This book facilitates both the theoretical background and applications of fuzzy, intuitionistic fuzzy and rough, fuzzy rough sets in the area of data science. This book provides various individual, soft computing, optimization and hybridization techniques of fuzzy and intuitionistic fuzzy sets with rough sets and their applications including data handling and that of type-2 fuzzy systems. Machine learning techniques are effectively implemented to solve a diversity of problems in pattern recognition, data mining and bioinformatics. To handle different nature of problems, including uncertainty, the book highlights the theory and recent developments on uncertainty, fuzzy systems, feature extraction, text categorization, multiscale modeling, soft computing, machine learning, deep learning, SMOTE, data handling, decision making, Diophantine fuzzy soft set, data envelopment analysis, centrally measures, social networks, Volterra–Fredholm integro-differential equation, Caputo fractional derivative, interval optimization, decision making, classification problems. This book is predominantly envisioned for researchers and students of data science, medical scientists and professional engineers.
Author(s): Tanmoy Som, Oscar Castillo, Anoop Kumar Tiwari, Shivam Shreevastava
Series: Forum for Interdisciplinary Mathematics
Publisher: Springer
Year: 2023
Language: English
Pages: 278
City: Singapore
Preface
Contents
About the Editors
1 Fuzzy Sets and Rough Sets: A Mathematical Narrative
1.1 Introduction
1.2 Algebraic Aspects
1.2.1 Initial Definitions
1.3 Generalizations
1.3.1 Fuzzy Sets
1.3.2 Rough Sets
1.4 Fuzzy Sets Vis a Vis Rough Sets
1.4.1 Fuzzy Rough Set
1.4.2 Rough Fuzzy Set
1.4.3 Some Other Approaches
1.4.4 Rough Membership Function-Based Approach
1.5 Logics
1.5.1 Fuzzy MP Rule
1.5.2 Rough Modus Ponens Rules
1.5.3 Rough Logics
1.6 Concluding Remarks
References
2 Enhancing the Prediction of Anti-cancer Peptides by Suitable Feature Extraction and FRFS with ACO Search Followed by Resampling
2.1 Introduction
2.2 Material and Methods
2.2.1 Dataset
2.2.2 Feature Extraction
2.2.3 Feature Selection
2.2.4 Balancing Protocol
2.2.5 Machine Learning Protocol
2.2.6 Performance Measures
2.3 Experimentation
2.4 Conclusion
References
3 New Methods of Vagueness and Uncertainty Quantification in Lattice Boltzmann Method-Based Solute Transport Model
3.1 Introduction
3.2 Solute Transport Model with Imprecise and Vague Parameters
3.3 Preliminaries of Fuzzy Set and Intuitionistic Fuzzy Set
3.3.1 Fuzzy Set
3.3.2 Basic Concept of Intuitionistic Fuzzy Set
3.3.3 Fuzzy Vertex Method
3.3.4 Fuzzy Vertex Method for IFS
3.4 Formulation of Fuzzy and Intuitionistic Fuzzy Lattice Boltzmann Scheme
3.5 Vagueness and Uncertainty Analysis of Solute Transport Model
3.5.1 Generation of Fuzzy and Intuitionistic Fuzzy Number
3.5.2 Development of LB Solver
3.5.3 LB-Based Numerical Solution
3.6 Conclusions
References
4 Fuzzy Rough Set-Based Feature Selection for Text Categorization
4.1 Introduction
4.2 Feature Selection
4.3 Fuzzy Rough Feature Selection
4.3.1 Supervised Fuzzy Rough Feature Selection
4.3.2 Unsupervised Fuzzy Rough Feature Selection
4.3.3 Reduction Calculation
4.4 Related Work
4.5 Potential of Fuzzy Rough Feature Selection in Text Categorization
4.5.1 Proposed Hybrid Landmark-Based Fuzzy Rough Feature Selection (LBFRFS)
4.5.2 Experimental Results
4.6 Conclusions
References
5 An Extensive Survey on Classification of Malaria Parasites in Patients Based on Fuzzy Approaches
5.1 Introduction
5.1.1 Fuzzy Logic
5.1.2 Fuzzy Logic Application on Disease Diagnosis
5.2 Fuzzy Logic and Fuzzy-Based Malaria Diagnosis Framework
5.2.1 Fuzzy Logic
5.2.2 Pre-processing the Data
5.2.3 Fuzzification and De-fuzzification
5.2.4 Feature Selection and Clustering
5.2.5 Malaria Parasite Classification from Blood Images
5.3 Classification of Malaria from Non-conventional Method
5.4 Detection of Malaria from Soft-Computing Methods
5.5 Conclusion and Future Research Scope
References
6 Application of Feature Extraction and Feature Selection Followed by SMOTE to Improve the Prediction of DNA-Binding Proteins
6.1 Introduction
6.2 Materials and Methods
6.2.1 Dataset
6.2.2 Input Features
6.2.3 Classification Protocol. RF Boosted
6.2.4 Optimal Balancing Protocol
6.2.5 Feature Selection Protocol
6.2.6 Performance Evaluation Metrics
6.3 Experimental Analysis
6.4 Conclusion
References
7 Perspectives of Soft Computing in Multiscale Modeling for Fluid Flow Systems
7.1 Introduction
7.2 Overview of Soft Computing
7.2.1 Why We Do Soft Computing?
7.3 Multiscale Modeling
7.4 Mathematical Structure of Classical LBM
7.4.1 Formulation of Boltzmann Transport Equation
7.4.2 Arrangements of Lattice Structure
7.4.3 Soft Computing in Multiscale Modeling
7.5 Fusion of Soft Computing and Multiscale Modeling
7.5.1 Definition of Fuzzy Set
7.5.2 Alpha (α)-Cut and Algebraic Properties of Fuzzy Number
7.5.3 Mathematical Structure of Fuzzy Lattice Boltzmann Scheme
7.5.4 Uncertainty Analysis
7.5.5 Results and Discussion
7.6 Conclusions
References
8 Various Generalizations of Fuzzy Sets in the Context of Soft Computing and Decision-Making
8.1 Preliminaries
8.2 Type-2 Fuzzy Sets and Systems
8.3 Intuitionistic Fuzzy Sets
8.4 Pythagorean Fuzzy Sets
8.5 Picture Fuzzy Sets
8.6 Spherical Fuzzy Sets
8.7 Fermatean Fuzzy Set as a q-Rung Orthopair Fuzzy Set
8.8 Hesitant Fuzzy Sets
8.9 Applications
8.10 Conclusions
References
9 A Linear Diophantine Fuzzy Soft Set-Based Decision-Making Approach Using Revised Max-Min Average Composition Method
9.1 Introduction
9.2 Preliminaries
9.3 Proposed Approach
9.4 Case Study and Comparative Analysis
9.5 Conclusion
References
10 Recent Developments in Fuzzy Dynamic Data Envelopment Analysis and Its Applications
10.1 Introduction
10.2 Overview of Dynamic DEA
10.3 Fuzzy Dynamic DEA
10.3.1 Fuzzy Set Theory
10.3.2 Fuzzy Set Theory and Dynamic DEA
10.4 Classification of FDDEA Studies
10.4.1 Theoretical Development of FDDEA Models with Different Fuzzy Sets
10.4.2 FDDEA with Network Structure
10.4.3 Applications of FDDEA
10.4.4 Integration of FDDEA with Other Techniques
10.5 Conclusion
References
11 Role of Centrality Measures in Link Prediction on Fuzzy Social Networks
11.1 Introduction
11.2 Preliminaries
11.2.1 Social Network
11.2.2 Centrality Measures
11.2.3 Link Prediction
11.3 Centrality-Based Link Prediction
11.3.1 Fuzzy Social Network Modeling
11.3.2 Similarity Index Computation
11.3.3 Likelihood Index Computation
11.4 Performance Analysis
11.4.1 AUC
11.4.2 Balanced Accuracy
11.4.3 F1-Score
11.5 Conclusion and Future Directions
References
12 Interval Solutions of Fractional Integro-differential Equations by Using Modified Adomian Decomposition Method
12.1 Introduction
12.2 Preliminaries
12.3 Modified Adomian Decomposition Method (MADM) [3]
12.4 Illustrative Examples
12.5 Conclusions
References
13 Generalized Hukuhara Subdifferentiability for Convex Interval-Valued Functions and Its Applications in Nonsmooth Interval Optimization
13.1 Introduction
13.2 Preliminaries and Terminologies
13.2.1 Fundamental Operations on Intervals
13.2.2 IVF and Its Calculus
13.3 gH-Subdifferential for Convex IVFs
13.4 Application on Nonsmooth Interval Optimization
13.5 Conclusion and Future Scopes
References
14 Rule-Based Classifiers for Identifying Fake Reviews in E-commerce: A Deep Learning System
14.1 Introduction
14.2 Related Work
14.3 Materials and Methods
14.3.1 E-commerce Product Reviews
14.3.2 Dataset Collection
14.3.3 Dataset Labeling
14.3.4 Preprocessing
14.3.5 TF-IDF and Word to Vector Methods
14.3.6 Classification Techniques
14.3.7 Evaluation Metrics
14.4 Experimental Results
14.4.1 Word Cloud
14.5 Conclusions and Future Research
References
