Pallavi Vijay Chavan

Decision Systems: Integrating Machine Learning, Fuzzy Logic, and Artificial Neural Networks provides readers with a comprehensive understanding of the principal techniques used to build effective decision-making systems. This book covers the fundamental principles and concepts of machine learning, fuzzy logic, and artificial neural networks, and explains how these techniques can be used to build intelligent decision-making systems that can learn from data, reason, and make accurate predictions. The book also presents a wide range of applications of machine learning, fuzzy logic, and artificial neural networks in various domains, such as engineering, medicine, finance, and robotics. The book also provides practical guidance on how to design and implement effective decision-making systems using these techniques and discusses the potential challenges and limitations of machine learning, fuzzy logic, and artificial neural networks, and how to overcome them. The book provides a stepwise approach to provide readers with the knowledge and tools they need to build intelligent decision-making systems, including a robust introduction to the mathematical concepts and principles necessary to understand the concepts and applications of Decision Systems and Machine Learning algorithms. Next, the book provides readers with an in-depth explanation and demonstration of two of the major machine learning techniques – Fuzzy Logic/Fuzzy Set Theory and Artificial Neural Networks – followed by an in-depth look at more advanced topics that play essential roles in making machine learning algorithms more useful in practice, including creating full-fledged Recurrent Networks and their mathematical foundations, Associative Memories, and Deep Learning networks such as Convolutional Neural Networks, Generative Adversarial Networks, Radial Basis Function Networks, Multilayer Perceptrons, and Self-Organizing Maps. The lynchpin of the book provides readers with an understanding of how the various types of techniques can be integrated to create dynamic Decision Systems. The book wraps up with coverage of challenges and opportunities in Decision Systems along with real-world applications of Decision Systems with case studies in healthcare, finance, education, social media, and agriculture.

This book will teach you the core concepts of blockchain technology in a concise manner through straightforward, concrete examples using a range of programming languages, including Python and Solidity. The 50 programs presented in this book are all you need to gain a firm understanding of blockchain and how to implement it. The book begins with an introduction to the fundamentals of blockchain technology, followed by a review of its types, framework, applications and challenges. Moving ahead, you will learn basic blockchain programming with hash functions, authentication code, and Merkle trees. You will then dive into the basics of bitcoin, including wallets, digital keys, transactions, digital signatures, and more. This is followed by a crash course on Ethereum programming, its network, and ecosystem. As you progress through the book, you will also learn about Hyperledger and put your newly-gained knowledge to work through case studies and example applications. After reading this book, you will understand blockchain’s underlying concepts and its common implementations. What You Will Learn Master theoretical and practical implementations of various blockchain components using PythonImplement hashing, Merkel trees, and smart contracts using the Solidity programming language for various applicationsGrasp the internal structure of EVM and its implementation in smart contractsUnderstand why blockchain plays an essential role in cryptocurrencies and identify possible applications beyond cryptocurrenciesInvestigate and apply alternative blockchain solutions using Hyperledger, including its integration and deploymentExplore research opportunities through case studies and gain an overview of implementation using various languages Who Is This Book For: Anyone who is new to blockchain and wants to gain anan understanding of how it works and can be implemented.

Automata Theory and Formal Languages presents the difficult concepts of automata theory in a straightforward manner, including discussions on diverse concepts and tools that play major roles in developing computing machines, algorithms and code. Automata theory includes numerous concepts such as finite automata, regular grammar, formal languages, context free and context sensitive grammar, push down automata, Turing machine, and decidability, which constitute the backbone of computing machines. This book enables readers to gain sufficient knowledge and experience to construct and solve complex machines. Each chapter begins with key concepts followed by a number of important examples that demonstrate the solution. The book explains concepts and simultaneously helps readers develop an understanding of their application with real-world examples, including application of Context Free Grammars in programming languages and Artificial Intelligence, and cellular automata in biomedical problems.