AI-Based Services for Smart Cities and Urban Infrastructure / Lyu Kangjuan (2021)

Public ISBD Titre : AI-Based Services for Smart Cities and Urban Infrastructure Type de document : document électronique Auteurs : Lyu Kangjuan, Auteur ; Hu Min, Auteur ; Du Juan, Auteur ; Sugumaran Vijayan, Auteur Editeur : Hershey [United States] : Engineering science reference Année de publication : 2021 Importance : 1 fichier PDF ISBN/ISSN/EAN : 978-1-79985-025-0 Note générale : Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École. Langues : Anglais (eng) Mots-clés : City planning--Technological innovations Smart cities Urbanization Artificial intelligence--Engineering applications Index. décimale : 004.8 Intelligence artificielle Résumé : Cities are the next frontier for artificial intelligence to permeate. As smart urban environments become possible, probable, and even preferred, artificial intelligence offers the chance for even further advancement through infrastructure and industry boosting. Opportunity overflows, but without thorough research to guide a complicated development and implementation process, urban environments can become disorganized and outright dangerous for citizens. AI-Based Services for Smart Cities and Urban Infrastructure is a collection of innovative research that explores artificial intelligence (AI) applications in urban planning. In addition, the book looks at how the internet of things and AI can work together to enable a real smart city and discusses state-of-the-art techniques in urban infrastructure design, construction, operation, maintenance, and management. While highlighting a broad range of topics including construction management, public transportation, and smart agriculture, this book is ideally designed for engineers, entrepreneurs, urban planners, architects, policymakers, researchers, academicians, and students. Série Note de contenu : Summary : I.Fundamentals of Smart Cities 1.Definition and History of Smart Cities 2.The Development of Smart Cities in the World 3.Core Technology of Smart Cities 4.Interpretation of the Construction Standard of Smart City Standard Systems 5.Framework and Structure of Smart Cities II.Infrastructure and Engineering Construction of Smart Cities 6.Infrastructure and Industry Economy 7.Intelligent Engineering Construction Management: On-Site Construction Management 8.Intelligent Engineering Construction Management: Long-Distance Construction Management 9.Performance Evaluation on the Intelligent Operation and Maintenance Mode of Public-Private 10.Policy Recommendations on the Application of AI to the Development of Smart Cities 11.Smart City Service III.Application of AI for Smart City Services 12.Application of Fuzzy Analytic Hierarchy Process for Evaluation of Ankara-Izmir High-Speed Train Project. 13.Applications of Artificial Intelligence for Smart Agriculture 14.The Role of AI-Based Integrated Physical Security Governance for Optimizing IoT Devices Connectivity in Smart Cities 15.Smart Home Environment AI-Based Services for Smart Cities and Urban Infrastructure [document électronique] / Lyu Kangjuan, Auteur ; Hu Min, Auteur ; Du Juan, Auteur ; Sugumaran Vijayan, Auteur . - Hershey [United States] : Engineering science reference, 2021 . - 1 fichier PDF. ISBN : 978-1-79985-025-0 Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École. Langues : Anglais (eng) Mots-clés : City planning--Technological innovations Smart cities Urbanization Artificial intelligence--Engineering applications Index. décimale : 004.8 Intelligence artificielle Résumé : Cities are the next frontier for artificial intelligence to permeate. As smart urban environments become possible, probable, and even preferred, artificial intelligence offers the chance for even further advancement through infrastructure and industry boosting. Opportunity overflows, but without thorough research to guide a complicated development and implementation process, urban environments can become disorganized and outright dangerous for citizens. AI-Based Services for Smart Cities and Urban Infrastructure is a collection of innovative research that explores artificial intelligence (AI) applications in urban planning. In addition, the book looks at how the internet of things and AI can work together to enable a real smart city and discusses state-of-the-art techniques in urban infrastructure design, construction, operation, maintenance, and management. While highlighting a broad range of topics including construction management, public transportation, and smart agriculture, this book is ideally designed for engineers, entrepreneurs, urban planners, architects, policymakers, researchers, academicians, and students. Série Note de contenu : Summary : I.Fundamentals of Smart Cities 1.Definition and History of Smart Cities 2.The Development of Smart Cities in the World 3.Core Technology of Smart Cities 4.Interpretation of the Construction Standard of Smart City Standard Systems 5.Framework and Structure of Smart Cities II.Infrastructure and Engineering Construction of Smart Cities 6.Infrastructure and Industry Economy 7.Intelligent Engineering Construction Management: On-Site Construction Management 8.Intelligent Engineering Construction Management: Long-Distance Construction Management 9.Performance Evaluation on the Intelligent Operation and Maintenance Mode of Public-Private 10.Policy Recommendations on the Application of AI to the Development of Smart Cities 11.Smart City Service III.Application of AI for Smart City Services 12.Application of Fuzzy Analytic Hierarchy Process for Evaluation of Ankara-Izmir High-Speed Train Project. 13.Applications of Artificial Intelligence for Smart Agriculture 14.The Role of AI-Based Integrated Physical Security Governance for Optimizing IoT Devices Connectivity in Smart Cities 15.Smart Home Environment

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Artificial intelligence-based smart power systems (2023)

Public ISBD Titre : Artificial intelligence-based smart power systems Type de document : texte imprimé Auteurs : Sanjeevikumar Padmanaban, Éditeur scientifique ; Sivaraman Palanisamy, Éditeur scientifique ; Sharmeela Chenniappan, Éditeur scientifique ; Jens Bo Holm-Nielsen, Éditeur scientifique Editeur : Piscataway, NJ : IEEE Press Année de publication : 2023 Autre Editeur : Hoboken, NJ : Wiley Importance : XXII, 378 p. Présentation : ill. Format : 26 cm ISBN/ISSN/EAN : 978-1-119-89396-7 Note générale : Ref. Bibliogr. en fin de chapitres. - Index Langues : Anglais (eng) Mots-clés : Smart power grids Artificial intelligence Réseaux électriques intelligents Intelligence artificielle Index. décimale : 004.89 Systèmes d'application d'intelligence artificielle. Systèmes basés sur la connaissance intelligente. Résumé : Authoritative resource describing artificial intelligence and advanced technologies in smart power systems with simulation examples and case studies Artificial Intelligence-based Smart Power Systems presents advanced technologies used in various aspects of smart power systems, especially grid-connected and industrial evolution. It covers many new topics such as distribution phasor measurement units, blockchain technologies for smart power systems, the application of deep learning and reinforced learning, and artificial intelligence techniques. The text also explores the potential consequences of artificial intelligence and advanced technologies in smart power systems in the forthcoming years. To enhance and reinforce learning, the editors include many learning resources throughout the text, including MATLAB, practical examples, and case studies. Note de contenu : Summary : 1. Introduction to Smart Power Systems 2. Modeling and Analysis of Smart Power System 3. Multilevel Cascaded Boost Converter Fed Multilevel Inverter for Renewable Energy Applications 4. Recent Advancements in Power Electronics for Modern Power Systems-Comprehensive Review on DC-Link Capacitors Concerning Power Density Maximization in Power Converters 5. Energy Storage Systems for Smart Power Systems 6. Real-Time Implementation and Performance Analysis of Supercapacitor for Energy Storage 7. Adaptive Fuzzy Logic Controller for MPPT Control in PMSG Wind Turbine Generator 8. A Novel Nearest Neighbor Searching-Based Fault Distance Location Method for HVDC Transmission Lines 9. Comparative Analysis of Machine Learning Approaches in Enhancing Power System Stability 10. Augmentation of PV-Wind Hybrid Technology with Adroit Neural Network, ANFIS, and PI Controllers Indeed Precocious DVR System 11. Deep Reinforcement Learning and Energy Price Prediction 12. Power Quality Conditioners in Smart Power System 13. The Role of Internet of Things in Smart Homes 14. Electric Vehicles and IoT in Smart Cities 15. Modeling and Simulation of Smart Power Systems Using HIL 16. Distribution Phasor Measurement Units (PMUs) in Smart Power Systems 17. Blockchain Technologies for Smart Power Systems 18. Power and Energy Management in Smart Power Systems Artificial intelligence-based smart power systems [texte imprimé] / Sanjeevikumar Padmanaban, Éditeur scientifique ; Sivaraman Palanisamy, Éditeur scientifique ; Sharmeela Chenniappan, Éditeur scientifique ; Jens Bo Holm-Nielsen, Éditeur scientifique . - Piscataway, NJ : IEEE Press : Hoboken, NJ : Wiley, 2023 . - XXII, 378 p. : ill. ; 26 cm. ISBN : 978-1-119-89396-7 Ref. Bibliogr. en fin de chapitres. - Index Langues : Anglais (eng) Mots-clés : Smart power grids Artificial intelligence Réseaux électriques intelligents Intelligence artificielle Index. décimale : 004.89 Systèmes d'application d'intelligence artificielle. Systèmes basés sur la connaissance intelligente. Résumé : Authoritative resource describing artificial intelligence and advanced technologies in smart power systems with simulation examples and case studies Artificial Intelligence-based Smart Power Systems presents advanced technologies used in various aspects of smart power systems, especially grid-connected and industrial evolution. It covers many new topics such as distribution phasor measurement units, blockchain technologies for smart power systems, the application of deep learning and reinforced learning, and artificial intelligence techniques. The text also explores the potential consequences of artificial intelligence and advanced technologies in smart power systems in the forthcoming years. To enhance and reinforce learning, the editors include many learning resources throughout the text, including MATLAB, practical examples, and case studies. Note de contenu : Summary : 1. Introduction to Smart Power Systems 2. Modeling and Analysis of Smart Power System 3. Multilevel Cascaded Boost Converter Fed Multilevel Inverter for Renewable Energy Applications 4. Recent Advancements in Power Electronics for Modern Power Systems-Comprehensive Review on DC-Link Capacitors Concerning Power Density Maximization in Power Converters 5. Energy Storage Systems for Smart Power Systems 6. Real-Time Implementation and Performance Analysis of Supercapacitor for Energy Storage 7. Adaptive Fuzzy Logic Controller for MPPT Control in PMSG Wind Turbine Generator 8. A Novel Nearest Neighbor Searching-Based Fault Distance Location Method for HVDC Transmission Lines 9. Comparative Analysis of Machine Learning Approaches in Enhancing Power System Stability 10. Augmentation of PV-Wind Hybrid Technology with Adroit Neural Network, ANFIS, and PI Controllers Indeed Precocious DVR System 11. Deep Reinforcement Learning and Energy Price Prediction 12. Power Quality Conditioners in Smart Power System 13. The Role of Internet of Things in Smart Homes 14. Electric Vehicles and IoT in Smart Cities 15. Modeling and Simulation of Smart Power Systems Using HIL 16. Distribution Phasor Measurement Units (PMUs) in Smart Power Systems 17. Blockchain Technologies for Smart Power Systems 18. Power and Energy Management in Smart Power Systems

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Artificial intelligence in industry 4.0 and 5G technology (2022)

Public ISBD Titre : Artificial intelligence in industry 4.0 and 5G technology Type de document : texte imprimé Auteurs : Pandian Vasant, Éditeur scientifique ; Elias Munapo, Éditeur scientifique ; J. Joshua Thomas (1973-....), Éditeur scientifique ; Gerhard-Wilhelm Weber, Éditeur scientifique Editeur : Hoboken, NJ : John Wiley et Sons, Inc. Année de publication : 2022 Importance : XXX, 321 p. Présentation : ill. Format : 24 cm ISBN/ISSN/EAN : 978-1-119-79876-7 Note générale : Ref. Bibliogr. en fin de chapitres. - Index Langues : Anglais (eng) Mots-clés : Artificial intelligence -- Industrial applications Industry 4.0 5G mobile communication systems Intelligence artificielle -- Applications industrielles Industrie Communications mobiles 5G Index. décimale : 004.896 Intelligence artificielle dans les systèmes industriels Résumé : Artificial Intelligence in Industry 4.0 and 5G Technology Explores innovative and value-added solutions for application problems in the commercial, business, and industry sectors As the pace of Artificial Intelligence (AI) technology innovation continues to accelerate, identifying the appropriate AI capabilities to embed in key decision processes has never been more critical to establishing competitive advantage. New and emerging analytics tools and technologies can be configured to optimize business value, change how an organization gains insights, and significantly improve the decision-making process across the enterprise. Artificial Intelligence in Industry 4.0 and 5G Technology helps readers solve real-world technological engineering optimization problems using evolutionary and swarm intelligence, mathematical programming, multi-objective optimization, and other cutting-edge intelligent optimization methods. Contributions from leading experts in the field present original research on both the theoretical and practical aspects of implementing new AI techniques in a variety of sectors, including Big Data analytics, smart manufacturing, renewable energy, smart cities, robotics, and the Internet of Things (IoT). Presents detailed information on meta-heuristic applications with a focus on technology and engineering sectors such as smart manufacturing, smart production, innovative cities, and 5G networks. Offers insights into the use of metaheuristic strategies to solve optimization problems in business, economics, finance, and industry where uncertainty is a factor. Provides guidance on implementing metaheuristics in different applications and hybrid technological systems. Describes various AI approaches utilizing hybrid meta-heuristics optimization algorithms, including meta-search engines for innovative research and hyper-heuristics algorithms for performance measurement. Artificial Intelligence in Industry 4.0 and 5G Technology is a valuable resource for IT specialists, industry professionals, managers and executives, researchers, scientists, engineers, and advanced students an up-to-date reference to innovative computing, uncertainty management, and optimization approaches. Note de contenu : Summary : 1. Dynamic Key-based Biometric End-User Authentication Proposal for IoT in Industry 2. Decision Support Methodology for Scheduling Orders in Additive Manufacturing 3. Significance of Consuming 5G-Built Artificial Intelligence in Smart Cities 4. Neural Network Approach to Segmentation of Economic Infrastructure Objects on High-Resolution Satellite Images 5. The Impact of Data Security on the Internet of Things 6. Sustainable Renewable Energy and Waste Management on Weathering Corporate Pollution 7. Adam Adaptive Optimization Method for Neural Network Models Regression in Image Recognition Tasks 8. Application of Integer Programming in Allocating Energy Resources in Rural Africa 9. Feasibility of Drones as the Next Step in Innovative Solution for Emerging Society 10. Designing a Distribution Network for a Soda Company: Formulation and Efficient Solution Procedure 11. Machine Learning and MCDM Approach to Characterize Student Attrition in Higher Education 12. A Concise Review on Recent Optimization and Deep Learning Applications in Blockchain Technology 13. Inventory Routing Problem with Fuzzy Demand and Deliveries with Priority 14. Comparison of Defuzzification Methods for Project Selection 15. Re-Identification-Based Models for Multiple Object Tracking Artificial intelligence in industry 4.0 and 5G technology [texte imprimé] / Pandian Vasant, Éditeur scientifique ; Elias Munapo, Éditeur scientifique ; J. Joshua Thomas (1973-....), Éditeur scientifique ; Gerhard-Wilhelm Weber, Éditeur scientifique . - Hoboken, NJ : John Wiley et Sons, Inc., 2022 . - XXX, 321 p. : ill. ; 24 cm. ISBN : 978-1-119-79876-7 Ref. Bibliogr. en fin de chapitres. - Index Langues : Anglais (eng) Mots-clés : Artificial intelligence -- Industrial applications Industry 4.0 5G mobile communication systems Intelligence artificielle -- Applications industrielles Industrie Communications mobiles 5G Index. décimale : 004.896 Intelligence artificielle dans les systèmes industriels Résumé : Artificial Intelligence in Industry 4.0 and 5G Technology Explores innovative and value-added solutions for application problems in the commercial, business, and industry sectors As the pace of Artificial Intelligence (AI) technology innovation continues to accelerate, identifying the appropriate AI capabilities to embed in key decision processes has never been more critical to establishing competitive advantage. New and emerging analytics tools and technologies can be configured to optimize business value, change how an organization gains insights, and significantly improve the decision-making process across the enterprise. Artificial Intelligence in Industry 4.0 and 5G Technology helps readers solve real-world technological engineering optimization problems using evolutionary and swarm intelligence, mathematical programming, multi-objective optimization, and other cutting-edge intelligent optimization methods. Contributions from leading experts in the field present original research on both the theoretical and practical aspects of implementing new AI techniques in a variety of sectors, including Big Data analytics, smart manufacturing, renewable energy, smart cities, robotics, and the Internet of Things (IoT). Presents detailed information on meta-heuristic applications with a focus on technology and engineering sectors such as smart manufacturing, smart production, innovative cities, and 5G networks. Offers insights into the use of metaheuristic strategies to solve optimization problems in business, economics, finance, and industry where uncertainty is a factor. Provides guidance on implementing metaheuristics in different applications and hybrid technological systems. Describes various AI approaches utilizing hybrid meta-heuristics optimization algorithms, including meta-search engines for innovative research and hyper-heuristics algorithms for performance measurement. Artificial Intelligence in Industry 4.0 and 5G Technology is a valuable resource for IT specialists, industry professionals, managers and executives, researchers, scientists, engineers, and advanced students an up-to-date reference to innovative computing, uncertainty management, and optimization approaches. Note de contenu : Summary : 1. Dynamic Key-based Biometric End-User Authentication Proposal for IoT in Industry 2. Decision Support Methodology for Scheduling Orders in Additive Manufacturing 3. Significance of Consuming 5G-Built Artificial Intelligence in Smart Cities 4. Neural Network Approach to Segmentation of Economic Infrastructure Objects on High-Resolution Satellite Images 5. The Impact of Data Security on the Internet of Things 6. Sustainable Renewable Energy and Waste Management on Weathering Corporate Pollution 7. Adam Adaptive Optimization Method for Neural Network Models Regression in Image Recognition Tasks 8. Application of Integer Programming in Allocating Energy Resources in Rural Africa 9. Feasibility of Drones as the Next Step in Innovative Solution for Emerging Society 10. Designing a Distribution Network for a Soda Company: Formulation and Efficient Solution Procedure 11. Machine Learning and MCDM Approach to Characterize Student Attrition in Higher Education 12. A Concise Review on Recent Optimization and Deep Learning Applications in Blockchain Technology 13. Inventory Routing Problem with Fuzzy Demand and Deliveries with Priority 14. Comparison of Defuzzification Methods for Project Selection 15. Re-Identification-Based Models for Multiple Object Tracking

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Convergence (2022)

Public ISBD Titre : Convergence : artificial intelligence and quantum computing Type de document : texte imprimé Auteurs : Greg Viggiano, Éditeur scientifique ; David Brin (1950-....), Préfacier, etc. Editeur : Hoboken, NJ : Wiley Année de publication : 2022 Importance : XLVIII, 272 p. Présentation : ill. Format : 23 cm ISBN/ISSN/EAN : 978-1-394-17410-2 Note générale : La couverture indique en plus "Social, economic, and policy impacts". - Notes bibliogr. p. 251-258. - Index Langues : Anglais (eng) Mots-clés : Artificial intelligence -- Social aspects Quantum computing -- Social aspects Artificial intelligence -- Economic aspects Quantum computing -- Economic aspects Intelligence artificielle -- Aspect social Informatique quantique -- Aspect social Intelligence artificielle -- Aspect économique Informatique quantique -- Aspect économique Index. décimale : 004.8 Intelligence artificielle Résumé : "Prepare for the coming convergence of AI and quantum computing. A collection of essays from 20 renowned, international authors working in industry, academia, and government, Convergence: Artificial Intelligence and Quantum Computing explains the impending convergence of artificial intelligence and quantum computing. A diversity of viewpoints is presented, each offering their view of this coming watershed event. In the book, you'll discover that we're on the cusp of seeing the stuff of science fiction become reality, with huge implications for ripping up the existing social fabric, global economy, and current geopolitical order. Along with an incisive foreword by Hugo- and Nebula-award winning author David Brin, you'll also find: Explorations of the increasing pace of technological development; Explanations of why seemingly unusual and surprising breakthroughs might be just around the corner; Maps to navigate the potential minefields that await us as AI and quantum computing come together A fascinating and thought-provoking compilation of insights from some of the leading technological voices in the world, Convergence convincingly argues that we should prepare for a world in which very little will remain the same and shows us how to get ready."-- Provided by publisher. Note de contenu : Summary : Part I. Policy and Regulatory Impacts Part II. Economic Impacts Part III. Social Impacts Convergence : artificial intelligence and quantum computing [texte imprimé] / Greg Viggiano, Éditeur scientifique ; David Brin (1950-....), Préfacier, etc. . - Hoboken, NJ : Wiley, 2022 . - XLVIII, 272 p. : ill. ; 23 cm. ISBN : 978-1-394-17410-2 La couverture indique en plus "Social, economic, and policy impacts". - Notes bibliogr. p. 251-258. - Index Langues : Anglais (eng) Mots-clés : Artificial intelligence -- Social aspects Quantum computing -- Social aspects Artificial intelligence -- Economic aspects Quantum computing -- Economic aspects Intelligence artificielle -- Aspect social Informatique quantique -- Aspect social Intelligence artificielle -- Aspect économique Informatique quantique -- Aspect économique Index. décimale : 004.8 Intelligence artificielle Résumé : "Prepare for the coming convergence of AI and quantum computing. A collection of essays from 20 renowned, international authors working in industry, academia, and government, Convergence: Artificial Intelligence and Quantum Computing explains the impending convergence of artificial intelligence and quantum computing. A diversity of viewpoints is presented, each offering their view of this coming watershed event. In the book, you'll discover that we're on the cusp of seeing the stuff of science fiction become reality, with huge implications for ripping up the existing social fabric, global economy, and current geopolitical order. Along with an incisive foreword by Hugo- and Nebula-award winning author David Brin, you'll also find: Explorations of the increasing pace of technological development; Explanations of why seemingly unusual and surprising breakthroughs might be just around the corner; Maps to navigate the potential minefields that await us as AI and quantum computing come together A fascinating and thought-provoking compilation of insights from some of the leading technological voices in the world, Convergence convincingly argues that we should prepare for a world in which very little will remain the same and shows us how to get ready."-- Provided by publisher. Note de contenu : Summary : Part I. Policy and Regulatory Impacts Part II. Economic Impacts Part III. Social Impacts

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Data Analytics Applied to the Mining Industry / ,Ali Soofastaei (2021)

Public ISBD Titre : Data Analytics Applied to the Mining Industry Type de document : document électronique Auteurs : ,Ali Soofastaei, Auteur Editeur : Boca Raton : CRC Press, Taylor and Francis Group,LLC Année de publication : 2021 Importance : 1 fichier PDF ISBN/ISSN/EAN : 978-0-429-78176-6 Note générale : Index Langues : Français (fre) Mots-clés : Artificial intelligence Mining engineering Mineral industries--Data processing Index. décimale : 004.62 Traitement de l'information (Data science) Résumé : Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes critical opportunity areas for mining optimization Brings experience and learning in digital transformation from adjacent sectors Note de contenu : Summary of the book : 1.Digital Transformation of Mining 2.Advanced Data Analytics 3.Data Collection, Storage, and Retrieval 4.Making Sense of Data 5.Analytics Toolsets 6.Process Analytics 7.Predictive Maintenance of Mining Machines Applying Advanced Data Analysis 8.Data Analytics forEnergy Efficiency and Gas Emission Reduction 9.Making Decisions Based on Analytics 10.Future Skills Requirements Data Analytics Applied to the Mining Industry [document électronique] / ,Ali Soofastaei, Auteur . - Boca Raton : CRC Press, Taylor and Francis Group,LLC, 2021 . - 1 fichier PDF. ISBN : 978-0-429-78176-6 Index Langues : Français (fre) Mots-clés : Artificial intelligence Mining engineering Mineral industries--Data processing Index. décimale : 004.62 Traitement de l'information (Data science) Résumé : Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes critical opportunity areas for mining optimization Brings experience and learning in digital transformation from adjacent sectors Note de contenu : Summary of the book : 1.Digital Transformation of Mining 2.Advanced Data Analytics 3.Data Collection, Storage, and Retrieval 4.Making Sense of Data 5.Analytics Toolsets 6.Process Analytics 7.Predictive Maintenance of Mining Machines Applying Advanced Data Analysis 8.Data Analytics forEnergy Efficiency and Gas Emission Reduction 9.Making Decisions Based on Analytics 10.Future Skills Requirements

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Deep learning for engineers / Tariq M. Arif (2024)  

Public ISBD Titre : Deep learning for engineers Type de document : document électronique Auteurs : Tariq M. Arif, Auteur ; Md Adilur Rahim, Auteur Editeur : London ; New York ; Boca Raton : CRC Press Année de publication : 2024 Importance : 1 fichier PDF Présentation : ill. ISBN/ISSN/EAN : 978-1-00-340292-3 Note générale : Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École Index Mots-clés : Engineering--Study and teaching Deep learning (Machine learning) Index. décimale : 004.8 Intelligence artificielle Résumé : Deep Learning for Engineers introduces the fundamental principles of deep learning along with an explanation of the basic elements required for understanding and applying deep learning models.As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of convolutional neural network (CNN) and recurrent neural network (RNN) architectures and their practical implementations in science and engineering are also discussed.This book includes exercise problems for all case studies focusing on various fine-tuning approaches in deep learning. Science and engineering students at both undergraduate and graduate levels, academic researchers, and industry professionals will find the contents useful. Note de contenu : Summary : 1. Basics of deep learning. 2. Computer vision fundamentals. 3. Natural language processing fundamentals. 4. Deep learning framework installation: pytorch and cuda. 5. Case study i: image classification. 6. Case study ii: object detection. 7. Case study iii: semantic segmentation. 8. Case study iv: image captioning. En ligne : https://research.ebsco.com/linkprocessor/plink?id=a4b6d807-80a1-33ec-be95-88f82d [...] Deep learning for engineers [document électronique] / Tariq M. Arif, Auteur ; Md Adilur Rahim, Auteur . - London ; New York ; Boca Raton : CRC Press, 2024 . - 1 fichier PDF : ill. ISBN : 978-1-00-340292-3 Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École Index Mots-clés : Engineering--Study and teaching Deep learning (Machine learning) Index. décimale : 004.8 Intelligence artificielle Résumé : Deep Learning for Engineers introduces the fundamental principles of deep learning along with an explanation of the basic elements required for understanding and applying deep learning models.As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of convolutional neural network (CNN) and recurrent neural network (RNN) architectures and their practical implementations in science and engineering are also discussed.This book includes exercise problems for all case studies focusing on various fine-tuning approaches in deep learning. Science and engineering students at both undergraduate and graduate levels, academic researchers, and industry professionals will find the contents useful. Note de contenu : Summary : 1. Basics of deep learning. 2. Computer vision fundamentals. 3. Natural language processing fundamentals. 4. Deep learning framework installation: pytorch and cuda. 5. Case study i: image classification. 6. Case study ii: object detection. 7. Case study iii: semantic segmentation. 8. Case study iv: image captioning. En ligne : https://research.ebsco.com/linkprocessor/plink?id=a4b6d807-80a1-33ec-be95-88f82d [...]

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Engineering intelligent systems / Barclay R. Brown (2023)

Public ISBD Titre : Engineering intelligent systems : systems engineering and design with artificial intelligence, visual modeling, and systems thinking Type de document : texte imprimé Auteurs : Barclay R. Brown, Auteur Editeur : Hoboken, NJ : John Wiley et Sons, Inc. Année de publication : 2023 Importance : XV, 365 p. Présentation : ill. Format : 24 cm ISBN/ISSN/EAN : 978-1-119-66559-5 Note générale : Ref. Bibliogr. - Index Langues : Anglais (eng) Mots-clés : Systems engineering Systems engineering -- Design Artificial intelligence Systems engineering -- Simulation methods Ingénierie des systèmes Intelligence artificielle Ingénierie des systèmes -- Méthodes de simulation Index. décimale : 004.8 Intelligence artificielle Résumé : Engineering Intelligent Systems Exploring the three key disciplines of intelligent systems As artificial intelligence (AI) and machine learning technology continue to develop and find new applications, advances in this field have generally been focused on the development of isolated software data analysis systems or of control systems for robots and other devices. By applying model-based systems engineering to AI, however, engineers can design complex systems that rely on AI-based components, resulting in larger, more complex intelligent systems that successfully integrate humans and AI. Engineering Intelligent Systems relies on Dr. Barclay R. Brown's 25 years of experience in software and systems engineering to propose an integrated perspective to the challenges and opportunities in the use of artificial intelligence to create better technological and business systems. While most recent research on the topic has focused on adapting and improving algorithms and devices, this book puts forth the innovative idea of transforming the systems in our lives, our societies, and our businesses into intelligent systems. At its heart, this book is about how to combine systems engineering and systems thinking with the newest technologies to design increasingly intelligent systems. Engineering Intelligent Systems readers will also find: An introduction to the fields of artificial intelligence with machine learning, model-based systems engineering (MBSE), and systems thinking--the key disciplines for making systems smarter An example of how to build a deep neural network in a spreadsheet, with no code or specialized mathematics required An approach to the visual representation of systems, using techniques from moviemaking, storytelling, visual systems design, and model-based systems engineering An analysis of the potential ability of computers to think, understand and become conscious and its implications for artificial intelligence Tools to allow for easier collaboration and communication among developers and engineers, allowing for better understanding between stakeholders, and creating a faster development cycle A systems thinking approach to people systems--systems that consist only of people and which form the basis for our organizations, communities and society Engineering Intelligent Systems offers an intriguing new approach to making systems more intelligent using artificial intelligence, machine learning, systems thinking, and system modeling and therefore will be of interest to all engineers and business professionals, particularly systems engineers. Note de contenu : Summary : Part I: systems and artificial intelligence 1. Artificial intelligence, science fiction, and fear 2. We live in a world of systems 3. The intelligence in the system: how artificial intelligence 4. Intelligent systems and the people they lov Part II: systems engineering for intelligent systems 5. Designing systems by drawing pictures and telling 6. Use cases: the superpower of systems engineering 7. Picturing systems with model based systems 8. A time for timeboxes and the use of usage processes Part III: systems thinking for intelligent systems 9. Solving hard problems with systems thinking 10. People systems: a new way to understand the world Engineering intelligent systems : systems engineering and design with artificial intelligence, visual modeling, and systems thinking [texte imprimé] / Barclay R. Brown, Auteur . - Hoboken, NJ : John Wiley et Sons, Inc., 2023 . - XV, 365 p. : ill. ; 24 cm. ISBN : 978-1-119-66559-5 Ref. Bibliogr. - Index Langues : Anglais (eng) Mots-clés : Systems engineering Systems engineering -- Design Artificial intelligence Systems engineering -- Simulation methods Ingénierie des systèmes Intelligence artificielle Ingénierie des systèmes -- Méthodes de simulation Index. décimale : 004.8 Intelligence artificielle Résumé : Engineering Intelligent Systems Exploring the three key disciplines of intelligent systems As artificial intelligence (AI) and machine learning technology continue to develop and find new applications, advances in this field have generally been focused on the development of isolated software data analysis systems or of control systems for robots and other devices. By applying model-based systems engineering to AI, however, engineers can design complex systems that rely on AI-based components, resulting in larger, more complex intelligent systems that successfully integrate humans and AI. Engineering Intelligent Systems relies on Dr. Barclay R. Brown's 25 years of experience in software and systems engineering to propose an integrated perspective to the challenges and opportunities in the use of artificial intelligence to create better technological and business systems. While most recent research on the topic has focused on adapting and improving algorithms and devices, this book puts forth the innovative idea of transforming the systems in our lives, our societies, and our businesses into intelligent systems. At its heart, this book is about how to combine systems engineering and systems thinking with the newest technologies to design increasingly intelligent systems. Engineering Intelligent Systems readers will also find: An introduction to the fields of artificial intelligence with machine learning, model-based systems engineering (MBSE), and systems thinking--the key disciplines for making systems smarter An example of how to build a deep neural network in a spreadsheet, with no code or specialized mathematics required An approach to the visual representation of systems, using techniques from moviemaking, storytelling, visual systems design, and model-based systems engineering An analysis of the potential ability of computers to think, understand and become conscious and its implications for artificial intelligence Tools to allow for easier collaboration and communication among developers and engineers, allowing for better understanding between stakeholders, and creating a faster development cycle A systems thinking approach to people systems--systems that consist only of people and which form the basis for our organizations, communities and society Engineering Intelligent Systems offers an intriguing new approach to making systems more intelligent using artificial intelligence, machine learning, systems thinking, and system modeling and therefore will be of interest to all engineers and business professionals, particularly systems engineers. Note de contenu : Summary : Part I: systems and artificial intelligence 1. Artificial intelligence, science fiction, and fear 2. We live in a world of systems 3. The intelligence in the system: how artificial intelligence 4. Intelligent systems and the people they lov Part II: systems engineering for intelligent systems 5. Designing systems by drawing pictures and telling 6. Use cases: the superpower of systems engineering 7. Picturing systems with model based systems 8. A time for timeboxes and the use of usage processes Part III: systems thinking for intelligent systems 9. Solving hard problems with systems thinking 10. People systems: a new way to understand the world

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Industrial applications of big data, AI, and blockchain / Mahmoud El Samad (2024)  

Public ISBD Titre : Industrial applications of big data, AI, and blockchain Type de document : document électronique Auteurs : Mahmoud El Samad, Auteur ; Ghalia Nassreddine, Auteur ; Hani El-Chaarani, Auteur ; Sam El Nemar, Auteur Editeur : IGI Global Année de publication : 2024 Collection : Advances in computational intelligence and robotics Importance : 1 fichier PDF Présentation : ill. ISBN/ISSN/EAN : 979-83693-10472-- Note générale : Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École. Bibliogr. p. 300 - 340 . - Index Langues : Anglais (eng) Mots-clés : Blockchains (Databases)--Industrial applications Big data--Industrial applications Artificial intelligence--Industrial applications Index. décimale : 004.8 Intelligence artificielle Résumé : Blockchain has become the cornerstone of technologies, supported by others including Big Data and Artificial Intelligence (AI). Originating from cryptocurrency, it transcends boundaries, finding resonance in finance, healthcare, e-governance, and beyond. While blockchain relies on a peer-to-peer approach, enabling nodes to collaborate without the shackles of a central authority, appropriate monitoring and updating of these technologies is a constant necessity. AI systems may generate false alerts of money laundering, leading to wasted resources and unnecessary investigations. Cybersecurity threats are becoming more sophisticated, and AI systems need to evolve accordingly. This is especially true for healthcare data systems, where data privacy and security concerns, especially with sensitive health information are paramount. Threads of automation in artificial intelligence (AI) weave through sectors such as business, finance, healthcare, marketing, and governance. Industrial Applications of Big Data, AI, and Blockchain delves into the pulsating realms of big data, AI, and blockchain. From natural language processing's eloquent interpretation of human language to the prowess of AI algorithms in predictive tasks, this book explores how AI enhances decision-making accuracy, catalyzing a paradigm shift in diverse industries. The book also navigates through big data, a robust infrastructure adorned with the quintessence of the 5V characteristics—velocity, volume, value, variety, and veracity. It is a stronghold for developers, offering an efficient sanctuary for advanced database systems, encompassing services like data cleaning, storage, processing, and analysis. The gravitational pull of big data attracts both researchers and industrialists. This book is ideal for researchers, business visionaries, tech enthusiasts, and curious minds eager to fathom the transformative potential of these technologies. From the AI in money laundering detection to the blockchain in healthcare, each chapter unfurls a narrative stitched. Note de contenu : In summary : 1. Artificial intelligence and big data analytics in green supply chain management: critical analysis. 2. Artificial intelligence applications in renewable power systems. 3. Artificial intelligence for money laundering detection. 4. Artificial intelligence in central banking: benefits and risks of ai for central banks. 5. Blockchain in healthcare. 6. Blockchain integration in upstream oil and gas: enhancing performance through innovation. 7. Blockchain technology through bitcoin and ethereum: a comprehensive review of their interdependent relationship. 8. Blockchain-based smart contracts: technical and usage aspects. 9. Cotton health-guard: ai-enhanced crop health assessment through image classification. 10. The role of social media presence, technology, and personalization in increasing sales and achieving sustainable business growth. En ligne : https://research.ebsco.com/linkprocessor/plink?id=8e4a4f32-df55-322c-acb6-3962b8 [...] Industrial applications of big data, AI, and blockchain [document électronique] / Mahmoud El Samad, Auteur ; Ghalia Nassreddine, Auteur ; Hani El-Chaarani, Auteur ; Sam El Nemar, Auteur . - IGI Global, 2024 . - 1 fichier PDF : ill.. - (Advances in computational intelligence and robotics) . ISBN : 979-83693-10472-- Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École. Bibliogr. p. 300 - 340 . - Index Langues : Anglais (eng) Mots-clés : Blockchains (Databases)--Industrial applications Big data--Industrial applications Artificial intelligence--Industrial applications Index. décimale : 004.8 Intelligence artificielle Résumé : Blockchain has become the cornerstone of technologies, supported by others including Big Data and Artificial Intelligence (AI). Originating from cryptocurrency, it transcends boundaries, finding resonance in finance, healthcare, e-governance, and beyond. While blockchain relies on a peer-to-peer approach, enabling nodes to collaborate without the shackles of a central authority, appropriate monitoring and updating of these technologies is a constant necessity. AI systems may generate false alerts of money laundering, leading to wasted resources and unnecessary investigations. Cybersecurity threats are becoming more sophisticated, and AI systems need to evolve accordingly. This is especially true for healthcare data systems, where data privacy and security concerns, especially with sensitive health information are paramount. Threads of automation in artificial intelligence (AI) weave through sectors such as business, finance, healthcare, marketing, and governance. Industrial Applications of Big Data, AI, and Blockchain delves into the pulsating realms of big data, AI, and blockchain. From natural language processing's eloquent interpretation of human language to the prowess of AI algorithms in predictive tasks, this book explores how AI enhances decision-making accuracy, catalyzing a paradigm shift in diverse industries. The book also navigates through big data, a robust infrastructure adorned with the quintessence of the 5V characteristics—velocity, volume, value, variety, and veracity. It is a stronghold for developers, offering an efficient sanctuary for advanced database systems, encompassing services like data cleaning, storage, processing, and analysis. The gravitational pull of big data attracts both researchers and industrialists. This book is ideal for researchers, business visionaries, tech enthusiasts, and curious minds eager to fathom the transformative potential of these technologies. From the AI in money laundering detection to the blockchain in healthcare, each chapter unfurls a narrative stitched. Note de contenu : In summary : 1. Artificial intelligence and big data analytics in green supply chain management: critical analysis. 2. Artificial intelligence applications in renewable power systems. 3. Artificial intelligence for money laundering detection. 4. Artificial intelligence in central banking: benefits and risks of ai for central banks. 5. Blockchain in healthcare. 6. Blockchain integration in upstream oil and gas: enhancing performance through innovation. 7. Blockchain technology through bitcoin and ethereum: a comprehensive review of their interdependent relationship. 8. Blockchain-based smart contracts: technical and usage aspects. 9. Cotton health-guard: ai-enhanced crop health assessment through image classification. 10. The role of social media presence, technology, and personalization in increasing sales and achieving sustainable business growth. En ligne : https://research.ebsco.com/linkprocessor/plink?id=8e4a4f32-df55-322c-acb6-3962b8 [...]

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Industrial quantum computing (2025)  

Public ISBD Titre : Industrial quantum computing : algorithms, blockchains, industry 4.0 Type de document : document électronique Auteurs : Umesh Kumar Lilhore, Éditeur scientifique ; Surjeet Dalal, Éditeur scientifique ; Vishal Dutt, Éditeur scientifique ; Magdalena Radulescu, Éditeur scientifique Editeur : Berlin : De Gruyter Année de publication : 2025 Importance : 1 fichier PDF (13.6 Mo) ISBN/ISSN/EAN : 978-3-11-135484-2 Note générale : Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École. Index Langues : Anglais (eng) Mots-clés : Manufacturing processes -- Data processing Industry 4.0 Quantum computing -- Industrial applications Index. décimale : 004.896 Intelligence artificielle dans les systèmes industriels Résumé : Industrial quantum computing'(IQC) covers the applications of quantum computing innovations in general industry and industry 4.0. This book presents the application of quantum computations to the financial sector, medical services, the logistics industry, and the manufacturing industry. Note de contenu : Summary : 1. Quantum computing in society: impacts and implications 2. Quantum computing with machine learning: opportunities and challenges 3. Quantum machine learning algorithms: a comprehensive review 4. Highlighting major issues with quantum computing in healthcare 5. Privacy and security for 6G's IoT-connected future in the age of quantum computing 6. Can quantum computers revolutionize health systems? 7. Industrial automation and quantum computing 8. Applications of quantum computing in financial planning and financial control 9. Quantum computing in machine learning: an overview 10. The impact of AI and automation on income inequality in BRICS countries and the role of structural factors and women's empowerment 11. Quantum computing and machine learning: a symbiotic relationship 12. Quantum-secured healthcare data and cybersecurity innovations in the era of Industry 5.0 13. Introduction to quantum computing and its revolution in industry and society 14. Advancing healthcare through the opportunities and challenges of quantum computing 15. Quantum computing in drug and chemical En ligne : https://research.ebsco.com/linkprocessor/plink?id=255a8868-b48e-3d61-8e97-da88e0 [...] Industrial quantum computing : algorithms, blockchains, industry 4.0 [document électronique] / Umesh Kumar Lilhore, Éditeur scientifique ; Surjeet Dalal, Éditeur scientifique ; Vishal Dutt, Éditeur scientifique ; Magdalena Radulescu, Éditeur scientifique . - Berlin : De Gruyter, 2025 . - 1 fichier PDF (13.6 Mo). ISBN : 978-3-11-135484-2 Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École. Index Langues : Anglais (eng) Mots-clés : Manufacturing processes -- Data processing Industry 4.0 Quantum computing -- Industrial applications Index. décimale : 004.896 Intelligence artificielle dans les systèmes industriels Résumé : Industrial quantum computing'(IQC) covers the applications of quantum computing innovations in general industry and industry 4.0. This book presents the application of quantum computations to the financial sector, medical services, the logistics industry, and the manufacturing industry. Note de contenu : Summary : 1. Quantum computing in society: impacts and implications 2. Quantum computing with machine learning: opportunities and challenges 3. Quantum machine learning algorithms: a comprehensive review 4. Highlighting major issues with quantum computing in healthcare 5. Privacy and security for 6G's IoT-connected future in the age of quantum computing 6. Can quantum computers revolutionize health systems? 7. Industrial automation and quantum computing 8. Applications of quantum computing in financial planning and financial control 9. Quantum computing in machine learning: an overview 10. The impact of AI and automation on income inequality in BRICS countries and the role of structural factors and women's empowerment 11. Quantum computing and machine learning: a symbiotic relationship 12. Quantum-secured healthcare data and cybersecurity innovations in the era of Industry 5.0 13. Introduction to quantum computing and its revolution in industry and society 14. Advancing healthcare through the opportunities and challenges of quantum computing 15. Quantum computing in drug and chemical En ligne : https://research.ebsco.com/linkprocessor/plink?id=255a8868-b48e-3d61-8e97-da88e0 [...]

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Intelligent and sustainable engineering systems for industry 4.0 and beyond (2025)  

Public ISBD Titre : Intelligent and sustainable engineering systems for industry 4.0 and beyond Type de document : document électronique Auteurs : Tulsi Pawan Fowdur, Éditeur scientifique ; Dragorad A. Milovanovic, Éditeur scientifique ; Zoran S. Bojkovic, Éditeur scientifique Editeur : London ; New York ; Boca Raton : CRC Press Année de publication : 2025 Importance : 1 fichier PDF ISBN/ISSN/EAN : 978-1-04-030218-7 Note générale : Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École. Index Langues : Anglais (eng) Mots-clés : Industry 4.0 Sustainable engineering Artificial intelligence -- Industry 4.0 Index. décimale : 004.896 Intelligence artificielle dans les systèmes industriels Résumé : The Fourth Industrial Revolution, Industry 4.0, aims to significantly improve the flexibility, versatility, usability and efficiency of future smart factories. However, the concept of Industry 4.0 is not only limited to the factory but also encompasses the entire life cycle of the product, that is, from production and suppliers, to end users. Industry 4.0 delivers seamless vertical and horizontal integration down the entire value chain and across all layers of the automation pyramid.Industry 4.0 has its roots in a project for the high?tech strategy of the German Government back in 2011, which led to the progression of cyber-physical systems into cyber-physical production systems (CPPS). CPPS can make intelligent decisions through real?time communication and cooperation between manufacturing entities. Smart Factory, which is based on CPPS and artificial intelligence (AI), is one of the key associated initiatives of Industry 4.0. This enables flexible production of high?quality personalized products with mass efficiency. Another important aspect of Industry 4.0 is sustainable engineering systems that can help make its processes align with the United Nations Sustainable Development Goals (UN SDGs). Sustainable and intelligent engineering systems such as 5G, Industrial IoT, robotics and automation, renewable energy, logistics and even intelligent waste management can be the main enablers of Industry 4.0.This is a multidisciplinary book and is meant for anyone with a basic engineering background interested in acquiring a solid foundation in the fundamental concepts and state?of?the?art research trends in Industry 4.0. It explores the application of AI and machine learning as well as sustainable engineering systems, which can be the main drivers for Industry 4.0 and beyond and have a significant impact on the UN SDGs. Note de contenu : Summary : 1. Industry 4.0, artificial intelligence and the un sdgs 2. 5g/6g-based sustainable systems for industry 4.0 3. Industrial internet of things for industry 4.0 4. Robotics and automation in industry 4.0: emerging trends and research directions 5. A paradigm shift towards sustainable production with additive manufacturing in the industry 4.0 era 6. Predictive maintenance for industry 4.0 7. Enabling technologies with industry 4.0 for renewable energy in africa 8. Solid waste management and industry 4.0: case study in mauritius 9. Logistic management and industry 4.0: within the construction industry 10. Drone technology in industry 4.0: challenges and obstacles in urban environments 11. Smart digitalization technologies for future resilient and sustainable energy systems 12. Blockchain technology integration in industry 4.0: challenges and potential solutions 13. Exploring 5g/6g energy-efficiency in mobile communications for sustainable future 14. Ai-powered agile project management in sustainable development of industry 4.0 15. Industry 4.0 intelligence on the edge En ligne : https://research.ebsco.com/linkprocessor/plink?id=ebb10246-67ce-36bf-bb28-278e01 [...] Intelligent and sustainable engineering systems for industry 4.0 and beyond [document électronique] / Tulsi Pawan Fowdur, Éditeur scientifique ; Dragorad A. Milovanovic, Éditeur scientifique ; Zoran S. Bojkovic, Éditeur scientifique . - London ; New York ; Boca Raton : CRC Press, 2025 . - 1 fichier PDF. ISBN : 978-1-04-030218-7 Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École. Index Langues : Anglais (eng) Mots-clés : Industry 4.0 Sustainable engineering Artificial intelligence -- Industry 4.0 Index. décimale : 004.896 Intelligence artificielle dans les systèmes industriels Résumé : The Fourth Industrial Revolution, Industry 4.0, aims to significantly improve the flexibility, versatility, usability and efficiency of future smart factories. However, the concept of Industry 4.0 is not only limited to the factory but also encompasses the entire life cycle of the product, that is, from production and suppliers, to end users. Industry 4.0 delivers seamless vertical and horizontal integration down the entire value chain and across all layers of the automation pyramid.Industry 4.0 has its roots in a project for the high?tech strategy of the German Government back in 2011, which led to the progression of cyber-physical systems into cyber-physical production systems (CPPS). CPPS can make intelligent decisions through real?time communication and cooperation between manufacturing entities. Smart Factory, which is based on CPPS and artificial intelligence (AI), is one of the key associated initiatives of Industry 4.0. This enables flexible production of high?quality personalized products with mass efficiency. Another important aspect of Industry 4.0 is sustainable engineering systems that can help make its processes align with the United Nations Sustainable Development Goals (UN SDGs). Sustainable and intelligent engineering systems such as 5G, Industrial IoT, robotics and automation, renewable energy, logistics and even intelligent waste management can be the main enablers of Industry 4.0.This is a multidisciplinary book and is meant for anyone with a basic engineering background interested in acquiring a solid foundation in the fundamental concepts and state?of?the?art research trends in Industry 4.0. It explores the application of AI and machine learning as well as sustainable engineering systems, which can be the main drivers for Industry 4.0 and beyond and have a significant impact on the UN SDGs. Note de contenu : Summary : 1. Industry 4.0, artificial intelligence and the un sdgs 2. 5g/6g-based sustainable systems for industry 4.0 3. Industrial internet of things for industry 4.0 4. Robotics and automation in industry 4.0: emerging trends and research directions 5. A paradigm shift towards sustainable production with additive manufacturing in the industry 4.0 era 6. Predictive maintenance for industry 4.0 7. Enabling technologies with industry 4.0 for renewable energy in africa 8. Solid waste management and industry 4.0: case study in mauritius 9. Logistic management and industry 4.0: within the construction industry 10. Drone technology in industry 4.0: challenges and obstacles in urban environments 11. Smart digitalization technologies for future resilient and sustainable energy systems 12. Blockchain technology integration in industry 4.0: challenges and potential solutions 13. Exploring 5g/6g energy-efficiency in mobile communications for sustainable future 14. Ai-powered agile project management in sustainable development of industry 4.0 15. Industry 4.0 intelligence on the edge En ligne : https://research.ebsco.com/linkprocessor/plink?id=ebb10246-67ce-36bf-bb28-278e01 [...]

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Machine learning on geographical data using Python / Joos Korstanje (2023)

Public ISBD Titre : Machine learning on geographical data using Python : introduction into geodata with applications and use cases Type de document : texte imprimé Auteurs : Joos Korstanje, Auteur Editeur : New York : Apress Année de publication : 2023 Importance : XV, 312 p. Présentation : ill. Format : 25 cm ISBN/ISSN/EAN : 978-1-4842-8286-1 Note générale : Index Langues : Anglais (eng) Mots-clés : Geodatabases Machine learning Python (Computer program language) Index. décimale : 004.89 Systèmes d'application d'intelligence artificielle. Systèmes basés sur la connaissance intelligente. Résumé : Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting, buffering, merging, dissolving, and erasing, with implementations in Python. Use cases and examples are included. The book also focuses on applying more advanced machine learning approaches to geographical data and presents interpolation, classification, regression, and clustering via examples and use cases. This book is your go-to resource for machine learning on geodata. It presents the basics of working with spatial data and advanced applications. Examples are presented using code (accessible at github.com/Apress/machine-learning-geographic-data-python) and facilitate learning by application. Note de contenu : Summary : Part I: General introduction Chapter 1: Introduction to Geodata Chapter 2: Coordinate Systems and Projections Chapter 3: Geodata Data Types Chapter 4: Creating Maps Part II: GIS operations Chapter 5: Clipping and Intersecting Chapter 6: Buffering Chapter 7: Merge and Dissolve Chapter 8: Erase Part III: Machine Learning and mathematics Chapter 9: Interpolation Chapter 10: Classification Chapter 11: Regression Chapter 12: Clustering Chapter 13: Conclusion Machine learning on geographical data using Python : introduction into geodata with applications and use cases [texte imprimé] / Joos Korstanje, Auteur . - New York : Apress, 2023 . - XV, 312 p. : ill. ; 25 cm. ISBN : 978-1-4842-8286-1 Index Langues : Anglais (eng) Mots-clés : Geodatabases Machine learning Python (Computer program language) Index. décimale : 004.89 Systèmes d'application d'intelligence artificielle. Systèmes basés sur la connaissance intelligente. Résumé : Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting, buffering, merging, dissolving, and erasing, with implementations in Python. Use cases and examples are included. The book also focuses on applying more advanced machine learning approaches to geographical data and presents interpolation, classification, regression, and clustering via examples and use cases. This book is your go-to resource for machine learning on geodata. It presents the basics of working with spatial data and advanced applications. Examples are presented using code (accessible at github.com/Apress/machine-learning-geographic-data-python) and facilitate learning by application. Note de contenu : Summary : Part I: General introduction Chapter 1: Introduction to Geodata Chapter 2: Coordinate Systems and Projections Chapter 3: Geodata Data Types Chapter 4: Creating Maps Part II: GIS operations Chapter 5: Clipping and Intersecting Chapter 6: Buffering Chapter 7: Merge and Dissolve Chapter 8: Erase Part III: Machine Learning and mathematics Chapter 9: Interpolation Chapter 10: Classification Chapter 11: Regression Chapter 12: Clustering Chapter 13: Conclusion

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TensorFlow pour le deep learning / Bharath Ramsundar (2018)

Public ISBD Titre : TensorFlow pour le deep learning : de la régression linéaire à l'apprentissage par renforcement Type de document : texte imprimé Auteurs : Bharath Ramsundar, Auteur ; Reza Bosagh Zadeh, Auteur ; Daniel Rougé (1952-2020 ; mathématicien), Traducteur Editeur : Paris : First Interactive Année de publication : 2018 Importance : XII, 245 p. Présentation : ill. Format : 23 cm ISBN/ISSN/EAN : 978-2-412-04116-1 Note générale : Sur la page de titre et la couverture l'éditeur (O'Reilly) de l'édition originale. - Index Langues : Français (fre) Langues originales : Anglais (eng) Mots-clés : TensorFlow (logiciel) Apprentissage profond Apprentissage automatique Index. décimale : 004.8 Intelligence artificielle Résumé : "Apprenez à résoudre des problèmes d'apprentissage automatique (même difficiles !) avec TensorFlow, la nouvelle bibliothèque logicielle révolutionnaire de Google pour le deep learning. Si vous avez une formation de base en algèbre linéaire et en calcul, ce livre pratique vous introduit dans les arcanes des principes fondamentaux de l'apprentissage automatique en vous montrant comment concevoir des systèmes capables de détecter des objets dans des images, de comprendre du texte et de prédire les propriétés de médicaments potentiels." (4e de couverture) Note de contenu : Au sommaires : 1. Introduction au deep learning 2. Introduction aux primitives de tensorFlow 3. Régression linéaires et logistique avec TensorFlow 4. Réseaux profonds entièrement connectés 5. Optimiser les hyperparamètres 6. Réseaux de neurones convolutifs 7. Réseaux de neurones récurrents 8. Apprentissage par renforcement 9. Entraîner de grands réseaux profonds 10. L'avenir du deep learning TensorFlow pour le deep learning : de la régression linéaire à l'apprentissage par renforcement [texte imprimé] / Bharath Ramsundar, Auteur ; Reza Bosagh Zadeh, Auteur ; Daniel Rougé (1952-2020 ; mathématicien), Traducteur . - Paris : First Interactive, 2018 . - XII, 245 p. : ill. ; 23 cm. ISBN : 978-2-412-04116-1 Sur la page de titre et la couverture l'éditeur (O'Reilly) de l'édition originale. - Index Langues : Français (fre) Langues originales : Anglais (eng) Mots-clés : TensorFlow (logiciel) Apprentissage profond Apprentissage automatique Index. décimale : 004.8 Intelligence artificielle Résumé : "Apprenez à résoudre des problèmes d'apprentissage automatique (même difficiles !) avec TensorFlow, la nouvelle bibliothèque logicielle révolutionnaire de Google pour le deep learning. Si vous avez une formation de base en algèbre linéaire et en calcul, ce livre pratique vous introduit dans les arcanes des principes fondamentaux de l'apprentissage automatique en vous montrant comment concevoir des systèmes capables de détecter des objets dans des images, de comprendre du texte et de prédire les propriétés de médicaments potentiels." (4e de couverture) Note de contenu : Au sommaires : 1. Introduction au deep learning 2. Introduction aux primitives de tensorFlow 3. Régression linéaires et logistique avec TensorFlow 4. Réseaux profonds entièrement connectés 5. Optimiser les hyperparamètres 6. Réseaux de neurones convolutifs 7. Réseaux de neurones récurrents 8. Apprentissage par renforcement 9. Entraîner de grands réseaux profonds 10. L'avenir du deep learning

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The convergence of artificial intelligence and blockchain technologies / Sam Goundar (2022)  

Public ISBD Titre : The convergence of artificial intelligence and blockchain technologies : challenges and opportunities Type de document : document électronique Auteurs : Sam Goundar, Auteur ; G. Suseendran, Auteur ; R. Anandan, Auteur Editeur : Singapore : World Scientific Publishing Année de publication : 2022 Importance : 1 fichier PDF Présentation : ill. ISBN/ISSN/EAN : 978-981-12-2507-9 Note générale : Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École Bibliogr. at the end of chapters . - Index Mots-clés : Internet of things--Case studies 5G mobile communication systems--Case studies Convergence (Telecommunication)--Case studies Multiagent systems--Case studies Blockchains (Databases)--Case studies Artificial intelligence--Case studies Big data--Case studies Index. décimale : 004.8:004.75 Intelligence artificielle avec le blockchain Résumé : This book covers the growing convergence between Blockchain and Artificial Intelligence for Big Data, Multi-Agent systems, the Internet of Things and 5G technologies. Using real case studies and project outcomes, it illustrates the intricate details of blockchain in these real-life scenarios. The contributions from this volume bring a state-of-the-art assessment of these rapidly evolving trends in a creative way and provide a key resource for all those involved in the study and practice of AI and Blockchain. Note de contenu : Summary : 1. An overview of blockchain technology: fundamental theories and concepts. 2. Blockchain and artificial intelligent for internet of things in E-HEALTH. 3. Data management and industries automatization using blockchain, AI, and IoT. 4. Using frost filterative fuzzified gravitational search-based shift invariant deep feature learning with blockchain for distributed pattern recognition. 5. The impact of blockchain on cloud and AI. 6. Blockchain-based secure authentication for an intelligent e-learning framework with optimized learning objects. 7. Indian corporate governance with relation to sarbanes oxley (sox) act: proposing business intelligence (bi) and blockchain as an integrated key strategy. En ligne : https://research.ebsco.com/linkprocessor/plink?id=8e942918-9318-3f03-afac-ca0a9b [...] The convergence of artificial intelligence and blockchain technologies : challenges and opportunities [document électronique] / Sam Goundar, Auteur ; G. Suseendran, Auteur ; R. Anandan, Auteur . - Singapore : World Scientific Publishing, 2022 . - 1 fichier PDF : ill. ISBN : 978-981-12-2507-9 Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École Bibliogr. at the end of chapters . - Index Mots-clés : Internet of things--Case studies 5G mobile communication systems--Case studies Convergence (Telecommunication)--Case studies Multiagent systems--Case studies Blockchains (Databases)--Case studies Artificial intelligence--Case studies Big data--Case studies Index. décimale : 004.8:004.75 Intelligence artificielle avec le blockchain Résumé : This book covers the growing convergence between Blockchain and Artificial Intelligence for Big Data, Multi-Agent systems, the Internet of Things and 5G technologies. Using real case studies and project outcomes, it illustrates the intricate details of blockchain in these real-life scenarios. The contributions from this volume bring a state-of-the-art assessment of these rapidly evolving trends in a creative way and provide a key resource for all those involved in the study and practice of AI and Blockchain. Note de contenu : Summary : 1. An overview of blockchain technology: fundamental theories and concepts. 2. Blockchain and artificial intelligent for internet of things in E-HEALTH. 3. Data management and industries automatization using blockchain, AI, and IoT. 4. Using frost filterative fuzzified gravitational search-based shift invariant deep feature learning with blockchain for distributed pattern recognition. 5. The impact of blockchain on cloud and AI. 6. Blockchain-based secure authentication for an intelligent e-learning framework with optimized learning objects. 7. Indian corporate governance with relation to sarbanes oxley (sox) act: proposing business intelligence (bi) and blockchain as an integrated key strategy. En ligne : https://research.ebsco.com/linkprocessor/plink?id=8e942918-9318-3f03-afac-ca0a9b [...]

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Understanding deep learning / Simon J.D. Prince (2023)  

Public ISBD Titre : Understanding deep learning Type de document : document électronique Auteurs : Simon J.D. Prince, Auteur Editeur : London : The MIT Press Année de publication : 2023 Importance : 1 fichier PDF Présentation : ill. ISBN/ISSN/EAN : 978-0-262-37710-2 Note générale : Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École Annexe p. 487 - 513 . - Bibliogr. p. 514 - 563 Mots-clés : Deep learning (Machine learning) Index. décimale : 004.8 Intelligence artificielle Résumé : An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.Deep learning is a fast-moving field with sweeping relevance in today's increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion modelsShort, focused chapters progress in complexity, easing students into difficult concepts Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of modelsStreamlined presentation separates critical ideas from background context and extraneous detailMinimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible Programming exercises offered in accompanying Python Notebooks Note de contenu : Summary : 1. Supervised learning. 2. Shallow neural networks. 3. Deep neural networks. 4. Loss functions. 5. Fitting models. 6. Gradients and initialization. 7. Measuring performance. 8. Regularization. 9. Convolutional networks. 10. Residual networks. ... En ligne : https://research.ebsco.com/linkprocessor/plink?id=ef37b04b-48ed-321a-a4bc-c73a26 [...] Understanding deep learning [document électronique] / Simon J.D. Prince, Auteur . - London : The MIT Press, 2023 . - 1 fichier PDF : ill. ISBN : 978-0-262-37710-2 Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École Annexe p. 487 - 513 . - Bibliogr. p. 514 - 563 Mots-clés : Deep learning (Machine learning) Index. décimale : 004.8 Intelligence artificielle Résumé : An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.Deep learning is a fast-moving field with sweeping relevance in today's increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion modelsShort, focused chapters progress in complexity, easing students into difficult concepts Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of modelsStreamlined presentation separates critical ideas from background context and extraneous detailMinimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible Programming exercises offered in accompanying Python Notebooks Note de contenu : Summary : 1. Supervised learning. 2. Shallow neural networks. 3. Deep neural networks. 4. Loss functions. 5. Fitting models. 6. Gradients and initialization. 7. Measuring performance. 8. Regularization. 9. Convolutional networks. 10. Residual networks. ... En ligne : https://research.ebsco.com/linkprocessor/plink?id=ef37b04b-48ed-321a-a4bc-c73a26 [...]

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