<span>Complex problems and systems, which prevail in the real world, cannot often be tackled and solved either by traditional methods offered by mathematics or even the traditional computer science (CS) and and artificial intelligence (AI)..). What is the way out of this dilemma? Advanced methodolog
Computational Intelligence and Mathematics for Tackling Complex Problems 3 (Studies in Computational Intelligence, 959)
✍ Scribed by István Á. Harmati (editor), László T. Kóczy (editor), Jesús Medina (editor), Eloísa Ramírez-Poussa (editor)
- Publisher
- Springer
- Year
- 2021
- Tongue
- English
- Leaves
- 223
- Edition
- 1st ed. 2022
- Category
- Library
No coin nor oath required. For personal study only.
✦ Synopsis
Complex problems and systems, which prevail in the real world, cannot often be tackled and solved either by traditional methods offered by mathematics or even the traditional computer science (CS) and and artificial intelligence (AI)..). What is the way out of this dilemma? Advanced methodologies, and tools and techniques, „mimicking” human reasoning or the behavior of animals, animal populations or certain parts of the living bod, based on traditional computer science science and the initial approaches of artificial intelligence are often referred to as biologically inspired methods, or often computational intelligence (CI). Computational intelligence offers effective and efficient solutions to many „unsolvable" problems problems. However, it is far from being a ready to use and complete collection of approaches, and is rather a continuously developing field without clear borders. The emerging new models and algorithms of computational intelligence are deeply rooted in the vast apparatus of traditional mathematics. Thus, the investigation of connections and synergy between mathematics and computational intelligence is an eminent goal which is periodically pursued by a group of mathematicians and computational intelligence researchers who regularly attand the annual European Symposia on Computational Intelligence and Mathematics (ESCIM). Some relevant papers from the last ESCIM-2020 are included in this volume.
✦ Table of Contents
Preface
Contents
Q-Fuzzy Subtopology
1 Preliminaries
1.1 Commutative Quantales
1.2 Q-Valued Sets
1.3 Q-Fuzzy Topologies
1.4 Q-Preorders and Alexandroff Q-Topologies
2 How to Generate an Alexandroff Strong Q-Topology Constructively
3 Q-Fuzzy Subtopology
4 Conclusion
References
Lattice-Valued Algebraic Structures Via Residuated Maps
1 Introduction
2 Preliminaries
3 Compatible Lattice-Valued Functions and -Algebras
4 Conclusion
References
Invariant Aggregation and Pre-aggregation Functions
1 Introduction
2 Transformations of Fusion Functions
3 Transformations of Fuzzy Integrals
References
Finite Model Property and Varieties of BL-Algebras
1 Introduction
2 BL-Algebras and Ordinal Sums
3 FMP for Varieties of BL-Algebras
4 Conclusions
References
Collection Integral on Infinite Spaces
1 Introduction
2 Preliminaries
3 Main Theorem and Examples
4 Countable Collections
5 Concluding Remarks
References
Duhamel Hereditary Integrals in Viscoelasticity
1 Introduction
2 Action Versus Reaction in Linear Viscoelastic Model
3 Constitutive Equations. Stress on Strain and Strain on Stress Dependence
4 Duhamel Hereditary Integral in Uniaxial Viscoelasticity. Maxwell Model
5 Conclusion
References
Weighted Penalty-Based Aggregation
1 Introduction
2 Penalty-Based Aggregation Functions
3 Weighted Penalty Functions
4 Concluding Remarks
References
Aggregation Operators in Fuzzy Relational Mathematical Morphology: Erosion and Dilation
1 Introduction
2 Preliminaries
2.1 Lattices, Quantales, Girard Quantales
2.2 L-Fuzzy Relations
3 Operators of Structured L-Fuzzy Relational Erosion and Dilation
4 Interrelations Between Fuzzy Relational Erosion and Dilation
5 Aggregation of Fuzzy Relational Erosion and Fuzzy Relational Dilation Operators
5.1 (wedge,vee)-Aggregation
5.2 (vee,wedge)-Aggregation
5.3 Aggregation on the Base of a Product-Coproduct (,) Pair
6 Conclusion
References
Characterization of the Infimum of Classes Induced by an Attribute Reduction in FCA
1 Introduction
2 Preliminaries
3 Characterizing the Infimum of Classes
4 Conclusions and Future Work
References
Extensions of Fuzzy Measures Based on Double Generalization of the Lovász Extension Formula
1 Introduction
2 Preliminaries
3 Generalizations of the Lovász Extension Formula
4 Conclusion
References
On the Intuitionistic Fuzzy Representations of Rough Real Functions
1 Introduction
2 Pawlak Approximation Spaces on Intervals
3 Rough Real Functions and Their Representations
4 Intuitionistic Fuzzy Set Representations of Rough Real Functions
4.1 Basic Notions and Notations
4.2 Intuitionistic Fuzzy Sets Derived from Rough Real Functions
4.3 Basic Relations and Operations with Derived Intuitionistic Fuzzy Sets
5 Conclusion
References
Automatic Generation of Linguistic Descriptions of Electricity Consumption in the Buildings of a Large Institution
1 Introduction
2 Related Work
3 LPN Design to Describe Building Electrical Consumption
4 Conclusions and Future Work
References
On the Selection the Rule Membership Functions and Fuzzy Rule Interpolation
1 Introduction
2 Image Filtering Methods and Antecedent Generation
3 Rule Base Selection
4 Conclusion and Further Outlook
References
Linguistic Descriptions of Data Via Fuzzy Formal Concept Analysis
1 Introduction
2 Fuzzy Concept Lattices
3 Transforming Datasets Into Residuated Contexts
4 Linguistic Descriptions By Using Irreducibles Concepts
5 Conclusions and Future Work
References
Rough Sets and Topology in AST: A Study Via Higher-Order Fuzzy Logic
1 Introduction
2 Preliminaries
2.1 Alternative Set Theory
2.2 Rough Set Theory
2.3 Fuzzy Sets in Fuzzy Type Theory
3 Rough Sets and Topology in AST Via Fuzzy Type Theory
3.1 Figures
3.2 Rough Fuzzy Sets
3.3 Fuzzy Topology Based on the Fuzzy Equality
4 Conclusion
References
olgga: An On-Line Generation-Less Genetic Algorithm
1 Introduction
2 On-Line Generation-Less Genetic Algorithm
2.1 Algorithm Overview
2.2 Selection Process
3 Dynamic Optimization with olgga
4 Performance Measurement
5 Conclusion
References
Some Dynamical Properties of Higher-Order Fuzzy Cognitive Maps
1 Introduction
2 Higher-Order Fuzzy Cognitive Maps
3 Some Dynamical Properties of Higher-Order Fuzzy Cognitive Maps
4 Conclusions and Future Work
References
A Fuzzy Declarative Approach to Classify Unlabeled Short Texts Based on Automatically Constructed WordNet Ontologies
1 Introduction
2 Related Work
3 A Declarative Approach to Text Classification
3.1 Pre-Processing Stage
3.2 Automatic Construction of WordNet-Based Ontologies
3.3 Classification Method
4 Experiments
5 Conclusions and Future Work
References
Automatic Recognition of Handwritten Urdu Characters
1 Introduction
1.1 The Problem Statement
2 Overview of the Literature
2.1 The Urdu Language
2.2 Modeling the Problem by Support Vector Machine
3 The Proposed Approach in Case of the Urdu Script
3.1 Data Preprocessing
3.2 Classification
4 Testing and Analysis
5 Conclusion and Future Work
References
Algorithms for Triggering General Regression Neural Network
1 Introduction
2 General Regression Neural Network
3 Learning Methods
4 Results and Discussion
5 Conclusions
References
Local Binary Pattern-Based Fingerprint Matching
1 Introduction
2 System Overview
3 Results
4 Conclusions
References
Database Incident Response and Forensic Preparation Through the Performance Features
1 Introduction
2 Methodology
2.1 Database Performance Feature Selection
2.2 Correlation Analysis
2.3 Prediction Future Behaviour
2.4 Stability Analysis and Change Point Detection
3 Case-Study
4 Conclusion
References
Formal Concept Analysis for Detecting Criminal Patterns
1 Introduction
2 Preliminaries
3 Analysis of Bilbao Crime Dataset
4 Conclusions and Future Work
References
Detection of Fraudulent Credit Card Transactions by Computational Intelligence Models as a Tool in Digital Forensics
1 Introduction
2 Data Set
3 Computational Intelligence Models
4 Comparative Analysis
4.1 Verification Method
4.2 Parameter Settings for CI Models
4.3 Results and Discussion
5 Conclusions
References
Data Mining Techniques for the Analysis of Student's Admission Data
1 Introduction
2 Data Mining Process and Data Mining Techniques
3 Research Methodology
4 Data Analysis and Results
5 Conclusions and Future Work
References
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