Böcker i Artificial Intelligence and Soft Computing for Industrial Transformation-serien

Covering the concepts and fundamentals of efficient energy systems, this volume, written and edited by a global team of experts, also goes into the practical applications that can be utilized across multiple industries, for both the engineer and the student.There is not a single industry which will not be transformed by machine learning and Internet of Things (IoT). IoT and machine learning have altogether changed the technological scenario by letting the user monitor and control things based on the prediction made by machine learning algorithms. There has been substantial progress in the usage of platforms, technologies and applications that are based on these technologies. These breakthrough technologies affect not just the software perspective of the industry, but they cut across areas like smart cities, smart healthcare, smart retail, smart monitoring, control, and others. Because of these "game changers," governments, along with top companies around the world, are investing heavily in its research and development. Keeping pace with the latest trends, endless research, and new developments is paramount to innovate systems that are not only user-friendly but also speak to the growing needs and demands of society.This volume is focused on saving energy at different levels of design and automation including the concept of machine learning automation and prediction modeling. It also deals with the design and analysis for IoT-enabled systems including energy saving aspects at different level of operation.The editors and contributors also cover the fundamental concepts of IoT and machine learning, including the latest research, technological developments, and practical applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in the area of IoT and machine technology, this is a must-have for any library.This outstanding new volume: Handles the fundamentals of system design including the concept of energy saving aspects at different levelsIs useful for all engineering students for learning the fundamentals of system design and automation with machine learning and IoTWill be helpful for researchers and designers to find out key parameters for future projects and current applicationsAudience: Engineers and scientists across many fields, including petroleum and process engineers, chemical engineers, electrical engineers working with power systems, and students at the university and post-graduate level studying energy topics

Dieses Buch beschreibt die Entwicklung und den neuartigen Einsatz von künstlicher Intelligenz in elektrischen Systemen. In einer zusammenfassenden Darstellung werden die Vorteile, Funktionen und künftige Rolle von KI und Computational Intelligence in Expertensystemen, beim maschinellen Lernen, bei der Mustererkennung, in künstlichen neuronalen Netzen, beim Deep Learning und in zahlreichen weiteren Bereichen betrachtet. Dabei werden die Anwendungsmöglichkeiten der KI auf vielen Gebieten aus verschiedenen Blickwinkeln analysiert. Die dabei angewandten Grundsätze und aktuellen Methoden dürften den Leserinnen und Lesern als nützliche Anleitung dienen.

Dieses Werk, das sich umfassend mit der Einführung von maschinellem Lernen, KI und dem IoT im Gesundheitswesen beschäftigt, richtet sich an Forschende, Fachkräfte im Gesundheitswesen, Wissenschaftler und Technologen.Die Nutzung von maschinellem Lernen und künstlicher Intelligenz im Internet der Dinge (IoT) für Anwendungen im Gesundheitswesen sowie die damit einhergehenden Herausforderungen werden ausführlich erörtert.Das IoT erzeugt gewaltige Datenmengen von unterschiedlicher Qualität. Die intelligente Verarbeitung und Analyse dieser Datenmengen sind der Schlüssel zur Entwicklung intelligenter IoT-Anwendungen, wodurch Raum für die Nutzung des maschinellen Lernens (ML) geschaffen wird. Mit ihren Recheninstrumenten, die bei der Erledigung bestimmter Aufgaben die menschliche Intelligenz ersetzen können, macht es die künstliche Intelligenz (KI) möglich, dass Computer aus Erfahrung lernen, sich an neue Eingaben anpassen und bisher von Menschen durchgeführte Aufgaben übernehmen. Da IoT-Plattformen eine Schnittstelle bieten, um Daten von unterschiedlichen Geräten zusammenzutragen, lassen sie sich leicht mit AI/ML-Systemen verbinden. Vor diesen Hintergrund besteht der Wert der KI in ihrer Fähigkeit, schnell Erkenntnisse aus Daten zu gewinnen, automatisch Muster zu erkennen und Anomalien in den von intelligenten Sensoren und Geräten erzeugten Daten zu erkennen ? aus Angaben zu Temperatur, Druck, Luftfeuchtigkeit, Luftqualität, Schwingungen und Geräuschen ? die für eine schnelle Diagnose extrem hilfreich sein können.

ARTIFICAL INTELLIGENCE for SUSTAINABLE APPLICATIONSThe objective of this book is to leverage the significance of artificial intelligence in achieving sustainable solutions using interdisciplinary research through innovative ideas.With the advent of recent technologies, the demand for Information and Communication Technology (ICT)-based applications such as artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), health care, data analytics, augmented reality/virtual reality, cyber-physical systems, and future generation networks, has increased drastically. In recent years, artificial intelligence has played a more significant role in everyday activities. While AI creates opportunities, it also presents greater challenges in the sustainable development of engineering applications. Therefore, the association between AI and sustainable applications is an essential field of research. Moreover, the applications of sustainable products have come a long way in the past few decades, driven by social and environmental awareness, and abundant modernization in the pertinent field. New research efforts are inevitable in the ongoing design of sustainable applications, which makes the study of communication between them a promising field to explore.This book highlights the recent advances in AI and its allied technologies with a special focus on sustainable applications. It covers theoretical background, a hands-on approach, and real-time use cases with experimental and analytical results.AudienceAI researchers as well as engineers in information technology and computer science.

CONVERGENCE OF DEEP LEARNING IN CYBER-IOT SYSTEMS AND SECURITY In-depth analysis of Deep Learning-based cyber-IoT systems and security which will be the industry leader for the next ten years. The main goal of this book is to bring to the fore unconventional cryptographic methods to provide cyber security, including cyber-physical system security and IoT security through deep learning techniques and analytics with the study of all these systems. This book provides innovative solutions and implementation of deep learning-based models in cyber-IoT systems, as well as the exposed security issues in these systems. The 20 chapters are organized into four parts. Part I gives the various approaches that have evolved from machine learning to deep learning. Part II presents many innovative solutions, algorithms, models, and implementations based on deep learning. Part III covers security and safety aspects with deep learning. Part IV details cyber-physical systems as well as a discussion on the security and threats in cyber-physical systems with probable solutions. Audience Researchers and industry engineers in computer science, information technology, electronics and communication, cybersecurity and cryptography.