Technology Innovation Pillars for Industry 4. 0: Challenges, Improvements, and Case Studies
Technology Innovation Pillars for Industry 4.0: Challenges, Improvements, and Case Studies discusses the latest innovations in the application of technologies to Industry 4.0 and the nine pillars and how they relate, support, and bridge the gap between the digital and physical worlds we live in
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton
Taylor & Francis Group
2024
|
| Ausgabe: | 1st ed |
| Schriftenreihe: | Intelligent Manufacturing and Industrial Engineering Series
|
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Zusammenfassung: | Technology Innovation Pillars for Industry 4.0: Challenges, Improvements, and Case Studies discusses the latest innovations in the application of technologies to Industry 4.0 and the nine pillars and how they relate, support, and bridge the gap between the digital and physical worlds we live in |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (181 Seiten) |
| ISBN: | 9781040049778 |
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| 505 | 8 | |a Cover -- Half Title -- Series Information -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the Editors -- List of Contributors -- Artificial Intelligence -- 1 Role of Artificial Intelligence in Telecommunication Systems: A Healthcare Perspective -- 1.1 Introduction -- 1.2 AI in Telecommunication -- 1.2.1 Predictive Maintenance -- 1.2.2 Network Optimization -- 1.2.3 Virtual Assistants and Chatbots -- 1.2.4 Fraud Detection and Prevention -- 1.2.5 Robotic Process Automation (RPA) -- 1.3 The Growing Role of Artificial Intelligence in Telehealth -- 1.3.1 Giving a More Accurate Diagnosis -- 1.3.2 Holding Doctors Back From Burning Out -- 1.3.3 Furnishing Elderly Patients With Better Medical Care -- 1.3.4 Patient Monitoring Convenience -- 1.3.5 Making Hospital Visits Easier -- 1.4 Developing Requirement for Virtual Consideration -- 1.4.1 Telehealth and Telemedicine Defined -- 1.4.2 Benefits for Patients -- 1.4.3 Benefits for Providers -- 1.4.4 Strengthening and the E-Patient -- 1.5 Significance of AI -- 1.6 Artificial Intelligence in Telehealth -- 1.7 How Is Artificial Intelligence Utilized in Medication? -- 1.7.1 AI for Infection Detection and Diagnosis -- 1.7.2 Customized Sickness Therapy -- 1.7.3 Artificial Intelligence in Clinical Imaging -- 1.7.4 Clinical Preliminary Effectiveness -- 1.7.5 Speed Up Drug Improvement -- 1.8 Artificial Intelligence in Medical Diagnosis -- 1.9 Artificial Intelligence in Medicine -- 1.9.1 Diagnose Diseases -- 1.9.1.1 How Machines Figure Out How to Diagnose -- 1.9.2 Develop Drugs Quicker -- 1.9.3 Customize Treatment -- 1.9.4 Improve Quality Change -- 1.10 Pros and Cons of Artificial Intelligence in Healthcare -- 1.11 Conclusion -- References -- 2 An Intelligent System Utilizing Bipolar Fuzzy Logic for Ensuring Semantic Interoperability and Privacy Preservation in Healthcare Systems | |
| 505 | 8 | |a 2.1 Introduction -- 2.2 Literature Review -- 2.3 Proposed System -- 2.3.1 BFS and Similarity Measure of Choquet Cosine -- 2.3.2 Decomposition of Singular Values -- 2.3.3 Computable Encryption -- 2.3.4 Medical Language -- 2.3.5 Interoperability Semantic Module -- 2.3.6 Conflict Resolution -- 2.3.7 Association of Vague Synonym Sets -- 2.3.8 Fuzzy Hypernymy and Fuzzy Hyponymy -- 2.3.9 HSDF (Healthcare Sign Description Framework) Development -- 2.3.10 Module for Generating EHR Vectors -- 2.3.11 The Encryption Stage -- 2.3.12 Phase of Decryption -- 2.4 Execution and Debate -- 2.4.1 Time for Calculation -- 2.4.1.1 Time for Encryption -- 2.4.1.2 Time for Decryption -- 2.4.1.3 Time of Communication -- 2.4.1.4 Timeframe for Completion -- 2.4.2 Time for Updating the Inverted Index -- 2.4.2.1 Linguistic Processing's Influence -- 2.4.2.2 Comparison of Index Structures -- 2.4.2.3 Data Anonymization -- 2.4.3 Security Analysis of Key Players -- 2.4.4 Contributions -- 2.5 Conclusion -- References -- 3 Graph Optimizations in Neural Networks By ONNX Model -- 3.1 Introduction -- 3.1.1 Graph and Optimization -- 3.2 Literature Survey -- 3.3 Methodology -- 3.3.1 Graph Optimization in ONNX -- 3.4 Results -- 3.4.1 Image Classification -- 3.4.2 Converting Into ONNX -- 3.5 Conclusion -- References -- 4 Convolutional Neural Network Architecture for Accurate Plant Classification -- 4.1 Introduction -- 4.2 Modeling of Proposed CNN-PC Model -- 4.3 System Architecture -- 4.4 CNN-PC Algorithm -- 4.5 Plant Dataset -- 4.6 Evaluation -- 4.7 Conclusion -- References -- Big Data Analytics -- 5 Big Data Visualizing With Augmented and Virtual Reality: Challenges and Research Agenda -- 5.1 Introduction -- 5.1.1 Big Data -- 5.1.2 Big Data Processing Methods -- 5.1.3 Augmented Reality -- 5.2 Augmented Reality (AR)-Based Visualization and Situated Visualization | |
| 505 | 8 | |a 5.2.1 Augmented Reality Visualization -- 5.2.2 Situated Visualization -- 5.2.3 Challenges With Augmented Reality Implementation -- 5.3 Data Visualization Methods -- 5.3.1 Classification -- 5.3.2 Analysis of Big Data Visualization Approaches -- 5.3.3 Big Data, IoT, and AR: Technology Convergence in Visualization Issues -- 5.4 Augmented Reality for Big Data -- 5.4.1 Retail and Banking -- 5.4.2 Healthcare -- 5.4.3 Industry -- 5.5 Issues and Challenges -- 5.5.1 Future Research Agenda and Data Visualization Challenges -- 5.6 Conclusion -- References -- Cloud and Security -- 6 Mathematical Model for Service-Selection Optimization and Scheduling in Cloud Manufacturing Using Sub-Task Scheduling With Fuzzy Inference Rule -- 6.1 Introduction -- 6.2 Problem Description -- 6.3 Service Selection -- 6.4 Overall Objective Function -- 6.5 Result and Discussion -- 6.6 Conclusion -- References -- IoT -- 7 Social Media Initiatives Through IoT to Link the Bridge Between Industrial Demands With Higher Education Millennial Students Through Experience Learning -- 7.1 Introduction -- 7.2 Research Gap -- 7.3 Objective -- 7.4 Review of Literature -- 7.5 Theoretical Framework -- 7.6 System Diagram -- 7.7 Conclusion -- References -- Digitization of Industrial Processes -- 8 Analyzing Consumer Product Feedback Dynamics With Confidence Intervals -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 Data Set -- 8.4 Data Pre-Processing -- 8.5 Proposed System -- 8.6 Implementation -- 8.7 Results -- 8.8 Conclusion -- References -- Information System in Industry -- 9 Amplifying the Effectiveness of a Learning Management System: Exploring the Impact of NEP-Compliant Curriculum Changes On Higher Education Institutions -- Objectives -- 9.1 Introduction -- 9.2 Significance of the Study -- 9.3 Review of Literature -- 9.3.1 National Education Policy and Curriculum Changes | |
| 505 | 8 | |a 9.3.2 Challenges and Opportunities of NEP-Aligned Curriculum On LMS Platforms -- 9.3.3 Current Gaps and Research Directions -- 9.4 Research Methodology -- 9.4.1 Research Design -- 9.4.2 Data Collection -- 9.4.3 Data Analysis -- 9.4.4 Presentation and Interpretation of Data -- 9.5 Limitations -- 9.6 Significance -- 9.7 Conclusion -- Bibliography -- Additive Manufacturing -- 10 The Future of Immersive Experience: Exploring Metaverse Application Development Technologies and Tools -- 10.1 Introduction -- 10.2 Literature Survey -- 10.3 Metaverse Architecture -- 10.4 Technologies and Tools for Metaverse Application Development -- 10.4.1 Game Engines (Unity, Unreal) -- 10.4.2 Virtual Reality and Augmented Reality Technologies -- 10.4.3 Blockchain Technology and Smart Contracts -- 10.4.4 Decentralized Platforms (Decentraland, Somnium Space) -- 10.5 Artificial Intelligence -- 10.6 Future System for Metaverse Application Development -- 10.6.1 Integration of Technologies and Tools for a Fully Immersive and Decentralized Virtual World -- 10.6.2 Use of Game Engines to Create 3D Environments and Objects -- 10.6.3 Integration of Virtual Reality and Augmented Reality Technologies for an Immersive Experience -- 10.6.4 Use of Blockchain Technology and Smart Contracts for User Ownership of Virtual Assets and Transactions -- 10.6.5 Incorporation of Decentralized Platforms for Interconnected Virtual Worlds -- 10.6.6 Use of Decentralized Autonomous Organizations (DAOs) for FAIR and Responsible Governance -- 10.6.7 Utilization of Artificial Intelligence to Create Intelligent NPCs for a More Realistic and Personalized Experience -- 10.7 Metaverse Security Challenges -- 10.8 Discussion -- 10.9 Conclusion -- References -- Index | |
| 520 | |a Technology Innovation Pillars for Industry 4.0: Challenges, Improvements, and Case Studies discusses the latest innovations in the application of technologies to Industry 4.0 and the nine pillars and how they relate, support, and bridge the gap between the digital and physical worlds we live in | ||
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Datensatz im Suchindex
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| author | Elngar, Ahmed A. |
| author_facet | Elngar, Ahmed A. |
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| contents | Cover -- Half Title -- Series Information -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the Editors -- List of Contributors -- Artificial Intelligence -- 1 Role of Artificial Intelligence in Telecommunication Systems: A Healthcare Perspective -- 1.1 Introduction -- 1.2 AI in Telecommunication -- 1.2.1 Predictive Maintenance -- 1.2.2 Network Optimization -- 1.2.3 Virtual Assistants and Chatbots -- 1.2.4 Fraud Detection and Prevention -- 1.2.5 Robotic Process Automation (RPA) -- 1.3 The Growing Role of Artificial Intelligence in Telehealth -- 1.3.1 Giving a More Accurate Diagnosis -- 1.3.2 Holding Doctors Back From Burning Out -- 1.3.3 Furnishing Elderly Patients With Better Medical Care -- 1.3.4 Patient Monitoring Convenience -- 1.3.5 Making Hospital Visits Easier -- 1.4 Developing Requirement for Virtual Consideration -- 1.4.1 Telehealth and Telemedicine Defined -- 1.4.2 Benefits for Patients -- 1.4.3 Benefits for Providers -- 1.4.4 Strengthening and the E-Patient -- 1.5 Significance of AI -- 1.6 Artificial Intelligence in Telehealth -- 1.7 How Is Artificial Intelligence Utilized in Medication? -- 1.7.1 AI for Infection Detection and Diagnosis -- 1.7.2 Customized Sickness Therapy -- 1.7.3 Artificial Intelligence in Clinical Imaging -- 1.7.4 Clinical Preliminary Effectiveness -- 1.7.5 Speed Up Drug Improvement -- 1.8 Artificial Intelligence in Medical Diagnosis -- 1.9 Artificial Intelligence in Medicine -- 1.9.1 Diagnose Diseases -- 1.9.1.1 How Machines Figure Out How to Diagnose -- 1.9.2 Develop Drugs Quicker -- 1.9.3 Customize Treatment -- 1.9.4 Improve Quality Change -- 1.10 Pros and Cons of Artificial Intelligence in Healthcare -- 1.11 Conclusion -- References -- 2 An Intelligent System Utilizing Bipolar Fuzzy Logic for Ensuring Semantic Interoperability and Privacy Preservation in Healthcare Systems 2.1 Introduction -- 2.2 Literature Review -- 2.3 Proposed System -- 2.3.1 BFS and Similarity Measure of Choquet Cosine -- 2.3.2 Decomposition of Singular Values -- 2.3.3 Computable Encryption -- 2.3.4 Medical Language -- 2.3.5 Interoperability Semantic Module -- 2.3.6 Conflict Resolution -- 2.3.7 Association of Vague Synonym Sets -- 2.3.8 Fuzzy Hypernymy and Fuzzy Hyponymy -- 2.3.9 HSDF (Healthcare Sign Description Framework) Development -- 2.3.10 Module for Generating EHR Vectors -- 2.3.11 The Encryption Stage -- 2.3.12 Phase of Decryption -- 2.4 Execution and Debate -- 2.4.1 Time for Calculation -- 2.4.1.1 Time for Encryption -- 2.4.1.2 Time for Decryption -- 2.4.1.3 Time of Communication -- 2.4.1.4 Timeframe for Completion -- 2.4.2 Time for Updating the Inverted Index -- 2.4.2.1 Linguistic Processing's Influence -- 2.4.2.2 Comparison of Index Structures -- 2.4.2.3 Data Anonymization -- 2.4.3 Security Analysis of Key Players -- 2.4.4 Contributions -- 2.5 Conclusion -- References -- 3 Graph Optimizations in Neural Networks By ONNX Model -- 3.1 Introduction -- 3.1.1 Graph and Optimization -- 3.2 Literature Survey -- 3.3 Methodology -- 3.3.1 Graph Optimization in ONNX -- 3.4 Results -- 3.4.1 Image Classification -- 3.4.2 Converting Into ONNX -- 3.5 Conclusion -- References -- 4 Convolutional Neural Network Architecture for Accurate Plant Classification -- 4.1 Introduction -- 4.2 Modeling of Proposed CNN-PC Model -- 4.3 System Architecture -- 4.4 CNN-PC Algorithm -- 4.5 Plant Dataset -- 4.6 Evaluation -- 4.7 Conclusion -- References -- Big Data Analytics -- 5 Big Data Visualizing With Augmented and Virtual Reality: Challenges and Research Agenda -- 5.1 Introduction -- 5.1.1 Big Data -- 5.1.2 Big Data Processing Methods -- 5.1.3 Augmented Reality -- 5.2 Augmented Reality (AR)-Based Visualization and Situated Visualization 5.2.1 Augmented Reality Visualization -- 5.2.2 Situated Visualization -- 5.2.3 Challenges With Augmented Reality Implementation -- 5.3 Data Visualization Methods -- 5.3.1 Classification -- 5.3.2 Analysis of Big Data Visualization Approaches -- 5.3.3 Big Data, IoT, and AR: Technology Convergence in Visualization Issues -- 5.4 Augmented Reality for Big Data -- 5.4.1 Retail and Banking -- 5.4.2 Healthcare -- 5.4.3 Industry -- 5.5 Issues and Challenges -- 5.5.1 Future Research Agenda and Data Visualization Challenges -- 5.6 Conclusion -- References -- Cloud and Security -- 6 Mathematical Model for Service-Selection Optimization and Scheduling in Cloud Manufacturing Using Sub-Task Scheduling With Fuzzy Inference Rule -- 6.1 Introduction -- 6.2 Problem Description -- 6.3 Service Selection -- 6.4 Overall Objective Function -- 6.5 Result and Discussion -- 6.6 Conclusion -- References -- IoT -- 7 Social Media Initiatives Through IoT to Link the Bridge Between Industrial Demands With Higher Education Millennial Students Through Experience Learning -- 7.1 Introduction -- 7.2 Research Gap -- 7.3 Objective -- 7.4 Review of Literature -- 7.5 Theoretical Framework -- 7.6 System Diagram -- 7.7 Conclusion -- References -- Digitization of Industrial Processes -- 8 Analyzing Consumer Product Feedback Dynamics With Confidence Intervals -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 Data Set -- 8.4 Data Pre-Processing -- 8.5 Proposed System -- 8.6 Implementation -- 8.7 Results -- 8.8 Conclusion -- References -- Information System in Industry -- 9 Amplifying the Effectiveness of a Learning Management System: Exploring the Impact of NEP-Compliant Curriculum Changes On Higher Education Institutions -- Objectives -- 9.1 Introduction -- 9.2 Significance of the Study -- 9.3 Review of Literature -- 9.3.1 National Education Policy and Curriculum Changes 9.3.2 Challenges and Opportunities of NEP-Aligned Curriculum On LMS Platforms -- 9.3.3 Current Gaps and Research Directions -- 9.4 Research Methodology -- 9.4.1 Research Design -- 9.4.2 Data Collection -- 9.4.3 Data Analysis -- 9.4.4 Presentation and Interpretation of Data -- 9.5 Limitations -- 9.6 Significance -- 9.7 Conclusion -- Bibliography -- Additive Manufacturing -- 10 The Future of Immersive Experience: Exploring Metaverse Application Development Technologies and Tools -- 10.1 Introduction -- 10.2 Literature Survey -- 10.3 Metaverse Architecture -- 10.4 Technologies and Tools for Metaverse Application Development -- 10.4.1 Game Engines (Unity, Unreal) -- 10.4.2 Virtual Reality and Augmented Reality Technologies -- 10.4.3 Blockchain Technology and Smart Contracts -- 10.4.4 Decentralized Platforms (Decentraland, Somnium Space) -- 10.5 Artificial Intelligence -- 10.6 Future System for Metaverse Application Development -- 10.6.1 Integration of Technologies and Tools for a Fully Immersive and Decentralized Virtual World -- 10.6.2 Use of Game Engines to Create 3D Environments and Objects -- 10.6.3 Integration of Virtual Reality and Augmented Reality Technologies for an Immersive Experience -- 10.6.4 Use of Blockchain Technology and Smart Contracts for User Ownership of Virtual Assets and Transactions -- 10.6.5 Incorporation of Decentralized Platforms for Interconnected Virtual Worlds -- 10.6.6 Use of Decentralized Autonomous Organizations (DAOs) for FAIR and Responsible Governance -- 10.6.7 Utilization of Artificial Intelligence to Create Intelligent NPCs for a More Realistic and Personalized Experience -- 10.7 Metaverse Security Challenges -- 10.8 Discussion -- 10.9 Conclusion -- References -- Index |
| ctrlnum | (ZDB-30-PQE)EBC31060858 (ZDB-30-PAD)EBC31060858 (ZDB-89-EBL)EBL31060858 (OCoLC)1439596140 (DE-599)BVBBV050102196 |
| dewey-full | 658.40380285 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.40380285 |
| dewey-search | 658.40380285 |
| dewey-sort | 3658.40380285 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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ind2=" "><subfield code="a">Cover -- Half Title -- Series Information -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the Editors -- List of Contributors -- Artificial Intelligence -- 1 Role of Artificial Intelligence in Telecommunication Systems: A Healthcare Perspective -- 1.1 Introduction -- 1.2 AI in Telecommunication -- 1.2.1 Predictive Maintenance -- 1.2.2 Network Optimization -- 1.2.3 Virtual Assistants and Chatbots -- 1.2.4 Fraud Detection and Prevention -- 1.2.5 Robotic Process Automation (RPA) -- 1.3 The Growing Role of Artificial Intelligence in Telehealth -- 1.3.1 Giving a More Accurate Diagnosis -- 1.3.2 Holding Doctors Back From Burning Out -- 1.3.3 Furnishing Elderly Patients With Better Medical Care -- 1.3.4 Patient Monitoring Convenience -- 1.3.5 Making Hospital Visits Easier -- 1.4 Developing Requirement for Virtual Consideration -- 1.4.1 Telehealth and Telemedicine Defined -- 1.4.2 Benefits for Patients -- 1.4.3 Benefits for Providers -- 1.4.4 Strengthening and the E-Patient -- 1.5 Significance of AI -- 1.6 Artificial Intelligence in Telehealth -- 1.7 How Is Artificial Intelligence Utilized in Medication? -- 1.7.1 AI for Infection Detection and Diagnosis -- 1.7.2 Customized Sickness Therapy -- 1.7.3 Artificial Intelligence in Clinical Imaging -- 1.7.4 Clinical Preliminary Effectiveness -- 1.7.5 Speed Up Drug Improvement -- 1.8 Artificial Intelligence in Medical Diagnosis -- 1.9 Artificial Intelligence in Medicine -- 1.9.1 Diagnose Diseases -- 1.9.1.1 How Machines Figure Out How to Diagnose -- 1.9.2 Develop Drugs Quicker -- 1.9.3 Customize Treatment -- 1.9.4 Improve Quality Change -- 1.10 Pros and Cons of Artificial Intelligence in Healthcare -- 1.11 Conclusion -- References -- 2 An Intelligent System Utilizing Bipolar Fuzzy Logic for Ensuring Semantic Interoperability and Privacy Preservation in Healthcare Systems</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2.1 Introduction -- 2.2 Literature Review -- 2.3 Proposed System -- 2.3.1 BFS and Similarity Measure of Choquet Cosine -- 2.3.2 Decomposition of Singular Values -- 2.3.3 Computable Encryption -- 2.3.4 Medical Language -- 2.3.5 Interoperability Semantic Module -- 2.3.6 Conflict Resolution -- 2.3.7 Association of Vague Synonym Sets -- 2.3.8 Fuzzy Hypernymy and Fuzzy Hyponymy -- 2.3.9 HSDF (Healthcare Sign Description Framework) Development -- 2.3.10 Module for Generating EHR Vectors -- 2.3.11 The Encryption Stage -- 2.3.12 Phase of Decryption -- 2.4 Execution and Debate -- 2.4.1 Time for Calculation -- 2.4.1.1 Time for Encryption -- 2.4.1.2 Time for Decryption -- 2.4.1.3 Time of Communication -- 2.4.1.4 Timeframe for Completion -- 2.4.2 Time for Updating the Inverted Index -- 2.4.2.1 Linguistic Processing's Influence -- 2.4.2.2 Comparison of Index Structures -- 2.4.2.3 Data Anonymization -- 2.4.3 Security Analysis of Key Players -- 2.4.4 Contributions -- 2.5 Conclusion -- References -- 3 Graph Optimizations in Neural Networks By ONNX Model -- 3.1 Introduction -- 3.1.1 Graph and Optimization -- 3.2 Literature Survey -- 3.3 Methodology -- 3.3.1 Graph Optimization in ONNX -- 3.4 Results -- 3.4.1 Image Classification -- 3.4.2 Converting Into ONNX -- 3.5 Conclusion -- References -- 4 Convolutional Neural Network Architecture for Accurate Plant Classification -- 4.1 Introduction -- 4.2 Modeling of Proposed CNN-PC Model -- 4.3 System Architecture -- 4.4 CNN-PC Algorithm -- 4.5 Plant Dataset -- 4.6 Evaluation -- 4.7 Conclusion -- References -- Big Data Analytics -- 5 Big Data Visualizing With Augmented and Virtual Reality: Challenges and Research Agenda -- 5.1 Introduction -- 5.1.1 Big Data -- 5.1.2 Big Data Processing Methods -- 5.1.3 Augmented Reality -- 5.2 Augmented Reality (AR)-Based Visualization and Situated Visualization</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">5.2.1 Augmented Reality Visualization -- 5.2.2 Situated Visualization -- 5.2.3 Challenges With Augmented Reality Implementation -- 5.3 Data Visualization Methods -- 5.3.1 Classification -- 5.3.2 Analysis of Big Data Visualization Approaches -- 5.3.3 Big Data, IoT, and AR: Technology Convergence in Visualization Issues -- 5.4 Augmented Reality for Big Data -- 5.4.1 Retail and Banking -- 5.4.2 Healthcare -- 5.4.3 Industry -- 5.5 Issues and Challenges -- 5.5.1 Future Research Agenda and Data Visualization Challenges -- 5.6 Conclusion -- References -- Cloud and Security -- 6 Mathematical Model for Service-Selection Optimization and Scheduling in Cloud Manufacturing Using Sub-Task Scheduling With Fuzzy Inference Rule -- 6.1 Introduction -- 6.2 Problem Description -- 6.3 Service Selection -- 6.4 Overall Objective Function -- 6.5 Result and Discussion -- 6.6 Conclusion -- References -- IoT -- 7 Social Media Initiatives Through IoT to Link the Bridge Between Industrial Demands With Higher Education Millennial Students Through Experience Learning -- 7.1 Introduction -- 7.2 Research Gap -- 7.3 Objective -- 7.4 Review of Literature -- 7.5 Theoretical Framework -- 7.6 System Diagram -- 7.7 Conclusion -- References -- Digitization of Industrial Processes -- 8 Analyzing Consumer Product Feedback Dynamics With Confidence Intervals -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 Data Set -- 8.4 Data Pre-Processing -- 8.5 Proposed System -- 8.6 Implementation -- 8.7 Results -- 8.8 Conclusion -- References -- Information System in Industry -- 9 Amplifying the Effectiveness of a Learning Management System: Exploring the Impact of NEP-Compliant Curriculum Changes On Higher Education Institutions -- Objectives -- 9.1 Introduction -- 9.2 Significance of the Study -- 9.3 Review of Literature -- 9.3.1 National Education Policy and Curriculum Changes</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">9.3.2 Challenges and Opportunities of NEP-Aligned Curriculum On LMS Platforms -- 9.3.3 Current Gaps and Research Directions -- 9.4 Research Methodology -- 9.4.1 Research Design -- 9.4.2 Data Collection -- 9.4.3 Data Analysis -- 9.4.4 Presentation and Interpretation of Data -- 9.5 Limitations -- 9.6 Significance -- 9.7 Conclusion -- Bibliography -- Additive Manufacturing -- 10 The Future of Immersive Experience: Exploring Metaverse Application Development Technologies and Tools -- 10.1 Introduction -- 10.2 Literature Survey -- 10.3 Metaverse Architecture -- 10.4 Technologies and Tools for Metaverse Application Development -- 10.4.1 Game Engines (Unity, Unreal) -- 10.4.2 Virtual Reality and Augmented Reality Technologies -- 10.4.3 Blockchain Technology and Smart Contracts -- 10.4.4 Decentralized Platforms (Decentraland, Somnium Space) -- 10.5 Artificial Intelligence -- 10.6 Future System for Metaverse Application Development -- 10.6.1 Integration of Technologies and Tools for a Fully Immersive and Decentralized Virtual World -- 10.6.2 Use of Game Engines to Create 3D Environments and Objects -- 10.6.3 Integration of Virtual Reality and Augmented Reality Technologies for an Immersive Experience -- 10.6.4 Use of Blockchain Technology and Smart Contracts for User Ownership of Virtual Assets and Transactions -- 10.6.5 Incorporation of Decentralized Platforms for Interconnected Virtual Worlds -- 10.6.6 Use of Decentralized Autonomous Organizations (DAOs) for FAIR and Responsible Governance -- 10.6.7 Utilization of Artificial Intelligence to Create Intelligent NPCs for a More Realistic and Personalized Experience -- 10.7 Metaverse Security Challenges -- 10.8 Discussion -- 10.9 Conclusion -- References -- Index</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Technology Innovation Pillars for Industry 4.0: Challenges, Improvements, and Case Studies discusses the latest innovations in the application of technologies to Industry 4.0 and the nine pillars and how they relate, support, and bridge the gap between the digital and physical worlds we live in</subfield></datafield><datafield 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| id | DE-604.BV050102196 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-18T19:05:06Z |
| institution | BVB |
| isbn | 9781040049778 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035439358 |
| oclc_num | 1439596140 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (181 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Taylor & Francis Group |
| record_format | marc |
| series2 | Intelligent Manufacturing and Industrial Engineering Series |
| spelling | Elngar, Ahmed A. Verfasser aut Technology Innovation Pillars for Industry 4. 0 Challenges, Improvements, and Case Studies 1st ed Boca Raton Taylor & Francis Group 2024 ©2024 1 Online-Ressource (181 Seiten) txt rdacontent c rdamedia cr rdacarrier Intelligent Manufacturing and Industrial Engineering Series Description based on publisher supplied metadata and other sources Cover -- Half Title -- Series Information -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the Editors -- List of Contributors -- Artificial Intelligence -- 1 Role of Artificial Intelligence in Telecommunication Systems: A Healthcare Perspective -- 1.1 Introduction -- 1.2 AI in Telecommunication -- 1.2.1 Predictive Maintenance -- 1.2.2 Network Optimization -- 1.2.3 Virtual Assistants and Chatbots -- 1.2.4 Fraud Detection and Prevention -- 1.2.5 Robotic Process Automation (RPA) -- 1.3 The Growing Role of Artificial Intelligence in Telehealth -- 1.3.1 Giving a More Accurate Diagnosis -- 1.3.2 Holding Doctors Back From Burning Out -- 1.3.3 Furnishing Elderly Patients With Better Medical Care -- 1.3.4 Patient Monitoring Convenience -- 1.3.5 Making Hospital Visits Easier -- 1.4 Developing Requirement for Virtual Consideration -- 1.4.1 Telehealth and Telemedicine Defined -- 1.4.2 Benefits for Patients -- 1.4.3 Benefits for Providers -- 1.4.4 Strengthening and the E-Patient -- 1.5 Significance of AI -- 1.6 Artificial Intelligence in Telehealth -- 1.7 How Is Artificial Intelligence Utilized in Medication? -- 1.7.1 AI for Infection Detection and Diagnosis -- 1.7.2 Customized Sickness Therapy -- 1.7.3 Artificial Intelligence in Clinical Imaging -- 1.7.4 Clinical Preliminary Effectiveness -- 1.7.5 Speed Up Drug Improvement -- 1.8 Artificial Intelligence in Medical Diagnosis -- 1.9 Artificial Intelligence in Medicine -- 1.9.1 Diagnose Diseases -- 1.9.1.1 How Machines Figure Out How to Diagnose -- 1.9.2 Develop Drugs Quicker -- 1.9.3 Customize Treatment -- 1.9.4 Improve Quality Change -- 1.10 Pros and Cons of Artificial Intelligence in Healthcare -- 1.11 Conclusion -- References -- 2 An Intelligent System Utilizing Bipolar Fuzzy Logic for Ensuring Semantic Interoperability and Privacy Preservation in Healthcare Systems 2.1 Introduction -- 2.2 Literature Review -- 2.3 Proposed System -- 2.3.1 BFS and Similarity Measure of Choquet Cosine -- 2.3.2 Decomposition of Singular Values -- 2.3.3 Computable Encryption -- 2.3.4 Medical Language -- 2.3.5 Interoperability Semantic Module -- 2.3.6 Conflict Resolution -- 2.3.7 Association of Vague Synonym Sets -- 2.3.8 Fuzzy Hypernymy and Fuzzy Hyponymy -- 2.3.9 HSDF (Healthcare Sign Description Framework) Development -- 2.3.10 Module for Generating EHR Vectors -- 2.3.11 The Encryption Stage -- 2.3.12 Phase of Decryption -- 2.4 Execution and Debate -- 2.4.1 Time for Calculation -- 2.4.1.1 Time for Encryption -- 2.4.1.2 Time for Decryption -- 2.4.1.3 Time of Communication -- 2.4.1.4 Timeframe for Completion -- 2.4.2 Time for Updating the Inverted Index -- 2.4.2.1 Linguistic Processing's Influence -- 2.4.2.2 Comparison of Index Structures -- 2.4.2.3 Data Anonymization -- 2.4.3 Security Analysis of Key Players -- 2.4.4 Contributions -- 2.5 Conclusion -- References -- 3 Graph Optimizations in Neural Networks By ONNX Model -- 3.1 Introduction -- 3.1.1 Graph and Optimization -- 3.2 Literature Survey -- 3.3 Methodology -- 3.3.1 Graph Optimization in ONNX -- 3.4 Results -- 3.4.1 Image Classification -- 3.4.2 Converting Into ONNX -- 3.5 Conclusion -- References -- 4 Convolutional Neural Network Architecture for Accurate Plant Classification -- 4.1 Introduction -- 4.2 Modeling of Proposed CNN-PC Model -- 4.3 System Architecture -- 4.4 CNN-PC Algorithm -- 4.5 Plant Dataset -- 4.6 Evaluation -- 4.7 Conclusion -- References -- Big Data Analytics -- 5 Big Data Visualizing With Augmented and Virtual Reality: Challenges and Research Agenda -- 5.1 Introduction -- 5.1.1 Big Data -- 5.1.2 Big Data Processing Methods -- 5.1.3 Augmented Reality -- 5.2 Augmented Reality (AR)-Based Visualization and Situated Visualization 5.2.1 Augmented Reality Visualization -- 5.2.2 Situated Visualization -- 5.2.3 Challenges With Augmented Reality Implementation -- 5.3 Data Visualization Methods -- 5.3.1 Classification -- 5.3.2 Analysis of Big Data Visualization Approaches -- 5.3.3 Big Data, IoT, and AR: Technology Convergence in Visualization Issues -- 5.4 Augmented Reality for Big Data -- 5.4.1 Retail and Banking -- 5.4.2 Healthcare -- 5.4.3 Industry -- 5.5 Issues and Challenges -- 5.5.1 Future Research Agenda and Data Visualization Challenges -- 5.6 Conclusion -- References -- Cloud and Security -- 6 Mathematical Model for Service-Selection Optimization and Scheduling in Cloud Manufacturing Using Sub-Task Scheduling With Fuzzy Inference Rule -- 6.1 Introduction -- 6.2 Problem Description -- 6.3 Service Selection -- 6.4 Overall Objective Function -- 6.5 Result and Discussion -- 6.6 Conclusion -- References -- IoT -- 7 Social Media Initiatives Through IoT to Link the Bridge Between Industrial Demands With Higher Education Millennial Students Through Experience Learning -- 7.1 Introduction -- 7.2 Research Gap -- 7.3 Objective -- 7.4 Review of Literature -- 7.5 Theoretical Framework -- 7.6 System Diagram -- 7.7 Conclusion -- References -- Digitization of Industrial Processes -- 8 Analyzing Consumer Product Feedback Dynamics With Confidence Intervals -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 Data Set -- 8.4 Data Pre-Processing -- 8.5 Proposed System -- 8.6 Implementation -- 8.7 Results -- 8.8 Conclusion -- References -- Information System in Industry -- 9 Amplifying the Effectiveness of a Learning Management System: Exploring the Impact of NEP-Compliant Curriculum Changes On Higher Education Institutions -- Objectives -- 9.1 Introduction -- 9.2 Significance of the Study -- 9.3 Review of Literature -- 9.3.1 National Education Policy and Curriculum Changes 9.3.2 Challenges and Opportunities of NEP-Aligned Curriculum On LMS Platforms -- 9.3.3 Current Gaps and Research Directions -- 9.4 Research Methodology -- 9.4.1 Research Design -- 9.4.2 Data Collection -- 9.4.3 Data Analysis -- 9.4.4 Presentation and Interpretation of Data -- 9.5 Limitations -- 9.6 Significance -- 9.7 Conclusion -- Bibliography -- Additive Manufacturing -- 10 The Future of Immersive Experience: Exploring Metaverse Application Development Technologies and Tools -- 10.1 Introduction -- 10.2 Literature Survey -- 10.3 Metaverse Architecture -- 10.4 Technologies and Tools for Metaverse Application Development -- 10.4.1 Game Engines (Unity, Unreal) -- 10.4.2 Virtual Reality and Augmented Reality Technologies -- 10.4.3 Blockchain Technology and Smart Contracts -- 10.4.4 Decentralized Platforms (Decentraland, Somnium Space) -- 10.5 Artificial Intelligence -- 10.6 Future System for Metaverse Application Development -- 10.6.1 Integration of Technologies and Tools for a Fully Immersive and Decentralized Virtual World -- 10.6.2 Use of Game Engines to Create 3D Environments and Objects -- 10.6.3 Integration of Virtual Reality and Augmented Reality Technologies for an Immersive Experience -- 10.6.4 Use of Blockchain Technology and Smart Contracts for User Ownership of Virtual Assets and Transactions -- 10.6.5 Incorporation of Decentralized Platforms for Interconnected Virtual Worlds -- 10.6.6 Use of Decentralized Autonomous Organizations (DAOs) for FAIR and Responsible Governance -- 10.6.7 Utilization of Artificial Intelligence to Create Intelligent NPCs for a More Realistic and Personalized Experience -- 10.7 Metaverse Security Challenges -- 10.8 Discussion -- 10.9 Conclusion -- References -- Index Technology Innovation Pillars for Industry 4.0: Challenges, Improvements, and Case Studies discusses the latest innovations in the application of technologies to Industry 4.0 and the nine pillars and how they relate, support, and bridge the gap between the digital and physical worlds we live in Internet of things Internet of things-Equipment and supplies Thillaiarasu, N. Sonstige oth Saravanan, T. Sonstige oth Balas, Valentina Emilia Sonstige oth Erscheint auch als Druck-Ausgabe Elngar, Ahmed A. Technology Innovation Pillars for Industry 4. 0 Boca Raton : Taylor & Francis Group,c2024 9781032482767 |
| spellingShingle | Elngar, Ahmed A. Technology Innovation Pillars for Industry 4. 0 Challenges, Improvements, and Case Studies Cover -- Half Title -- Series Information -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the Editors -- List of Contributors -- Artificial Intelligence -- 1 Role of Artificial Intelligence in Telecommunication Systems: A Healthcare Perspective -- 1.1 Introduction -- 1.2 AI in Telecommunication -- 1.2.1 Predictive Maintenance -- 1.2.2 Network Optimization -- 1.2.3 Virtual Assistants and Chatbots -- 1.2.4 Fraud Detection and Prevention -- 1.2.5 Robotic Process Automation (RPA) -- 1.3 The Growing Role of Artificial Intelligence in Telehealth -- 1.3.1 Giving a More Accurate Diagnosis -- 1.3.2 Holding Doctors Back From Burning Out -- 1.3.3 Furnishing Elderly Patients With Better Medical Care -- 1.3.4 Patient Monitoring Convenience -- 1.3.5 Making Hospital Visits Easier -- 1.4 Developing Requirement for Virtual Consideration -- 1.4.1 Telehealth and Telemedicine Defined -- 1.4.2 Benefits for Patients -- 1.4.3 Benefits for Providers -- 1.4.4 Strengthening and the E-Patient -- 1.5 Significance of AI -- 1.6 Artificial Intelligence in Telehealth -- 1.7 How Is Artificial Intelligence Utilized in Medication? -- 1.7.1 AI for Infection Detection and Diagnosis -- 1.7.2 Customized Sickness Therapy -- 1.7.3 Artificial Intelligence in Clinical Imaging -- 1.7.4 Clinical Preliminary Effectiveness -- 1.7.5 Speed Up Drug Improvement -- 1.8 Artificial Intelligence in Medical Diagnosis -- 1.9 Artificial Intelligence in Medicine -- 1.9.1 Diagnose Diseases -- 1.9.1.1 How Machines Figure Out How to Diagnose -- 1.9.2 Develop Drugs Quicker -- 1.9.3 Customize Treatment -- 1.9.4 Improve Quality Change -- 1.10 Pros and Cons of Artificial Intelligence in Healthcare -- 1.11 Conclusion -- References -- 2 An Intelligent System Utilizing Bipolar Fuzzy Logic for Ensuring Semantic Interoperability and Privacy Preservation in Healthcare Systems 2.1 Introduction -- 2.2 Literature Review -- 2.3 Proposed System -- 2.3.1 BFS and Similarity Measure of Choquet Cosine -- 2.3.2 Decomposition of Singular Values -- 2.3.3 Computable Encryption -- 2.3.4 Medical Language -- 2.3.5 Interoperability Semantic Module -- 2.3.6 Conflict Resolution -- 2.3.7 Association of Vague Synonym Sets -- 2.3.8 Fuzzy Hypernymy and Fuzzy Hyponymy -- 2.3.9 HSDF (Healthcare Sign Description Framework) Development -- 2.3.10 Module for Generating EHR Vectors -- 2.3.11 The Encryption Stage -- 2.3.12 Phase of Decryption -- 2.4 Execution and Debate -- 2.4.1 Time for Calculation -- 2.4.1.1 Time for Encryption -- 2.4.1.2 Time for Decryption -- 2.4.1.3 Time of Communication -- 2.4.1.4 Timeframe for Completion -- 2.4.2 Time for Updating the Inverted Index -- 2.4.2.1 Linguistic Processing's Influence -- 2.4.2.2 Comparison of Index Structures -- 2.4.2.3 Data Anonymization -- 2.4.3 Security Analysis of Key Players -- 2.4.4 Contributions -- 2.5 Conclusion -- References -- 3 Graph Optimizations in Neural Networks By ONNX Model -- 3.1 Introduction -- 3.1.1 Graph and Optimization -- 3.2 Literature Survey -- 3.3 Methodology -- 3.3.1 Graph Optimization in ONNX -- 3.4 Results -- 3.4.1 Image Classification -- 3.4.2 Converting Into ONNX -- 3.5 Conclusion -- References -- 4 Convolutional Neural Network Architecture for Accurate Plant Classification -- 4.1 Introduction -- 4.2 Modeling of Proposed CNN-PC Model -- 4.3 System Architecture -- 4.4 CNN-PC Algorithm -- 4.5 Plant Dataset -- 4.6 Evaluation -- 4.7 Conclusion -- References -- Big Data Analytics -- 5 Big Data Visualizing With Augmented and Virtual Reality: Challenges and Research Agenda -- 5.1 Introduction -- 5.1.1 Big Data -- 5.1.2 Big Data Processing Methods -- 5.1.3 Augmented Reality -- 5.2 Augmented Reality (AR)-Based Visualization and Situated Visualization 5.2.1 Augmented Reality Visualization -- 5.2.2 Situated Visualization -- 5.2.3 Challenges With Augmented Reality Implementation -- 5.3 Data Visualization Methods -- 5.3.1 Classification -- 5.3.2 Analysis of Big Data Visualization Approaches -- 5.3.3 Big Data, IoT, and AR: Technology Convergence in Visualization Issues -- 5.4 Augmented Reality for Big Data -- 5.4.1 Retail and Banking -- 5.4.2 Healthcare -- 5.4.3 Industry -- 5.5 Issues and Challenges -- 5.5.1 Future Research Agenda and Data Visualization Challenges -- 5.6 Conclusion -- References -- Cloud and Security -- 6 Mathematical Model for Service-Selection Optimization and Scheduling in Cloud Manufacturing Using Sub-Task Scheduling With Fuzzy Inference Rule -- 6.1 Introduction -- 6.2 Problem Description -- 6.3 Service Selection -- 6.4 Overall Objective Function -- 6.5 Result and Discussion -- 6.6 Conclusion -- References -- IoT -- 7 Social Media Initiatives Through IoT to Link the Bridge Between Industrial Demands With Higher Education Millennial Students Through Experience Learning -- 7.1 Introduction -- 7.2 Research Gap -- 7.3 Objective -- 7.4 Review of Literature -- 7.5 Theoretical Framework -- 7.6 System Diagram -- 7.7 Conclusion -- References -- Digitization of Industrial Processes -- 8 Analyzing Consumer Product Feedback Dynamics With Confidence Intervals -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 Data Set -- 8.4 Data Pre-Processing -- 8.5 Proposed System -- 8.6 Implementation -- 8.7 Results -- 8.8 Conclusion -- References -- Information System in Industry -- 9 Amplifying the Effectiveness of a Learning Management System: Exploring the Impact of NEP-Compliant Curriculum Changes On Higher Education Institutions -- Objectives -- 9.1 Introduction -- 9.2 Significance of the Study -- 9.3 Review of Literature -- 9.3.1 National Education Policy and Curriculum Changes 9.3.2 Challenges and Opportunities of NEP-Aligned Curriculum On LMS Platforms -- 9.3.3 Current Gaps and Research Directions -- 9.4 Research Methodology -- 9.4.1 Research Design -- 9.4.2 Data Collection -- 9.4.3 Data Analysis -- 9.4.4 Presentation and Interpretation of Data -- 9.5 Limitations -- 9.6 Significance -- 9.7 Conclusion -- Bibliography -- Additive Manufacturing -- 10 The Future of Immersive Experience: Exploring Metaverse Application Development Technologies and Tools -- 10.1 Introduction -- 10.2 Literature Survey -- 10.3 Metaverse Architecture -- 10.4 Technologies and Tools for Metaverse Application Development -- 10.4.1 Game Engines (Unity, Unreal) -- 10.4.2 Virtual Reality and Augmented Reality Technologies -- 10.4.3 Blockchain Technology and Smart Contracts -- 10.4.4 Decentralized Platforms (Decentraland, Somnium Space) -- 10.5 Artificial Intelligence -- 10.6 Future System for Metaverse Application Development -- 10.6.1 Integration of Technologies and Tools for a Fully Immersive and Decentralized Virtual World -- 10.6.2 Use of Game Engines to Create 3D Environments and Objects -- 10.6.3 Integration of Virtual Reality and Augmented Reality Technologies for an Immersive Experience -- 10.6.4 Use of Blockchain Technology and Smart Contracts for User Ownership of Virtual Assets and Transactions -- 10.6.5 Incorporation of Decentralized Platforms for Interconnected Virtual Worlds -- 10.6.6 Use of Decentralized Autonomous Organizations (DAOs) for FAIR and Responsible Governance -- 10.6.7 Utilization of Artificial Intelligence to Create Intelligent NPCs for a More Realistic and Personalized Experience -- 10.7 Metaverse Security Challenges -- 10.8 Discussion -- 10.9 Conclusion -- References -- Index Internet of things Internet of things-Equipment and supplies |
| title | Technology Innovation Pillars for Industry 4. 0 Challenges, Improvements, and Case Studies |
| title_auth | Technology Innovation Pillars for Industry 4. 0 Challenges, Improvements, and Case Studies |
| title_exact_search | Technology Innovation Pillars for Industry 4. 0 Challenges, Improvements, and Case Studies |
| title_full | Technology Innovation Pillars for Industry 4. 0 Challenges, Improvements, and Case Studies |
| title_fullStr | Technology Innovation Pillars for Industry 4. 0 Challenges, Improvements, and Case Studies |
| title_full_unstemmed | Technology Innovation Pillars for Industry 4. 0 Challenges, Improvements, and Case Studies |
| title_short | Technology Innovation Pillars for Industry 4. 0 |
| title_sort | technology innovation pillars for industry 4 0 challenges improvements and case studies |
| title_sub | Challenges, Improvements, and Case Studies |
| topic | Internet of things Internet of things-Equipment and supplies |
| topic_facet | Internet of things Internet of things-Equipment and supplies |
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