfaces -- 16.1 Introduction -- 16.2 Background and Related Works -- 16.2.1 Eigenfaces -- 16.2.2 Linear Discriminant Analysis (LDA) -- 16.3 Datasets -- 16.4 Architecture, Models and Data Preparation -- 16.5 Results -- 16.5.1 Metrics Comparison and Outliers Detection -- 16.5.2 Eigenfaces -- 16.5.3 Face Space -- 16.5.4 Projection of an Image on the Face Space -- 16.5.5 Face Recognition -- 16.6 Conclusions -- References -- 17 Genetic Algorithm for the Optimization of the Unequal-Area Facility Layout Problem -- 17.1 Introduction -- 17.2 The Unequal-Area Facility Layout Problem -- 17.3 Genetic Algorithm for the Optimization of the UAFLP -- 17.3.1 Solution Encoding and Representation -- 17.3.2 Fitness Function -- 17.3.3 Selection Operator -- 17.3.4 Crossover and Mutation Operators -- 17.3.5 Validation of the GA for Optimizing the UAFLP -- 17.4 Results of the GA Optimization for the Case of the Garment Industry -- 17.5 Conclusions -- References -- 18 Microsimulation Calibration Integrating Synthetic Population Generation and Complex Interaction Clusters to Evaluate COVID-19 Spread -- 18.1 Introduction -- 18.2 Agent-Based Microsimulation and Its Application to Disease Spread -- 18.3 Synthetic Population Generation -- 18.4 Synthetic Population Generation Integrated with Complex Interaction Clusters -- 18.5 Application of the Proposed Synthetic Population Generation -- 18.6 Microsimulation of COVID-19 Spread -- 18.7 Conclusions -- References -- 19 A Decision Support System for Container Handling Operations at a Seaport Terminal with Disturbances: Design and Concepts -- 19.1 Introduction -- 19.2 Related Work -- 19.2.1 Yard Operations -- 19.2.2 DSS for Container Terminals -- 19.2.3 Disturbances in Container Terminals -- 19.3 Disturbances Characterization: Case Study of Chilean Ports -- 19.4 DSS Proposal and Concepts.
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Handbook on decision making, Volume 3: trends and challenges in intelligent decision support systems
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Verantwortlichkeitsangabe: | edited by Julian Andres Zapata-Cortes [and three others] |
Autor/in / Beteiligte Person: | Zapata-Cortes, Julian Andres [editor.] |
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Veröffentlichung: | Cham, Switzerland: Springer, [2023] |
Medientyp: | Sammelwerk, Teil eines Werkes |
Datenträgertyp: | Elektronische Ressource |
Umfang: | 1 online resource (466 pages) |
ISBN: | 3-031-08246-X |
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