Deep Learning Techniques for Automation and Industrial Applications

Artificial intelligence learning is the fastest-growing field in computer science. Deep learning algorithms and techniques are found to be useful in different areas, such as automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delays in children. Deep Learning Techniques for Automation and Industrial Applications presents a concise introduction to the recent
advances in the field of artificial intelligence (AI). The broad-ranging discussion herein covers the algorithms and applications in the areas of AI, reasoning, machine learning, neural networks, reinforcement learning, and their applications in various domains like agriculture and healthcare.
Applying deep learning techniques or algorithms successfully in these areas requires a concerted effort, fostering integrative research between experts ranging from diverse disciplines, from data science to visualization. This book provides state-of-the-art approaches to deep learning in these areas. It covers detection and prediction, as well as future framework development, building service systems, and analytical aspects. For all these topics, various approaches to deep learning, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms, are explained.
The successful application of deep learning techniques to enable meaningful, cost-effective, personalized cloud security service is a primary and current goal. However, realizing this goal requires effective understanding, application, and amalgamation of deep learning and several other computing technologies to deploy such a system effectively. This book helps to clarify certain key mechanisms of technology to realize a successful system.
It enables the processing of very large datasets to help with precise and comprehensive forecasts of risk and delivers recommended actions that improve outcomes for consumers.