Machine Learning Methods for Engineering Application Development E-Book
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- Herausgeber: Bentham Science Publishers
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
This book is a quick review of machine learning methods for engineeringapplications. It provides an introduction to the principles of machine learningand common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field.Next, it offers some guidelines on applying machine learning methods to softwareengineering tasks. Finally, it gives an outlook into some of the futuredevelopments and possibly new research areas of machine learning and artificialintelligence in general.Techniques highlighted in the book include: Bayesian models, supportvector machines, decision tree induction, regression analysis, and recurrent andconvolutional neural network. Finally, it also intends to be a reference book. Key Features:Describes real-world problems that can be solved using machine learningExplains methods for directly applying machine learning techniques to concrete real-world problemsExplains concepts used in Industry 4.0 platforms, including the use and integration of AI, ML, Big Data, NLP, and the Internet of Things (IoT). It does not require prior knowledge of the machine learning This book is meantto be an introduction to artificial intelligence (AI), machine earning, and itsapplications in Industry 4.0. It explains the basic mathematical principlesbut is intended to be understandable for readers who do not have a backgroundin advanced mathematics.
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Seitenzahl: 316
Veröffentlichungsjahr: 2003
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FOREWORD
As I reviewed the manuscript prior to writing this Foreword, I was fascinated by many unique features that I would like to share with you. The book can best be described as concise yet detailed. There is more useful information packed into its 12 chapters than seen in most books twice its size. Therefore, it gives me great pleasure to contribute to this foreword.
This book is an enthusiastic celebration of many Machine Learning Techniques for Engineering Applications. It is also a unique tribute to many academicians and researchers who were involved in their study and contributed to society. Still another element is provided by many interesting technical details and an abundance of illustrations in the form of figures and tables. On top of that, there are innumerable machine learning algorithms for Intelligent Systems, Computational Linguistics, Natural Language Processing, Information Retrieval, Neural Networks, Social Networks, Recommender Systems, etc., indeed, to anyone with a fascination with the world of machine learning. This book can be read on two different levels. First, it may be read by ordinary people with a limited, if any, scientific background. Throughout, the book has been written with this audience in mind. The second group of readers will be represented by professionals from academia, government agencies and researchers. I do feel that everybody in the scientific community agrees with the content and ideas put forth in this book, and I hope that the information and knowledge presented will become a useful guideline for the research community and scholars.
This book contains so much useful information, and the chapters contain many pearls. I hope that this book will become a primer for teachers, teacher educators, and professional developers, helping teachers across the world to learn, teach, and practice machine learning techniques for various applications.
PREFACE
Machine learning deals with the issue of how to build programs that improve their performance at some tasks through experience. Machine learning (ML) plays a major role in the fourth industrial revolution, and we see a lot of evolution in various machine learning methodologies. AI techniques are widely used by practicing engineers to solve real-world problems. Industry 4.0 refers to the introduction of digital technologies and the development of skills, resources, and high-tech for the evolution of Industrial Factories. The concepts of Artificial Intelligence (AI), Machine Learning, and its applications in Industry 4.0 are popular among researchers. Several industrial applications are being designed and deployed. Herein, we share a few examples of machine learning that we use every day and perhaps have no idea that they are driven by ML-like Virtual Personal Assistants, Predictions, Videos Surveillance, Social Media Services, Email Spam and Malware Filtering, Online Customer Support, Search Engine Result Refining, Product Recommendations, and Online Fraud Detection. Besides, numerous researchers from diversified domains are working towards the amalgamation of these technologies.
The chapters of this book are organized into five parts, Machine Learning Essentials, Applied Machine Learning, Surveillance Systems, Machine Learning in IoT and Cyber Security, and 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 and 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.
This book describes the most common Artificial Intelligence (AI), Machine Learning and its applications in Industry 4.0, including 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 working 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.
List of Contributors
Cutting Edge Techniques of Adaptive Machine Learning for Image Processing and Computer Vision
P. Sasikumar1,*,T. Saravanan2Abstract
Computers, systems, applications, and technology, in general, are becoming more commonly used, advanced, scalable, and thus effective in modern times. Because of its widespread use, it undergoes various advancements on a regular basis. A fast-paced life is also associated with modern times. This way of life necessitates that our systems behave similarly. Adaptive Machine Learning (AML) can do things that conventional machine learning cannot. It will easily adjust to new information and determine the significance of that information. Adaptive machine learning uses a variety of data collection, grouping, and analysis methods due to its single-channeled structure. It gathers, analyses, and learns from the information. That is why it is adaptive: as long as new data is presented, the system can learn and update. This single-channeled device acts on any piece of input it receives in order to improve potential forecasts and outcomes. Furthermore, since the entire process happens in real-time, it can immediately adjust to new actions. High efficiency and impeccably precise accuracy are two of AML's main advantages. The system does not become outdated or redundant because it is constantly running in real-time. So, incorporating the three core concepts of agility, strength, and efficiency better explains AML.
Agility helps systems to respond rapidly and without hesitation. The systems achieve new levels of proficiency and accuracy as a result of their power, and they can find new ways to operate flawlessly at lower costs as a result of their performance. This chapter covers the preparation, regularisation, and structure of deep neural networks such as convolutional and generative adversarial networks. New information in the reinforcement learning chapter includes a description of t-SNE, a standard dimensionality reduction approach, as well as multilayer perceptrons on auto encoders and the word2vec network. As a consequence, these suggestions will assist readers in applying what they have learned.
