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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches
Theory and Practical Applications

by Fouzi Harrou,Ying Sun,Amanda S. Hering,Muddu Madakyaru,abdelkader Dairi

  • Publisher : Elsevier
  • Release : 2020-07-03
  • Pages : 328
  • ISBN : 0128193662
  • Language : En, Es, Fr & De
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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches – such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches – to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems. Uses a data-driven based approach to fault detection and attribution Provides an in-depth understanding of fault detection and attribution in complex and multivariate systems Familiarises you with the most suitable data-driven based techniques including multivariate statistical techniques and deep learning-based methods Includes case studies and comparison of different methods

Road Traffic Modeling and Management

Road Traffic Modeling and Management
Using Statistical Monitoring and Deep Learning

by Fouzi Harrou,Abdelhafid Zeroual,Mohamad Mazen Hittawe,Ying Sun

  • Publisher : Elsevier
  • Release : 2021-10-05
  • Pages : 268
  • ISBN : 0128234334
  • Language : En, Es, Fr & De
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Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing road traffic monitoring and management. The book examines commonly used traffic analysis methodologies as well the emerging methods that use deep learning methods. Other sections discuss how to understand statistical models and machine learning algorithms and how to apply them to traffic modeling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, this book is ideal for researchers and practitioners who want to improve the performance of intelligent transportation systems. Provides integrated, up-to-date and complete coverage of the key components for intelligent transportation systems: traffic modeling, forecasting, estimation and monitoring Uses methods based on video and time series data for traffic modeling and forecasting Includes case studies, key processes guidance and comparisons of different methodologies

Advanced Systems for Biomedical Applications

Advanced Systems for Biomedical Applications
A Book

by Olfa Kanoun,Nabil Derbel

  • Publisher : Springer Nature
  • Release : 2021-07-19
  • Pages : 286
  • ISBN : 3030712214
  • Language : En, Es, Fr & De
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The book highlights recent developments in the field of biomedical systems covering a wide range of technological aspects, methods, systems and instrumentation techniques for diagnosis, monitoring, treatment, and assistance. Biomedical systems are becoming increasingly important in medicine and in special areas of application such as supporting people with disabilities and under pandemic conditions. They provide a solid basis for supporting people and improving their health care. As such, the book offers a key reference guide about novel medical systems for students, engineers, designers, and technicians.

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
A Book

by Chris Aldrich,Lidia Auret

  • Publisher : Springer Science & Business Media
  • Release : 2013-06-15
  • Pages : 374
  • ISBN : 1447151852
  • Language : En, Es, Fr & De
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This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.

Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control

Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control
A Book

by Ch. Venkateswarlu,Rama Rao Karri

  • Publisher : Elsevier
  • Release : 2022-01-31
  • Pages : 366
  • ISBN : 0323900682
  • Language : En, Es, Fr & De
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Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control presents various mechanistic model based state estimators and data-driven model based state estimators with a special emphasis on their development and applications to process monitoring, fault diagnosis and control. The design and analysis of different state estimators are highlighted with a number of applications and case studies concerning to various real chemical and biochemical processes. The book starts with the introduction of basic concepts, extending to classical methods and successively leading to advances in this field. Design and implementation of various classical and advanced state estimation methods to solve a wide variety of problems makes this book immensely useful for the audience working in different disciplines in academics, research and industry in areas concerning to process monitoring, fault diagnosis, control and related disciplines. • Describes various classical and advanced versions of mechanistic model based state estimation algorithms. • Describes various data-driven model based state estimation techniques. • Highlights a number of real applications of mechanistic model based and data-driven model based state estimators/soft sensors. • Beneficial to those associated with process monitoring, fault diagnosis, online optimization, control and related areas.

Multivariate Statistical Process Control

Multivariate Statistical Process Control
Process Monitoring Methods and Applications

by Zhiqiang Ge,Zhihuan Song

  • Publisher : Springer Science & Business Media
  • Release : 2012-11-28
  • Pages : 194
  • ISBN : 1447145135
  • Language : En, Es, Fr & De
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Given their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Multivariate statistical process control (MSPC) is one of the most popular data-based methods for process monitoring and is widely used in various industrial areas. Effective routines for process monitoring can help operators run industrial processes efficiently at the same time as maintaining high product quality. Multivariate Statistical Process Control reviews the developments and improvements that have been made to MSPC over the last decade, and goes on to propose a series of new MSPC-based approaches for complex process monitoring. These new methods are demonstrated in several case studies from the chemical, biological, and semiconductor industrial areas. Control and process engineers, and academic researchers in the process monitoring, process control and fault detection and isolation (FDI) disciplines will be interested in this book. It can also be used to provide supplementary material and industrial insight for graduate and advanced undergraduate students, and graduate engineers. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Advanced methods for fault diagnosis and fault-tolerant control

Advanced methods for fault diagnosis and fault-tolerant control
A Book

by Steven X. Ding

  • Publisher : Springer Nature
  • Release : 2020-11-24
  • Pages : 658
  • ISBN : 3662620049
  • Language : En, Es, Fr & De
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The major objective of this book is to introduce advanced design and (online) optimization methods for fault diagnosis and fault-tolerant control from different aspects. Under the aspect of system types, fault diagnosis and fault-tolerant issues are dealt with for linear time-invariant and time-varying systems as well as for nonlinear and distributed (including networked) systems. From the methodological point of view, both model-based and data-driven schemes are investigated.To allow for a self-contained study and enable an easy implementation in real applications, the necessary knowledge as well as tools in mathematics and control theory are included in this book. The main results with the fault diagnosis and fault-tolerant schemes are presented in form of algorithms and demonstrated by means of benchmark case studies. The intended audience of this book are process and control engineers, engineering students and researchers with control engineering background.

Performance Assessment for Process Monitoring and Fault Detection Methods

Performance Assessment for Process Monitoring and Fault Detection Methods
A Book

by Kai Zhang

  • Publisher : Springer
  • Release : 2016-10-04
  • Pages : 153
  • ISBN : 3658159715
  • Language : En, Es, Fr & De
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The objective of Kai Zhang and his research is to assess the existing process monitoring and fault detection (PM-FD) methods. His aim is to provide suggestions and guidance for choosing appropriate PM-FD methods, because the performance assessment study for PM-FD methods has become an area of interest in both academics and industry. The author first compares basic FD statistics, and then assesses different PM-FD methods to monitor the key performance indicators of static processes, steady-state dynamic processes and general dynamic processes including transient states. He validates the theoretical developments using both benchmark and real industrial processes.

Advances in Production Management Systems. Production Management for Data-Driven, Intelligent, Collaborative, and Sustainable Manufacturing

Advances in Production Management Systems. Production Management for Data-Driven, Intelligent, Collaborative, and Sustainable Manufacturing
IFIP WG 5.7 International Conference, APMS 2018, Seoul, Korea, August 26-30, 2018, Proceedings, Part I

by Ilkyeong Moon,Gyu M. Lee,Jinwoo Park,Dimitris Kiritsis,Gregor von Cieminski

  • Publisher : Springer
  • Release : 2018-08-24
  • Pages : 570
  • ISBN : 3319997041
  • Language : En, Es, Fr & De
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The two-volume set IFIP AICT 535 and 536 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2018, held in Seoul, South Korea, in August 2018. The 129 revised full papers presented were carefully reviewed and selected from 149 submissions. They are organized in the following topical sections: lean and green manufacturing; operations management in engineer-to-order manufacturing; product-service systems, customer-driven innovation and value co-creation; collaborative networks; smart production for mass customization; global supply chain management; knowledge based production planning and control; knowledge based engineering; intelligent diagnostics and maintenance solutions for smart manufacturing; service engineering based on smart manufacturing capabilities; smart city interoperability and cross-platform implementation; manufacturing performance management in smart factories; industry 4.0 - digital twin; industry 4.0 - smart factory; and industry 4.0 - collaborative cyber-physical production and human systems.

Machine Learning for Cyber Physical Systems

Machine Learning for Cyber Physical Systems
Selected papers from the International Conference ML4CPS 2017

by Jürgen Beyerer,Alexander Maier,Oliver Niggemann

  • Publisher : Springer
  • Release : 2019-04-09
  • Pages : 87
  • ISBN : 3662590840
  • Language : En, Es, Fr & De
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The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 25th-26th, 2017. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning

IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning
Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers

by Joao Gama,Sepideh Pashami,Albert Bifet,Moamar Sayed-Mouchawe,Holger Fröning,Franz Pernkopf,Gregor Schiele,Michaela Blott

  • Publisher : Springer Nature
  • Release : 2021-01-09
  • Pages : 310
  • ISBN : 3030667707
  • Language : En, Es, Fr & De
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This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.

Advanced Mathematical And Computational Tools In Metrology And Testing Xii

Advanced Mathematical And Computational Tools In Metrology And Testing Xii
A Book

by Franco Pavese,Alistair B Forbes,Nien Fan Zhang,Anna G Chunovkina

  • Publisher : World Scientific
  • Release : 2022-01-13
  • Pages : 548
  • ISBN : 9811242399
  • Language : En, Es, Fr & De
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This volume contains original, refereed contributions by researchers from national metrology institutes, universities and laboratories across the world involved in metrology and testing. The volume has been produced by the International Measurement Confederation Technical Committee 21, Mathematical Tools for Measurements and is the twelfth in the series. The papers cover topics in numerical analysis and computational tools, statistical inference, regression, calibration and metrological traceability, computer science and data provenance, and describe applications in a wide range of application domains. This volume is useful to all researchers, engineers and practitioners who need to characterize the capabilities of measurement systems and evaluate measurement data. It will also be of interest to scientists and engineers concerned with the reliability, trustworthiness and reproducibility of data and data analytics in data-driven systems in engineering, environmental and life sciences.

Metal Additive Manufacturing

Metal Additive Manufacturing
A Book

by Ehsan Toyserkani,Dyuti Sarker,Osezua Obehi Ibhadode,Farzad Liravi,Paola Russo,Katayoon Taherkhani

  • Publisher : John Wiley & Sons
  • Release : 2022-02-07
  • Pages : 624
  • ISBN : 111921078X
  • Language : En, Es, Fr & De
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METAL ADDITIVE MANUFACTURING A comprehensive review of additive manufacturing processes for metallic structures Additive Manufacturing (AM)—also commonly referred to as 3D printing—builds three-dimensional objects by adding materials layer by layer. Recent years have seen unprecedented investment in additive manufacturing research and development by governments and corporations worldwide. This technology has the potential to replace many conventional manufacturing processes, enable the development of new industry practices, and transform the entire manufacturing enterprise. Metal Additive Manufacturing provides an up-to-date review of all essential physics of metal additive manufacturing techniques with emphasis on both laser-based and non-laser-based additive manufacturing processes. This comprehensive volume covers fundamental processes and equipment, governing physics and modelling, design and topology optimization, and more. The text adresses introductory, intermediate, and advanced topics ranging from basic additive manufacturing process classification to practical and material design aspects of additive manufacturability. Written by a panel of expert authors in the field, this authoritative resource: Provides a thorough analysis of AM processes and their theoretical foundations Explains the classification, advantages, and applications of AM processes Describes the equipment required for different AM processes for metallic structures, including laser technologies, positioning devices, feeder and spreader mechanisms, and CAD software Discusses the opportunities, challenges, and current and emerging trends within the field Covers practical considerations, including design for AM, safety, quality assurance, automation, and real-time control of AM processes Includes illustrative cases studies and numerous figures and tables Featuring material drawn from the lead author’s research and professional experience on laser additive manufacturing, Metal Additive Manufacturing is an important source for manufacturing professionals, research and development engineers in the additive industry, and students and researchers involved in mechanical, mechatronics, automatic control, and materials engineering and science.

Data-Driven and Model-Based Methods for Fault Detection and Diagnosis

Data-Driven and Model-Based Methods for Fault Detection and Diagnosis
A Book

by Majdi Mansouri,Mohamed-Faouzi Harkat,Hazem Nounou,Mohamed N. Nounou

  • Publisher : Elsevier
  • Release : 2020-02-05
  • Pages : 322
  • ISBN : 0128191651
  • Language : En, Es, Fr & De
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Data-Driven and Model-Based Methods for Fault Detection and Diagnosis covers techniques that improve the quality of fault detection and enhance monitoring through chemical and environmental processes. The book provides both the theoretical framework and technical solutions. It starts with a review of relevant literature, proceeds with a detailed description of developed methodologies, and then discusses the results of developed methodologies, and ends with major conclusions reached from the analysis of simulation and experimental studies. The book is an indispensable resource for researchers in academia and industry and practitioners working in chemical and environmental engineering to do their work safely. Outlines latent variable based hypothesis testing fault detection techniques to enhance monitoring processes represented by linear or nonlinear input-space models (such as PCA) or input-output models (such as PLS) Explains multiscale latent variable based hypothesis testing fault detection techniques using multiscale representation to help deal with uncertainty in the data and minimize its effect on fault detection Includes interval PCA (IPCA) and interval PLS (IPLS) fault detection methods to enhance the quality of fault detection Provides model-based detection techniques for the improvement of monitoring processes using state estimation-based fault detection approaches Demonstrates the effectiveness of the proposed strategies by conducting simulation and experimental studies on synthetic data

Time Series Analysis

Time Series Analysis
Data, Methods, and Applications

by Chun-Kit Ngan

  • Publisher : BoD – Books on Demand
  • Release : 2019-11-06
  • Pages : 130
  • ISBN : 1789847788
  • Language : En, Es, Fr & De
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This book aims to provide readers with the current information, developments, and trends in a time series analysis, particularly in time series data patterns, technical methodologies, and real-world applications. This book is divided into three sections and each section includes two chapters. Section 1 discusses analyzing multivariate and fuzzy time series. Section 2 focuses on developing deep neural networks for time series forecasting and classification. Section 3 describes solving real-world domain-specific problems using time series techniques. The concepts and techniques contained in this book cover topics in time series research that will be of interest to students, researchers, practitioners, and professors in time series forecasting and classification, data analytics, machine learning, deep learning, and artificial intelligence.

Thermomechanics and Infrared Imaging, Inverse Problem Methodologies, Mechanics of Additive and Advanced Manufactured Materials, and Advancements in Optical Methods and Digital Image Correlation, Volume 4

Thermomechanics and Infrared Imaging, Inverse Problem Methodologies, Mechanics of Additive and Advanced Manufactured Materials, and Advancements in Optical Methods and Digital Image Correlation, Volume 4
Proceedings of the 2021 Annual Conference on Experimental and Applied Mechanics

by Sharlotte L. B. Kramer

  • Publisher : Springer Nature
  • Release : 2022
  • Pages : 109
  • ISBN : 3030867455
  • Language : En, Es, Fr & De
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Thermomechanics & Infrared Imaging, Inverse Problem Methodologies and Mechanics of Additive & Advanced Manufactured Materials, and Advancement of Optical Methods & Digital Image Correlation, Volume 4 of the Proceedings of the 2021 SEM Annual Conference & Exposition on Experimental and Applied Mechanics, the fourth volume of four from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on a wide range of areas, including: Test Design and Inverse Method Algorithms Inverse Problems: Virtual Fields Method Material Characterizations Using Thermography Fatigue, Damage & Fracture Evaluation Using Infrared Thermography Mechanics of Additive & Advanced Manufactured Materials DIC Methods & Its Applications Photoelasticity and Interferometry Applications Micro-Optics and Microscopic Systems Multiscale and New Developments in Optical Methods .

Data Science-Based Full-Lifespan Management of Lithium-ion Battery

Data Science-Based Full-Lifespan Management of Lithium-ion Battery
Manufacturing, Operation and Reutilization

by Kailong Liu,Yujie Wang,Xin Lai

  • Publisher : Springer Nature
  • Release : 2022-06-19
  • Pages : 290
  • ISBN : 3031013409
  • Language : En, Es, Fr & De
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This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers.

Data-driven Detection and Diagnosis of Faults in Traction Systems of High-speed Trains

Data-driven Detection and Diagnosis of Faults in Traction Systems of High-speed Trains
A Book

by Hongtian Chen,Bin Jiang,Ningyun Lu,Wen Chen

  • Publisher : Springer Nature
  • Release : 2020-04-25
  • Pages : 160
  • ISBN : 3030462633
  • Language : En, Es, Fr & De
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This book addresses the needs of researchers and practitioners in the field of high-speed trains, especially those whose work involves safety and reliability issues in traction systems. It will appeal to researchers and graduate students at institutions of higher learning, research labs, and in the industrial R&D sector, catering to a readership from a broad range of disciplines including intelligent transportation, electrical engineering, mechanical engineering, chemical engineering, the biological sciences and engineering, economics, ecology, and the mathematical sciences.

Advanced Mapping of Environmental Data

Advanced Mapping of Environmental Data
A Book

by Mikhail Kanevski

  • Publisher : John Wiley & Sons
  • Release : 2013-05-10
  • Pages : 352
  • ISBN : 1118623266
  • Language : En, Es, Fr & De
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This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.

Advancing Smarter and More Secure Industrial Applications Using AI, IoT, and Blockchain Technology

Advancing Smarter and More Secure Industrial Applications Using AI, IoT, and Blockchain Technology
A Book

by Saini, Kavita,Raj, Pethuru

  • Publisher : IGI Global
  • Release : 2021-12-24
  • Pages : 309
  • ISBN : 179988368X
  • Language : En, Es, Fr & De
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There is no doubt that there has been much excitement regarding the pioneering contributions of artificial intelligence (AI), the internet of things (IoT), and blockchain technologies and tools in visualizing and realizing smarter as well as sophisticated systems and services. However, researchers are being bombarded with various machine and deep learning algorithms, which are categorized as a part and parcel of the enigmatic AI discipline. The knowledge discovered gets disseminated to actuators and other concerned systems in order to empower them to intelligently plan and insightfully execute appropriate tasks with clarity and confidence. The IoT processes in conjunction with the AI algorithms and blockchain technology are bound to lay out a stimulating foundation for producing and sustaining smarter systems for society. Advancing Smarter and More Secure Industrial Applications Using AI, IoT, and Blockchain Technology articulates and accentuates various AI algorithms, fresh innovations in the IoT, and blockchain spaces. The domain of transforming raw data to information and to relevant knowledge is gaining prominence with the availability of data ingestion, processing, mining, analytics algorithms, platforms, frameworks, and other accelerators. Covering topics such as blockchain applications, Industry 4.0, and cryptography, this book serves as a comprehensive guide for AI researchers, faculty members, IT professionals, academicians, students, researchers, and industry professionals.