Pallavi Vijay Chavan – författare

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.

Presents the concepts of Automata Theory and Formal Languages in an easy-to-understand approach Helps the readers understand key concepts by solving real-world examples. Provides the readers with a simple approach to connect the theory with the latest trend like software testing, cybersecurity, artificial intelligence, and machine learning. Includes a wide coverage of applications of automata theory and formal languages.

Machine learning (ML) and deep learning (DL) algorithms are invaluable resources for Industry 4.0 and allied areas and are considered as the future of computing. A subfield called neural networks, to recognize and understand patterns in data, helps a machine carry out tasks in a manner similar to humans. The intelligent models developed using ML and DL are effectively designed and are fully investigated – bringing in practical applications in many fields such as health care, agriculture and security. These algorithms can only be successfully applied in the context of data computing and analysis. Today, ML and DL have created conditions for potential developments in detection and prediction. Apart from these domains, ML and DL are found useful in analysing the social behaviour of humans. With the advancements in the amount and type of data available for use, it became necessary to build a means to process the data and that is where deep neural networks prove their importance. These networks are capable of handling a large amount of data in such fields as finance and images. This book also exploits key applications in Industry 4.0 including: · Fundamental models, issues and challenges in ML and DL.· Comprehensive analyses and probabilistic approaches for ML and DL.· Various applications in healthcare predictions such as mental health, cancer, thyroid disease, lifestyle disease and cardiac arrhythmia.· Industry 4.0 applications such as facial recognition, feather classification, water stress prediction, deforestation control, tourism and social networking. · Security aspects of Industry 4.0 applications suggest remedial actions against possible attacks and prediction of associated risks. - Information is presented in an accessible way for students, researchers and scientists, business innovators and entrepreneurs, sustainable assessment and management professionals.This book equips readers with a knowledge of data analytics, ML and DL techniques for applications defined under the umbrella of Industry 4.0. This book offers comprehensive coverage, promising ideas and outstanding research contributions, supporting further development of ML and DL approaches by applying intelligence in various applications.

Machine learning (ML) and deep learning (DL) algorithms are invaluable resources for Industry 4.0 and allied areas and are considered as the future of computing. A subfield called neural networks, to recognize and understand patterns in data, helps a machine carry out tasks in a manner similar to humans. The intelligent models developed using ML and DL are effectively designed and are fully investigated – bringing in practical applications in many fields such as health care, agriculture and security. These algorithms can only be successfully applied in the context of data computing and analysis. Today, ML and DL have created conditions for potential developments in detection and prediction. Apart from these domains, ML and DL are found useful in analysing the social behaviour of humans. With the advancements in the amount and type of data available for use, it became necessary to build a means to process the data and that is where deep neural networks prove their importance. These networks are capable of handling a large amount of data in such fields as finance and images. This book also exploits key applications in Industry 4.0 including: · Fundamental models, issues and challenges in ML and DL.· Comprehensive analyses and probabilistic approaches for ML and DL.· Various applications in healthcare predictions such as mental health, cancer, thyroid disease, lifestyle disease and cardiac arrhythmia.· Industry 4.0 applications such as facial recognition, feather classification, water stress prediction, deforestation control, tourism and social networking. · Security aspects of Industry 4.0 applications suggest remedial actions against possible attacks and prediction of associated risks. - Information is presented in an accessible way for students, researchers and scientists, business innovators and entrepreneurs, sustainable assessment and management professionals.This book equips readers with a knowledge of data analytics, ML and DL techniques for applications defined under the umbrella of Industry 4.0. This book offers comprehensive coverage, promising ideas and outstanding research contributions, supporting further development of ML and DL approaches by applying intelligence in various applications.

Decision-making is a fundamental process that influences outcomes across a wide range of domains, including business, healthcare, scientific research, and automation. With the increasing availability of data and the growing computational power of modern systems, decision-making models have become more sophisticated and capable of providing highly accurate and efficient solutions. The ability to develop, analyze, and implement these models has become crucial for professionals and researchers working in fields that rely on data-driven decision-making.This book explores the evolution and significance of decision systems, covering both foundational theories and advanced methodologies. It introduces readers to the essential principles of decision-making models, illustrating their applications through practical case studies and real-world scenarios. The discussion begins with a focus on traditional decision-making techniques and gradually progresses to more advanced topics, including machine learning-based approaches, the integration of artificial intelligence, and the role of fuzzy logic in decision support systems. Furthermore, ethical considerations in decision-making and strategies for mitigating bias are examined, ensuring that models remain fair and transparent.Throughout this book, each chapter builds on the previous one, providing a structured and comprehensive learning experience. By the time readers complete this book, they will have gained an in-depth understanding of decision-making frameworks, their applications, and the future directions of research in this dynamic field. Whether one is a student, a researcher, or an industry professional, this book serves as a valuable guide to mastering the complexities of decision systems and applying them effectively in various domains.Covers foundational concepts, advanced theories, and real-world applications, ensuring readers gain a thorough understanding of Decision SystemsPresents the foundational mathematics behind the various techniques covered, including stepwise mathematical formula development, R and Python code syntax listings for the worked examples, and stepwise methods and procedures for application algorithmsIllustrates how fuzzy logic and neural networks can be integrated with other disciplines like machine learning, optimization, and data science to create powerful hybrid solutions

This book covers the topic of data science in a comprehensive manner and synthesizes both fundamental and advanced topics of a research area that has now reached its maturity. The book starts with the basic concepts of data science. It highlights the types of data and their use and importance, followed by a discussion on a wide range of applications of data science and widely used techniques in data science.Key Features• Provides an internationally respected collection of scientific research methods, technologies and applications in the area of data science.• Presents predictive outcomes by applying data science techniques to real-life applications.• Provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods.• Gives the reader a variety of intelligent applications that can be designed using data science and its allied fields.The book is aimed primarily at advanced undergraduates and graduates studying machine learning and data science. Researchers and professionals will also find this book useful.

This book covers the topic of data science in a comprehensive manner and synthesizes both fundamental and advanced topics of a research area that has now reached its maturity. The book starts with the basic concepts of data science. It highlights the types of data and their use and importance, followed by a discussion on a wide range of applications of data science and widely used techniques in data science.Key Features• Provides an internationally respected collection of scientific research methods, technologies and applications in the area of data science.• Presents predictive outcomes by applying data science techniques to real-life applications.• Provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods.• Gives the reader a variety of intelligent applications that can be designed using data science and its allied fields.The book is aimed primarily at advanced undergraduates and graduates studying machine learning and data science. Researchers and professionals will also find this book useful.

The book guides the smart computing community toward future directions in cognitive research and demonstrates how these advances can be applied to next-generation smart systems. It further identifies applications that demonstrate innovative ways of utilizing cognition-based smart computing for solving various real-world problems.• Presents cognitive modeling in machine consciousness and human cognition.• Covers artificial intelligence (AI) and its applications in machine self-learning, human-computer interaction, natural language (NL) processing, cognitive models, and architectures.• Discusses machine learning (ML) and cognitive computing for mobile communications, and wireless networks.• Explains the foundation, framework, and applications of cognition based smart computing in a single volume.• Showcases key issues and challenges in the emerging area of cognitive computation and promotes an interdisciplinary understanding of the diversity in the adaptation of cognition in smart computing.The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

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 LearnMaster 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.

Artificial intelligence advancements, machine intelligence innovations, and semantic web developments together make up semantic intelligence technologies. The edited book integrates artifi cial intelligence, machine learning, IoT, blockchain, and natural language processing with semantic web technologies. This book also aims to offer real-life solutions to the pressing issues currently being faced by semantic web technologies.

Gain practical knowledge of application implementation using various programming approaches in predictive analytics. This book serves as a comprehensive guide for both beginners and professionals in the field of predictive analytics, offering core principles and practical insights without requiring an extensive mathematics or statistics background.The book starts with an introduction to analytics in decision making, protective analytics basics, and implementation in various industries. The book then takes you through types of regression, and simple linear regression in detail, followed by a demonstration of R Studio and SAS. Multiple Linear Regression is discussed next along with MLR model diagnostics. The book covers Multivariate Analysis and teaches you how to work with Principal Components Analysis, Factor Analysis, and much more. You also learn Time series Analysis with an understanding of Autoregressive Moving Average (ARMA) Models.After reading the book, you will be able to put predictive analytics principles into practice.What You Will LearnUnderstand modeling, estimating, and evaluating models for forecastingImplement Partial F-Test and Variable Selection MethodDemonstrate each analysis model in R Studio and SASUnderstand SLR and MLR Analysis modelsWho This Book Is ForStudents and professionals in the field of data analysis and intelligence applications