The chapters of “Machine Learning Methods for Engineering Application Development“ book are organized into five parts Machine Learning Essentials, Applied Machine Learning, Surveillance Systems, Machine Learning in IoT and Cyber Security, Intelligent Systems, Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This Book deals with the subject of applying machine learning methods to so are engineering. In these books, we first provide the characteristics and applicability of some frequently utilized machine learning algorithms. We then summarize and analyze the existing work and discuss some general issues in this niche area. Finally, we offer some guidelines on applying machine learning methods to software engineering tasks.
The book ‘Machine Learning Methods for Engineering Application Development“ describes the most common Artificial Intelligence (AI), Machine Learning, and its applications in Industry 4.0, techniques such as Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural networks. It first introduces the principles of machine learning; it then covers the basic methods including the mathematical foundations. The biggest part of the book provides common machine learning algorithms and their applications. Finally, the book gives an outlook into some of the future developments and possibly new research areas of machine learning and artificial intelligence in general.
This book is meant to be an introduction to Artificial Intelligence (AI), Machine Learning, and its applications in Industry 4.0. It does not require prior knowledge in this area. It covers some of the basic mathematical principles but intends to be understandable even without a background in mathematics. It can be read chapter-wise and intends to be comprehensible, even when not starting in the beginning. Finally, it also intends to be a reference book.
Key Features:
• Describes real-world problems that can be solved using Machine Learning
• Provides methods for directly applying Machine Learning techniques to concrete real-world problems
• Research outputs require to work in Industry 4.0 platforms, including the use and integration of AI, ML, Big Data, NLP, and the Internet of Things (IoT).
• We welcome new developments in statistics, mathematics, and computing that are relevant to the machine learning perspective, including foundations, systems, innovative applications, and other research contributions related to the overall design of machine learning and models and algorithms that are relevant for AI.
Editors:
- Dr. Prasad Lokulwar is an Associate Professor at the Department of Computer Science and Engineering, G H Raisoni College of Engineering, Nagpur.
- Dr. Basant Verma is a Professor. He works in the Department of Computer Science and Engineering, G H Raisoni College of Engineering, Nagpur.
- Dr. N. Thillaiarasu is an Associate Professor and he teaches at the School of Computing and Information technology REVA University, Banglore, India
- Dr. Kailash Kumar, Assistant Professor works at Saudi Electronic University, Riyadh, Kingdom of Saudi Arabia
- Dr. Mahip Bartere, anAssociate Professor. Works in Department of Computer Science and Engineering, G H Raisoni University, Amaravati
- Mr. Dharam Singh is an Associate Professor. He is a Senior Developer at Machine Learning and BI, Wallmart, USA.
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