Ebook: Innovative Engineering with AI Applications

Innovative Engineering with AI Applications demonstrates how we can innovate in different engineering domains as well as how to make most business problems simpler by applying AI to them.

This book presents a study on current developments, trends, and the future usage of Artificial Intelligence (AI). The impending research on AI applications—like improvements in agricultural systems, security systems, web services, etc.—has shown the usefulness of AI in engineering, as well as in Deep Learning tools and models.

Engineering advancements combined with Artificial Intelligence, have resulted in a hyper-connected society in which smart devices are not only used to exchange data but also have increased capabilities. These devices are becoming more context-aware and smarter by the day. This timely book shows how organizations, who want to innovate and adapt, can enter new markets using expertise in various emerging technologies (e.g. data, AI, system architecture, blockchain), and can build technology-based business models, a culture of innovation, and high-performing networks. The book specifies an approach that anyone can use to better architect, design, and more effectively build things that are technically novel, useful, and valuable, and to do so efficiently, on-time, and repeatable.

This book takes foundational steps toward analyzing stress among teaching professionals using deep learning algorithms. Various deep learning models are discussed, with a practical approach that employs the MNIST dataset. These models help to solve various complex problems in the domains of computer vision and natural language processing (NLP). Also introduced are some emerging and interdisciplinary domains that are associated with Deep Learning and AI. The book touches upon the core concept of deep learning technology, which is fruitful for a beginner in this area. Furthermore, it compiles the various applications of AI in agriculture, such as irrigation, weeding, spraying with sensors, and other means that are facilitated by robots and drones. This book covers most of the popular and important deep-learning neural network models.

Audience:
The book is essential to AI product developers, business leaders in all industries and organizational domains. Researchers, academicians, and students in the AI field will also benefit from reading this book.