Название: How Machine Learning is Innovating Today's World: A Concise Technical GuideАвтор: Arindam Dey, Sukanta Nayak, Ranjan Kumar, Sachi Nandan Mohanty
Издательство: Wiley-Scrivener
Год: 2024
Страниц: 477
Язык: английский
Формат: pdf (true)
Размер: 50.6 MB
Provides a comprehensive understanding of the latest advancements and practical applications of Machine Learning techniques.
Machine Learning (ML), a branch of Artificial Intelligence (AI), has gained tremendous momentum in recent years, revolutionizing the way we analyze data, make predictions, and solve complex problems. As researchers and practitioners in the field, the editors of this book recognize the importance of disseminating knowledge and fostering collaboration to further advance this dynamic discipline. How Machine Learning is Innovating Today's World is a timely book and presents a diverse collection of 25 chapters that delve into the remarkable ways that ML is transforming various fields and industries. It provides a comprehensive understanding of the practical applications of ML techniques.
The research of developing complex algorithms whose accuracy increases over time is known as Machine Learning. To solve the problem of generating and dealing with large, powerful computers in a quickly changing and dynamic environment, Machine Learning approaches have become more important in many computers’ maintenance and advancement tasks. Machines’ intelligence strategies have been shown to be extremely applicable across a wide range of industries. It is not unexpected that many tasks that go into the creation and upkeep of technology may well be rethought as knowledge difficulties and accosted in terms of comprehending processes. Over the past 20 years, interest in using Machine Learning algorithms in software design has increased, along with some positive publications and results. In this paper, we have conducted a review of prior research on Machine Learning approaches and provided a broad overview, including benefits and drawbacks and a comparison of a few existing algorithms.
The chapters herein cover a wide range of topics, each contributing a unique perspective to the broader landscape of machine learning. First is a comprehensive analysis of various tokenization techniques and the sequence-to-sequence model in natural language processing. Next, Chapter 2 explores the evaluation of English language readability using ML models, followed by a detailed study of text analysis for information retrieval through natural language processing (NLP) in the subsequent chapter.
Chapter 4 investigates Machine Learning’s role in maximizing cotton yield with a focus on fertilizer selection, and Chapter 5 delves into the application of reinforcement learning approaches to supply chain management. The following chapter examines the performance analysis of converting algorithms to source code using natural language processing in Java, and Chapter 7 presents an alternate approach to solving differential equations utilizing artificial neural networks with optimization techniques.
The exploration of the subject continues with a comparative study of different techniques of text-to-SQL query conversion in Chapter 8, and the next chapter examines ML approaches to catalysis. After that, Chapter 10 presents the systematic study of text generation and classification using tokenization in natural language processing, followed by the classification of livestock diseases using ML algorithms in Chapter 11.
Chapter 12 provides a closer look at the application of ML in image enhancement techniques, and the following chapter demonstrates the prediction of book genres using natural language processing. Additionally, Chapter 14 delves into efficient leader selection for inter-cluster flying ad-hoc networks, and the subsequent chapter provides a comprehensive survey of applications powered by GPT-3 and DALL-E.
Recommender systems’ domain of application is discussed in Chapter 16, and the next chapter reviews mood detection, emoji generation, and classification using tokenization and CNN. Chapter 18 delves into a new variation of the exam scheduling problem using graph coloring, and Chapter 19 examines the intersection of software engineering and Machine Learning applications.
Moreover, Chapter 20 explores ML strategies for indeterminate information systems in complex bipolar neutrosophic environments, and the rise of AI-generated news videos is scrutinized in Chapter 21. The next section highlights ML applications in battery management systems, while the healthcare industry is covered in Chapter 23. The book’s final chapter presents how to enhance resource management in precision farming through AI-based irrigation optimization.
Audience
The book will be extremely useful to professionals, post-graduate research scholars, policymakers, corporate managers, and anyone with technical interests looking to understand how Machine Learning and Artificial Intelligence can benefit their work.
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