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Human Judgment : How Accurate Is It, and How Can It Get Better? / / John Wilcox
| Human Judgment : How Accurate Is It, and How Can It Get Better? / / John Wilcox |
| Autore | Wilcox John |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (157 pages) |
| Disciplina | 153.46 |
| Collana | SpringerBriefs in Psychology Series |
| Soggetto topico |
Decision making - Psychological aspects
Judgment Criteri Presa de decisions Aspectes psicològics |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783031192050
9783031192043 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Testimonies -- Acknowledgments -- Contents -- Chapter 1: Introduction -- 1.1 Judgmental Accuracy: Why It Is Important -- 1.2 The Focus of This Book -- 1.3 How This Book Was Researched -- 1.4 Intended Audiences and How to Read This Book -- 1.5 Structure of This Book -- References -- Chapter 2: What Is Judgmental Accuracy: Concepts and Measurement -- 2.1 What Judgmental Accuracy Involves: Correspondence and Confidence -- 2.1.1 Objective Truth and the Correspondence Theory -- 2.1.2 Misconceptions About Objective Truth -- 2.1.2.1 Objection #1: The Diversity of Opinions About Truth -- 2.1.2.2 Objection #2: The Subjectivity of Truths About Money or Other Topics -- 2.1.2.3 Objection #3: Track Record of Failures to Grasp Truth -- 2.1.2.4 Objection #4: There Is No Way to Tell Truth -- 2.1.2.5 Objection #5: Truth Depends on Language -- 2.1.2.6 Objection #6: The Ambiguity and Vagueness of Language -- 2.1.2.7 Objection #7: Alternative Definitions of Objectivity -- 2.1.3 Why Does It Matter? -- 2.1.4 Degrees of Confidence -- 2.2 How Do We Measure Judgmental Accuracy: Calibration, Resolution, and Friends -- 2.2.1 Measurement Validity: Internal and External -- 2.2.2 A Good Measure of Accuracy: Binned Calibration and Resolution -- 2.2.3 Less Good Measures of Accuracy -- 2.2.3.1 Unbinned Calibration -- 2.2.3.2 Brier Scores -- 2.2.4 Measures of Collective Accuracy -- 2.2.4.1 Unweighted Binned Calibration -- 2.2.4.2 Unbinned Calibration -- 2.2.4.3 Brier Scores -- 2.2.4.4 Weighted Binned Calibration -- 2.3 Summary -- References -- Chapter 3: What We Think: The Accuracy of Our Judgments -- 3.1 Who Is Accurate: How Society Flies Blind -- 3.2 How Accurate Are Cultures: Inaccuracy in Cross-Cultural Psychology -- 3.3 How Accurate Are Medical Professionals: Inaccuracy in Medicine -- 3.4 How Accurate Are Political Experts: Inaccuracy in Political Judgment.
3.5 How Accurate Are Judges and Juries: Inaccuracy in Law -- 3.6 Other Evidence of Inaccuracy: Disagreement -- 3.7 Contexts with Underconfidence -- 3.8 Summary -- References -- Chapter 4: How We Evaluate Our Thinking: The Accuracy of Our Metacognition -- 4.1 Evidence of Metacognitive Inaccuracy -- 4.2 Explanations of Metacognitive Inaccuracy -- 4.3 Summary -- References -- Chapter 5: How We Think: The Rationality of Our Reasoning -- 5.1 Rationality, Heuristics, and Biases -- 5.2 Dual-Process Theory: System 1 and System 2 -- 5.3 Misconceptions About Heuristics and Type 1 Processing -- 5.4 Search Heuristics and Inference Heuristics -- 5.4.1 Motivation, Search Heuristics, and Confirmation Bias -- 5.4.2 Availability Heuristic -- 5.4.3 Representativeness Heuristic -- 5.4.4 Anchoring Heuristic -- 5.4.5 Motivated Reasoning -- 5.5 Social Influences -- 5.6 Summary -- References -- Chapter 6: How We Were Made: The Evolutionary Origins of Thought -- 6.1 Evolution, Functions, and the Intellectualist View -- 6.2 Mercier and Sperber's Interactionist Approach -- 6.3 Critical Evaluation of Mercier and Sperber's Arguments -- 6.4 Tangential Interlude: The Harm of Confirmation Bias -- 6.5 Summary -- References -- Chapter 7: What Correlates with Accuracy: The Empirical Epistemology of Optimal Cognition -- 7.1 Empirical Epistemology -- 7.2 The Domain Generality of Empirical Epistemology -- 7.3 Insights from Empirical Epistemology -- 7.3.1 Situational Variables -- 7.3.2 Motivational Variables -- 7.3.3 Cognitive Variables -- 7.3.4 Metacognitive Variables -- 7.3.5 What Does Not Correlate with Accuracy -- 7.4 Summary -- References -- Chapter 8: How Can We Get More Accurate: Recommendations About Human Judgment -- 8.1 Category 1: Improving Our Own Judgments -- 8.1.1 Foster Motivation -- 8.1.2 Become Accountable -- 8.1.3 Track Your Accuracy. 8.1.4 Be Your Own Skeptic: Expect Inaccuracy and Embrace Humility -- 8.1.5 Beware of Intuition -- 8.1.6 Practice Active Open-Minded Thinking -- 8.1.7 Gather Subject-Specific Knowledge and from Diverse Sources -- 8.1.8 Use Statistics, Especially Base Rates -- 8.1.9 Average Estimates from Conflicting Sources -- 8.1.10 Test for Scope Sensitivity -- 8.1.11 Do Postmortems -- 8.1.12 Take Some Training -- 8.2 Category 2: Estimating the Accuracy of Other Sources -- 8.2.1 Be Skeptical of Judgment, But Not Too Skeptical -- 8.2.2 Estimate Accuracy Based on Track Records -- 8.2.3 Look for Models or Theories with Track Records of Accuracy -- 8.2.4 Pay Attention to Qualifiers -- 8.2.5 Do Not Estimate Accuracy Based on One-Off Successes or Failures -- 8.2.6 Do Not Always Estimate Accuracy from Years of Experience, Education, Fame, or Confidence Levels -- 8.2.7 Trust Experts, But Not Too Much -- 8.2.8 Listen to Non-Experts, But Not Uncritically -- 8.2.9 Beware of Negative Social Influences -- 8.2.10 Tolerate Length and Nuance -- 8.3 Category 3: Managing Businesses or Other Organizations -- 8.3.1 Adopt all the Recommendations in the Previous Category -- 8.3.2 Promote Motivation and Accountability in Your Organization -- 8.3.3 Measure Track Records -- 8.3.4 Give Feedback -- 8.3.5 Expect Backlash from the Inaccurate -- 8.3.6 When Possible, Create Teams, Especially of Those with the Best Track Records -- 8.3.7 Give Training -- 8.3.8 Make Accuracy Profitable -- References -- Chapter 9: Conclusion -- Appendix: Judgments and Emotions -- The Close Connection Between Judgment and Emotions -- Cognitive Behavioral Therapy -- Step One: Understanding Emotions Via Understanding Their Underlying Judgments -- Step Two: Challenging Judgments -- References -- Index. |
| Record Nr. | UNINA-9910639879803321 |
Wilcox John
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Information and communication technologies for agriculture . Theme III, : decision / / Dionysis D. Bochtis [and four others], editors
| Information and communication technologies for agriculture . Theme III, : decision / / Dionysis D. Bochtis [and four others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (303 pages) |
| Disciplina | 338.10285 |
| Collana | Springer Optimization and its Applications |
| Soggetto topico |
Agriculture - Decision making
Agriculture - Decision making - Methodology Enginyeria agronòmica Agricultura Presa de decisions |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-84152-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Part I: Value Chain -- Agricultural Information Model -- 1 Introduction -- 2 Related Work -- 3 Technical Requirements -- 3.1 Core Data Modeling Requirements -- 3.2 Semantic Interoperability Requirements -- 4 AIM Design -- 4.1 Meta-model Layer -- 4.2 Cross-Domain Layer -- Cross-Domain Integration Process -- 4.3 Domain Layer -- Domain Layer Requirements -- AIM Domain-Specific Ontologies -- 5 Semantic Interoperability -- 6 Implementation -- 6.1 Meta-model Implementation -- 6.2 Cross-Domain Implementation -- 6.3 Domain-Specific Implementation -- 7 Methodology for Profiles -- 8 Exemplary Use Cases -- 9 Conclusions and Future Work -- References -- Development of a Framework for Implementing o- on the Beef Cattle Value Chain -- 1 Introduction -- 2 Related Work -- 2.1 Frameworks for IoT in Agri-food Value Chains -- 2.2 General Frameworks for IoT and the IoT-A -- 3 Methodology -- 4 Results -- 4.1 Overview of the Beef Cattle Value Chain -- 4.2 Requirements and Services Identification -- 4.3 IoT-A for the Beef Cattle Value Chain -- 5 Discussion -- 6 Conclusions -- References -- Food Business Information Systems in Western Greece -- 1 Introduction -- 2 Literature Review -- 2.1 Studies from 1990 to 2000 -- 2.2 Studies from 2001 to 2005 -- 2.3 Studies from 2006 to 2010 -- 2.4 Studies from 2011 to 2015 -- 2.5 Studies from 2016 Until Today -- 3 Methodology -- 4 Results -- 4.1 Adoption of Human Resources Information Systems -- 4.2 Adoption of Accounting and Financial Information System -- 4.3 Adoption of Sales and Marketing Information Systems -- 4.4 Adoption of Operational Information Systems -- 4.5 Adoption of Production Information Systems -- 4.6 Analysis of Software Packages Applications in Food Businesses of Western Greece -- 5 Conclusions -- References -- Part II: Primary Production.
From Precision Agriculture to Agriculture 4.0: Integrating ICT in Farming -- 1 Introduction -- 2 Agriculture 4.0 Constituents -- 2.1 Internet of Things (IoT) -- 2.2 Artificial Intelligence (AI) -- 2.3 Machine Learning (ML) -- 2.4 Big Data Analytics -- 2.5 Wireless Sensor Networks (WSN) -- 2.6 Blockchain -- 2.7 Cloud Computing -- 2.8 Automated Guided Vehicles -- 2.9 5G Technology -- 3 Discussion -- References -- On the Routing of Unmanned Aerial Vehicles (UAVs) in Precision Farming Sampling Missions -- 1 Introduction -- 2 Types of UAVs and Their Use -- 3 UAVs Applications in Precision Agriculture -- 4 UAVs Route Planning -- 5 Algorithms for Solving TSP -- 5.1 Exact Algorithms -- Dynamic Programming -- Branch-and-Bound Algorithms -- Branch-and-Cut Algorithms -- 5.2 Algorithms for Sub-optimal Solutions -- Approximation Algorithms -- Christofides-Serdyukov Algorithm -- Heuristics -- Nearest Neighbor Algorithm -- Multiple Fragment Algorithm -- k-Opt or Lin-Kernighan Heuristics -- Metaheuristics -- Genetic Algorithms -- Ant Colony Optimization -- 6 Demonstration of UAVs Routing in Agriculture -- 6.1 Single TSP (sTSP) -- 6.2 Multiple TSP (mTSP) (Without a Fixed Depot) -- 6.3 Multiple TSP, Single (Fixed) Depot (mTSPsD) -- 6.4 Multiple TSP, Multiple (Fixed) Depots (mTSPmD) -- 6.5 Multiple TSP, Multiple (Fixed) Depots and Constrained Travelling Distance (mTSPmDcT) -- 7 Conclusions -- References -- 3D Scenery Construction of Agricultural Environments for Robotics Awareness -- 1 Introduction -- 1.1 Depth Cameras -- 2 Point Cloud Processing and Digitalization -- 2.1 3D Mapping -- 2.2 Digital Twin -- 2.3 Simulation Environments -- 2.4 Aim of This Chapter -- 3 Demonstrative Scenario: An In-field Application -- 3.1 Point Cloud Data Acquisition -- 3.2 Point Cloud Data Processing -- 3.3 Orchard´s Simulation Environment -- 4 Conclusions -- References. A Weed Control Unmanned Ground Vehicle Prototype for Precision Farming Activities: The Case of Red Rice -- 1 Introduction -- 2 Literature Review -- 3 Materials and Methods -- 3.1 Research Design -- 3.2 Case Study -- 4 Robot Prototype Development -- 4.1 Rod Mechanism -- 4.2 Autonomous Vehicle -- 5 Results -- 5.1 Simulation Environment -- 5.2 Real-World Environment -- 6 Conclusions -- References -- Decision-Making and Decision Support System for a Successful Weed Management -- 1 Introduction -- 1.1 The Introduction of Decision Support Systems (DSS) in Agriculture -- 1.2 The Development of DSSs in Terms of Weed Management -- 2 Factors Affecting Decision-Making Process in DSSs for Weed Management -- 2.1 Weed Emergence and Weed Flora Composition in the Field -- 2.2 The Impact of Weed Competition on Crops´ Productivity -- 3 Factors Affecting Decision-Making Either in the Short- or in the Long-Term Period and Future Challenges of DSSs Developed fo... -- 4 Conclusion -- References -- Zephyrus: Grain Aeration Strategy Based on the Prediction of Temperature and Moisture Fronts -- 1 Introduction -- 2 Methodology -- 2.1 Theory Basis of Zephyrus Control Strategy -- 2.2 Description of Zephyrus Control Strategy -- 2.3 Experimental Evaluation of Zephyrus Control Strategy -- 2.4 Comparison of Zephyrus with Other Aeration Controllers -- 3 Results and Discussion -- 3.1 Experimental Evaluation of Zephyrus Control Strategy -- 3.2 Comparison of Zephyrus with Other Aeration Controllers -- 4 Conclusions -- References -- Decision-Making Applications on Smart Livestock Farming -- 1 Smart Livestock Farming -- 1.1 Concepts and Fundamentals -- 1.2 Smart Livestock Farming Models Implemented On-farm Actions -- Pig Production -- Poultry Production -- Dairy and Beef Production -- 2 Tools for Implementing Decision-Making Applications in Smart Livestock Farming. 2.1 Paraconsistent Logic Applications -- Applications -- Poultry Production -- 2.2 Pig production -- 2.3 Use of Machine Learning on Livestock Production -- Applications -- Dairy Production -- Poultry Production -- Pig Production -- 2.4 Technical Challenges -- 3 Final Remarks -- References -- Part III: Environment -- Programmable Process Structures of Unified Elements for Model-Based Planning and Operation of Complex Agri-environmental Proce... -- 1 Introduction -- 1.1 Functionality Modeling of Complex Process Systems -- 1.2 Structural Modeling of Complex Systems -- 2 Methodology -- 3 Results and Discussion -- 3.1 Recirculation Aquaculture System -- Challenge -- Experimental Unit -- Conceptual Model -- PPS Implementation of the Model -- Validation of a Pilot Experiment -- Study of a Complete Fish Grading Process -- Simulation-Based Design -- Experiences About the Applied Methodology -- 3.2 Ecosystem-Involved Fishpond -- Challenge -- Investigated Production Site -- Conceptual Model -- PPS Implementation of the Model -- Validation of the Model -- Simulation of Various Managerial Strategies -- Effect of Climate Change on Production of Fishpond -- Experiences About the Applied Methodology -- 3.3 Agroforestry Site -- Challenge -- Experimental Site -- Conceptual Model -- PPS Implementation of the Model -- Illustration of Simulation-Based Analysis -- Experiences About the Applied Methodology -- 4 Concluding Discussion -- References -- Monitoring and Estimation of Sugarcane Burning in the Middle Paranapanema Basin, Brazil, Using Linear Mixed Models -- 1 Introduction -- 2 Material and Methods -- 2.1 Topographic Survey -- 2.2 Statistical Modeling -- 3 Results and Discussion -- 4 Conclusions -- References -- A Decision Support System for Green Crop Fertilization Planning -- 1 Introduction -- 2 System Description -- 3 Case Study Demonstration. 3.1 The Demonstrated Crops -- 3.2 Fertilization Scenario -- 3.3 Input Parameters -- 4 Results -- 5 Discussion -- 6 Conclusions -- References -- Knowledge Elicitation and Modeling of Agroecological Management Strategies -- 1 Introduction -- 2 Agroecological Farm Management -- 3 Developing a Farm-Management Model -- 4 Decision-Relevant Concepts -- 4.1 Activities, Operations, and Resources -- 4.2 Goals and Plans -- 4.3 Preferences and Priorities -- 4.4 Events and Reactions -- 5 Example of an Agroecological Management Strategy -- 6 Discussion and Conclusion -- References. |
| Record Nr. | UNISA-996472036703316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Information and communication technologies for agriculture . Theme III, : decision / / Dionysis D. Bochtis [and four others], editors
| Information and communication technologies for agriculture . Theme III, : decision / / Dionysis D. Bochtis [and four others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (303 pages) |
| Disciplina | 338.10285 |
| Collana | Springer Optimization and its Applications |
| Soggetto topico |
Agriculture - Decision making
Agriculture - Decision making - Methodology Enginyeria agronòmica Agricultura Presa de decisions |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-84152-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Part I: Value Chain -- Agricultural Information Model -- 1 Introduction -- 2 Related Work -- 3 Technical Requirements -- 3.1 Core Data Modeling Requirements -- 3.2 Semantic Interoperability Requirements -- 4 AIM Design -- 4.1 Meta-model Layer -- 4.2 Cross-Domain Layer -- Cross-Domain Integration Process -- 4.3 Domain Layer -- Domain Layer Requirements -- AIM Domain-Specific Ontologies -- 5 Semantic Interoperability -- 6 Implementation -- 6.1 Meta-model Implementation -- 6.2 Cross-Domain Implementation -- 6.3 Domain-Specific Implementation -- 7 Methodology for Profiles -- 8 Exemplary Use Cases -- 9 Conclusions and Future Work -- References -- Development of a Framework for Implementing o- on the Beef Cattle Value Chain -- 1 Introduction -- 2 Related Work -- 2.1 Frameworks for IoT in Agri-food Value Chains -- 2.2 General Frameworks for IoT and the IoT-A -- 3 Methodology -- 4 Results -- 4.1 Overview of the Beef Cattle Value Chain -- 4.2 Requirements and Services Identification -- 4.3 IoT-A for the Beef Cattle Value Chain -- 5 Discussion -- 6 Conclusions -- References -- Food Business Information Systems in Western Greece -- 1 Introduction -- 2 Literature Review -- 2.1 Studies from 1990 to 2000 -- 2.2 Studies from 2001 to 2005 -- 2.3 Studies from 2006 to 2010 -- 2.4 Studies from 2011 to 2015 -- 2.5 Studies from 2016 Until Today -- 3 Methodology -- 4 Results -- 4.1 Adoption of Human Resources Information Systems -- 4.2 Adoption of Accounting and Financial Information System -- 4.3 Adoption of Sales and Marketing Information Systems -- 4.4 Adoption of Operational Information Systems -- 4.5 Adoption of Production Information Systems -- 4.6 Analysis of Software Packages Applications in Food Businesses of Western Greece -- 5 Conclusions -- References -- Part II: Primary Production.
From Precision Agriculture to Agriculture 4.0: Integrating ICT in Farming -- 1 Introduction -- 2 Agriculture 4.0 Constituents -- 2.1 Internet of Things (IoT) -- 2.2 Artificial Intelligence (AI) -- 2.3 Machine Learning (ML) -- 2.4 Big Data Analytics -- 2.5 Wireless Sensor Networks (WSN) -- 2.6 Blockchain -- 2.7 Cloud Computing -- 2.8 Automated Guided Vehicles -- 2.9 5G Technology -- 3 Discussion -- References -- On the Routing of Unmanned Aerial Vehicles (UAVs) in Precision Farming Sampling Missions -- 1 Introduction -- 2 Types of UAVs and Their Use -- 3 UAVs Applications in Precision Agriculture -- 4 UAVs Route Planning -- 5 Algorithms for Solving TSP -- 5.1 Exact Algorithms -- Dynamic Programming -- Branch-and-Bound Algorithms -- Branch-and-Cut Algorithms -- 5.2 Algorithms for Sub-optimal Solutions -- Approximation Algorithms -- Christofides-Serdyukov Algorithm -- Heuristics -- Nearest Neighbor Algorithm -- Multiple Fragment Algorithm -- k-Opt or Lin-Kernighan Heuristics -- Metaheuristics -- Genetic Algorithms -- Ant Colony Optimization -- 6 Demonstration of UAVs Routing in Agriculture -- 6.1 Single TSP (sTSP) -- 6.2 Multiple TSP (mTSP) (Without a Fixed Depot) -- 6.3 Multiple TSP, Single (Fixed) Depot (mTSPsD) -- 6.4 Multiple TSP, Multiple (Fixed) Depots (mTSPmD) -- 6.5 Multiple TSP, Multiple (Fixed) Depots and Constrained Travelling Distance (mTSPmDcT) -- 7 Conclusions -- References -- 3D Scenery Construction of Agricultural Environments for Robotics Awareness -- 1 Introduction -- 1.1 Depth Cameras -- 2 Point Cloud Processing and Digitalization -- 2.1 3D Mapping -- 2.2 Digital Twin -- 2.3 Simulation Environments -- 2.4 Aim of This Chapter -- 3 Demonstrative Scenario: An In-field Application -- 3.1 Point Cloud Data Acquisition -- 3.2 Point Cloud Data Processing -- 3.3 Orchard´s Simulation Environment -- 4 Conclusions -- References. A Weed Control Unmanned Ground Vehicle Prototype for Precision Farming Activities: The Case of Red Rice -- 1 Introduction -- 2 Literature Review -- 3 Materials and Methods -- 3.1 Research Design -- 3.2 Case Study -- 4 Robot Prototype Development -- 4.1 Rod Mechanism -- 4.2 Autonomous Vehicle -- 5 Results -- 5.1 Simulation Environment -- 5.2 Real-World Environment -- 6 Conclusions -- References -- Decision-Making and Decision Support System for a Successful Weed Management -- 1 Introduction -- 1.1 The Introduction of Decision Support Systems (DSS) in Agriculture -- 1.2 The Development of DSSs in Terms of Weed Management -- 2 Factors Affecting Decision-Making Process in DSSs for Weed Management -- 2.1 Weed Emergence and Weed Flora Composition in the Field -- 2.2 The Impact of Weed Competition on Crops´ Productivity -- 3 Factors Affecting Decision-Making Either in the Short- or in the Long-Term Period and Future Challenges of DSSs Developed fo... -- 4 Conclusion -- References -- Zephyrus: Grain Aeration Strategy Based on the Prediction of Temperature and Moisture Fronts -- 1 Introduction -- 2 Methodology -- 2.1 Theory Basis of Zephyrus Control Strategy -- 2.2 Description of Zephyrus Control Strategy -- 2.3 Experimental Evaluation of Zephyrus Control Strategy -- 2.4 Comparison of Zephyrus with Other Aeration Controllers -- 3 Results and Discussion -- 3.1 Experimental Evaluation of Zephyrus Control Strategy -- 3.2 Comparison of Zephyrus with Other Aeration Controllers -- 4 Conclusions -- References -- Decision-Making Applications on Smart Livestock Farming -- 1 Smart Livestock Farming -- 1.1 Concepts and Fundamentals -- 1.2 Smart Livestock Farming Models Implemented On-farm Actions -- Pig Production -- Poultry Production -- Dairy and Beef Production -- 2 Tools for Implementing Decision-Making Applications in Smart Livestock Farming. 2.1 Paraconsistent Logic Applications -- Applications -- Poultry Production -- 2.2 Pig production -- 2.3 Use of Machine Learning on Livestock Production -- Applications -- Dairy Production -- Poultry Production -- Pig Production -- 2.4 Technical Challenges -- 3 Final Remarks -- References -- Part III: Environment -- Programmable Process Structures of Unified Elements for Model-Based Planning and Operation of Complex Agri-environmental Proce... -- 1 Introduction -- 1.1 Functionality Modeling of Complex Process Systems -- 1.2 Structural Modeling of Complex Systems -- 2 Methodology -- 3 Results and Discussion -- 3.1 Recirculation Aquaculture System -- Challenge -- Experimental Unit -- Conceptual Model -- PPS Implementation of the Model -- Validation of a Pilot Experiment -- Study of a Complete Fish Grading Process -- Simulation-Based Design -- Experiences About the Applied Methodology -- 3.2 Ecosystem-Involved Fishpond -- Challenge -- Investigated Production Site -- Conceptual Model -- PPS Implementation of the Model -- Validation of the Model -- Simulation of Various Managerial Strategies -- Effect of Climate Change on Production of Fishpond -- Experiences About the Applied Methodology -- 3.3 Agroforestry Site -- Challenge -- Experimental Site -- Conceptual Model -- PPS Implementation of the Model -- Illustration of Simulation-Based Analysis -- Experiences About the Applied Methodology -- 4 Concluding Discussion -- References -- Monitoring and Estimation of Sugarcane Burning in the Middle Paranapanema Basin, Brazil, Using Linear Mixed Models -- 1 Introduction -- 2 Material and Methods -- 2.1 Topographic Survey -- 2.2 Statistical Modeling -- 3 Results and Discussion -- 4 Conclusions -- References -- A Decision Support System for Green Crop Fertilization Planning -- 1 Introduction -- 2 System Description -- 3 Case Study Demonstration. 3.1 The Demonstrated Crops -- 3.2 Fertilization Scenario -- 3.3 Input Parameters -- 4 Results -- 5 Discussion -- 6 Conclusions -- References -- Knowledge Elicitation and Modeling of Agroecological Management Strategies -- 1 Introduction -- 2 Agroecological Farm Management -- 3 Developing a Farm-Management Model -- 4 Decision-Relevant Concepts -- 4.1 Activities, Operations, and Resources -- 4.2 Goals and Plans -- 4.3 Preferences and Priorities -- 4.4 Events and Reactions -- 5 Example of an Agroecological Management Strategy -- 6 Discussion and Conclusion -- References. |
| Record Nr. | UNINA-9910568296603321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine learning and data analytics for solving business problems : methods, applications, and case studies / / edited by Bader Alyoubi, [and four others]
| Machine learning and data analytics for solving business problems : methods, applications, and case studies / / edited by Bader Alyoubi, [and four others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (214 pages) |
| Disciplina | 780 |
| Collana | Unsupervised and Semi-Supervised Learning |
| Soggetto topico |
Machine learning
Aprenentatge automàtic Presa de decisions Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-18483-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910635386903321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine learning and data analytics for solving business problems : methods, applications, and case studies / / edited by Bader Alyoubi, [and four others]
| Machine learning and data analytics for solving business problems : methods, applications, and case studies / / edited by Bader Alyoubi, [and four others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (214 pages) |
| Disciplina | 780 |
| Collana | Unsupervised and Semi-Supervised Learning |
| Soggetto topico |
Machine learning
Aprenentatge automàtic Presa de decisions Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-18483-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996503550603316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine learning for practical decision making : a multidisciplinary perspective with applications from healthcare, engineering and business analytics / / Christo El Morr [and three others]
| Machine learning for practical decision making : a multidisciplinary perspective with applications from healthcare, engineering and business analytics / / Christo El Morr [and three others] |
| Autore | El Morr Christo <1966-> |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (475 pages) |
| Disciplina | 658.403 |
| Collana | International series in operations research & management science |
| Soggetto topico |
Decision making - Data processing
Machine learning Presa de decisions Processament de dades Aprenentatge automàtic |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-16990-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Chapter 1: Introduction to Machine Learning -- 1.1 Introduction to Machine Learning -- 1.2 Origin of Machine Learning -- 1.3 Growth of Machine Learning -- 1.4 How Machine Learning Works -- 1.5 Machine Learning Building Blocks -- 1.5.1 Data Management and Exploration -- 1.5.1.1 Data, Information, and Knowledge -- 1.5.1.2 Big Data -- 1.5.1.3 OLAP Versus OLTP -- 1.5.1.4 Databases, Data Warehouses, and Data Marts -- 1.5.1.5 Multidimensional Analysis Techniques -- 1.5.1.5.1 Slicing and Dicing -- 1.5.1.5.2 Pivoting -- 1.5.1.5.3 Drill-Down, Roll-Up, and Drill-Across -- 1.5.2 The Analytics Landscape -- 1.5.2.1 Types of Analytics (Descriptive, Diagnostic, Predictive, Prescriptive) -- 1.5.2.1.1 Descriptive Analytics -- 1.5.2.1.2 Diagnostic Analytics -- 1.5.2.1.3 Predictive Analytics -- 1.5.2.1.4 Prescriptive Analytics -- 1.6 Conclusion -- 1.7 Key Terms -- 1.8 Test Your Understanding -- 1.9 Read More -- 1.10 Lab -- 1.10.1 Introduction to R -- 1.10.2 Introduction to RStudio -- 1.10.2.1 RStudio Download and Installation -- 1.10.2.2 Install a Package -- 1.10.2.3 Activate Package -- 1.10.2.4 User Readr to Load Data -- 1.10.2.5 Run a Function -- 1.10.2.6 Save Status -- 1.10.3 Introduction to Python and Jupyter Notebook IDE -- 1.10.3.1 Python Download and Installation -- 1.10.3.2 Jupyter Download and Installation -- 1.10.3.3 Load Data and Plot It Visually -- 1.10.3.4 Save the Execution -- 1.10.3.5 Load a Saved Execution -- 1.10.3.6 Upload a Jupyter Notebook File -- 1.10.4 Do It Yourself -- References -- Chapter 2: Statistics -- 2.1 Overview of the Chapter -- 2.2 Definition of General Terms -- 2.3 Types of Variables -- 2.3.1 Measures of Central Tendency -- 2.3.1.1 Measures of Dispersion -- 2.4 Inferential Statistics -- 2.4.1 Data Distribution -- 2.4.2 Hypothesis Testing -- 2.4.3 Type I and II Errors.
2.4.4 Steps for Performing Hypothesis Testing -- 2.4.5 Test Statistics -- 2.4.5.1 Student´s t-test -- 2.4.5.2 One-Way Analysis of Variance -- 2.4.5.3 Chi-Square Statistic -- 2.4.5.4 Correlation -- 2.4.5.5 Simple Linear Regression -- 2.5 Conclusion -- 2.6 Key Terms -- 2.7 Test Your Understanding -- 2.8 Read More -- 2.9 Lab -- 2.9.1 Working Example in R -- 2.9.1.1 Statistical Measures Overview -- 2.9.1.2 Central Tendency Measures in R -- 2.9.1.3 Dispersion in R -- 2.9.1.4 Statistical Test Using p-value in R -- 2.9.2 Working Example in Python -- 2.9.2.1 Central Tendency Measure in Python -- 2.9.2.2 Dispersion Measures in Python -- 2.9.2.3 Statistical Testing Using p-value in Python -- 2.9.3 Do It Yourself -- 2.9.4 Do More Yourself (Links to Available Datasets for Use) -- References -- Chapter 3: Overview of Machine Learning Algorithms -- 3.1 Introduction -- 3.2 Data Mining -- 3.3 Analytics and Machine Learning -- 3.3.1 Terminology Used in Machine Learning -- 3.3.2 Machine Learning Algorithms: A Classification -- 3.4 Supervised Learning -- 3.4.1 Multivariate Regression -- 3.4.1.1 Multiple Linear Regression -- 3.4.1.2 Multiple Logistic Regression -- 3.4.2 Decision Trees -- 3.4.3 Artificial Neural Networks -- 3.4.3.1 Perceptron -- 3.4.4 Naïve Bayes Classifier -- 3.4.5 Random Forest -- 3.4.6 Support Vector Machines (SVM) -- 3.5 Unsupervised Learning -- 3.5.1 K-Means -- 3.5.2 K-Nearest Neighbors (KNN) -- 3.5.3 AdaBoost -- 3.6 Applications of Machine Learning -- 3.6.1 Machine Learning Demand Forecasting and Supply Chain Performance [42] -- 3.6.2 A Case Study on Cervical Pain Assessment with Motion Capture [43] -- 3.6.3 Predicting Bank Insolvencies Using Machine Learning Techniques [44] -- 3.6.4 Deep Learning with Convolutional Neural Network for Objective Skill Evaluation in Robot-Assisted Surgery [45] -- 3.7 Conclusion -- 3.8 Key Terms. 3.9 Test Your Understanding -- 3.10 Read More -- 3.11 Lab -- 3.11.1 Machine Learning Overview in R -- 3.11.1.1 Caret Package -- 3.11.1.2 ggplot2 Package -- 3.11.1.3 mlBench Package -- 3.11.1.4 Class Package -- 3.11.1.5 DataExplorer Package -- 3.11.1.6 Dplyr Package -- 3.11.1.7 KernLab Package -- 3.11.1.8 Mlr3 Package -- 3.11.1.9 Plotly Package -- 3.11.1.10 Rpart Package -- 3.11.2 Supervised Learning Overview -- 3.11.2.1 KNN Diamonds Example -- 3.11.2.1.1 Loading KNN Algorithm Package -- 3.11.2.1.2 Loading Dataset for KNN -- 3.11.2.1.3 Preprocessing Data -- 3.11.2.1.4 Scaling Data -- 3.11.2.1.5 Splitting Data and Applying KNN Algorithm -- 3.11.2.1.6 Model Performance -- 3.11.3 Unsupervised Learning Overview -- 3.11.3.1 Loading K-Means Clustering Package -- 3.11.3.2 Loading Dataset for K-Means Clustering Algorithm -- 3.11.3.3 Preprocessing Data -- 3.11.3.4 Executing K-Means Clustering Algorithm -- 3.11.3.5 Results Discussion -- 3.11.4 Python Scikit-Learn Package Overview -- 3.11.5 Python Supervised Learning Machine (SML) -- 3.11.5.1 Using Scikit-Learn Package -- 3.11.5.2 Loading Diamonds Dataset Using Python -- 3.11.5.3 Preprocessing Data -- 3.11.5.4 Splitting Data and Executing Linear Regression Algorithm -- 3.11.5.5 Model Performance Explanation -- 3.11.5.6 Classification Performance -- 3.11.6 Unsupervised Machine Learning (UML) -- 3.11.6.1 Loading Dataset for Hierarchical Clustering Algorithm -- 3.11.6.2 Running Hierarchical Algorithm and Plotting Data -- 3.11.7 Do It Yourself -- 3.11.8 Do More Yourself -- References -- Chapter 4: Data Preprocessing -- 4.1 The Problem -- 4.2 Data Preprocessing Steps -- 4.2.1 Data Collection -- 4.2.2 Data Profiling, Discovery, and Access -- 4.2.3 Data Cleansing and Validation -- 4.2.4 Data Structuring -- 4.2.5 Feature Selection -- 4.2.6 Data Transformation and Enrichment. 4.2.7 Data Validation, Storage, and Publishing -- 4.3 Feature Engineering -- 4.3.1 Feature Creation -- 4.3.2 Transformation -- 4.3.3 Feature Extraction -- 4.4 Feature Engineering Techniques -- 4.4.1 Imputation -- 4.4.1.1 Numerical Imputation -- 4.4.1.2 Categorical Imputation -- 4.4.2 Discretizing Numerical Features -- 4.4.3 Converting Categorical Discrete Features to Numeric (Binarization) -- 4.4.4 Log Transformation -- 4.4.5 One-Hot Encoding -- 4.4.6 Scaling -- 4.4.6.1 Normalization (Min-Max Normalization) -- 4.4.6.2 Standardization (Z-Score Normalization) -- 4.4.7 Reduce the Features Dimensionality -- 4.5 Overfitting -- 4.6 Underfitting -- 4.7 Model Selection: Selecting the Best Performing Model of an Algorithm -- 4.7.1 Model Selection Using the Holdout Method -- 4.7.2 Model Selection Using Cross-Validation -- 4.7.3 Evaluating Model Performance in Python -- 4.8 Data Quality -- 4.9 Key Terms -- 4.10 Test Your Understanding -- 4.11 Read More -- 4.12 Lab -- 4.12.1 Working Example in Python -- 4.12.1.1 Read the Dataset -- 4.12.1.2 Split the Dataset -- 4.12.1.3 Impute Data -- 4.12.1.4 One-Hot-Encode Data -- 4.12.1.5 Scale Numeric Data: Standardization -- 4.12.1.6 Create Pipelines -- 4.12.1.7 Creating Models -- 4.12.1.8 Cross-Validation -- 4.12.1.9 Hyperparameter Finetuning -- 4.12.2 Working Example in Weka -- 4.12.2.1 Missing Values -- 4.12.2.2 Discretization (or Binning) -- 4.12.2.3 Data Normalization and Standardization -- 4.12.2.4 One-Hot-Encoding (Nominal to Numeric) -- 4.12.3 Do It Yourself -- 4.12.3.1 Lenses Dataset -- 4.12.3.2 Nested Cross-Validation -- 4.12.4 Do More Yourself -- References -- Chapter 5: Data Visualization -- 5.1 Introduction -- 5.2 Presentation and Visualization of Information -- 5.2.1 A Taxonomy of Graphs -- 5.2.2 Relationships and Graphs -- 5.2.3 Dashboards -- 5.2.4 Infographics -- 5.3 Building Effective Visualizations. 5.4 Data Visualization Software -- 5.5 Conclusion -- 5.6 Key Terms -- 5.7 Test Your Understanding -- 5.8 Read More -- 5.9 Lab -- 5.9.1 Working Example in Tableau -- 5.9.1.1 Getting a Student Copy of Tableau Desktop -- 5.9.1.2 Learning with Tableau´s how-to Videos and Resources -- 5.9.2 Do It Yourself -- 5.9.2.1 Assignment 1: Introduction to Tableau -- 5.9.2.2 Assignment 2: Data Manipulation and Basic Charts with Tableau -- 5.9.3 Do More Yourself -- 5.9.3.1 Assignment 3: Charts and Dashboards with Tableau -- 5.9.3.2 Assignment 4: Analytics with Tableau -- References -- Chapter 6: Linear Regression -- 6.1 The Problem -- 6.2 A Practical Example -- 6.3 The Algorithm -- 6.3.1 Modeling the Linear Regression -- 6.3.2 Gradient Descent -- 6.3.3 Gradient Descent Example -- 6.3.4 Batch Versus Stochastic Gradient Descent -- 6.3.5 Examples of Error Functions -- 6.3.6 Gradient Descent Types -- 6.3.6.1 Stochastic Gradient Descent -- 6.3.6.2 Batch Gradient -- 6.4 Final Notes: Advantages, Disadvantages, and Best Practices -- 6.5 Key Terms -- 6.6 Test Your Understanding -- 6.7 Read More -- 6.8 Lab -- 6.8.1 Working Example in R -- 6.8.1.1 Load Diabetes Dataset -- 6.8.1.2 Preprocess Diabetes Dataset -- 6.8.1.3 Choose Dependent and Independent Variables -- 6.8.1.4 Visualize Your Dataset -- 6.8.1.5 Split Data into Test and Train Datasets -- 6.8.1.6 Create Linear Regression Model and Visualize it -- 6.8.1.7 Calculate Confusion Matrix -- 6.8.1.8 Gradient Descent -- 6.8.2 Working Example in Python -- 6.8.2.1 Load USA House Prices Dataset -- 6.8.2.2 Explore Housing Prices Visually -- 6.8.2.3 Preprocess Data -- 6.8.2.4 Split Data and Scale Features -- 6.8.2.5 Create and Visualize Model Using the LinearRegression Algorithm -- 6.8.2.6 Evaluate Performance of LRM -- 6.8.2.7 Optimize LRM Manually with Gradient Descent. 6.8.2.8 Create and Visualize a Model Using the Stochastic Gradient Descent (SGD). |
| Record Nr. | UNINA-9910633918303321 |
|
El Morr Christo <1966->
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine learning for practical decision making : a multidisciplinary perspective with applications from healthcare, engineering and business analytics / / Christo El Morr [and three others]
| Machine learning for practical decision making : a multidisciplinary perspective with applications from healthcare, engineering and business analytics / / Christo El Morr [and three others] |
| Autore | El Morr Christo <1966-> |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (475 pages) |
| Disciplina | 658.403 |
| Collana | International series in operations research & management science |
| Soggetto topico |
Decision making - Data processing
Machine learning Presa de decisions Processament de dades Aprenentatge automàtic |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-16990-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Chapter 1: Introduction to Machine Learning -- 1.1 Introduction to Machine Learning -- 1.2 Origin of Machine Learning -- 1.3 Growth of Machine Learning -- 1.4 How Machine Learning Works -- 1.5 Machine Learning Building Blocks -- 1.5.1 Data Management and Exploration -- 1.5.1.1 Data, Information, and Knowledge -- 1.5.1.2 Big Data -- 1.5.1.3 OLAP Versus OLTP -- 1.5.1.4 Databases, Data Warehouses, and Data Marts -- 1.5.1.5 Multidimensional Analysis Techniques -- 1.5.1.5.1 Slicing and Dicing -- 1.5.1.5.2 Pivoting -- 1.5.1.5.3 Drill-Down, Roll-Up, and Drill-Across -- 1.5.2 The Analytics Landscape -- 1.5.2.1 Types of Analytics (Descriptive, Diagnostic, Predictive, Prescriptive) -- 1.5.2.1.1 Descriptive Analytics -- 1.5.2.1.2 Diagnostic Analytics -- 1.5.2.1.3 Predictive Analytics -- 1.5.2.1.4 Prescriptive Analytics -- 1.6 Conclusion -- 1.7 Key Terms -- 1.8 Test Your Understanding -- 1.9 Read More -- 1.10 Lab -- 1.10.1 Introduction to R -- 1.10.2 Introduction to RStudio -- 1.10.2.1 RStudio Download and Installation -- 1.10.2.2 Install a Package -- 1.10.2.3 Activate Package -- 1.10.2.4 User Readr to Load Data -- 1.10.2.5 Run a Function -- 1.10.2.6 Save Status -- 1.10.3 Introduction to Python and Jupyter Notebook IDE -- 1.10.3.1 Python Download and Installation -- 1.10.3.2 Jupyter Download and Installation -- 1.10.3.3 Load Data and Plot It Visually -- 1.10.3.4 Save the Execution -- 1.10.3.5 Load a Saved Execution -- 1.10.3.6 Upload a Jupyter Notebook File -- 1.10.4 Do It Yourself -- References -- Chapter 2: Statistics -- 2.1 Overview of the Chapter -- 2.2 Definition of General Terms -- 2.3 Types of Variables -- 2.3.1 Measures of Central Tendency -- 2.3.1.1 Measures of Dispersion -- 2.4 Inferential Statistics -- 2.4.1 Data Distribution -- 2.4.2 Hypothesis Testing -- 2.4.3 Type I and II Errors.
2.4.4 Steps for Performing Hypothesis Testing -- 2.4.5 Test Statistics -- 2.4.5.1 Student´s t-test -- 2.4.5.2 One-Way Analysis of Variance -- 2.4.5.3 Chi-Square Statistic -- 2.4.5.4 Correlation -- 2.4.5.5 Simple Linear Regression -- 2.5 Conclusion -- 2.6 Key Terms -- 2.7 Test Your Understanding -- 2.8 Read More -- 2.9 Lab -- 2.9.1 Working Example in R -- 2.9.1.1 Statistical Measures Overview -- 2.9.1.2 Central Tendency Measures in R -- 2.9.1.3 Dispersion in R -- 2.9.1.4 Statistical Test Using p-value in R -- 2.9.2 Working Example in Python -- 2.9.2.1 Central Tendency Measure in Python -- 2.9.2.2 Dispersion Measures in Python -- 2.9.2.3 Statistical Testing Using p-value in Python -- 2.9.3 Do It Yourself -- 2.9.4 Do More Yourself (Links to Available Datasets for Use) -- References -- Chapter 3: Overview of Machine Learning Algorithms -- 3.1 Introduction -- 3.2 Data Mining -- 3.3 Analytics and Machine Learning -- 3.3.1 Terminology Used in Machine Learning -- 3.3.2 Machine Learning Algorithms: A Classification -- 3.4 Supervised Learning -- 3.4.1 Multivariate Regression -- 3.4.1.1 Multiple Linear Regression -- 3.4.1.2 Multiple Logistic Regression -- 3.4.2 Decision Trees -- 3.4.3 Artificial Neural Networks -- 3.4.3.1 Perceptron -- 3.4.4 Naïve Bayes Classifier -- 3.4.5 Random Forest -- 3.4.6 Support Vector Machines (SVM) -- 3.5 Unsupervised Learning -- 3.5.1 K-Means -- 3.5.2 K-Nearest Neighbors (KNN) -- 3.5.3 AdaBoost -- 3.6 Applications of Machine Learning -- 3.6.1 Machine Learning Demand Forecasting and Supply Chain Performance [42] -- 3.6.2 A Case Study on Cervical Pain Assessment with Motion Capture [43] -- 3.6.3 Predicting Bank Insolvencies Using Machine Learning Techniques [44] -- 3.6.4 Deep Learning with Convolutional Neural Network for Objective Skill Evaluation in Robot-Assisted Surgery [45] -- 3.7 Conclusion -- 3.8 Key Terms. 3.9 Test Your Understanding -- 3.10 Read More -- 3.11 Lab -- 3.11.1 Machine Learning Overview in R -- 3.11.1.1 Caret Package -- 3.11.1.2 ggplot2 Package -- 3.11.1.3 mlBench Package -- 3.11.1.4 Class Package -- 3.11.1.5 DataExplorer Package -- 3.11.1.6 Dplyr Package -- 3.11.1.7 KernLab Package -- 3.11.1.8 Mlr3 Package -- 3.11.1.9 Plotly Package -- 3.11.1.10 Rpart Package -- 3.11.2 Supervised Learning Overview -- 3.11.2.1 KNN Diamonds Example -- 3.11.2.1.1 Loading KNN Algorithm Package -- 3.11.2.1.2 Loading Dataset for KNN -- 3.11.2.1.3 Preprocessing Data -- 3.11.2.1.4 Scaling Data -- 3.11.2.1.5 Splitting Data and Applying KNN Algorithm -- 3.11.2.1.6 Model Performance -- 3.11.3 Unsupervised Learning Overview -- 3.11.3.1 Loading K-Means Clustering Package -- 3.11.3.2 Loading Dataset for K-Means Clustering Algorithm -- 3.11.3.3 Preprocessing Data -- 3.11.3.4 Executing K-Means Clustering Algorithm -- 3.11.3.5 Results Discussion -- 3.11.4 Python Scikit-Learn Package Overview -- 3.11.5 Python Supervised Learning Machine (SML) -- 3.11.5.1 Using Scikit-Learn Package -- 3.11.5.2 Loading Diamonds Dataset Using Python -- 3.11.5.3 Preprocessing Data -- 3.11.5.4 Splitting Data and Executing Linear Regression Algorithm -- 3.11.5.5 Model Performance Explanation -- 3.11.5.6 Classification Performance -- 3.11.6 Unsupervised Machine Learning (UML) -- 3.11.6.1 Loading Dataset for Hierarchical Clustering Algorithm -- 3.11.6.2 Running Hierarchical Algorithm and Plotting Data -- 3.11.7 Do It Yourself -- 3.11.8 Do More Yourself -- References -- Chapter 4: Data Preprocessing -- 4.1 The Problem -- 4.2 Data Preprocessing Steps -- 4.2.1 Data Collection -- 4.2.2 Data Profiling, Discovery, and Access -- 4.2.3 Data Cleansing and Validation -- 4.2.4 Data Structuring -- 4.2.5 Feature Selection -- 4.2.6 Data Transformation and Enrichment. 4.2.7 Data Validation, Storage, and Publishing -- 4.3 Feature Engineering -- 4.3.1 Feature Creation -- 4.3.2 Transformation -- 4.3.3 Feature Extraction -- 4.4 Feature Engineering Techniques -- 4.4.1 Imputation -- 4.4.1.1 Numerical Imputation -- 4.4.1.2 Categorical Imputation -- 4.4.2 Discretizing Numerical Features -- 4.4.3 Converting Categorical Discrete Features to Numeric (Binarization) -- 4.4.4 Log Transformation -- 4.4.5 One-Hot Encoding -- 4.4.6 Scaling -- 4.4.6.1 Normalization (Min-Max Normalization) -- 4.4.6.2 Standardization (Z-Score Normalization) -- 4.4.7 Reduce the Features Dimensionality -- 4.5 Overfitting -- 4.6 Underfitting -- 4.7 Model Selection: Selecting the Best Performing Model of an Algorithm -- 4.7.1 Model Selection Using the Holdout Method -- 4.7.2 Model Selection Using Cross-Validation -- 4.7.3 Evaluating Model Performance in Python -- 4.8 Data Quality -- 4.9 Key Terms -- 4.10 Test Your Understanding -- 4.11 Read More -- 4.12 Lab -- 4.12.1 Working Example in Python -- 4.12.1.1 Read the Dataset -- 4.12.1.2 Split the Dataset -- 4.12.1.3 Impute Data -- 4.12.1.4 One-Hot-Encode Data -- 4.12.1.5 Scale Numeric Data: Standardization -- 4.12.1.6 Create Pipelines -- 4.12.1.7 Creating Models -- 4.12.1.8 Cross-Validation -- 4.12.1.9 Hyperparameter Finetuning -- 4.12.2 Working Example in Weka -- 4.12.2.1 Missing Values -- 4.12.2.2 Discretization (or Binning) -- 4.12.2.3 Data Normalization and Standardization -- 4.12.2.4 One-Hot-Encoding (Nominal to Numeric) -- 4.12.3 Do It Yourself -- 4.12.3.1 Lenses Dataset -- 4.12.3.2 Nested Cross-Validation -- 4.12.4 Do More Yourself -- References -- Chapter 5: Data Visualization -- 5.1 Introduction -- 5.2 Presentation and Visualization of Information -- 5.2.1 A Taxonomy of Graphs -- 5.2.2 Relationships and Graphs -- 5.2.3 Dashboards -- 5.2.4 Infographics -- 5.3 Building Effective Visualizations. 5.4 Data Visualization Software -- 5.5 Conclusion -- 5.6 Key Terms -- 5.7 Test Your Understanding -- 5.8 Read More -- 5.9 Lab -- 5.9.1 Working Example in Tableau -- 5.9.1.1 Getting a Student Copy of Tableau Desktop -- 5.9.1.2 Learning with Tableau´s how-to Videos and Resources -- 5.9.2 Do It Yourself -- 5.9.2.1 Assignment 1: Introduction to Tableau -- 5.9.2.2 Assignment 2: Data Manipulation and Basic Charts with Tableau -- 5.9.3 Do More Yourself -- 5.9.3.1 Assignment 3: Charts and Dashboards with Tableau -- 5.9.3.2 Assignment 4: Analytics with Tableau -- References -- Chapter 6: Linear Regression -- 6.1 The Problem -- 6.2 A Practical Example -- 6.3 The Algorithm -- 6.3.1 Modeling the Linear Regression -- 6.3.2 Gradient Descent -- 6.3.3 Gradient Descent Example -- 6.3.4 Batch Versus Stochastic Gradient Descent -- 6.3.5 Examples of Error Functions -- 6.3.6 Gradient Descent Types -- 6.3.6.1 Stochastic Gradient Descent -- 6.3.6.2 Batch Gradient -- 6.4 Final Notes: Advantages, Disadvantages, and Best Practices -- 6.5 Key Terms -- 6.6 Test Your Understanding -- 6.7 Read More -- 6.8 Lab -- 6.8.1 Working Example in R -- 6.8.1.1 Load Diabetes Dataset -- 6.8.1.2 Preprocess Diabetes Dataset -- 6.8.1.3 Choose Dependent and Independent Variables -- 6.8.1.4 Visualize Your Dataset -- 6.8.1.5 Split Data into Test and Train Datasets -- 6.8.1.6 Create Linear Regression Model and Visualize it -- 6.8.1.7 Calculate Confusion Matrix -- 6.8.1.8 Gradient Descent -- 6.8.2 Working Example in Python -- 6.8.2.1 Load USA House Prices Dataset -- 6.8.2.2 Explore Housing Prices Visually -- 6.8.2.3 Preprocess Data -- 6.8.2.4 Split Data and Scale Features -- 6.8.2.5 Create and Visualize Model Using the LinearRegression Algorithm -- 6.8.2.6 Evaluate Performance of LRM -- 6.8.2.7 Optimize LRM Manually with Gradient Descent. 6.8.2.8 Create and Visualize a Model Using the Stochastic Gradient Descent (SGD). |
| Record Nr. | UNISA-996499867703316 |
|
El Morr Christo <1966->
|
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Optimization and Decision Science [[electronic resource] ] : ODS, Virtual Conference, November 19, 2020 / / edited by Raffaele Cerulli, Mauro Dell'Amico, Francesca Guerriero, Dario Pacciarelli, Antonio Sforza
| Optimization and Decision Science [[electronic resource] ] : ODS, Virtual Conference, November 19, 2020 / / edited by Raffaele Cerulli, Mauro Dell'Amico, Francesca Guerriero, Dario Pacciarelli, Antonio Sforza |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (249 pages) |
| Disciplina | 519.6 |
| Collana | AIRO Springer Series |
| Soggetto topico |
Computer science - Mathematics
Discrete mathematics Operations research Management science Discrete Mathematics in Computer Science Operations Research, Management Science Optimització matemàtica Presa de decisions |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-86841-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466551903316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Optimization and Decision Science [[electronic resource] ] : ODS, Virtual Conference, November 19, 2020 / / edited by Raffaele Cerulli, Mauro Dell'Amico, Francesca Guerriero, Dario Pacciarelli, Antonio Sforza
| Optimization and Decision Science [[electronic resource] ] : ODS, Virtual Conference, November 19, 2020 / / edited by Raffaele Cerulli, Mauro Dell'Amico, Francesca Guerriero, Dario Pacciarelli, Antonio Sforza |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (249 pages) |
| Disciplina | 519.6 |
| Collana | AIRO Springer Series |
| Soggetto topico |
Computer science - Mathematics
Discrete mathematics Operations research Management science Discrete Mathematics in Computer Science Operations Research, Management Science Optimització matemàtica Presa de decisions |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-86841-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910520065503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimization and Decision Science: Operations Research, Inclusion and Equity : ODS, Florence, Italy, August 30—September 2, 2022 / / edited by Paola Cappanera, Matteo Lapucci, Fabio Schoen, Marco Sciandrone, Fabio Tardella, Filippo Visintin
| Optimization and Decision Science: Operations Research, Inclusion and Equity : ODS, Florence, Italy, August 30—September 2, 2022 / / edited by Paola Cappanera, Matteo Lapucci, Fabio Schoen, Marco Sciandrone, Fabio Tardella, Filippo Visintin |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (354 pages) |
| Disciplina | 658.403 |
| Collana | AIRO Springer Series |
| Soggetto topico |
Operations research
Management science Operations Research, Management Science Investigació operativa Presa de decisions |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-031-28863-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Georgia Fargetta, Laura Rosa Maria Scrimali. Time-Dependent Generalized Nash Equilibria in Social Media Platforms -- 2 Annamaria Barbagallo, Serena Guarino Lo Bianco. A general Cournot-Nash equilibrium principle and applications to the COVID-19 pandemic -- 3 M. Ángeles Caraballo, Asunción Zapata, Luisa Monroy, Amparo Mármol. Environmental damage reduction: when countries face conflicting objectives -- 4 Lorenzo Lampariello, Simone Sagratella, Valerio Giuseppe Sasso. Nonsmooth Hierarchical Multi Portfolio Selection -- 5 Mauro Passacantando, Fabio Raciti. A multiclass network international migration model under shared regulations -- 6 Luca Bravi, Andrew Harbourne-Thomas, Alessandro Lori, Peter Mitchell, Samuele Salti, Leonardo Taccari, Francesco Sambo. GPS data mining to infer fleet operations for personalised product upselling -- 7 Cristiano Cervellera, Danilo Macciò, Francesco Rebora. Voronoi recursive binary trees for the optimization of nonlinear functionals -- 8 Alberto Ceselli and Elia Togni. Mathematical Programming and Machine Learning for a Task Allocation Game -- 9 Leo Liberti, Benedetto Manca, Antoine Oustry, and Pierre-Louis Poirion. Random projections for semidefinite programming -- 10 Ana Garcia-Bernabeu, Adolfo Hilario-Caballero, José Vicente Salcedo, Francisco Salas-Molina. Approaches to ESG - Integration in Portfolio Optimization using MOEAs -- 11 Milan Hladík. Complexity Issues in Interval Linear Programming -- 12 Patrizia Beraldi and Sara Khodaparasti. Dynamic Pricing in the Electricity Retail Market: A Stochastic Bi-Level Approach -- 13 Edoardo Fadda, Daniele Giovanni Gioia and Paolo Brandimarte. Robust Approaches for a Two-Stage Assembly-to-Order Problem -- 14 Giulia Ansuini, Antonio Frangioni, Laura Galli, Giovanni Nardini, Giovanni Stea. Bi-dimensional Assignment in 5G Periodic Scheduling -- 15 Meisam Pour-Massahian-Tafti, Matthieu Godichaud and Lionel Amodeo. Capacitated Disassembly Lot-Sizing Problem with Disposal Decisions for Multiple Product Types with Parts Commonality -- 16 Nikola Obrenović, Selin Ataç, Stefano Bortolomiol, Sanja Brdar, Oskar Marko,Vladimir Crnojević. The Crop Plant Scheduling Problem -- 17 Elena Ausonio, Patrizia Bagnerini, Mauro Gaggero. A MILP Formulation and a Metaheuristic Approach for the Scheduling of Drone Landings and Payload Changes on an Automatic Platform -- 18 Rosita Guido, Gabriele Zangara, Giuseppina Ambrogio, Domenico Conforti. A flexible job shop scheduling model for Sustainable Manufacturing -- 19 Annarita De Maio, Roberto Musmanno, Aurora Skrame, Francesca Vocaturo. Selection of Cultural Sites via Optimization -- 20 Carla De Francesco and Luigi De Giovanni. Integer Linear Programming Formulations for the Fleet Quickest Routing Problem on Grids -- 21 Massimo Di Gangi and Antonio Polimeni. C-Weibit discrete choice model: a path based approach -- 22 Cristiano Cervellera, Danilo Macciò, Francesco Rebora. Receding-horizon dynamic optimization of port-city traffic interactions over shared urban infrastructure -- 23 David Di Lorenzo, Tommaso Bianconcini, Leonardo Taccari, Marco Gualtieri, Paolo Raiconi, Alessandro Lori. Ten years of Routist: vehicle routing lessons learned from practice -- 24 Maksim Lalić, Nikola Obrenović, Sanja Brdar, Ivan Luković, Michel Bierlaire. Assisting Passengers on Rerouted Train Service Using Vehicle Sharing System -- 25 Maurizio Boccia, Andrea Mancuso, Adriano Masone, Francesco Messina, Antonio Sforza, Claudio Sterle. Optimization for surgery department management: an application to a hospital in Naples -- 26 Christian Piermarini, Massimo Roma. The Ambulance Diversion phenomenon in an Emergency Department network: a case study -- 27 Nicolas Zufferey, Marie-Sklaerder Vié,Leandro Coelho. Reducing the supply-chain nervosity thanks to flexible planning -- 28 EP. Mezatio, MM. Aghelinejad, L. Amodeo, I. Ferreira. Design forward and reverse closed-loop supply chain to improve economic and environmental performances -- 29 Tatiana Grimard, Nadia Lehoux and Luc Lebel. Supply chain design and cost allocation in a collaborative three-echelon supply network: A literature review -- 30 S. Baldassarre, G. Bruno, M. Cavola and E. Pipicelli. A mathematical model to locate Services of General Economic Interest. |
| Record Nr. | UNINA-9910734827303321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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