Bøger i Artificial Intelligence and Soft Computing for Industrial Transformation serien

The world is experiencing an unprecedented period of change and growth through all the electronic and technilogical developments and everyone on the planet has been impacted. What was once 'science fiction', today it is a reality.This book explores the world of many of once unthinkable advancements by explaining current technologies in great detail. Each chapter focuses on a different aspect - Machine Vision, Pattern Analysis and Image Processing - Advanced Trends in Computational Intelligence and Data Analytics - Futuristic Communication Technologies - Disruptive Technologies for Future Sustainability. The chapters include the list of topics that spans all the areas of smart intelligent systems and computing such as: Data Mining with Soft Computing, Evolutionary Computing, Quantum Computing, Expert Systems, Next Generation Communication, Blockchain and Trust Management, Intelligent Biometrics, Multi-Valued Logical Systems, Cloud Computing and security etc. An extensive list of bibliographic references at the end of each chapter guides the reader to probe further into application area of interest to him/her.

Die Nutzung von KI bei der Umgestaltung von Unternehmensprozessen betrifft sowohl die Managementmethoden als auch die Computertechnologie. In diesem Werk geht es darum, wie sich künstliche Intelligenz auf verschiedene Berufe auswirkt und welche Bedeutung die Algorithmen und Technologien des maschinellen Lernens haben. Vor dem Hintergrund der Anwendung von KI bei der Neugestaltung von Geschäftsprozessen werden in erster Linie neue Geschäftsmodelle sowie die Einsatzreife und -bereitschaft der KI auf organisatorischer, technologischer, finanzieller und kultureller Ebene betrachtet. Das Buch informiert ausführlich und detailliert über maschinelles Lernen und die entsprechenden Anwendungen in den Bereichen Robotik, Blockchain und Internet der Dinge. Zudem wird der Einfluss der KI auf Strategien und Verfahren im Finanzbereich, menschliche Fähigkeiten und Werte, innovative Beschaffungsstrategien, innovative Produktionsmethoden sowie beim Einsatz auf Marketing- und Verkaufsplattformen erörtert.

BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVALThis book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications.Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient's data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients' biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients.AudienceResearchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.

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

FUZZY INTELLIGENT SYSTEMSA comprehensive guide to Expert Systems and Fuzzy Logic that is the backbone of artificial intelligence.The objective in writing the book is to foster advancements in the field and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and those in education and research covering a broad cross section of technical disciplines.Fuzzy Intelligent Systems: Methodologies, Techniques, and Applications comprises state-of-the-art chapters detailing how expert systems are built and how the fuzzy logic resembling human reasoning, powers them. Engineers, both current and future, need systematic training in the analytic theory and rigorous design of fuzzy control systems to keep up with and advance the rapidly evolving field of applied control technologies. As a consequence, expert systems with fuzzy logic capabilities make for a more versatile and innovative handling of problems. This book showcases the combination of fuzzy logic and neural networks known as a neuro-fuzzy system, which results in a hybrid intelligent system by combining a human-like reasoning style of neural networks.AudienceResearchers and students in computer science, Internet of Things, artificial intelligence, machine learning, big data analytics and information and communication technology-related fields. Students will gain a thorough understanding of fuzzy control systems theory by mastering its contents.

In den 16 Kapiteln dieses Buches werden neue Architekturen, Vernetzungsparadigmen, vertrauenswürdige Strukturen und Plattformen für die Integration von Anwendungen in verschiedenen Geschäftsbereichen und Branchen erörtert, die für die Einführung (stationärer oder mobiler) ?intelligenter Dinge? in kollaborativen autonomen Flotten erforderlich sind. Die neuen Anwendungen beschleunigen den Fortschritt der Paradigmen beim Design autonomer Systeme und die Verbreitung des Internets der robotischen Dinge (IoRT). Kollaborative robotische Dinge können im IoRT mit anderen Dingen kommunizieren, selbständig lernen, sicher mit der Welt, Menschen und anderen Dingen interagieren und sich Eigenschaften wie Selbstwartung, Selbstwahrnehmung, Selbstheilung und Ausfallsicherheit aneignen. Durch die Allgegenwärtigkeit der robotischen Dinge gewinnt das ?Internet der robotischen Dinge?, das die Sensoren und die Objekte robotischer Dinge miteinander verbindet, zunehmend an Popularität.

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.

ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMSRenewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design.Due to the importance of renewable energy in today's world, this book was designed to enhance the reader's knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business.AudienceThe primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.

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.