Fuzzy Logic(2021)[Carter et al][]

Preface
Contents
Fuzzy Logic, a Logician’s Perspective
	1 Introduction
	2 Ancient Greece
	3 Twentieth Century
	4 Modern Origins of Fuzzy Logic
	5 What Is a Fuzzy Set?
	6 What Is Fuzzy Logic?
	7 Reception
	8 Conclusions
	References
A Fuzzy Approach to Sentiment Analysis at the Sentence Level
	1 A Fuzzy Approach to Sentiment Analysis
	2 Introduction
	3 Research Methodology
	4 A Hybrid Approach to the SA Problem at the Sentence Level
		4.1 Component 1: The Sentiment/Opinion Lexicon
		4.2 Component 2: Semantic Rules (SR)
		4.3 Component 3: Fuzzy Sets Approach to the SA Problem
		4.4 Description of the Process Implemented in Our Hybrid Approach
		4.5 Experimental Results
		4.6 Performance Comparison Against Machine Learning and State of the Art
	5 Conclusions
	References
Consensus in Sentiment Analysis
	1 Sentiment Aggregation by Consensus
	2 Introduction
	3 Fuzzy Majority in Collective Decision Making Modelled with an IOWA Operator
		3.1 The Linguistic Quantifier in Fuzzy Logic
		3.2 Linguistic Quantifiers as Soft Specifications of Majority-Based Aggregation
	4 The Proposed IOWA Approach to Sentiment Analysis
		4.1 The Concept of Fuzzy Majority Implemented Using IOWA Operators
		4.2 Fuzzy Majority in Determining Intensity of the Polarity of Predetermined Subjectivity
		4.3 Experimental Results Obtained
		4.4 Datasets Used
		4.5 Utilised Comparison Criteria
		4.6 Non-OWA Aggregation—The Outputs of the Three Classification Methods Combined Without the Application of the IOWA Operator
		4.7 OWA Aggregation Using Operator IOWAmost
		4.8 Specific Examples of Applying the IOWAmost Operator
		4.9 The Tolerance Value α and Its Role
	5 Conclusion
	References
Fostering Positive Personalisation Through Fuzzy Clustering
	1 Introduction
	2 Personalisation
		2.1 How to Personalise?
		2.2 Models of Personalisation
	3 Clustering
		3.1 Crisp Clustering Versus Fuzzy Clustering
		3.2 The K-Means and Fuzzy C-Means (FCM) Clustering
		3.3 The K-Means and Fuzzy C-Means (FCM) Algorithms
	4 Case Study: FCM Clustering to Enable Personalisation for Targeted Promotions in Grocery Retail
		4.1 Identifying Target Customers
		4.2 Creating a Target Customer Stratification Matrix
		4.3 Customer Treatment Approach
		4.4 Creating and Using Clusters for Personalisation
		4.5 Measuring the Accuracy of the Personalisation Approach
	5 Summary of Key Points
	References
Diagnosing Alzheimer's Disease Using a Self-organising Fuzzy Classifier
	1 Introduction
	2 Preliminary
		2.1 The Self-organising Fuzzy Classifier
		2.2 Data Summary
	3 Methodology
		3.1 Data Pre-processing
		3.2 Modelling Approach
		3.3 Feature Selection
		3.4 Model Development
	4 Experimentation
	5 Conclusion and Future Work
	References
Autism Spectrum Disorder Classification Using a Self-organising Fuzzy Classifier
	1 Introduction
	2 Literature Review
	3 Materials and Methods
		3.1 Participants
		3.2 Data Pre-processing
		3.3 Feature Selection and Modelling Approach
		3.4 Feature Selection
		3.5 Modelling
		3.6 Evaluation
		3.7 Results
	4 Conclusion
	References
An Outlier Detection Informed Aggregation Approach for Group Decision-Making
	1 Introduction
	2 Preliminaries
		2.1 Pairwise Comparisons of Alternatives Based on PFSs from the Positive and Negative Views
		2.2 Consensus Measure
	3 The Proposed Method
		3.1 The Outlier Detection Method
		3.2 The Aggregation Method
	4 Experimentation and Analysis
	5 Conclusion
	References
Novel Aggregation Functions Based  on Domain Partition with Concentrate Region of Data
	1 Introduction
	2 Related Works
	3 Proposed Mixed Aggregation Functions
		3.1 Analyzing and Classifying Partitioned Sub-regions  of Domain
		3.2 Bi-variate Aggregation Functions
		3.3 Multivariate Aggregation Functions
	4 Experimentation
		4.1 Experimental Settings
		4.2 Implementation
	5 Conclusion
	References
Applying Fuzzy Pattern Trees for the Assessment of Corneal Nerve Tortuosity
	1 Introduction
	2 Preliminaries
		2.1 Triangular Norms
		2.2 Ordered Weighted Averaging
	3 Method
		3.1 Nerve Fiber Segmentation and Tortuosity Measurements
		3.2 Image-Level Feature Extraction
		3.3 Top-Down Generation of Fuzzy Pattern Trees
	4 Experimental Analysis
	5 Conclusion and Future Work
	References
A Mamdani Fuzzy Logic Inference System to Estimate Project Cost
	1 Introduction
	2 Literature Review and Motivation
	3 The System
		3.1 System Design Overview
		3.2 Project Effort Fuzzy Inference System
		3.3 Project Cost Fuzzy Inference System
	4 Experimental Design and Evaluation
		4.1 Project Cost Fuzzy System Tests
	5 Discussion
	6 Conclusion
	References
Artificial Intelligence in FPS Games: NPC Difficulty Effects on Gameplay
	1 Introduction
	2 Background and Motivation
	3 System Overview
		3.1 Design Considerations
		3.2 Fuzzy Inference Sub-system: NPC Sensual Skill
		3.3 Variable Justification
		3.4 Fuzzy Inference Sub-system: NPC Objective Potential
		3.5 Variable Justification
		3.6 Fuzzy Inference Sub-system: NPC Weapon Lethality
		3.7 Variable Justification
		3.8 Fuzzy Inference Sub-system: NPC Difficulty
		3.9 Variable Justification
	4 Experimental Design and Evaluation
		4.1 Initial Fuzzy System Design: MATLAB
		4.2 System Functionality Testing
		4.3 Rule Base Adjustments
		4.4 Membership Function Testing
		4.5 Defuzzification Method Testing
	5 Nominated Defuzzification Methods
	6 Discussion
	7 Conclusion
	References
Adaptive Cruise Control Using Fuzzy Logic
	1 Introduction
	2 Literature Review
		2.1 Why Does the World Need ITS?
		2.2 A Brief History of Automobiles and Cruise Control
		2.3 A Brief Introduction to Fuzzy Logic
		2.4 Better Technology Better systems
		2.5 Better Inputs: Stronger Outputs
		2.6 The Human Element
	3 System Overview
		3.1 Design Considerations
		3.2 System Designs
	4 System Testing and Evaluation
		4.1 System Testing
		4.2 Test 1
		4.3 Test 2
		4.4 Test 3
		4.5 Modifications Based on These Tests
		4.6 Defuzzification Testing
		4.7 Aggregation Versus Implication
		4.8 Implication and Weighting
	5 Final System and Discussion
	6 Conclusion
	References
Automatic Camera Flash Using a Mamdani Type One Fuzzy Inference System
	1 Introduction
	2 Literature Review
		2.1 Auto-Focus
		2.2 Auto-Exposure
		2.3 Conclusions
	3 System Design
		3.1 Overview
		3.2 Input: Distance from Subject
		3.3 Input: Ambient Light Level
		3.4 Input: Camera Aperture
		3.5 Input: Camera Shutter Speed
		3.6 Output: Flash Intensity
	4 The Rule Base
	5 Testing Defuzzification Methods
		5.1 Defuzzification Method: Centroid
		5.2 Testing Centroid Defuzzification
		5.3 Defuzzification Method: Bisector
		5.4 Testing Bisector Defuzzification
		5.5 Defuzzification Methods: LOM, MOM and SOM
		5.6 Testing LOM Defuzzification
		5.7 Testing MOM Defuzzification
		5.8 Testing SOM Defuzzification
		5.9 Conclusions from Testing Defuzzification Methods
	6 Critical Reflection
	7 Conclusion
	References
The Application of Fuzzy Logic in Determining Outcomes of eSports Events
	1 Introduction
	2 What is eSports?
	3 Literature Review
	4 System Design
	5 The Future of eSports Analytical Tools
	6 Conclusion
	References
Water Carbonation Fuzzy Inference System
	1 Introduction
	2 Motivation
	3 System Overview
		3.1 Design
		3.2 Fuzzy Inference System
		3.3 Description of System
		3.4 Parameter and Interval Justification
		3.5 Operator, Implication and Aggregation Justification
	4 Experimental Design, Testing and Evaluation
		4.1 Initial Design
	5 Testing
		5.1 Test 1
		5.2 System Expectation
		5.3 Test 2
		5.4 System Expectation
		5.5 Test 3
		5.6 Test 4
		5.7 Test 5
		5.8 Test Comparison
	6 Discussion
	7 Conclusion
	References