Soft Computing Techniques in Engineering, Health, Mathematical and Social Sciences

✍ Scribed by Pradip Debnath; S A Mohiuddine

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 233
• Category: Library

✦ Synopsis

Soft computing techniques are no longer limited to the arena of computer science. The discipline has an exponentially growing demand in other branches of science and engineering and even into health and social science. This book contains theory and applications of soft computing in engineering, health, and social and applied sciences. Different soft computing techniques such as artificial neural networks, fuzzy systems, evolutionary algorithms and hybrid systems are discussed. It also contains important chapters in machine learning and clustering. This book presents a survey of the existing knowledge and also the current state of art development through original new contributions from the researchers. This book may be used as a one-stop reference book for a broad range of readers worldwide interested in soft computing. In each chapter, the preliminaries have been presented first and then the advanced discussion takes place. Learners and researchers from a wide variety of backgrounds will find several useful tools and techniques to develop their soft computing skills. This book is meant for graduate students, faculty and researchers willing to expand their knowledge in any branch of soft computing. The readers of this book will require minimum prerequisites of undergraduate studies in computation and mathematics.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
Chapter 1 Revisiting the Machine Learning Algorithms and Applications in Engineering and Computer Science
1.1 Introduction
1.2 Different Learning Approaches Used by Machine Learning
1.3 Algorithms Used by Different Learning Approaches
1.3.1 Supervised Learning Algorithms
1.3.2 Unsupervised Learning Algorithms
1.3.3 Semi-supervised Learning Algorithms
1.3.4 Reinforcement Learning Algorithms
1.4 Analysis of the Learning Algorithms
1.5 Related Works in the Field of Engineering and Computer Science
1.6 Discussion
1.7 Conclusion
References
Chapter 2 Detection and Prevention of Cancer through Artificial Intelligence and Machine Learning
2.1 Introduction
2.2 Types of Cancer
2.3 Types of Cancer Growth
2.3.1 Exponential Growth
2.3.2 Gompertz Growth
2.3.3 Logistic Growth
2.4 Machine Learning and Its Types
2.5 ML in the Field of Cancer Research
2.5.1 Role in Screening and Diagnosis
2.5.2 Role in Prognostication and Risk Stratification
2.5.3 Role of Deep Learning in Image Construction and Radiation Oncology
2.5.4 Role in Treatment
2.6 Conclusion
References
Chapter 3 A Review on Soft Computing Techniques in Nanomagnetism and Its Impact on Biomedical Applications
3.1 Introduction
3.2 Types of Soft Computing Techniques
3.3 Soft Computing in Material Science and Nanomagnetism
3.4 Impact of Soft Computing on Biomedical Applications
3.5 Conclusion
References
Chapter 4 A Mediative Fuzzy Logic-Based Approach to the Goal Programming Problem: In the Context of Multi-Objective Solid Transportation Problem
4.1 Introduction: Background of Relevant Work
4.2 Basic Concepts Related to the Work
4.2.1 Fuzzy Set
4.2.2 Intuitionistic Fuzzy Set
4.2.3 Mediative Fuzzy Logic
4.2.4 Transportation Problem
4.2.5 Solid Transportation Problem
4.2.6 Solid Transportation Problem with Multiple Objectives
4.2.7 Goal Programming
4.2.8 Fuzzy Goal Programming (FGP)
4.3 Mathematical Structure Formulation for the Proposed Algorithm (MFMSTP)
4.4 Numerical Example
4.5 Conclusion
Acknowledgments
References
Chapter 5 H-U-R Stability Results of Mixed-Type Additive-Quadratic Functional Equation in Fuzzy β-Normed Spaces by Two Different Approaches
5.1 Introduction
5.2 General Solution
5.3 Hyers-Ulam-Rassias Stability: Direct Method
5.3.1 Stability Results When φ Is Even
5.3.2 Stability Results When φ Is Odd
5.4 Hyers-Ulam-Rassias Stability: Fixed-Point Method
5.4.1 Stability Results When φ Is Even
5.4.2 Stability Results When φ is Odd
References
Chapter 6 Tauberian Theorems for Intuitionistic Fuzzy Normed Spaces
6.1 Introduction
6.2 Preliminaries
6.3 Main Results
6.4 Conclusion
References
Chapter 7 Picture Fuzzy Soft Matrices
7.1 Introduction
7.2 Picture Fuzzy Soft Matrices
7.3 Operations on PFSM
7.4 Operations on PFSM
7.5 Recall of VIKOR Method
7.6 Conclusion
References
Chapter 8 Cubic n-Inner Product Space
8.1 Introduction
8.2 Preliminaries
8.3 Cubic n-Inner Product Space
8.4 Cubic Intuitionistic Linear Space
References
Chapter 9 Convergence Methods for Double Sequences and Applications in Neutrosophic Normed Spaces
9.1 Introduction
9.2 Main Results
9.3 Conclusion
References
Chapter 10 Intuitionistic Fuzzy Generalized Lucas Ideal Convergent Sequence Spaces Associated with Orlicz Function
10.1 Introduction and Preliminaries
10.2 Intuitionistic Generalized Lucas I − Convergent Sequence Spaces
10.3 Intuitionistic Generalized Lucas Statistical Convergent Sequence Spaces
10.4 Conclusion
References
Chapter 11 Soft Computing Techniques in Social Sciences: The Recent Developments
11.1 Introduction
11.2 On Contemplating about Problem Solving
11.3 On Defining Soft Computing
11.4 On Describing Features of Soft Computing
11.5 On Differentiating between Hard Computing and Soft Computing
11.6 On Analysing Soft Computing Techniques
11.6.1 Artificial Neural Network
11.6.2 Genetic Algorithm
11.6.3 Probabilistic Logic
11.6.4 Machine Learning
11.6.5 Bayesian Statistics
11.6.6 Fuzzy Logic
11.7 On Discussing Applications of Soft Computing Techniques
11.8 On Discussing about Soft Computing Techniques in Social Science
11.9 Conclusion
References
Chapter 12 An Approach Based on Fuzzy Logic for Analysis on Product Development in Open Innovation Context
12.1 Introduction
12.2 Literature Review
12.3 Research Method
12.3.1 Open Innovation and Virtual Enterprises
12.3.2 Project and Risk Management
12.3.3 Analytical Hierarchy Process
12.4 Proposed Model
12.5 Case Study
12.6 Results and Discussion
12.7 Conclusions
References
Index