Soft Computing in Interdisciplinary Sciences (Studies in Computational Intelligence, 988)

✍ Scribed by S. Chakraverty (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 264
• Category: Library

✦ Synopsis

This book meets the present and future needs for the interaction between various science and technology/engineering areas on the one hand and different branches of soft computing on the other. Soft computing is the recent development about the computing methods which include fuzzy set theory/logic, evolutionary computation (EC), probabilistic reasoning, artificial neural networks, machine learning, expert systems, etc. Soft computing refers to a partnership of computational techniques in computer science, artificial intelligence, machine learning, and some other engineering disciplines, which attempt to study, model, and analyze complex problems from different interdisciplinary problems. This, as opposed to traditional computing, deals with approximate models and gives solutions to complex real-life problems. Unlike hard computing, soft computing is tolerant of imprecision, uncertainty, partial truth, and approximations.

Interdisciplinary sciences includevarious challenging problems of science and engineering. Recent developments in soft computing are the bridge to handle different interdisciplinary science and engineering problems. In recent years, the correspondingly increased dialog between these disciplines has led to this new book.

This is done, firstly, by encouraging the ways that soft computing may be applied in traditional areas, as well as point towards new and innovative areas of applications and secondly, by encouraging other scientific disciplines to engage in a dialog with the above computation algorithms outlining their problems to both access new methods as well as to suggest innovative developments within itself.

✦ Table of Contents

Preface
Contents
Editor and Contributors
Recent Trends in Interval Regression: Applications in Predicting Dengue Outbreaks
1 Introduction
2 Theory and Applications
2.1 The Centre Method (CM)
2.2 Centre and Range Method (CRM)
2.3 Constrained Centre and Range Method (CCRM)
2.4 Applications of CM, CRM and CCRM
2.5 Interval Least Squares Algorithm
2.6 Applications of Interval Regression Based on Interval Least Squares Algorithm
2.7 Fuzzy Number
2.8 Fuzzy Regression
2.9 Fuzzy Linear Regression Using the Possibilistic Linear Regression Method
2.10 Application of Possibilistic Linear Regression (PLR) Method
2.11 Possibilistic Linear Regression with Least Squares (PLRLS) Method
2.12 Application of Possibilistic Linear Regression with Least Squares (PLRLS) Method
2.13 Fuzzy Linear Regression Using the Multi-objective Fuzzy Linear Regression (MOFLR) Method
2.14 Application of MOFLR Method
3 Conclusion and Discussion
References
Fuzzy-Affine Approach in Dynamic Analysis of Uncertain Structural Systems
1 Introduction
2 Preliminaries
2.1 Fuzzy Number
2.2 Different Types of Fuzzy Number
2.3 α-Cut Technique of Fuzzy Number [2]
2.4 Affine Arithmetic
2.5 Conversion of Interval to Affine and Vice Versa
2.6 Affine Arithmetic Operations
3 Fuzzy-Affine Approach
3.1 Fuzzy-Affine form of TFN
3.2 Fuzzy-Affine form of TrFN
3.3 Fuzzy-Affine Approach
3.4 Efficacy of Fuzzy-Affine Approach
4 Proposed Method
5 Numerical Examples
6 Conclusion
References
Fuzzy Application: Develop a Weather Index
1 Introduction
2 Preliminaries
2.1 Fuzzy Sets
2.2 Triangular Membership Function
2.3 Basic Fuzzy Algebraic Operations Defined on Triangular Fuzzy Number
2.4 Linguistic Variable
2.5 Fuzzy Pairwise Comparison Matrix
2.6 Analytic Hierarchy Process (AHP)
2.7 Value of Degree of Fuzziness
2.8 Convex Combination
3 Methodology
4 Results and Discussion
5 Conclusion
References
Type-2 Fuzzy Linear Eigenvalue Problems with Application in Dynamic Structures
1 Introduction
2 Preliminaries
2.1 Type-1 Fuzzy Numbers
2.2 Parametric Form of Fuzzy Number
2.3 Type-2 Fuzzy Set
2.4 Vertical Slice of Type-2 Fuzzy Set
2.5 r1-Plane of Type-2 Fuzzy Set
2.6 Footprint of Uncertainty
2.7 Lower Membership Function(LMF) and Upper Membership Function(LMF) of a Type-2 Fuzzy Set
2.8 Principle Set of tildeA ch4hamrawi2011type
2.9 r2- Cut of r1- Plane ch4hamrawi2011type
2.10 Triangular Perfect Quasi Type-2 Fuzzy Numbers ch4mazandarani2014differentiability
3 Type-2 Fuzzy Linear Eigenvalue Problem
4 Proposed Method to Solve Type-2 Fuzzy Linear Eigenvalue Problem
5 Numerical Examples
6 Conclusion
References
Fuzzy Dynamical System in Alcohol-Related Health Risk Behaviors and Beliefs
1 Introduction
1.1 Mathematical Preliminaries
2 The Mathematical Model
3 Model Analysis
3.1 Steady State Solutions
4 Fuzzy Dynamical Systems
4.1 Fuzzy Model Risk Reproduction Number
4.2 Stability Analysis of Risk-Free Equilibrium
4.3 Risk Control in Fuzzy Epidemic System
5 Discussion and Conclusion
References
Curriculum Learning-Based Artificial Neural Network Model for Solving Differential Equations
1 Introduction
2 Artificial Neural Network
3 Curriculum Learning
4 General Formulation for Differential Equations
4.1 Construction for First-Order IVP
4.2 Construction for Second-Order IVP
4.3 Construction for Second-Order BVP
5 First-Order ODEs
6 Higher Order ODEs
7 Conclusion
References
Analysis of EEG Signal for Drowsy Detection: A Machine Learning Approach
1 Introduction
2 Background: Drowsiness Detection
3 EEG Signal Acquisition and Artifact Removing
4 Machine Learning-Based Drowsiness Detection
5 Deep Learning-Based Drowsiness Detection Methods
6 Experimental Results
6.1 Machine Learning Classification
6.2 Deep Learning Classification
7 Discussion
8 Conclusion
References
Uncertain Structural Parameter Identification by Intelligent Neural Training
1 Introduction
2 Interval Arithmetic
3 Learning Algorithm for Single-Layer Interval Neural Network
4 System Identification of Interval Structural Parameter
5 Results and Discussion
6 Conclusions
References
Security Issues on IoT Communication and Evolving Solutions
1 Introduction
2 Review of State-of-the-Art Security and Privacy Solutions
2.1 Security and Privacy Solution Review Based on Communication Layers
2.2 Security and Privacy Solution Review Based on State-of-the-Art Techniques
3 Conclusion
References
Causality and Its Applications
1 Introduction
1.1 An Introduction to Causality
1.2 A Representation of Causal Model
2 Causal Identification
2.1 The Quantitative Analysis of Causality
2.2 Causal Inference: A Qualitative Analysis
3 Machine Learning, Deep Learning and Causal Reasoning
3.1 Deep Learning and the Black Box
3.2 Predictive Versus Prescriptive Analysis
3.3 Integrating Causality into Machine Learning and Deep Learning
3.4 Causal Applications in Machine Learning, Deep Learning
References
Hybrid Evolutionary Computing-based Association Rule Mining
1 Introduction
2 Literature Review
3 Association Rule Mining Using Firefly Optimization, Particle Swarm Optimization, Threshold Accepting-based Techniques
3.1 Firefly Optimization Algorithm (FFO)
3.2 Threshold Acceptance (TA)
3.3 Binary Firefly Optimization (BFFO)
3.4 Particle Swarm Optimization (PSO)
3.5 Binary PSO
3.6 Feature Selection
4 BFFO/BFFO-TA/BPSO-TA for Association Rule Mining
4.1 Binary Transformation
4.2 Rule Representation
4.3 Objective Function
4.4 Special Cases
4.5 Advantages of the Proposed Approaches
5 Results and Discussion
5.1 Books Dataset
5.2 Food Dataset
5.3 Grocery Dataset
5.4 XYZ Bank Dataset
5.5 Bakery Dataset
5.6 Clickstream Dataset
6 Conclusions
References
Toward Sarcasm Detection in Reviews—A Dual Parametric Approach with Emojis and Ratings
1 Introduction
2 Related Works
3 Dataset Preparation
3.1 Dataset Extraction
3.2 Data Pre-processing
4 Methodology
4.1 Overview
4.2 Rating Extraction
4.3 Emoji Extraction
4.4 Adverbs and Adjectives Extraction
4.5 Feature Sentiment Score Calculation
4.6 Opinion Word Sentiment Score Calculation
4.7 Emoji Sentiment Score Calculation
4.8 Sarcastic Review Detection
5 Results and Discussion
5.1 Data
5.2 Ground Truth
5.3 Results and Discussion
6 Conclusion and Future Work
References
Toward Sarcasm Detection in Reviews—A Dual Parametric Approach with Emojis and Ratings
1 Introduction
2 Related Works
3 Dataset Preparation
3.1 Dataset Extraction
3.2 Data Pre-processing
4 Methodology
4.1 Overview
4.2 Rating Extraction
4.3 Emoji Extraction
4.4 Adverbs and Adjectives Extraction
4.5 Feature Sentiment Score Calculation
4.6 Opinion Word Sentiment Score Calculation
4.7 Emoji Sentiment Score Calculation
4.8 Sarcastic Review Detection
5 Results and Discussion
5.1 Data
5.2 Ground Truth
5.3 Results and Discussion
6 Conclusion and Future Work
References