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Advance Trends in Soft Computing : Proceedings of WCSC 2013, December 16-18, San Antonio, Texas, USA / / edited by Mo Jamshidi, Vladik Kreinovich, Janusz Kacprzyk
| Advance Trends in Soft Computing : Proceedings of WCSC 2013, December 16-18, San Antonio, Texas, USA / / edited by Mo Jamshidi, Vladik Kreinovich, Janusz Kacprzyk |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (XII, 468 p. 163 illus., 40 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Studies in Fuzziness and Soft Computing |
| Soggetto topico |
Computational intelligence
Data mining Optical data processing Artificial intelligence Computational Intelligence Data Mining and Knowledge Discovery Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence |
| ISBN | 3-319-03674-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Synthesis and Research of Neuro-Fuzzy Model of Ecopyrogenesis Multi-Circuit Circulatory System -- Investigation of Ordered Weighted Averaging Weights for Estimating fuzzy validity of Geometric Shapes -- Processing Quantities with Heavy-Tailed Distribution of Measurement Uncertainty:How to Estimate the Tails of the Results of Data Processing -- A Logic for Qualified Syllogisms -- Flexible Querying Using Criterion Trees -- Non-stationary Time Series Clustering with Application to Climate Systems -- A Generalized Fuzzy T-norm Formulation of Fuzzy Modularity for Community Detection in Social Networks -- Soft Computing Models in Online Real Estate -- Constraints Preserving Genetic Algorithm for Learning Fuzzy Measures with an Application to Ontology Matching -- Topology Preservation in Fuzzy Self-Organizing Maps -- Designing Type-2 Fuzzy Controllers Using Lyapunov Approach for Trajectory Tracking -- Decentralized Adaptive Fuzzy Control Applied to a Robot Manipulator -- Modeling, Planning, Decision-making and Control in Fuzzy Environment -- Knowledge Integration for Uncertainty Management -- Co-reference resolution in Persian corpora -- Real-Time Implementation of a Neural Inverse Optimal Control for a Linear Induction Motor -- Preliminary Results on a New Fuzzy Cognitive Map Structure -- Time Series Image Data Analysis for Sport Skill -- Towards Incremental A-r-Star -- Comparative Analysis of Evaluation Algorithms for Decision-Making in Transport Logistics -- Handling Big Data with Fuzzy Based Classification Approach -- OWA based Model for Talent Selection in Cricket.-Knowledge Representation in ISpace Based Man-Machine Communicatio -- An alpha-level OWA Implementation of Bounded Rationality for Fuzzy Route Selection -- Indices for Introspection of the Choquet Integral -- Artificial neural network modeling of slaughter house wastewater removal of COD and TSS by electro coagulation -- Memetic Algorithm for Solving the Task of Providing Group Anonymity, Oleg Chertov -- Takagi-Sugeno approximation of a Mamdani fuzzy system -- Alpha-Rooting Image Enhancement Using a Traditional Algorithm and Genetic Algorithm -- Learning User’s Characteristics in Collaborative Filtering Through Genetic Algorithms: Some New Results, Oswaldo Velez-Langs, Angelica De Anotonio Fuzzy Sets Can Be Interpreted as Limits of Crisp Sets and This Can Help to Fuzzify Crisp Notions -- How to Gauge Accuracy of Measurements and of Expert Estimates: Beyond Normal Distributions -- Automatic Tuning of SOM Neural Network by using Evolutionary Algorithms: An Application to the SHM Problem -- Density-Based Fuzzy Clustering as a First Step to Learning Rules: Challenges and Solutions -- Computing Covariance and Correlation in Optimally Privacy-Protected Statistical Databases: Feasible Algorithms -- Feature Selection with Fuzzy Entropy to Find Similar Cases-Computing Intensive Definition of Products, Laszlo Horvath -- PSO Optimal Tracking Control for State-Dependent Coefficient Nonlinear Systems -- Delphi-Neural Approach to Clinical Decision Making: A Preliminary Study -- Contextual bipolar queries -- Landing of a Quadcopter on a Mobile Base using Fuzzy Logic -- An Innovative Process for Qualitative Group Decision Making employing Fuzzy-Neural Decision Analyzer -- Preprocessing Method for Support Vector Machines Based on Center of. |
| Record Nr. | UNINA-9910299487403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Algebraic approach to data processing : techniques and applications / / Julio C. Urenda and Vladik Kreinovich
| Algebraic approach to data processing : techniques and applications / / Julio C. Urenda and Vladik Kreinovich |
| Autore | Urenda Julio C. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (246 pages) |
| Disciplina | 005.7 |
| Collana | Studies in big data |
| Soggetto topico |
Big data
Computational intelligence Computer science - Mathematics |
| ISBN | 3-031-16780-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- 1 Introduction -- 1.1 What Is Data Processing and Why Do We Need It? -- 1.2 Why Algebraic Approach? -- 1.3 What We Do in This Book: An Overview -- 1.4 Thanks -- References -- 2 What Are the Most Natural and the Most Frequent Transformations -- 2.1 Main Idea: Numerical Values Change When We Change a Measuring Unit and/or Starting Point -- 2.2 Scaling Transformations -- 2.3 Shifts -- 2.4 Linear Transformations -- 2.5 Geometric Transformations -- 2.6 Beyond Linear Transformations -- 2.7 Permutations -- References -- 3 Which Functions and Which Families of Functions Are Invariant -- 3.1 Why Do We Need Invariant Functions -- 3.2 What Does It Mean for a Function to Be Invariant -- 3.3 Example: Scale-Invariant Functions of One Variable -- 3.4 What If We Have Both Shift- and Scale-Invariance? -- 3.5 Which Families of Functions Are Invariant: Case of Shift-Invariance -- 3.6 Which Families of Functions Are Invariant: Case of Scale-Invariance -- 3.7 What If We Have Both Shift- and Scale-Invariance -- 3.8 Which Linear Transformations Are Shift-Invariant -- References -- 4 What Is the General Relation Between Invariance and Optimality -- 4.1 What Is an Optimality Criterion -- 4.2 We Need a Final Optimality Criterion -- 4.3 It Is Often Reasonable to Require That the Optimality Criterion Be Invariant -- 4.4 Main Result of This Chapter -- 5 General Application: Dynamical Systems -- 5.1 Problem: Why a Linear-Based Classification Often Works in Nonlinear Cases -- 5.2 Our Explanation -- References -- 6 First Application to Physics: Why Liquids? -- 6.1 Two Applications to Physics: Summary -- 6.2 Problem: Why Liquids? -- 6.3 Towards a Formulation of the Problem in Precise Terms -- 6.4 Main Result of This Chapter -- References -- 7 Second Application to Physics: Warping of Our Galaxy -- 7.1 Formulation of the Problem.
7.2 Analysis of the Problem and the Resulting Explanation -- References -- 8 Application to Electrical Engineering: Class-D Audio Amplifiers -- 8.1 Applications to Engineering: Summary -- 8.2 Problem: Why Class-D Audio Amplifiers Work Well? -- 8.3 Why Pulses -- 8.4 Why the Pulse's Duration Should Linearly Depend … -- References -- 9 Application to Mechanical Engineering: Wood Structures -- 9.1 Problem: Need for a Theoretical Explanation of an Empirical Fact -- 9.2 Our Explanation: Main Idea -- 9.3 Our Explanation: Details -- 9.4 Proof -- References -- 10 Medical Application: Prevention -- 10.1 Problem: How to Best Maintain Social Distance -- 10.2 Towards Formulating This Problem in Precise Terms -- 10.3 Solution -- Reference -- 11 Medical Application: Testing -- 11.1 Problem: Optimal Group Testing -- 11.2 What Was Proposed -- 11.3 Resulting Problem -- 11.4 Let Us Formulate This Problem in Precise Terms -- 11.5 Solution -- References -- 12 Medical Application: Diagnostics, Part 1 -- 12.1 Problem: Diagnosing Lung Disfunctions in Children -- 12.2 First Pre-processing Stage: Scale-Invariant Smoothing -- 12.3 Which Order Polynomials Should We Use? -- 12.4 Second Pre-processing Stage: Using the Approximating Polynomials to Distinguish Between Different Diseases -- 12.5 Third Pre-processing Stage: Scale-Invariant Similarity/Dissimilarity Measures -- 12.6 How to Select α: Need to Have Efficient and Robust Estimates -- 12.7 Scale-Invariance Helps to Take Into Account That Signal Informativeness Decreases with Time -- 12.8 Pre-processing Summarized: What Information Serves as An Input to a Neural Network -- 12.9 The Results of Training Neural Networks on These Pre-processed Data -- References -- 13 Medical Application: Diagnostics, Part 2 -- 13.1 Problem: Why Hierarchical Multiclass Classification Works Better Than Direct Classification -- 13.2 Our Explanation. References -- 14 Medical Application: Diagnostics, Part 3 -- 14.1 Problem: Which Fourier Components Are Most Informative -- 14.2 Main Idea -- 14.3 First Case Study: Human Color Vision -- 14.4 Second Case Study: Classifying Lung Dysfunctions -- References -- 15 Medical Application: Treatment -- 15.1 Problem: Geometric Aspects of Wound Healing -- 15.2 What Are Natural Symmetries Here and What Are the Resulting Cell Shapes: Case of Undamaged Skin -- 15.3 What If the Skin Is Damaged: Resulting Symmetries and Cell Shapes -- 15.4 Geometric Symmetries Also Explain Observed Cell Motions -- References -- 16 Applications to Economics: How Do People Make Decisions, Part 1 -- References -- 17 Application to Economics: How Do People Make Decisions, Part 2 -- 17.1 Problem: Need to Consider Multiple Scenarios -- 17.2 Our Explanation -- References -- 18 Application to Economics: How Do People Make Decisions, Part 3 -- 18.1 Problem: Using Experts -- 18.2 Towards an Explanation -- References -- 19 Application to Economics: How Do People Make Decisions, Part 4 -- 19.1 Why Should We Play Down Emotions -- 19.2 Towards Explanation -- References -- 20 Application to Economics: Stimuli, Part 1 -- 20.1 Problem: Why Rewards Work Better Than Punishment -- 20.2 Analysis of the Problem -- 20.3 Our Explanation -- References -- 21 Application to Economics: Stimuli, Part 2 -- 21.1 Problem: Why Top Experts Are Paid So Much -- 21.2 Our Explanation -- References -- 22 Application to Economics: Investment -- 22.1 1/n Investment: Formulation of the Problem -- 22.2 Our Explanation -- 22.3 Discussion -- References -- 23 Application to Social Sciences: When Revolutions Happen -- 23.1 Formulation of the Problem -- 23.2 Analysis of the Problem -- References -- 24 Application to Education: General -- 24.1 Problem: Is Immediate Repetition Good for Learning?. 24.2 Analysis of the Problem and the Resulting Explanation -- References -- 25 Application to Education: Specific -- 25.1 Problem: Why Derivative -- 25.2 Invariance Naturally Leads to the Derivative -- Reference -- 26 Application to Mathematics: Why Necessary Conditions Are Often Sufficient -- 26.1 Formulation of the Problem -- 26.2 Analysis of the Problem -- 26.3 How Can We Formalize What Is Not Abnormal -- 26.4 Resulting Explanation of the TONCAS Phenomenon -- References -- 27 Data Processing: Neural Techniques, Part 1 -- 27.1 Machine Learning Is Needed to Analyze Complex Systems -- 27.2 Neural Networks and Deep Learning: A Brief Reminder -- 27.3 Why Traditional Neural Networks -- 27.4 Why Sigmoid Activation Function: Idea -- 27.5 Why Sigmoid-Derivation -- 27.6 Limit Cases -- 27.7 We Need Multi-layer Neural Networks -- 27.8 Which Activation Function Should We Use -- 27.9 This Leads Exactly to Squashing Functions -- 27.10 Why Rectified Linear Functions -- References -- 28 Data Processing: Neural Techniques, Part 2 -- 28.1 Problem: Spiking Neural Networks -- 28.2 Analysis of the Problem and the First Result -- 28.3 Main Result: Spikes Are, in Some Reasonable Sense, Optimal -- References -- 29 Data Processing: Fuzzy Techniques, Part 1 -- 29.1 Why Fuzzy Techniques -- 29.2 Fuzzy Techniques: Main Ideas -- 29.3 Fuzzy Techniques: Logic -- References -- 30 Data Processing: Neural and Fuzzy Techniques -- 30.1 Problem: Computations Should Be Fast and Understandable -- 30.2 Definitions and the Main Results -- 30.3 Auxiliary Result: What Can We Do with Two-Layer Networks -- References -- 31 Data Processing: Fuzzy Techniques, Part 2 -- 31.1 Problem: Which Fuzzy Techniques to Use? -- 31.2 Analysis of the Problem -- 31.3 Which Symmetric Membership Functions Should We … -- 31.4 Which Hedge Operations and Negation Operations Should We Select -- 31.5 Proofs. References -- 32 Data Processing: Fuzzy Techniques, Part 3 -- 32.1 Problem: Which Fuzzy Degrees to Use? -- 32.2 Definitions and the Main Result -- 32.3 How General Is This Result? -- 32.4 What If We Allow Unlimited Number of ``And''-Operations and Negations: Case Study -- References -- 33 Data Processing: Fuzzy Techniques, Part 4 -- 33.1 Problem: How to Explain Commonsense Reasoning -- 33.2 Our Explanation -- 33.3 Auxiliary Result: Why the Usual Quantifiers? -- References -- 34 Data Processing: Probabilistic Techniques, Part 1 -- 34.1 Problem: How to Represent Interval Uncertainty -- 34.2 Analysis of the Problem -- 34.3 Our Results -- References -- 35 Data Processing: Probabilistic Techniques, Part 2 -- 35.1 Problem: How to Represent General Uncertainty -- 35.2 Definitions and the Main Result -- 35.3 Consequence -- References -- 36 Data Processing: Probabilistic Techniques, Part 3 -- 36.1 Problem: Experts Don't Perform Well in Unusual Situations -- 36.2 Our Explanation -- References -- 37 Data Processing: Beyond Traditional Techniques -- 37.1 DNA Computing: Introduction -- 37.2 Computing Without Computing-Quantum Version: A Brief Reminder -- 37.3 Computing Without Computing-Version Involving Acausal Processes: A Reminder -- 37.4 Computing Without Computing-DNA Version -- 37.5 DNA Computing Without Computing Is Somewhat Less … -- 37.6 First Related Result: Security Is More Difficult to Achieve than Privacy -- 37.7 Second Related Result: Data Storage Is More Difficult Than Data Transmission -- References -- Appendix References -- -- Index. |
| Record Nr. | UNISA-996495562903316 |
Urenda Julio C.
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Algebraic approach to data processing : techniques and applications / / Julio C. Urenda and Vladik Kreinovich
| Algebraic approach to data processing : techniques and applications / / Julio C. Urenda and Vladik Kreinovich |
| Autore | Urenda Julio C. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (246 pages) |
| Disciplina | 005.7 |
| Collana | Studies in big data |
| Soggetto topico |
Big data
Computational intelligence Computer science - Mathematics |
| ISBN | 3-031-16780-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- 1 Introduction -- 1.1 What Is Data Processing and Why Do We Need It? -- 1.2 Why Algebraic Approach? -- 1.3 What We Do in This Book: An Overview -- 1.4 Thanks -- References -- 2 What Are the Most Natural and the Most Frequent Transformations -- 2.1 Main Idea: Numerical Values Change When We Change a Measuring Unit and/or Starting Point -- 2.2 Scaling Transformations -- 2.3 Shifts -- 2.4 Linear Transformations -- 2.5 Geometric Transformations -- 2.6 Beyond Linear Transformations -- 2.7 Permutations -- References -- 3 Which Functions and Which Families of Functions Are Invariant -- 3.1 Why Do We Need Invariant Functions -- 3.2 What Does It Mean for a Function to Be Invariant -- 3.3 Example: Scale-Invariant Functions of One Variable -- 3.4 What If We Have Both Shift- and Scale-Invariance? -- 3.5 Which Families of Functions Are Invariant: Case of Shift-Invariance -- 3.6 Which Families of Functions Are Invariant: Case of Scale-Invariance -- 3.7 What If We Have Both Shift- and Scale-Invariance -- 3.8 Which Linear Transformations Are Shift-Invariant -- References -- 4 What Is the General Relation Between Invariance and Optimality -- 4.1 What Is an Optimality Criterion -- 4.2 We Need a Final Optimality Criterion -- 4.3 It Is Often Reasonable to Require That the Optimality Criterion Be Invariant -- 4.4 Main Result of This Chapter -- 5 General Application: Dynamical Systems -- 5.1 Problem: Why a Linear-Based Classification Often Works in Nonlinear Cases -- 5.2 Our Explanation -- References -- 6 First Application to Physics: Why Liquids? -- 6.1 Two Applications to Physics: Summary -- 6.2 Problem: Why Liquids? -- 6.3 Towards a Formulation of the Problem in Precise Terms -- 6.4 Main Result of This Chapter -- References -- 7 Second Application to Physics: Warping of Our Galaxy -- 7.1 Formulation of the Problem.
7.2 Analysis of the Problem and the Resulting Explanation -- References -- 8 Application to Electrical Engineering: Class-D Audio Amplifiers -- 8.1 Applications to Engineering: Summary -- 8.2 Problem: Why Class-D Audio Amplifiers Work Well? -- 8.3 Why Pulses -- 8.4 Why the Pulse's Duration Should Linearly Depend … -- References -- 9 Application to Mechanical Engineering: Wood Structures -- 9.1 Problem: Need for a Theoretical Explanation of an Empirical Fact -- 9.2 Our Explanation: Main Idea -- 9.3 Our Explanation: Details -- 9.4 Proof -- References -- 10 Medical Application: Prevention -- 10.1 Problem: How to Best Maintain Social Distance -- 10.2 Towards Formulating This Problem in Precise Terms -- 10.3 Solution -- Reference -- 11 Medical Application: Testing -- 11.1 Problem: Optimal Group Testing -- 11.2 What Was Proposed -- 11.3 Resulting Problem -- 11.4 Let Us Formulate This Problem in Precise Terms -- 11.5 Solution -- References -- 12 Medical Application: Diagnostics, Part 1 -- 12.1 Problem: Diagnosing Lung Disfunctions in Children -- 12.2 First Pre-processing Stage: Scale-Invariant Smoothing -- 12.3 Which Order Polynomials Should We Use? -- 12.4 Second Pre-processing Stage: Using the Approximating Polynomials to Distinguish Between Different Diseases -- 12.5 Third Pre-processing Stage: Scale-Invariant Similarity/Dissimilarity Measures -- 12.6 How to Select α: Need to Have Efficient and Robust Estimates -- 12.7 Scale-Invariance Helps to Take Into Account That Signal Informativeness Decreases with Time -- 12.8 Pre-processing Summarized: What Information Serves as An Input to a Neural Network -- 12.9 The Results of Training Neural Networks on These Pre-processed Data -- References -- 13 Medical Application: Diagnostics, Part 2 -- 13.1 Problem: Why Hierarchical Multiclass Classification Works Better Than Direct Classification -- 13.2 Our Explanation. References -- 14 Medical Application: Diagnostics, Part 3 -- 14.1 Problem: Which Fourier Components Are Most Informative -- 14.2 Main Idea -- 14.3 First Case Study: Human Color Vision -- 14.4 Second Case Study: Classifying Lung Dysfunctions -- References -- 15 Medical Application: Treatment -- 15.1 Problem: Geometric Aspects of Wound Healing -- 15.2 What Are Natural Symmetries Here and What Are the Resulting Cell Shapes: Case of Undamaged Skin -- 15.3 What If the Skin Is Damaged: Resulting Symmetries and Cell Shapes -- 15.4 Geometric Symmetries Also Explain Observed Cell Motions -- References -- 16 Applications to Economics: How Do People Make Decisions, Part 1 -- References -- 17 Application to Economics: How Do People Make Decisions, Part 2 -- 17.1 Problem: Need to Consider Multiple Scenarios -- 17.2 Our Explanation -- References -- 18 Application to Economics: How Do People Make Decisions, Part 3 -- 18.1 Problem: Using Experts -- 18.2 Towards an Explanation -- References -- 19 Application to Economics: How Do People Make Decisions, Part 4 -- 19.1 Why Should We Play Down Emotions -- 19.2 Towards Explanation -- References -- 20 Application to Economics: Stimuli, Part 1 -- 20.1 Problem: Why Rewards Work Better Than Punishment -- 20.2 Analysis of the Problem -- 20.3 Our Explanation -- References -- 21 Application to Economics: Stimuli, Part 2 -- 21.1 Problem: Why Top Experts Are Paid So Much -- 21.2 Our Explanation -- References -- 22 Application to Economics: Investment -- 22.1 1/n Investment: Formulation of the Problem -- 22.2 Our Explanation -- 22.3 Discussion -- References -- 23 Application to Social Sciences: When Revolutions Happen -- 23.1 Formulation of the Problem -- 23.2 Analysis of the Problem -- References -- 24 Application to Education: General -- 24.1 Problem: Is Immediate Repetition Good for Learning?. 24.2 Analysis of the Problem and the Resulting Explanation -- References -- 25 Application to Education: Specific -- 25.1 Problem: Why Derivative -- 25.2 Invariance Naturally Leads to the Derivative -- Reference -- 26 Application to Mathematics: Why Necessary Conditions Are Often Sufficient -- 26.1 Formulation of the Problem -- 26.2 Analysis of the Problem -- 26.3 How Can We Formalize What Is Not Abnormal -- 26.4 Resulting Explanation of the TONCAS Phenomenon -- References -- 27 Data Processing: Neural Techniques, Part 1 -- 27.1 Machine Learning Is Needed to Analyze Complex Systems -- 27.2 Neural Networks and Deep Learning: A Brief Reminder -- 27.3 Why Traditional Neural Networks -- 27.4 Why Sigmoid Activation Function: Idea -- 27.5 Why Sigmoid-Derivation -- 27.6 Limit Cases -- 27.7 We Need Multi-layer Neural Networks -- 27.8 Which Activation Function Should We Use -- 27.9 This Leads Exactly to Squashing Functions -- 27.10 Why Rectified Linear Functions -- References -- 28 Data Processing: Neural Techniques, Part 2 -- 28.1 Problem: Spiking Neural Networks -- 28.2 Analysis of the Problem and the First Result -- 28.3 Main Result: Spikes Are, in Some Reasonable Sense, Optimal -- References -- 29 Data Processing: Fuzzy Techniques, Part 1 -- 29.1 Why Fuzzy Techniques -- 29.2 Fuzzy Techniques: Main Ideas -- 29.3 Fuzzy Techniques: Logic -- References -- 30 Data Processing: Neural and Fuzzy Techniques -- 30.1 Problem: Computations Should Be Fast and Understandable -- 30.2 Definitions and the Main Results -- 30.3 Auxiliary Result: What Can We Do with Two-Layer Networks -- References -- 31 Data Processing: Fuzzy Techniques, Part 2 -- 31.1 Problem: Which Fuzzy Techniques to Use? -- 31.2 Analysis of the Problem -- 31.3 Which Symmetric Membership Functions Should We … -- 31.4 Which Hedge Operations and Negation Operations Should We Select -- 31.5 Proofs. References -- 32 Data Processing: Fuzzy Techniques, Part 3 -- 32.1 Problem: Which Fuzzy Degrees to Use? -- 32.2 Definitions and the Main Result -- 32.3 How General Is This Result? -- 32.4 What If We Allow Unlimited Number of ``And''-Operations and Negations: Case Study -- References -- 33 Data Processing: Fuzzy Techniques, Part 4 -- 33.1 Problem: How to Explain Commonsense Reasoning -- 33.2 Our Explanation -- 33.3 Auxiliary Result: Why the Usual Quantifiers? -- References -- 34 Data Processing: Probabilistic Techniques, Part 1 -- 34.1 Problem: How to Represent Interval Uncertainty -- 34.2 Analysis of the Problem -- 34.3 Our Results -- References -- 35 Data Processing: Probabilistic Techniques, Part 2 -- 35.1 Problem: How to Represent General Uncertainty -- 35.2 Definitions and the Main Result -- 35.3 Consequence -- References -- 36 Data Processing: Probabilistic Techniques, Part 3 -- 36.1 Problem: Experts Don't Perform Well in Unusual Situations -- 36.2 Our Explanation -- References -- 37 Data Processing: Beyond Traditional Techniques -- 37.1 DNA Computing: Introduction -- 37.2 Computing Without Computing-Quantum Version: A Brief Reminder -- 37.3 Computing Without Computing-Version Involving Acausal Processes: A Reminder -- 37.4 Computing Without Computing-DNA Version -- 37.5 DNA Computing Without Computing Is Somewhat Less … -- 37.6 First Related Result: Security Is More Difficult to Achieve than Privacy -- 37.7 Second Related Result: Data Storage Is More Difficult Than Data Transmission -- References -- Appendix References -- -- Index. |
| Record Nr. | UNINA-9910617307203321 |
|
Urenda Julio C.
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Algebraic Techniques and Their Use in Describing and Processing Uncertainty : To the Memory of Professor Elbert A. Walker / / edited by Hung T. Nguyen, Vladik Kreinovich
| Algebraic Techniques and Their Use in Describing and Processing Uncertainty : To the Memory of Professor Elbert A. Walker / / edited by Hung T. Nguyen, Vladik Kreinovich |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (173 pages) |
| Disciplina |
006.3
512 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Neural networks (Computer science) Computational Intelligence Mathematical Models of Cognitive Processes and Neural Networks |
| ISBN | 3-030-38565-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Specker Algebras: A Survey -- Least Square Approximations and Linear Values of Cooperative Game -- Elementary Divisor Domains as Endomorphism Rings -- A Topos View of the Type-2 Fuzzy Truth Value Algebra -- A Symmetry-Based Explanation of the Main Idea Behind Chubanov's Linear Programming Algorithm -- Why Bohmian Approach to Quantum Econometrics: An Algebraic Explanation -- Direct Decompositions of Matrices. |
| Record Nr. | UNINA-9910484508303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Algorithmic Aspects of Analysis, Prediction, and Control in Science and Engineering : An Approach Based on Symmetry and Similarity / / by Jaime Nava, Vladik Kreinovich
| Algorithmic Aspects of Analysis, Prediction, and Control in Science and Engineering : An Approach Based on Symmetry and Similarity / / by Jaime Nava, Vladik Kreinovich |
| Autore | Nava Jaime |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (160 p.) |
| Disciplina |
006.3
620 629.8 |
| Collana | Studies in Systems, Decision and Control |
| Soggetto topico |
Computational intelligence
Artificial intelligence Control engineering Computational Intelligence Artificial Intelligence Control and Systems Theory |
| ISBN | 3-662-44955-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction: Symmetries and Similarities as a Methodology for Algorithmics of Analysis, Prediction, and Control in Science and Engineering.- Algorithmic Aspects of Real-Life Systems Analysis: Approach Based on Symmetry and Simila -- Algorithmic Aspects of Prediction: An Approach Based on Symmetry and Similarity -- Algorithmic Aspects of Control: Approach Based on Symmetry and Similarity -- Possible Ideas for FutureWork. |
| Record Nr. | UNINA-9910299704503321 |
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Nava Jaime
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| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applications of fuzzy techniques : proceedings of the 2022 annual conference of the North American Fuzzy Information Processing Society, NAFIPS 2022 / / edited by Scott Dick, Vladik Kreinovich and Pawan Lingras
| Applications of fuzzy techniques : proceedings of the 2022 annual conference of the North American Fuzzy Information Processing Society, NAFIPS 2022 / / edited by Scott Dick, Vladik Kreinovich and Pawan Lingras |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (375 pages) |
| Disciplina | 511.3 |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Fuzzy logic
Fuzzy sets |
| ISBN | 3-031-16038-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- How to Elicit Complex-Valued Fuzzy Degrees -- 1 Formulation of the Problem -- 2 Analysis of the Problem -- 3 So How to Elicit Complex-Valued Fuzzy Degrees: Algorithm and Discussion -- 4 Conclusions -- References -- Flutter Mitigation via Fuzzy Gain Scheduling of a Passivity-Based Controller -- 1 Introduction -- 1.1 Problem -- 1.2 Genetic Fuzzy Control -- 1.3 Benchmark Aerodynamic Controls Technology Model -- 2 Methodology -- 2.1 Standard Controller -- 2.2 Fuzzy Gain Scheduled Controller -- 3 Results and Discussion -- 4 Conclusions -- References -- A New Weighting Method in Fuzzy Multi-criteria Decision Making: Selected Element Reduction Approach (SERA) -- 1 Introduction -- 2 Preliminaries -- 3 Methodology -- 4 Application -- 5 Conclusion -- References -- Genetic Fuzzy System for Pitch Control on a F-4 Phantom -- 1 Introduction -- 2 F-4 Fighter Pitch Angle Dynamic Behavior -- 3 Genetic Takagi-Sugeno-Kang Fuzzy Inference System -- 4 Conclusions and Future Works -- References -- Analyzing the Sars-Cov-2 Pandemic Outbreak Using Fuzzy Sets and the SIR Model -- 1 Introduction -- 2 The Sars-Cov-2 Pandemic -- 3 Basic Concepts on Fuzzy Sets -- 4 SIR Model -- 4.1 Fuzzy Solution and Asymptotic Behavior -- 4.2 Time-Varying Basic Reproductive Number -- 5 Application to Sars-Cov-2 Outbreak -- 5.1 Results -- 5.2 Comparing and Forecasting -- 6 Conclusion -- References -- Hybrid Fuzzy-LQR Control for Time Optimal Spacecraft Docking -- 1 Introduction -- 1.1 Related Work -- 2 Background -- 2.1 Linear Quadratic Regulator Control -- 2.2 Fuzzy-Based Bang-Bang Control -- 2.3 Genetic Algorithms -- 3 Methodology -- 3.1 Spacecraft Docking Problem -- 3.2 GA Approach -- 3.3 LQR Controller Approach -- 3.4 Fuzzy-Based Bang-Bang Controller Approach -- 3.5 Hybrid Fuzzy-LQR Controller Approach -- 4 Results -- 5 Conclusion -- References.
An Experimental Study on Fuzzy Markov Chains Under Mn Generalized Mean Relation -- 1 Introduction and Motivation -- 2 Fuzzy Markov Chains -- 2.1 Normalized Fuzzy Transition Matrix -- 2.2 Mn Generalized Mean and Its Use in Computing the Limiting Distribution of P -- 3 Illustrative Examples -- 3.1 Discussion of the Results -- 4 Concluding Remarks -- References -- An Approach to Simulation of Fuzzy Linguistic Variables -- 1 Introduction and Motivation -- 2 Basics on Fuzzy Numbers -- 2.1 Fuzzy Linguistic Variables -- 3 An Approach to Fuzzy Linguistic Random Variate Generation -- 3.1 Fuzzy Random Variate Generation for Linguistic Variables -- 3.2 Fuzzy Random Linguistic Value Generation -- 3.3 Fuzzy Random Variate Generation -- 4 Illustrative Example -- 5 Concluding Remarks -- References -- Why Sine Membership Functions -- 1 Formulation of the Problem -- 2 Our Explanation -- 3 Conclusions -- References -- Agricultural Yield Prediction by Difference Equations on Data-Induced Cumulative Possibility Distributions -- 1 Introduction -- 2 Mathematical Background -- 2.1 Elementary Lattice Theory Definitions -- 2.2 The Cone of Intervals' Numbers (INs) and a Novel Interpretation -- 2.3 Differential Intervals' Number (IN) Models -- 3 Novel Algorithms -- 3.1 Difference Intervals' Number (IN) Models -- 3.2 Algorithms for Training and Testing -- 4 Experiments and Results -- 4.1 Data Acquisition -- 4.2 Data Preprocessing and Experiments -- 4.3 Discussion -- 5 Conclusion -- References -- Commonsense-Continuous Dynamical Systems - Stationary States, Prediction, and Reconstruction of the Past: Fuzzy-Based Analysis -- 1 Formulation of the Problem -- 2 Mathematical Continuity vs. Commonsense Continuity: Analysis of the Difference -- 3 Useful Corollary -- 4 Auxiliary Corollaries: Predicting the Future and Reconstructing the Past -- 5 Conclusions -- References. Why Gaussian Copulas Are Ubiquitous in Economics: Fuzzy-Related Explanation -- 1 Formulation of the Problem -- 2 Analysis of the Problem and the Resulting Explanation -- References -- A Note on Caputo Fractional Derivative in the Space of Linearly Correlated Fuzzy Numbers -- 1 Introduction -- 2 Preliminaries -- 2.1 Interactivity -- 2.2 The Space RF(A) -- 3 Caputo Derivative in RF(A) -- 4 Fractional Logistic Model in RF(A) for Cumulative Cases of COVID-19 -- 5 Conclusion -- References -- Data Driven Level Set Method in Fuzzy Modeling and Forecasting -- 1 Introduction -- 2 Data Driven Level Set Method -- 3 Model Accuracy and Transparency -- 4 Electric Power Load Forecasting -- 5 Conclusion -- References -- Semi-supervised Physics-Informed Genetic Fuzzy System for IoT BLE Localization -- 1 Introduction -- 2 Background -- 3 Dataset Description -- 4 Methodology -- 4.1 Semi-supervised Label Propagation -- 4.2 Physics-Informed Genetic Fuzzy System -- 5 Results and Discussion -- 6 Conclusion -- References -- The Constraint Interval Theory: A Solution for Interval Differential Equations -- 1 Introduction -- 2 Standard Interval Arithmetic and Constraint Interval Arithmetic -- 3 Solution of Initial Value Problem for Interval Linear Differential Equations System -- 4 Conclusion -- References -- Classification of Rice Using Genetic Fuzzy Cascading System -- 1 Introduction -- 1.1 Need of Fuzzy Techniques for Explainable AI (XAI) -- 1.2 Genetic Fuzzy Systems and Cascading -- 1.3 Classification of Rice -- 2 Methodology -- 2.1 Fuzzy Inference System (FIS) -- 2.2 Fuzzy Cascading -- 2.3 Genetic Algorithm -- 3 Results -- 4 Conclusion -- 4.1 Challenges and Future Work -- References -- On a New Contrapositivisation Technique for Fuzzy Implications Constructed from Grouping Functions -- 1 Introduction -- 2 Preliminaries -- 3 Contrapositivisation Techniques. 4 (G,N)-Contrapositivisation -- 5 Final Remarks -- References -- Genetically Trained Fuzzy Cognitive Maps for Effects Based Operations -- 1 Introduction -- 2 Methodology -- 2.1 Forward Propagating Knowledge Graph Solution -- 2.2 Genetic Source-Determination Knowledge Graph Solution -- 2.3 Fuzzy Inference System Integration -- 3 Results and Discussion -- 3.1 Forward Propagating Solver Results -- 3.2 Genetic Source-Determination Results -- 4 Conclusions and Future Work -- References -- Genetic Fuzzy Controller for the Homicidal Chauffeur Differential Game -- 1 Introduction -- 1.1 Related Work on the Homicidal Chauffeur -- 2 Methodology -- 2.1 Optimal Control Solution -- 2.2 Genetic Fuzzy System -- 2.3 Noise Addition -- 3 Results -- 3.1 Results of Noise Added -- 4 Discussion -- 5 Summary and Conclusions -- References -- Use of Fuzzy PID Controller for Pitch Control of a Wind Turbine -- 1 Introduction -- 2 Background and Preliminaries -- 2.1 Wind Turbine Model -- 2.2 Genetic Algorithm -- 2.3 Genetic Fuzzy System -- 3 Methodology -- 4 Results -- 5 Conclusion and Future Works -- References -- Special Tolerance Left Solution for Course Assignment Problem with Interval Workload Constraint -- 1 Introduction -- 2 Special Tolerance Left Solution to System of Interval Linear Equations -- 2.1 Definition and Characteristics of Tolerance Solution to System of Interval Linear Equations -- 2.2 Definition and Characteristics of Special Tolerance Left Solution to System of Interval Linear Equations -- 3 Course Assignment Problem with Interval Workload Constraint -- 4 Results -- 5 Conclusion -- References -- Passive Fault-Tolerant Control Scheme for Nonlinear Level Control System with Parameter Uncertainty and Actuator Fault -- 1 Introduction -- 2 Uncertain Benchmark Level Control System -- 2.1 Uncertain Benchmark Two-Tank Level Control System. 2.2 Benchmark Two-Tank Level Control System Mathematical Modeling -- 3 Proposed Methodology for Passive FTC -- 3.1 Data Generation Layer -- 3.2 Pre-processing Layer -- 3.3 Training Layer -- 4 Implementation and Results -- 4.1 Implementation Setup -- 4.2 Simulation Results -- 5 Conclusion and Future Work -- References -- Can Physically-Trained Genetic Fuzzy Learning Algorithm Improve Pitch Control in Wind Turbines? -- 1 Introduction -- 2 WT Model -- 3 Genetic Fuzzy Methodology -- 3.1 Genetic Algorithm -- 3.2 Training the GFS -- 3.3 Structure of the GFS -- 4 Results -- 4.1 Training the GFS -- 4.2 Testing the GFS -- 5 Conclusions and Future Work -- References -- Generating Interval Type-2 Fuzzy Inputs from Smoothed Data for Fuzzy Rule-Based Systems -- 1 Introduction -- 2 Some Background Information -- 2.1 Type-1 and Type-2 Fuzzy Sets -- 2.2 Non-Singleton Fuzzy Logic Systems -- 2.3 Smoothing Using Penalized Least Squares Regression -- 2.4 Signal-to-Noise Ratio -- 3 Problem Statement and Methodology -- 3.1 Algorithm 1: Stable Noise in Both Training and Test Sets -- 3.2 Algorithm 2: Varying Noise Levels in the Application Phase -- 4 Experimental Results in Time Series Prediction -- 5 Concluding Remarks -- References -- Subsethood Measures on a Bounded Lattice of Continuous Fuzzy Numbers with an Application in Approximate Reasoning -- 1 Introduction -- 2 Some Mathematical Background on Lattice Theory and Subsethood Measures -- 3 Some Facts Regarding Subsethood and Inclusion Measures -- 4 A Bounded Lattice of Continuous Fuzzy Numbers for Analogical Reasoning -- 5 An Outline of an Application in Mechanical Condition Monitoring -- 6 Conclusions -- References -- Why Ideas First Appear in Informal Form? Why It Is Very Difficult to Know Yourself? Fuzzy-Based Explanation -- 1 Formulation of the Problem. 2 Analysis of the Problem Explains the Need for Informal Ideas. |
| Record Nr. | UNINA-9910627275403321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Behavioral Predictive Modeling in Economics / / edited by Songsak Sriboonchitta, Vladik Kreinovich, Woraphon Yamaka
| Behavioral Predictive Modeling in Economics / / edited by Songsak Sriboonchitta, Vladik Kreinovich, Woraphon Yamaka |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (445 pages) |
| Disciplina | 330.015195 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Economic theory Computational Intelligence Economic Theory/Quantitative Economics/Mathematical Methods |
| ISBN | 3-030-49728-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Behavioral Predictive Modeling in Economics -- Conclusion. |
| Record Nr. | UNINA-9910484560003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Beyond Traditional Probabilistic Methods in Economics / / edited by Vladik Kreinovich, Nguyen Ngoc Thach, Nguyen Duc Trung, Dang Van Thanh
| Beyond Traditional Probabilistic Methods in Economics / / edited by Vladik Kreinovich, Nguyen Ngoc Thach, Nguyen Duc Trung, Dang Van Thanh |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XIV, 1157 p. 206 illus., 124 illus. in color.) |
| Disciplina | 330.015195 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Artificial intelligence Economic theory Computational Intelligence Artificial Intelligence Economic Theory/Quantitative Economics/Mathematical Methods |
| ISBN | 3-030-04200-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484183603321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Biomedical and other applications of soft computing / / Hoang Phuong Nguyen, Vladik Kreinovich, editors
| Biomedical and other applications of soft computing / / Hoang Phuong Nguyen, Vladik Kreinovich, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (277 pages) |
| Disciplina | 610.28 |
| Collana | Studies in computational intelligence |
| Soggetto topico |
Biomedical engineering
Soft computing |
| ISBN | 3-031-08580-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Question-Answering System over Knowledge Graphs Using Analogical-Problem-Solving Approach -- 1 Introduction -- 2 Background -- 2.1 RDF Knowledge Graphs and RDF Query Language -- 2.2 Dependency Parser -- 3 LingTeQA: An Analogical-Problem-Solving QA System -- 3.1 LingTeQA: Representing Questions -- 3.2 LingTeQA: Generating Templates -- 4 LingTeQA: Answering Questions -- 4.1 LingTeQA: Answering Question Containing Linguistic Terms -- 4.2 LingTeQA: Answering Questions by Generating Linguistic Summaries -- 4.3 LingTeQA: Defining Linguistic Terms with a User-friendly Web Interface -- 5 Related Work -- 5.1 Question Answering -- 5.2 Linguistic Summarization of Numeric Data -- 6 Result and Conclusion -- References -- Fuzzy Transform on 1-D Manifolds -- 1 Introduction -- 2 Preliminaries -- 2.1 Fuzzy Partition -- 2.2 Topological Manifold -- 2.3 Properties of Manifold upper MM -- 3 Fuzzy Partition of a Manifold -- 4 Hilbert Space on a Manifold -- 4.1 Subspace upper L 2 Superscript m Baseline left parenthesis ModifyingAbove upper A With quotation dash Subscript i Baseline right parenthesisL2m(overlineAi) -- 5 upper F Superscript mFm-transform -- 5.1 upper F Superscript 0F0-transform and its Inverse -- 6 Conclusion -- References -- A Systematic Review of Privacy-Preserving Blockchain in e-Medicine -- 1 Introduction -- 2 Blockchain and the Electronic Health Records Model -- 2.1 Conventional Electronic Health Records Model -- 2.2 Blockchain-Based Privacy-Preserving Models in Electric Health Records -- 3 Privacy-Preserving Identity Management Systems and Platforms in Blockchain -- 4 Future Research Directions -- 5 Conclusions -- References -- Why Rectified Linear Neurons: Two Convexity-Related Explanations -- 1 Formulation of the Problem -- 2 Why Convexity -- 3 First Convexity-Related Explanation.
4 Second Convexity-Related Explanation -- References -- How to Work? How to Study? Shall We Cram for the Exams? and How Is This Related to Life on Earth? -- 1 When to Switch Activities: Formulation of the Problem -- 2 Let Us Formulate This Problem in Precise Terms -- 3 Analysis of the Problem -- 4 Resulting Recommendations -- 5 How Is This Related to Life on Earth? -- References -- Why Quantum Techniques Are a Good First Approximation to Social and Economic Phenomena, and What Next -- 1 Formulation of the Problem -- 2 Where Can Such an Explanation Come from: General Analysis -- 3 First Explanation: Quantum Formulas Provide a Good Description for Many Phenomena in General -- 4 Second Explanation: Quantum Formulas Are the Computationally Fastest Way to Describe Nonlinear Phenomena -- 5 Third Explanation: Quantum Physics Has Many Solved Problems -- 6 Beyond Quantum -- References -- How the Pavement's Lifetime Depends on the Stress Level and on the Dry Density: An Explanation of Empirical Formulas -- 1 First Problem: Dependence on Stress -- 2 Analysis of the Problem -- 3 Invariance Requirement -- 4 Resulting Explanation -- 5 Second Problem: Dependence on Dry Density -- References -- Freedom of Will, Physics, and Human Intelligence: An Idea -- 1 Three Fundamental Challenges -- 2 How We Can Solve These Challenges: An Idea -- 3 Let Us Summarize Our Findings -- References -- Why Normalized Difference Vegetation Index (NDVI)? -- 1 Formulation of the Problem -- 2 Towards an Explanation: General Analysis of the Problem -- 3 First Result: Characterizing All Natural Functions of Two Variables -- 4 Scale Invariance -- References -- Binary Image Classification Using Convolutional Neural Network for V2V Communication Systems -- 1 Introduction -- 2 Related Work -- 2.1 Traditional Approaches -- 2.2 Deep Learning Approaches -- 3 Preliminaries of VOCC Systems. 4 Proposed Algorithm -- 5 Experiment Analyses -- 5.1 Dataset -- 5.2 Evaluation metrics -- 5.3 Result -- 6 Conclusion -- References -- Topic Model-Machine Learning Classifier Integrations on Geocoded Twitter Data -- 1 Introduction -- 2 Related Literature -- 3 Data -- 4 Methods -- 4.1 Topic Models Formulation -- 4.2 From Topic Models to Machine Learning Classifiers -- 4.3 Artificial Neural Networks -- 5 Results -- 5.1 Topic Models -- 5.2 Machine Learning Classifiers -- 5.3 Artificial Neural Networks -- 6 Conclusion -- 7 Appendix -- References -- Shop Product Tracking and Early Fire Detection Using Edge Devices -- 1 Introduction -- 2 Related Work -- 2.1 Real World Applications -- 2.2 Technologies -- 3 Method -- 3.1 Architecture -- 3.2 Product Recognition Module -- 3.3 Fire Detection Module -- 3.4 Product Tracking Module -- 4 Experiments -- 4.1 Datasets -- 4.2 Choosing Algorithms -- 4.3 Application Testing -- 5 Conclusions -- References -- SDNs Delay Prediction Using Machine Learning Algorithms -- 1 Introduction -- 2 Related Work -- 2.1 Machine Learning Algorithms -- 2.2 Evaluation Metrics -- 3 Method -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Training and Evaluation -- 4 Experiments -- 4.1 Datasets and Data Preprocessing -- 4.2 Comparison Between Different Approaches -- 5 Conclusions -- References -- A Linear Neural Network Approach for Solving Partial Differential Equations on Porous Domains -- 1 Introduction -- 2 The Proposed Radial Basis Function Network Technique -- 2.1 Integrated RBF Network Discretisation for Extended Domain -- 3 Numerical Solutions -- 3.1 Elliptic Partial Differential Equation -- 3.2 Parabolic Partial Differential Equation -- 4 Concluding Remarks -- References -- Accuracy Measures and the Convexity of ROC Curves for Binary Classification Problems -- 1 Introduction -- 2 Convexity of the ROC Curves. 3 Inequalities Relating Different Measures of Accuracy -- References -- Stochastic Simulations of Airborne Particles in a Fibre Matrix -- 1 Introduction -- 2 Langevin Equation -- 3 SDE Solver -- 3.1 Euler Method -- 3.2 Milstein Method -- 4 Langevin Equation for Particles' Zig-Zac Motion -- 5 Conclusion -- References -- Disease Diagnosis Based on Symptoms Description -- 1 Introduction -- 2 Overview of Related Works -- 3 Methodology -- 3.1 Background -- 3.2 Datasets, Input Pre-processing, Model Selection and Training -- 3.3 The Proposed Architecture for Diagnosis Based Symptom Descriptions -- 4 Results -- 4.1 Evaluation and Comparison with Prediction Performance of Bi-RNN -- 4.2 Practical Testing -- 5 Conclusion and Future Works -- References -- Chest X-Ray Image Analysis with ResNet50, SMOTE and SafeSMOTE -- 1 Introduction -- 2 Related Work -- 3 The Method -- 4 Experiments -- 5 Conclusions -- References -- Weakly Supervised Localization of the Abnormal Regions in Breast Cancer X-Ray Images Using Patches Classification -- 1 Introduction -- 2 Related Works -- 3 A Method of Weakly-Supervised Localization of the Cancer Regions in Breast Cancer X-Ray Images -- 3.1 Data Pre-processing -- 3.2 Generating Patches -- 3.3 Patch Augmentation -- 3.4 Patch Training -- 3.5 Heatmaps Calculation -- 4 Conclusions -- References -- Effects Evaluation of Data Augmentation Techniques on Common Seafood Types Classification Tasks -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Data Collection -- 3.2 Data Preprocessing and Augmentation Techniques -- 3.3 Split Data for the Evaluation -- 3.4 Building a Classification Model Using MobileNetV2 -- 4 Experimental Results -- 4.1 Hyper-parameters Fine-Tuning -- 4.2 Effect of Data Augmentation Techniques on Seafood Types Classification -- 5 Conclusion -- References. Image Caption Generator with a Combination Between Convolutional Neural Network and Long Short-Term Memory -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Preprocessing -- 3.2 Training with Deep Learning Approaches -- 4 Experimental Results -- 4.1 Dataset Description -- 4.2 Performance Comparison -- 4.3 Conclusion -- References -- Clothing Classification Using Shallow Convolutional Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Method -- 4.1 Overview -- 4.2 CNN-1 -- 4.3 CNN-2 -- 5 Experimental Results -- 6 Discussion -- 7 Conclusion -- References -- Similar Vietnamese Document Detection in Online Assignment Submission System -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Data Preprocessing and Cosine Similarity Computation -- 3.2 The Semantic Similarity Between Sentences -- 3.3 Calculate the Similarity Between the Documents -- 4 Experimental Results -- 4.1 Data Description -- 4.2 Performance Evaluation -- 4.3 Similarity Between Sentences -- 4.4 Similarity Between Documents -- 4.5 The Application to Assignment Submission System -- 5 Conclusion -- References -- A Study of Causal Modeling with Time Delay for Frost Forecast Using Machine Learning from Data -- 1 Introduction -- 2 Causal Modeling with Time Delay -- 3 Input Variable Granulation -- 4 Implementation and Experiments -- 4.1 Datasets -- 4.2 Input and Output -- 4.3 Model Implementation -- 5 Experiments -- 5.1 Granulation for Air Temperature and Vapor Pressure -- 6 Conclusion and Future Work -- 6.1 Conclusion -- 6.2 Future Work -- References. |
| Record Nr. | UNINA-9910733714703321 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Bounded Rationality in Decision Making Under Uncertainty: Towards Optimal Granularity / / by Joe Lorkowski, Vladik Kreinovich
| Bounded Rationality in Decision Making Under Uncertainty: Towards Optimal Granularity / / by Joe Lorkowski, Vladik Kreinovich |
| Autore | Lorkowski Joe |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (167 pages) |
| Disciplina | 620 |
| Collana | Studies in Systems, Decision and Control |
| Soggetto topico |
Computational intelligence
Cognitive psychology Artificial intelligence Computational Intelligence Cognitive Psychology Artificial Intelligence |
| ISBN | 3-319-62214-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Human Decisions Are Often Suboptimal: Phenomenon of Bounded Rationality -- Towards Explaining Other Aspects of Human Decision Making -- Towards Explaining Heuristic Techniques (Such as Fuzzy) in Expert Decision Making -- Decision Making Under Uncertainty and Restrictions on Computation Resources: From Heuristic to Optimal Techniques -- Conclusions and Future Work. |
| Record Nr. | UNINA-9910299873603321 |
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Lorkowski Joe
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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