This book presents a set of soft computing approaches and their application in data analytics, classification model, and control. The basics of fuzzy logic implementation for advanced hybrid fuzzy driven optimization methods has been covered in the book. The various soft computing techniques, including Fuzzy Logic, Rough Sets, Neutrosophic Sets, Type-2 Fuzzy logic, Neural Networks, Generative Adversarial Networks, and Evolutionary Computation have been discussed and they are used on variety of applications including data analytics, classification model, and control.

The book is divided into two thematic parts. The first thematic section covers the various soft computing approaches for text classification and data analysis, while the second section focuses on the fuzzy driven optimization methods for the control systems. The chapters has been written and edited by active researchers, which cover hypotheses and practical considerations; provide insights into the design ofhybrid algorithms for applications in data analytics, classification model, and engineering control.

Presents improved fuzzy sets and intelligent and optimization algorithms Bridges between data science, machine learning and soft computing methods Covers applications of metaheuristics in medical imaging and dynamical system control

Klappentext
This book presents a set of soft computing approaches and their application in data analytics, classification model, and control. The basics of fuzzy logic implementation for advanced hybrid fuzzy driven optimization methods has been covered in the book. The various soft computing techniques, including Fuzzy Logic, Rough Sets, Neutrosophic Sets, Type-2 Fuzzy logic, Neural Networks, Generative Adversarial Networks, and Evolutionary Computation have been discussed and they are used on variety of applications including data analytics, classification model, and control. The book is divided into two thematic parts. The first thematic section covers the various soft computing approaches for text classification and data analysis, while the second section focuses on the fuzzy driven optimization methods for the control systems. The chapters has been written and edited by active researchers, which cover hypotheses and practical considerations; provide insights into the design ofhybrid algorithms for applications in data analytics, classification model, and engineering control.

Inhalt

Chapter 1: An Optimization of Fuzzy Rough Set Nearest Neighbor Classification Model using Krill Herd Algorithm for Sentiment Text Analytics.- Chapter 2: Fuzzy Wavelet Neural Network with Social Spider Optimization Algorithm for Pattern Recognition in Medical Domain.- Chapter 3: Fuzzy with Gravitational Search Algorithm Tuned Radial Basis Function Network for Medical Disease Diagnosis and Classification Model.- Chapter 4: Optimal Neutrosophic Rules based Feature Extraction for Data Classification using Deep Learning Model.- Chapter 5: Self-Evolving Interval Type-2 Fuzzy Neural Network Design for The Synchronization of Chaotic Systems.- Chapter 6: Categorizing Relations via Semi-Supervised Learning using a Hybrid Tolerance Rough Sets and Genetic Algorithm Approach.- Chapter 7: Data-driven Fuzzy C-Means Equivalent Turbine-governor for Power System Frequency Response.- Chapter 8: Multicriteria group decision making using a novel similarity measure for triangular fuzzy numbers based on their newly defined expected values and variances.- Chapter 9: Bangla Printed Character Generation from Handwritten Character Using GAN.