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Submit a ProposalFuzzy Logic Applications in Computer Science and Mathematics
 | Edited by Rahul Kar, Dac-Nhuong Le, Gunjan Mukherjee and Biswadip Basu Mallik and Ashok Kumar Shaw
Series: Advances in Learning Analytics for Intelligent Cloud-IoT Systems Copyright: 2023 | Expected Pub Date: 2023/07/30 ISBN: 9781394174539 | Hardcover | 297 pages Price: $195 USD  |
One Line Description
The prime objective of developing this book is to provide meticulous details about the basic and advanced concepts of fuzzy logic and its all-around applications to different fields of mathematics and engineering.
Audience
This book will be read and used by a broad audience including applied mathematicians, computer scientists, and industry engineers.
This book will be read and used by a broad audience including applied mathematicians, computer scientists, and industry engineers.
Description
The basic steps of fuzzy inference systems starting from the core foundation of the fuzzy concepts are presented in this book. The fuzzy theory is a mathematical concept and, at the same time, it is applied to many versatile engineering fields and research domains related to computer science. The fuzzy system offers some knowledge about uncertainty and is also related to the theory of probability. A fuzzy logic-based model acts as the classifier for many different types of data belonging to several classes.
Covered in this book are topics such as the fundamental concepts of mathematics, fuzzy logic concepts, probability and possibility theories, and evolutionary computing to some extent. The combined fields of neural network and fuzzy domain (known as the neuro-fuzzy system) are explained and elaborated. Each chapter has been produced in a very lucid manner, with grading from simple to complex to accommodate the anticipated different audiences. The application-oriented approach is the unique feature of this book.
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The basic steps of fuzzy inference systems starting from the core foundation of the fuzzy concepts are presented in this book. The fuzzy theory is a mathematical concept and, at the same time, it is applied to many versatile engineering fields and research domains related to computer science. The fuzzy system offers some knowledge about uncertainty and is also related to the theory of probability. A fuzzy logic-based model acts as the classifier for many different types of data belonging to several classes.
Covered in this book are topics such as the fundamental concepts of mathematics, fuzzy logic concepts, probability and possibility theories, and evolutionary computing to some extent. The combined fields of neural network and fuzzy domain (known as the neuro-fuzzy system) are explained and elaborated. Each chapter has been produced in a very lucid manner, with grading from simple to complex to accommodate the anticipated different audiences. The application-oriented approach is the unique feature of this book.
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Author / Editor Details
Rahul Kar, PhD, is working as a state-aided teacher of mathematics, Faculty of Kalyani Mahavidyalaya, Nadia, West Bengal, India.
Rahul Kar, PhD, is working as a state-aided teacher of mathematics, Faculty of Kalyani Mahavidyalaya, Nadia, West Bengal, India.
Dac-Nhuong Le, PhD, is an associate professor of computer science and deputy head of the Faculty of Information Technology, Haiphong University, Vietnam.
Gunjan Mukherjee, PhD, is an assistant professor in the Department of Computational Science, Brainware University, Kolkata, India.
Biswadip Basu Mallik, PhD, is a senior assistant professor of mathematics in the Department of Basic Sciences & Humanities, Institute of Engineering & Management, Kolkata, India.
Ashok Kumar Shaw, PhD, is an applied mathematician and professor and Dean of R&D at the Budge Budge Institute of Technology, Kolkata, India.
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Table of Contents
Preface
1. Decision Making Using Fuzzy Logic Using Multicriteria
Panem Charanarur, Srinivasa Rao Gundu and J.Vijaylaxmi
1.1 Introduction
1.2 Fuzzy Logic
1.3 Decision Making
1.4 Literature Review
1.5 Conclusion
Acknowledgment
References
2. Application of Fuzzy Logic in the Context of Risk Management
Sudipta Adhikary and Kaushik Banerjee
2.1 Introduction
2.2 Objectives of Risk Management
2.3 Improved Risk Estimation
2.3.1 Point-Wise Calculations on a Curve
2.3.2 Estimation of a Curve
2.3.3 Accuracy in Quantification is Raised
2.3.4 The Problems with the Basic Quantification Approach
2.4 Threat at Quantification Matrix
2.4.1 Qualitative Matrix
2.4.2 Errors in Scaling
2.4.3 Band Width at Various Scales
2.5 Fundamental Definitions
2.5.1 Positioning Statement
2.5.2 Risk Under the Level of Tolerance
2.5.3 Risk Elimination
2.6 Fuzzy Logic
2.7 Risk Related to Fuzzy Matrix
2.8 Conclusion
Bibliography
3. Use of Fuzzy Logic for Controlling Greenhouse Environment: A Study Through the Lens of Web Monitoring
Kaushik Banerjee and Sudipta Adhikary
3.1 Introduction
3.2 Design (Hardware)
3.2.1 Sensor for Measuring Soil Moisture
3.2.2 Sensor for Measuring Humidity and Temperature
3.3 Programming Arduino Mega Board
3.3.1 Fuzzification
3.3.2 Fuzzy Inference
3.3.3 Communication via Remote Connections and a Web Server
3.4 Implementation of a Prototype
3.5 Results
3.6 Conclusion
Bibliography
4. Fuzzy Logics and Marketing Decisions
Mohammed Majeed
4.1 Introduction
4.2 Literature
4.2.1 Fuzzy Logic (FL)
4.2.2 FL Application in Marketing
4.2.2.1 Communication and Advertising
4.2.2.2 Customer Service and Satisfaction
4.2.2.3 Customer Segmentation
4.2.2.4 CRM
4.2.2.5 Pricing
4.2.2.6 Evaluation of a Product
4.2.2.7 Uncertainty in the Development of New Products
4.2.2.8 Decision Making
4.2.2.9 Consumer Nation Identity (CNI)
4.2.2.10 Quality of Service
4.3 Conclusion
4.4 Further Studies
References
5. A Method for Ranking Fuzzy Numbers Based on Their Value, Ambiguity, Fuzziness, and Vagueness
Sunayana Saikia and Rituparna Chutia
5.1 Introduction
5.2 Preliminaries
5.2.1 Definitions and Concepts
5.3 The Designed Method
5.4 Validate the Reasonableness of the Suggested Ranking Algorithm
5.5 Comparative Analysis and Numerical Examples
5.6 Application
5.7 Conclusions
References
6. Evacuation of Attributes to Translucent TNSET in Mathematics Using Rough Topology
Kala Raja Mohan, R. Narmada Devi, Nagadevi Bala Nagaram, Sathish Kumar Kumaravel and Regan Murugesan
6.1 Introduction
6.2 Basic Concepts of Rough Topology
6.2.1 Conditional Attribute
6.2.2 Decision Attribute
6.2.3 Rough Topology
6.2.4 Lower Approximation
6.2.5 Upper Approximation
6.2.6 Boundary Region
6.2.7 Basis
6.2.8 Information System
6.2.9 Core
6.3 Algorithm
6.4 Information System
6.5 Working Procedure
6.6 Conclusion
References
7. Design of Type-2 Fuzzy Controller for Hybrid Multi-Area Power System
Susmit Chakraborty, Arindam Mondal and Soumen Biswas
7.1 Introduction
7.2 Plant Model
7.3 Controller Design
7.3.1 Proportional Integral Derivative (PID) Controller
7.3.2 Fractional Order Proportional Integral Derivative (FOPID) Controller
7.3.3 Type-2-Fuzzy Logic
7.4 Levenberg–Marquardt Algorithm
7.5 Optimization of Controller Parameters Using CASO Algorithm
7.6 Result and Analysis
7.6.1 Without Disturbances
7.6.2 With Disturbances
7.7 Conclusion
Appendix
References
8. Alzheimer’s Detection and Classification Using Fine-Tuned Convolutional Neural Network
Anooja Ali, Sarvamangala D. R., Meenakshi Sundaram A. and Rashmi C.
8.1 Introduction
8.2 Literature Review
8.3 Methodology
8.3.1 Dataset
8.3.2 Pre-Processing
8.4 Implementation and Results
8.5 Conclusion
References
9. Design of Fuzzy Logic-Based Smart Cars Using Scilab
Josiga S., Maheswari R. and Subbulakshmi T.
9.1 Introduction
9.2 Literature Survey
9.2.1 Fuzzy Logic for Automobile Industry
9.2.2 Fuzzy Logic for Smart Cars
9.2.3 Fuzzy Logic for Driver Behavior Detection
9.2.4 Fuzzy Logic Applications for Common Industry
9.3 Proposed Fuzzy Inference System for Smart Cars
9.3.1 Fuzzification
9.3.2 Membership Functions
9.3.3 Rule Base
9.3.4 Example Rules
9.3.5 Defuzzification
9.4 Implementation Details and Results
9.5 Conclusion and Future Work
References
10. Financial Planning and Decision Making for Students Using Fuzzy Logic
G. Surya Deepan and T. Subbulakshmi
10.1 Introduction
10.2 Literature Review
10.3 System Architecture
10.3.1 Input
10.3.2 Fuzzification
10.3.3 Membership Function
10.3.3.1 Necessity
10.3.3.2 Cost Percentage
10.3.3.3 Quality
10.3.4 Fuzzy Rule Base
10.3.5 Fuzzy Output
10.3.6 Defuzzification
10.4 Conclusion and Future Scope
References
11. A Novel Fuzzy Logic (FL) Algorithm for the Automatic Detection of Oral Cancer
M. Praveena Kiruba bai and G. Arumugam
11.1 Introduction
11.1.1 Significance of Pre-Processing
11.2 Image Enhancement
11.3 Gabor Transform
11.4 Image Transformation
11.5 Adaptive Networks: Architecture
11.5.1 Classification of Images
11.6 Results and Discussions
11.7 Conclusion
Bibliography
12. A Study on Decision Making of Difficulties Faced by Indian Workers Abroad by Using Rough Topology
Nagadevi Bala Nagaram, R. Narmada Devi , Kala Raja Mohan, Regan Murugesan and Sathish Kumar Kumaravel
12.1 Introduction
12.1.1 Problems Faced by the Indian Workers
12.2 Fundamental Idea of Rough Topology
12.2.1 Conditional Attribute
12.2.2 Decision Attribute
12.2.3 Rough Topology
12.2.4 Lower Approximation
12.2.5 Upper Approximation
12.2.6 Boundary Region
12.2.7 Basis
12.2.8 Information System
12.2.9 Core
12.3 Algorithm
12.4 Information System
12.5 Working Procedure
12.6 Conclusion
References
13. Case Study on Fuzzy Logic: Fuzzy Logic-Based PID Controller to Tune the DC Motor Speed
Devendra Kumar Somwanshi
13.1 Introduction
13.1.1 DC Motor
13.1.2 DC Motor Speed Control Methods
13.1.2.1 PID Controller
13.1.2.2 Fuzzy-Based PID Controller
13.1.2.3 Micro Controller-Based PID Controller
13.1.2.4 Genetic Algorithm-Based PID Controller
13.2 Literature Review
13.2.1 Common Findings
13.2.2 Comparative Analysis of Research Works Reviewed
13.2.3 Strengths in the Literature Reviewed
13.2.4 Weaknesses in the Literature Reviewed
13.2.5 Findings in the Literature Reviewed
13.3 Design of Fuzzy-Based PID Controller
13.3.1 Fuzzy Controller
13.3.2 Flowchart for Fuzzy Controller
13.3.3 Fuzzy Logic Controller Membership Function and FAM Table
13.3.4 Rules for the Fuzzy Controller
13.3.5 Simulation Diagram of FLC
13.3.6 Fuzzy-Based PID Controller
13.3.6.1 Fuzzy Block Design
13.3.6.2 Flowchart for Fuzzy-PID Controller
13.3.6.3 Simulation Diagram of Fuzzy-PID Controller
13.4 Experimental Work and Results Analysis
13.5 Conclusion and Future Scope
References
14. Application of Intuitionistic Fuzzy Network Using Efficient Domination
A. Meenakshi, J. Senbagamalar and A. Kannan
14.1 Introduction
14.2 Efficient Domination in Intuitionistic Fuzzy Graph (IFG)
14.3 Main Frame Work
14.3.1 Construction of IFN from Sub IFN
14.4 Secret Key
14.4.1 Encryption Algorithm
14.4.2 Decryption Algorithm
14.5 Illustration
14.5.1 Construction of IFN from Sub IFN
14.5.2 Secret Key
14.5.3 Encryption Algorithm
14.5.4 Decryption Algorithm
14.6 Conclusion
References
15. Analysis of Parameters Related to Malaria with Comparative Study on Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps
Regan Murugesan, Sathish Kumar Kumaravel, Kala Raja Mohan, Narmada Devi Rathinam and Suresh Rasappan
15.1 Introduction
15.2 Parameters of Malaria
15.3 Fuzzy Cognitive Map
15.3.1 Matrix Representation of FCM
15.4 Neutrosophic Cognitive Map
15.4.1 Matrix Representation of NCM
15.5 Comparison and Discussion
15.6 Conclusion
References
16. Applications of Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps on Analysis of Dengue Fever
Sathish Kumar Kumaravel, Regan Murugesan, Nagadevi Bala Nagaram, Suresh Rasappan and G. Yamini
16.1 Introduction
16.2 Parameters of Dengue
16.3 Fuzzy Cognitive Maps
16.3.1 Matrix Representation of FCM
16.4 Neutrosophic Cognitive Map
16.4.1 Matrix Representation of NCM
16.5 Comparison and Discussion
16.6 Conclusion
References
17. A Comprehensive Review and Analysis of the Plethora of Branches of Medical Science and Bioinformatics Based on Fuzzy Logic
Partha Sarker and Siddhartha Roy
17.1 Introduction
17.2 Previous Work
17.3 Fuzzy Logic in Medical Fields and Bioinformatics
17.3.1 Applied Fuzzy Logic in Medical Areas
17.3.2 Applied Fuzzy Logic in Bioinformatics
17.4 Review of Published Work and In-Depth Analysis
17.5 Conclusion
References
Index
Back to Top
Preface
1. Decision Making Using Fuzzy Logic Using Multicriteria
Panem Charanarur, Srinivasa Rao Gundu and J.Vijaylaxmi
1.1 Introduction
1.2 Fuzzy Logic
1.3 Decision Making
1.4 Literature Review
1.5 Conclusion
Acknowledgment
References
2. Application of Fuzzy Logic in the Context of Risk Management
Sudipta Adhikary and Kaushik Banerjee
2.1 Introduction
2.2 Objectives of Risk Management
2.3 Improved Risk Estimation
2.3.1 Point-Wise Calculations on a Curve
2.3.2 Estimation of a Curve
2.3.3 Accuracy in Quantification is Raised
2.3.4 The Problems with the Basic Quantification Approach
2.4 Threat at Quantification Matrix
2.4.1 Qualitative Matrix
2.4.2 Errors in Scaling
2.4.3 Band Width at Various Scales
2.5 Fundamental Definitions
2.5.1 Positioning Statement
2.5.2 Risk Under the Level of Tolerance
2.5.3 Risk Elimination
2.6 Fuzzy Logic
2.7 Risk Related to Fuzzy Matrix
2.8 Conclusion
Bibliography
3. Use of Fuzzy Logic for Controlling Greenhouse Environment: A Study Through the Lens of Web Monitoring
Kaushik Banerjee and Sudipta Adhikary
3.1 Introduction
3.2 Design (Hardware)
3.2.1 Sensor for Measuring Soil Moisture
3.2.2 Sensor for Measuring Humidity and Temperature
3.3 Programming Arduino Mega Board
3.3.1 Fuzzification
3.3.2 Fuzzy Inference
3.3.3 Communication via Remote Connections and a Web Server
3.4 Implementation of a Prototype
3.5 Results
3.6 Conclusion
Bibliography
4. Fuzzy Logics and Marketing Decisions
Mohammed Majeed
4.1 Introduction
4.2 Literature
4.2.1 Fuzzy Logic (FL)
4.2.2 FL Application in Marketing
4.2.2.1 Communication and Advertising
4.2.2.2 Customer Service and Satisfaction
4.2.2.3 Customer Segmentation
4.2.2.4 CRM
4.2.2.5 Pricing
4.2.2.6 Evaluation of a Product
4.2.2.7 Uncertainty in the Development of New Products
4.2.2.8 Decision Making
4.2.2.9 Consumer Nation Identity (CNI)
4.2.2.10 Quality of Service
4.3 Conclusion
4.4 Further Studies
References
5. A Method for Ranking Fuzzy Numbers Based on Their Value, Ambiguity, Fuzziness, and Vagueness
Sunayana Saikia and Rituparna Chutia
5.1 Introduction
5.2 Preliminaries
5.2.1 Definitions and Concepts
5.3 The Designed Method
5.4 Validate the Reasonableness of the Suggested Ranking Algorithm
5.5 Comparative Analysis and Numerical Examples
5.6 Application
5.7 Conclusions
References
6. Evacuation of Attributes to Translucent TNSET in Mathematics Using Rough Topology
Kala Raja Mohan, R. Narmada Devi, Nagadevi Bala Nagaram, Sathish Kumar Kumaravel and Regan Murugesan
6.1 Introduction
6.2 Basic Concepts of Rough Topology
6.2.1 Conditional Attribute
6.2.2 Decision Attribute
6.2.3 Rough Topology
6.2.4 Lower Approximation
6.2.5 Upper Approximation
6.2.6 Boundary Region
6.2.7 Basis
6.2.8 Information System
6.2.9 Core
6.3 Algorithm
6.4 Information System
6.5 Working Procedure
6.6 Conclusion
References
7. Design of Type-2 Fuzzy Controller for Hybrid Multi-Area Power System
Susmit Chakraborty, Arindam Mondal and Soumen Biswas
7.1 Introduction
7.2 Plant Model
7.3 Controller Design
7.3.1 Proportional Integral Derivative (PID) Controller
7.3.2 Fractional Order Proportional Integral Derivative (FOPID) Controller
7.3.3 Type-2-Fuzzy Logic
7.4 Levenberg–Marquardt Algorithm
7.5 Optimization of Controller Parameters Using CASO Algorithm
7.6 Result and Analysis
7.6.1 Without Disturbances
7.6.2 With Disturbances
7.7 Conclusion
Appendix
References
8. Alzheimer’s Detection and Classification Using Fine-Tuned Convolutional Neural Network
Anooja Ali, Sarvamangala D. R., Meenakshi Sundaram A. and Rashmi C.
8.1 Introduction
8.2 Literature Review
8.3 Methodology
8.3.1 Dataset
8.3.2 Pre-Processing
8.4 Implementation and Results
8.5 Conclusion
References
9. Design of Fuzzy Logic-Based Smart Cars Using Scilab
Josiga S., Maheswari R. and Subbulakshmi T.
9.1 Introduction
9.2 Literature Survey
9.2.1 Fuzzy Logic for Automobile Industry
9.2.2 Fuzzy Logic for Smart Cars
9.2.3 Fuzzy Logic for Driver Behavior Detection
9.2.4 Fuzzy Logic Applications for Common Industry
9.3 Proposed Fuzzy Inference System for Smart Cars
9.3.1 Fuzzification
9.3.2 Membership Functions
9.3.3 Rule Base
9.3.4 Example Rules
9.3.5 Defuzzification
9.4 Implementation Details and Results
9.5 Conclusion and Future Work
References
10. Financial Planning and Decision Making for Students Using Fuzzy Logic
G. Surya Deepan and T. Subbulakshmi
10.1 Introduction
10.2 Literature Review
10.3 System Architecture
10.3.1 Input
10.3.2 Fuzzification
10.3.3 Membership Function
10.3.3.1 Necessity
10.3.3.2 Cost Percentage
10.3.3.3 Quality
10.3.4 Fuzzy Rule Base
10.3.5 Fuzzy Output
10.3.6 Defuzzification
10.4 Conclusion and Future Scope
References
11. A Novel Fuzzy Logic (FL) Algorithm for the Automatic Detection of Oral Cancer
M. Praveena Kiruba bai and G. Arumugam
11.1 Introduction
11.1.1 Significance of Pre-Processing
11.2 Image Enhancement
11.3 Gabor Transform
11.4 Image Transformation
11.5 Adaptive Networks: Architecture
11.5.1 Classification of Images
11.6 Results and Discussions
11.7 Conclusion
Bibliography
12. A Study on Decision Making of Difficulties Faced by Indian Workers Abroad by Using Rough Topology
Nagadevi Bala Nagaram, R. Narmada Devi , Kala Raja Mohan, Regan Murugesan and Sathish Kumar Kumaravel
12.1 Introduction
12.1.1 Problems Faced by the Indian Workers
12.2 Fundamental Idea of Rough Topology
12.2.1 Conditional Attribute
12.2.2 Decision Attribute
12.2.3 Rough Topology
12.2.4 Lower Approximation
12.2.5 Upper Approximation
12.2.6 Boundary Region
12.2.7 Basis
12.2.8 Information System
12.2.9 Core
12.3 Algorithm
12.4 Information System
12.5 Working Procedure
12.6 Conclusion
References
13. Case Study on Fuzzy Logic: Fuzzy Logic-Based PID Controller to Tune the DC Motor Speed
Devendra Kumar Somwanshi
13.1 Introduction
13.1.1 DC Motor
13.1.2 DC Motor Speed Control Methods
13.1.2.1 PID Controller
13.1.2.2 Fuzzy-Based PID Controller
13.1.2.3 Micro Controller-Based PID Controller
13.1.2.4 Genetic Algorithm-Based PID Controller
13.2 Literature Review
13.2.1 Common Findings
13.2.2 Comparative Analysis of Research Works Reviewed
13.2.3 Strengths in the Literature Reviewed
13.2.4 Weaknesses in the Literature Reviewed
13.2.5 Findings in the Literature Reviewed
13.3 Design of Fuzzy-Based PID Controller
13.3.1 Fuzzy Controller
13.3.2 Flowchart for Fuzzy Controller
13.3.3 Fuzzy Logic Controller Membership Function and FAM Table
13.3.4 Rules for the Fuzzy Controller
13.3.5 Simulation Diagram of FLC
13.3.6 Fuzzy-Based PID Controller
13.3.6.1 Fuzzy Block Design
13.3.6.2 Flowchart for Fuzzy-PID Controller
13.3.6.3 Simulation Diagram of Fuzzy-PID Controller
13.4 Experimental Work and Results Analysis
13.5 Conclusion and Future Scope
References
14. Application of Intuitionistic Fuzzy Network Using Efficient Domination
A. Meenakshi, J. Senbagamalar and A. Kannan
14.1 Introduction
14.2 Efficient Domination in Intuitionistic Fuzzy Graph (IFG)
14.3 Main Frame Work
14.3.1 Construction of IFN from Sub IFN
14.4 Secret Key
14.4.1 Encryption Algorithm
14.4.2 Decryption Algorithm
14.5 Illustration
14.5.1 Construction of IFN from Sub IFN
14.5.2 Secret Key
14.5.3 Encryption Algorithm
14.5.4 Decryption Algorithm
14.6 Conclusion
References
15. Analysis of Parameters Related to Malaria with Comparative Study on Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps
Regan Murugesan, Sathish Kumar Kumaravel, Kala Raja Mohan, Narmada Devi Rathinam and Suresh Rasappan
15.1 Introduction
15.2 Parameters of Malaria
15.3 Fuzzy Cognitive Map
15.3.1 Matrix Representation of FCM
15.4 Neutrosophic Cognitive Map
15.4.1 Matrix Representation of NCM
15.5 Comparison and Discussion
15.6 Conclusion
References
16. Applications of Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps on Analysis of Dengue Fever
Sathish Kumar Kumaravel, Regan Murugesan, Nagadevi Bala Nagaram, Suresh Rasappan and G. Yamini
16.1 Introduction
16.2 Parameters of Dengue
16.3 Fuzzy Cognitive Maps
16.3.1 Matrix Representation of FCM
16.4 Neutrosophic Cognitive Map
16.4.1 Matrix Representation of NCM
16.5 Comparison and Discussion
16.6 Conclusion
References
17. A Comprehensive Review and Analysis of the Plethora of Branches of Medical Science and Bioinformatics Based on Fuzzy Logic
Partha Sarker and Siddhartha Roy
17.1 Introduction
17.2 Previous Work
17.3 Fuzzy Logic in Medical Fields and Bioinformatics
17.3.1 Applied Fuzzy Logic in Medical Areas
17.3.2 Applied Fuzzy Logic in Bioinformatics
17.4 Review of Published Work and In-Depth Analysis
17.5 Conclusion
References
Index
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