Cyber-Physical Systems : Foundations and Techniques |
Autore | Sharma Uzzal |
Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
Descrizione fisica | 1 online resource (340 pages) |
Altri autori (Persone) |
NandParma
ChatterjeeJyotir Moy JainVishal JhanjhiNoor Zaman SujathaR |
Soggetto genere / forma | Electronic books. |
ISBN |
1-119-83663-8
1-119-83662-X |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910585797703321 |
Sharma Uzzal
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Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
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Lo trovi qui: Univ. Federico II | ||
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Deep Learning for Personalized Healthcare Services / / ed. by Vishal Jain, Hadi Hedayati, Salahddine Krit, Omer Deperlioglu, Jyotir Moy Chatterjee |
Pubbl/distr/stampa | Berlin ; ; Boston : , : De Gruyter, , [2021] |
Descrizione fisica | 1 online resource (XVIII, 250 p.) |
Collana | Intelligent Biomedical Data Analysis |
Soggetto topico | COMPUTERS / Social Aspects / General |
Soggetto genere / forma | Electronic books. |
ISBN | 3-11-070812-4 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Frontmatter -- Preface -- Acknowledgments -- Contents -- Short Biography of Editors -- List of Contributors -- Deep learning for health and medicine -- Exploring Indian Yajna and mantra sciences for personalized health: pandemic threats and possible cures in twenty-first-century healthcare -- Advanced deep learning techniques and applications in healthcare services -- Visualizations of human bioelectricity with internal symptom captures: the Indo-Vedic concepts on Healthcare 4.0 -- Early cancer predictions using ensembles of machine learning and deep learning -- Deep learning in patient management and clinical decision making -- Patient health record system -- Prediction of multiclass cervical cancer using deep machine learning algorithms in healthcare services -- Comparative analysis for detecting skin cancer using SGD-based optimizer on a CNN versus DCNN architecture and ResNet-50 versus AlexNet on Adam optimizer -- Coronary heart disease analysis using two deep learning algorithms, CNN and RNN, and their sensitivity analyses -- An overview of the technological performance of deep learning in modern medicine -- Index |
Record Nr. | UNINA-9910554282103321 |
Berlin ; ; Boston : , : De Gruyter, , [2021] | ||
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Lo trovi qui: Univ. Federico II | ||
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Internet of Things and Its Applications |
Autore | Nandan Mohanty Sachi |
Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2022 |
Descrizione fisica | 1 online resource (562 pages) |
Altri autori (Persone) |
ChatterjeeJyotir Moy
SatpathySuneeta |
Collana | EAI/Springer Innovations in Communication and Computing Ser. |
Soggetto genere / forma | Electronic books. |
ISBN |
9783030775285
9783030775278 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Intro -- Preface -- Acknowledgment -- Contents -- About the Authors -- Part I: IoT -Foundations, Architectures & -- Smart Services -- Internet of Things: Basic Concepts and Decorum of Smart Services -- 1 Introduction -- 1.1 Level of IoT -- 1.2 Discussion of Major Components for IoT-Based Smart Farming -- 2 IoT's Role in Application -- 2.1 WSNs -- 2.2 Characteristics of the Wireless Sensor Network -- 2.3 Wireless Architecture -- 2.4 Network Topology Construction Phase with Efficient Processing -- 2.5 IoT Agricultural Network Architecture -- 3 Cloud and Fog Infrastructure for Data Security -- 4 COVID Handling Using IoT -- 5 Conclusion -- References -- IoT Framework, Architecture Services, Platforms, and Reference Models -- 1 Introduction -- 1.1 Definitions -- 1.2 IoT Technologies -- 1.2.1 Radio-Frequency Identification (RFID) -- 1.2.2 Internet Protocol (IP) -- 1.2.3 Electronic Product Code (EPC) -- 1.2.4 Barcode -- 1.2.5 Wireless Fidelity -- 1.2.6 Bluetooth -- 1.2.7 Zigbee -- 1.2.8 Near Field Communication (NFC) -- 1.2.9 Wireless Sensor Networks (WSN) -- 1.3 IoT Framework -- 1.4 IoT Architecture -- 1.4.1 Four Stages of IoT Architecture -- 1.4.2 Basic IoT Architecture -- 1.4.3 Three-Layered Architecture -- 1.4.4 Four-Layered Architecture -- 1.4.5 Five-Layered Architecture -- 1.4.6 European FP7 Research Project -- 1.4.7 ITU Architecture and IoT Forum Architecture -- 1.4.8 Qian Xiao Cong, Zhang Jidong Architecture -- 1.4.9 Cloud-Based Architectures -- 1.5 IoT Platform -- 1.5.1 Google Cloud Platform -- 1.5.2 IBM BlueMix -- 1.5.3 ThingWorx -- 1.5.4 Microsoft Azure Cloud -- 1.5.5 ThingSpeak -- 1.5.6 Digital Service Cloud -- 1.5.7 Zetta -- 1.5.8 Yaler -- 1.5.9 Amazon Web Services -- 1.5.10 Seven Levels of IoT Reference Model -- 1.6 Brief Introduction to IoT Analytics -- 1.7 Challenges of IoT -- 1.8 Conclusion -- References.
Part II: Smart Healthcare & -- IoT -- A Check on WHO Protocol Implementation for COVID-19 Using IoT -- 1 Introduction -- 2 Literature Survey -- 2.1 Literature Survey Conclusion -- 3 Dataset -- 4 Proposed System -- 4.1 Designed Convolutional Neural Network -- 4.2 Raspberry Pi's Setup -- 4.2.1 Pi Camera -- 4.2.2 MLX90614 Non-contact Temperature Sensor -- 5 Implementation -- 5.1 CNN Algorithm -- 6 Results -- 7 Conclusion -- References -- Design and Implementation of an Internet of Things (IoT) Architecture for the Acquisition of Relevant Variables in the Study of Failures in Medical Equipment: A Case Study -- 1 Introduction -- 2 Related Works -- 3 Proposed Work -- 3.1 System Architecture and Variables Measured -- 3.1.1 Sensing Layer -- 3.1.2 Network Layer -- 3.1.3 The Service Layer -- 4 Results -- 4.1 System Architecture and Variables Measured -- 5 Discussion -- 6 Conclusions -- 7 Future Work -- References -- A Novel IoT-Based Solution for Respiratory Flow Diagnosis -- 1 Introduction -- 2 Related Works -- 3 Overview of Acquisition and Control Modules -- 3.1 Proposed System to Measure Exhaled Airflow Rate -- 4 Design of Experiment -- 5 Results and Discussion -- 6 Conclusion -- References -- Deep Learning Application in Classification of Brain Metastases: Sensor Usage in Medical Diagnosis for Next Gen Healthcare -- 1 Introduction -- 1.1 Brain Tumor -- 1.2 Big Data Analytics in Health Informatics -- 1.3 Machine Learning in Healthcare -- 1.4 Sensors for Internet of Things -- 1.5 Let Us Look at Some Stats to See the Progress of IOT in Healthcare -- 1.6 Challenges and Critical Issues of IOT in Healthcare -- 1.7 Machine Learning and Artificial Intelligence (AI) for Health Informatics -- 1.8 Health Sensor Data Management -- 1.9 Multimodal Data Fusion for Healthcare. 1.10 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT -- 1.11 Role of Technology in Addressing the Problem of Integration of Healthcare System -- 2 Literature Survey -- 3 System Design and Methodology -- 3.1 System Design -- 3.2 CNN Architecture -- 3.3 Block Diagram -- 3.4 Algorithm(s) -- 4 Our Experimental Results, Interpretation, and Discussion -- 4.1 Experimental Setup -- 4.2 Implementation Details -- 4.3 Snapshots of Interfaces -- 5 Novelty in Our Work -- 6 Future Scope, Possible Applications, and Limitations -- 7 Recommendations and Consideration -- 8 Conclusions -- 9 Performance Evaluations -- 9.1 Comparison with Other Algorithms -- Annex -- Key Terms and Definitions -- B. Additional Readings -- References -- Implementation of Smart Control of Wheelchair for a Disabled Person -- 1 Introduction -- 2 Related Work -- 3 System Design -- 4 Results and Discussion -- 5 Conclusion -- References -- Application of the Internet of Things (IoT) in Biomedical Engineering: Present Scenario and Challenges -- 1 Introduction -- 2 Applications to Health Care -- 2.1 Health Monitoring System -- 2.2 Remote Steady ECG Checking -- 2.3 Telemedicine Innovation -- 2.4 RFID Applications to Assist the Elderly to Live Independently -- 2.5 Portable Medicine -- 2.6 Utilizations of RFID Wristbands -- 2.7 GPS Positioning Applications for Patients with Heart Disease -- 2.8 Prediction of Protein Structure -- 3 Specialized Problems Facing Medical IoT -- 3.1 Node Versatility and Dynamic Large-Scale System: The Board in Enormous Scale Systems -- 3.2 Information Completeness and Data Compression -- 3.3 Information Security -- 3.4 Duplicate Medicine Detection -- 4 Conclusion -- References. Risk Stratification for Subjects Suffering from Lung Carcinoma: Healthcare 4.0 Approach with Medical Diagnosis Using Computational Intelligence -- 1 Introduction -- 1.1 Motivation to the Study -- 1.1.1 Problem Statements -- 1.1.2 Authors' Contributions -- 1.1.3 Research Manuscript Organization -- 1.2 Definitions -- 1.2.1 Computer-Aided Diagnosis System (CADe or CADx) -- 1.2.2 Sensors for the Internet of Things -- 1.2.3 Wireless and Wearable Sensors for Health Informatics -- 1.2.4 Remote Human's Health and Activity Monitoring -- 1.2.5 Decision-Making Systems for Sensor Data -- 1.2.6 Artificial Intelligence (AI) and Machine Learning for Health Informatics -- 1.2.7 Health Sensor Data Management -- 1.2.8 Multimodal Data Fusion for Healthcare -- 1.2.9 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT -- 2 Literature Review -- 3 Proposed Systems -- 3.1 Framework or Architecture of the Work -- 3.2 Model Steps and Parameters -- 3.3 Discussions -- 4 Experimental Results and Analysis -- 4.1 Tissue Characterization and Risk Stratification -- 4.2 Samples of Cancer Data and Analysis -- 5 Novelties -- 6 Future Scopes, Limitations, and Possible Applications -- 7 Recommendations and Considerations -- 8 Conclusions -- Annex -- Key Terms and Definitions -- Additional Readings (Addendum) -- Data Set -- Snapshots of the Implementation -- References -- The Fusion of IOT and Wireless Body Area Network -- 1 Introduction -- 1.1 WBAN System Architecture -- 1.2 Applications of WBANs -- 1.2.1 Cardiovascular Application -- 1.2.2 Cancer Detection -- 1.2.3 Blood Glucose Monitoring -- 1.2.4 Stress Monitoring -- 1.2.5 Artificial Retina -- 1.2.6 General Health Monitoring -- 1.2.7 Non-medical Applications -- 2 Review of Existing Works -- 3 Fusion of IoT with WBAN -- 3.1 Starting Stage -- 3.2 Cluster Evolution. 3.3 Sensed Information Stage -- 3.4 Choice of Forwarder Stage -- 3.5 Consumed Energy as well as Routing Stage -- 3.6 Model of Network -- 3.6.1 Model of Energy -- 3.6.2 Model of Path Loss -- 3.6.3 Particle Swarm Optimization Algorithm -- Initialization -- Fitness Function's Evaluation -- Hunting -- Particles' Upgraded Velocity as well as Allocation -- Local Best as well as Global Best Upgrading -- 3.7 Optimized Approaches -- 3.7.1 System Model -- 3.7.2 Starting Stage -- Transmission of Data Stage -- 4 MC-MAC Strategy for Interference Reduction Inside WBANs -- 4.1 WBANs and Healthcare -- 4.2 Protocols of Multi-channel -- 5 Conclusion -- References -- Part III: Smart Education & -- IoT -- Paradigms of Smart Education with IoT Approach -- 1 Introduction -- 2 Meaning of "Smart" in Smart Education -- 2.1 Smart Campus -- 2.2 Smart Learner -- 2.3 Handheld Devices -- 2.4 Smart Tracking and Monitoring System -- 2.5 Smart Learning Environment -- 2.6 Smart Pedagogies -- 2.7 Increased Security -- 2.8 Smart Learning for Disable Students -- 3 IoT in Smart Education -- 4 Conclusion -- References -- Automated Electric Power Saving System in University Classrooms Using Internet of Things -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Algorithm Used for Implementing the Model -- 4 Results and Effectiveness of Proposed Methodology -- 5 Advantages, Disadvantages, and Applications of Using Proposed Methodology -- 6 Conclusion and Future Directions -- 6.1 Conclusion -- 6.2 Future Directions -- References -- Part IV: Smart Banking & -- IoT -- Smart Banking in Financial Transactions of Migrants: A Study on the In-Migrants of the Gajapati District of Odisha -- 1 Introduction -- 2 Review of Literature -- 3 Objectives -- 4 Methodology. 5 Availability and Accessibility of Smart Banking Facilities to Migrant Workers Staying in the Gajapati District of Odisha. |
Record Nr. | UNINA-9910510574303321 |
Nandan Mohanty Sachi
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Cham : , : Springer International Publishing AG, , 2022 | ||
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Lo trovi qui: Univ. Federico II | ||
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Internet of things and its applications / / Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Suneeta Satpathy, editors |
Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
Descrizione fisica | 1 online resource (562 pages) |
Disciplina | 004.678 |
Collana | EAI/Springer innovations in communication and computing |
Soggetto topico |
Internet of things
Internet in medicine Internet in education |
ISBN | 3-030-77528-3 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Intro -- Preface -- Acknowledgment -- Contents -- About the Authors -- Part I: IoT -Foundations, Architectures & -- Smart Services -- Internet of Things: Basic Concepts and Decorum of Smart Services -- 1 Introduction -- 1.1 Level of IoT -- 1.2 Discussion of Major Components for IoT-Based Smart Farming -- 2 IoT's Role in Application -- 2.1 WSNs -- 2.2 Characteristics of the Wireless Sensor Network -- 2.3 Wireless Architecture -- 2.4 Network Topology Construction Phase with Efficient Processing -- 2.5 IoT Agricultural Network Architecture -- 3 Cloud and Fog Infrastructure for Data Security -- 4 COVID Handling Using IoT -- 5 Conclusion -- References -- IoT Framework, Architecture Services, Platforms, and Reference Models -- 1 Introduction -- 1.1 Definitions -- 1.2 IoT Technologies -- 1.2.1 Radio-Frequency Identification (RFID) -- 1.2.2 Internet Protocol (IP) -- 1.2.3 Electronic Product Code (EPC) -- 1.2.4 Barcode -- 1.2.5 Wireless Fidelity -- 1.2.6 Bluetooth -- 1.2.7 Zigbee -- 1.2.8 Near Field Communication (NFC) -- 1.2.9 Wireless Sensor Networks (WSN) -- 1.3 IoT Framework -- 1.4 IoT Architecture -- 1.4.1 Four Stages of IoT Architecture -- 1.4.2 Basic IoT Architecture -- 1.4.3 Three-Layered Architecture -- 1.4.4 Four-Layered Architecture -- 1.4.5 Five-Layered Architecture -- 1.4.6 European FP7 Research Project -- 1.4.7 ITU Architecture and IoT Forum Architecture -- 1.4.8 Qian Xiao Cong, Zhang Jidong Architecture -- 1.4.9 Cloud-Based Architectures -- 1.5 IoT Platform -- 1.5.1 Google Cloud Platform -- 1.5.2 IBM BlueMix -- 1.5.3 ThingWorx -- 1.5.4 Microsoft Azure Cloud -- 1.5.5 ThingSpeak -- 1.5.6 Digital Service Cloud -- 1.5.7 Zetta -- 1.5.8 Yaler -- 1.5.9 Amazon Web Services -- 1.5.10 Seven Levels of IoT Reference Model -- 1.6 Brief Introduction to IoT Analytics -- 1.7 Challenges of IoT -- 1.8 Conclusion -- References.
Part II: Smart Healthcare & -- IoT -- A Check on WHO Protocol Implementation for COVID-19 Using IoT -- 1 Introduction -- 2 Literature Survey -- 2.1 Literature Survey Conclusion -- 3 Dataset -- 4 Proposed System -- 4.1 Designed Convolutional Neural Network -- 4.2 Raspberry Pi's Setup -- 4.2.1 Pi Camera -- 4.2.2 MLX90614 Non-contact Temperature Sensor -- 5 Implementation -- 5.1 CNN Algorithm -- 6 Results -- 7 Conclusion -- References -- Design and Implementation of an Internet of Things (IoT) Architecture for the Acquisition of Relevant Variables in the Study of Failures in Medical Equipment: A Case Study -- 1 Introduction -- 2 Related Works -- 3 Proposed Work -- 3.1 System Architecture and Variables Measured -- 3.1.1 Sensing Layer -- 3.1.2 Network Layer -- 3.1.3 The Service Layer -- 4 Results -- 4.1 System Architecture and Variables Measured -- 5 Discussion -- 6 Conclusions -- 7 Future Work -- References -- A Novel IoT-Based Solution for Respiratory Flow Diagnosis -- 1 Introduction -- 2 Related Works -- 3 Overview of Acquisition and Control Modules -- 3.1 Proposed System to Measure Exhaled Airflow Rate -- 4 Design of Experiment -- 5 Results and Discussion -- 6 Conclusion -- References -- Deep Learning Application in Classification of Brain Metastases: Sensor Usage in Medical Diagnosis for Next Gen Healthcare -- 1 Introduction -- 1.1 Brain Tumor -- 1.2 Big Data Analytics in Health Informatics -- 1.3 Machine Learning in Healthcare -- 1.4 Sensors for Internet of Things -- 1.5 Let Us Look at Some Stats to See the Progress of IOT in Healthcare -- 1.6 Challenges and Critical Issues of IOT in Healthcare -- 1.7 Machine Learning and Artificial Intelligence (AI) for Health Informatics -- 1.8 Health Sensor Data Management -- 1.9 Multimodal Data Fusion for Healthcare. 1.10 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT -- 1.11 Role of Technology in Addressing the Problem of Integration of Healthcare System -- 2 Literature Survey -- 3 System Design and Methodology -- 3.1 System Design -- 3.2 CNN Architecture -- 3.3 Block Diagram -- 3.4 Algorithm(s) -- 4 Our Experimental Results, Interpretation, and Discussion -- 4.1 Experimental Setup -- 4.2 Implementation Details -- 4.3 Snapshots of Interfaces -- 5 Novelty in Our Work -- 6 Future Scope, Possible Applications, and Limitations -- 7 Recommendations and Consideration -- 8 Conclusions -- 9 Performance Evaluations -- 9.1 Comparison with Other Algorithms -- Annex -- Key Terms and Definitions -- B. Additional Readings -- References -- Implementation of Smart Control of Wheelchair for a Disabled Person -- 1 Introduction -- 2 Related Work -- 3 System Design -- 4 Results and Discussion -- 5 Conclusion -- References -- Application of the Internet of Things (IoT) in Biomedical Engineering: Present Scenario and Challenges -- 1 Introduction -- 2 Applications to Health Care -- 2.1 Health Monitoring System -- 2.2 Remote Steady ECG Checking -- 2.3 Telemedicine Innovation -- 2.4 RFID Applications to Assist the Elderly to Live Independently -- 2.5 Portable Medicine -- 2.6 Utilizations of RFID Wristbands -- 2.7 GPS Positioning Applications for Patients with Heart Disease -- 2.8 Prediction of Protein Structure -- 3 Specialized Problems Facing Medical IoT -- 3.1 Node Versatility and Dynamic Large-Scale System: The Board in Enormous Scale Systems -- 3.2 Information Completeness and Data Compression -- 3.3 Information Security -- 3.4 Duplicate Medicine Detection -- 4 Conclusion -- References. Risk Stratification for Subjects Suffering from Lung Carcinoma: Healthcare 4.0 Approach with Medical Diagnosis Using Computational Intelligence -- 1 Introduction -- 1.1 Motivation to the Study -- 1.1.1 Problem Statements -- 1.1.2 Authors' Contributions -- 1.1.3 Research Manuscript Organization -- 1.2 Definitions -- 1.2.1 Computer-Aided Diagnosis System (CADe or CADx) -- 1.2.2 Sensors for the Internet of Things -- 1.2.3 Wireless and Wearable Sensors for Health Informatics -- 1.2.4 Remote Human's Health and Activity Monitoring -- 1.2.5 Decision-Making Systems for Sensor Data -- 1.2.6 Artificial Intelligence (AI) and Machine Learning for Health Informatics -- 1.2.7 Health Sensor Data Management -- 1.2.8 Multimodal Data Fusion for Healthcare -- 1.2.9 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT -- 2 Literature Review -- 3 Proposed Systems -- 3.1 Framework or Architecture of the Work -- 3.2 Model Steps and Parameters -- 3.3 Discussions -- 4 Experimental Results and Analysis -- 4.1 Tissue Characterization and Risk Stratification -- 4.2 Samples of Cancer Data and Analysis -- 5 Novelties -- 6 Future Scopes, Limitations, and Possible Applications -- 7 Recommendations and Considerations -- 8 Conclusions -- Annex -- Key Terms and Definitions -- Additional Readings (Addendum) -- Data Set -- Snapshots of the Implementation -- References -- The Fusion of IOT and Wireless Body Area Network -- 1 Introduction -- 1.1 WBAN System Architecture -- 1.2 Applications of WBANs -- 1.2.1 Cardiovascular Application -- 1.2.2 Cancer Detection -- 1.2.3 Blood Glucose Monitoring -- 1.2.4 Stress Monitoring -- 1.2.5 Artificial Retina -- 1.2.6 General Health Monitoring -- 1.2.7 Non-medical Applications -- 2 Review of Existing Works -- 3 Fusion of IoT with WBAN -- 3.1 Starting Stage -- 3.2 Cluster Evolution. 3.3 Sensed Information Stage -- 3.4 Choice of Forwarder Stage -- 3.5 Consumed Energy as well as Routing Stage -- 3.6 Model of Network -- 3.6.1 Model of Energy -- 3.6.2 Model of Path Loss -- 3.6.3 Particle Swarm Optimization Algorithm -- Initialization -- Fitness Function's Evaluation -- Hunting -- Particles' Upgraded Velocity as well as Allocation -- Local Best as well as Global Best Upgrading -- 3.7 Optimized Approaches -- 3.7.1 System Model -- 3.7.2 Starting Stage -- Transmission of Data Stage -- 4 MC-MAC Strategy for Interference Reduction Inside WBANs -- 4.1 WBANs and Healthcare -- 4.2 Protocols of Multi-channel -- 5 Conclusion -- References -- Part III: Smart Education & -- IoT -- Paradigms of Smart Education with IoT Approach -- 1 Introduction -- 2 Meaning of "Smart" in Smart Education -- 2.1 Smart Campus -- 2.2 Smart Learner -- 2.3 Handheld Devices -- 2.4 Smart Tracking and Monitoring System -- 2.5 Smart Learning Environment -- 2.6 Smart Pedagogies -- 2.7 Increased Security -- 2.8 Smart Learning for Disable Students -- 3 IoT in Smart Education -- 4 Conclusion -- References -- Automated Electric Power Saving System in University Classrooms Using Internet of Things -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Algorithm Used for Implementing the Model -- 4 Results and Effectiveness of Proposed Methodology -- 5 Advantages, Disadvantages, and Applications of Using Proposed Methodology -- 6 Conclusion and Future Directions -- 6.1 Conclusion -- 6.2 Future Directions -- References -- Part IV: Smart Banking & -- IoT -- Smart Banking in Financial Transactions of Migrants: A Study on the In-Migrants of the Gajapati District of Odisha -- 1 Introduction -- 2 Review of Literature -- 3 Objectives -- 4 Methodology. 5 Availability and Accessibility of Smart Banking Facilities to Migrant Workers Staying in the Gajapati District of Odisha. |
Record Nr. | UNINA-9910522931903321 |
Cham, Switzerland : , : Springer, , [2022] | ||
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Lo trovi qui: Univ. Federico II | ||
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Internet of Things Use Cases for the Healthcare Industry [[electronic resource] /] / edited by Pethuru Raj, Jyotir Moy Chatterjee, Abhishek Kumar, B. Balamurugan |
Edizione | [1st ed. 2020.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
Descrizione fisica | 1 online resource (XII, 296 p. 79 illus., 59 illus. in color.) |
Disciplina | 610.28563 |
Soggetto topico |
Computer communication systems
Computer engineering Internet of things Embedded computer systems Health informatics Input-output equipment (Computers) Computer Communication Networks Cyber-physical systems, IoT Health Informatics Input/Output and Data Communications |
ISBN | 3-030-37526-9 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | AI in Health Sector -- Real-Time Smart Healthcare Model using IoT -- A Fog Based Approach for Real-Time Analytics of IoT-Enabled Healthcare -- Applications of IoT in Indoor Air Quality Monitoring Systems -- CloudIoT for Smart Healthcare: Architecture, Issues and Challenges -- Impact of IoT on the Healthcare Producers: Epitomizing Pharmaceutical Drug Discovery Process -- Cyber-Security Threats in Medical Devices -- Smart Healthcare Use Cases and Applications -- IoT Use Cases and Applications -- Internet of Things for Ambient Assisted Living - An Overview -- Smart Health care Applications and Real Time Analytics through Edge Computing -- The Role of Blockchain for Medical Electronics Security -- Clinical Data Analysis using IoT Data Analytics Platforms -- Internet of Things - Tools and Technologies in Healthcare -- Clinical data analysis using IoT -- Security Issues in IoT and Healthcare Devices. |
Record Nr. | UNISA-996465460403316 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
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Lo trovi qui: Univ. di Salerno | ||
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Internet of Things Use Cases for the Healthcare Industry / / edited by Pethuru Raj, Jyotir Moy Chatterjee, Abhishek Kumar, B. Balamurugan |
Edizione | [1st ed. 2020.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
Descrizione fisica | 1 online resource (XII, 296 p. 79 illus., 59 illus. in color.) |
Disciplina | 610.28563 |
Soggetto topico |
Computer communication systems
Computer engineering Internet of things Embedded computer systems Health informatics Input-output equipment (Computers) Computer Communication Networks Cyber-physical systems, IoT Health Informatics Input/Output and Data Communications |
ISBN | 3-030-37526-9 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | AI in Health Sector -- Real-Time Smart Healthcare Model using IoT -- A Fog Based Approach for Real-Time Analytics of IoT-Enabled Healthcare -- Applications of IoT in Indoor Air Quality Monitoring Systems -- CloudIoT for Smart Healthcare: Architecture, Issues and Challenges -- Impact of IoT on the Healthcare Producers: Epitomizing Pharmaceutical Drug Discovery Process -- Cyber-Security Threats in Medical Devices -- Smart Healthcare Use Cases and Applications -- IoT Use Cases and Applications -- Internet of Things for Ambient Assisted Living - An Overview -- Smart Health care Applications and Real Time Analytics through Edge Computing -- The Role of Blockchain for Medical Electronics Security -- Clinical Data Analysis using IoT Data Analytics Platforms -- Internet of Things - Tools and Technologies in Healthcare -- Clinical data analysis using IoT -- Security Issues in IoT and Healthcare Devices. |
Record Nr. | UNINA-9910410049303321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
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Lo trovi qui: Univ. Federico II | ||
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Machine Learning with Health Care Perspective : Machine Learning and Healthcare / / edited by Vishal Jain, Jyotir Moy Chatterjee |
Edizione | [1st ed. 2020.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
Descrizione fisica | 1 online resource (418 pages) |
Disciplina | 610.28563 |
Collana | Learning and Analytics in Intelligent Systems |
Soggetto topico |
Computational intelligence
Biomedical engineering Computational Intelligence Biomedical Engineering and Bioengineering |
ISBN | 3-030-40850-7 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Chapter 1: Machine learning for Healthcare: Introduction -- Chapter 2: Artificial Intelligence in Medical Diagnosis: Methods, algorithms and applications -- Chapter 3: Intelligent Learning Analytics in Healthcare Sector Using Machine Learning -- Chapter 4: Unsupervised Learning on Healthcare Survey Data with Particle Swarm Optimization -- Chapter 5: Machine Learning for Healthcare Diagnostics -- Chapter 6: Disease Detection System (DDS) Using Machine Learning Technique -- Chapter 7: Knowledge Discovery (Feature Identification) from Teeth, Wrist and Femur Images to determine Human Age and Gender -- Chapter 8: Deep Learning Solutions for Skin Cancer Detection and Diagnosis -- Chapter 9: Security of Healthcare Systems with Smart Health Records using Cloud Technology -- Chapter 10: Intelligent Heart Disease Prediction on Physical and Mental Parameters: A ML Based IoT and Big Data Application and Analysis -- Chapter 11: Medical Text and image processing: Applications, issues and challenges -- Chapter 12: Machine Learning Methods for Managing Parkinson’s Disease -- Chapter 13: An Efficient Method for Computer-aided Diagnosis of Cardiac Arrhythmias -- Chapter 14: Clinical decision support systems and predictive analytics -- Chapter 15: Yajna and Mantra Science Bringing Health and Comfort to Indo-Asian Public: A Healthcare 4.0 Approach and Computational Study -- Chapter 16: Identifying Diseases and Diagnosis using Machine Learning. |
Record Nr. | UNINA-9910483000003321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
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Lo trovi qui: Univ. Federico II | ||
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A roadmap for enabling Industry 4.0 by artificial intelligence / / edited by Jyotir Moy Chatterjee, Harish Garg and R. N. Thakur |
Pubbl/distr/stampa | Hoboken, NJ : , : Wiley, , [2023] |
Descrizione fisica | 1 online resource (339 pages) |
Soggetto topico |
Artificial intelligence - Industrial applications
Industry 4.0 |
ISBN |
1-119-90514-1
1-119-90513-3 9781119904854 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Artificial Intelligence-The Driving Force of Industry 4.0 -- 1.1 Introduction -- 1.2 Methodology -- 1.3 Scope of AI in Global Economy and Industry 4.0 -- 1.3.1 Artificial Intelligence-Evolution and Implications -- 1.3.2 Artificial Intelligence and Industry 4.0-Investments and Returns on Economy -- 1.3.3 The Driving Forces for Industry 4.0 -- 1.4 Artificial Intelligence-Manufacturing Sector -- 1.4.1 AI Diversity-Applications to Manufacturing Sector -- 1.4.2 Future Roadmap of AI-Prospects to Manufacturing Sector in Industry 4.0 -- 1.5 Conclusion -- References -- Chapter 2 Industry 4.0, Intelligent Manufacturing, Internet of Things, Cloud Computing: An Overview -- 2.1 Introduction -- 2.2 Industrial Transformation/Value Chain Transformation -- 2.2.1 First Scenario: Reducing Waste and Increasing Productivity Using IIoT -- 2.2.2 Second Scenario: Selling Outcome (User Demand)-Based Services Using IIoT -- 2.3 IIoT Reference Architecture -- 2.4 IIoT Technical Concepts -- 2.5 IIoT and Cloud Computing -- 2.6 IIoT and Security -- References -- Chapter 3 Artificial Intelligence of Things (AIoT) and Industry 4.0-Based Supply Chain (FMCG Industry) -- 3.1 Introduction -- 3.2 Concepts -- 3.2.1 Internet of Things -- 3.2.2 The Industrial Internet of Things (IIoT) -- 3.2.3 Artificial Intelligence of Things (AIoT) -- 3.3 AIoT-Based Supply Chain -- 3.4 Conclusion -- References -- Chapter 4 Application of Artificial Intelligence in Forecasting the Demand for Supply Chains Considering Industry 4.0 -- 4.1 Introduction -- 4.2 Literature Review -- 4.2.1 Summary of the First Three Industrial Revolutions -- 4.2.2 Emergence of Industry 4.0 -- 4.2.3 Some of the Challenges of Industry 4.0 -- 4.3 Application of Artificial Intelligence in Supply Chain Demand Forecasting -- 4.4 Proposed Approach.
4.4.1 Mathematical Model -- 4.4.2 Advantages of the Proposed Model -- 4.5 Discussion and Conclusion -- References -- Chapter 5 Integrating IoT and Deep Learning-The Driving Force of Industry 4.0 -- 5.1 Motivation and Background -- 5.2 Bringing Intelligence Into IoT Devices -- 5.3 The Foundation of CR-IoT Network -- 5.3.1 Various AI Technique in CR-IoT Network -- 5.3.2 Artificial Neural Network (ANN) -- 5.3.3 Metaheuristic Technique -- 5.3.4 Rule-Based System -- 5.3.5 Ontology-Based System -- 5.3.6 Probabilistic Models -- 5.4 The Principles of Deep Learning and Its Implementation in CR-IoT Network -- 5.5 Realization of CR-IoT Network in Daily Life Examples -- 5.6 AI-Enabled Agriculture and Smart Irrigation System-Case Study -- 5.7 Conclusion -- References -- Chapter 6 A Systematic Review on Blockchain Security Technology and Big Data Employed in Cloud Environment -- 6.1 Introduction -- 6.2 Overview of Blockchain -- 6.3 Components of Blockchain -- 6.3.1 Data Block -- 6.3.2 Smart Contracts -- 6.3.3 Consensus Algorithms -- 6.4 Safety Issues in Blockchain Technology -- 6.5 Usage of Big Data Framework in Dynamic Supply Chain System -- 6.6 Machine Learning and Big Data -- 6.6.1 Overview of Shallow Models -- 6.6.1.1 Support Vector Machine (SVM) -- 6.6.1.2 Artificial Neural Network (ANN) -- 6.6.1.3 K-Nearest Neighbor (KNN) -- 6.6.1.4 Clustering -- 6.6.1.5 Decision Tree -- 6.7 Advantages of Using Big Data for Supply Chain and Blockchain Systems -- 6.7.1 Replenishment Planning -- 6.7.2 Optimizing Orders -- 6.7.3 Arranging and Organizing -- 6.7.4 Enhanced Demand Structuring -- 6.7.5 Real-Time Management of the Supply Chain -- 6.7.6 Enhanced Reaction -- 6.7.7 Planning and Growth of Inventories -- 6.8 IoT-Enabled Blockchains -- 6.8.1 Securing IoT Applications by Utilizing Blockchain -- 6.8.2 Blockchain Based on Permission -- 6.8.3 Blockchain Improvements in IoT. 6.8.3.1 Blockchain Can Store Information Coming from IoT Devices -- 6.8.3.2 Secure Data Storage with Blockchain Distribution -- 6.8.3.3 Data Encryption via Hash Key and Tested by the Miners -- 6.8.3.4 Spoofing Attacks and Data Loss Prevention -- 6.8.3.5 Unauthorized Access Prevention Using Blockchain -- 6.8.3.6 Exclusion of Centralized Cloud Servers -- 6.9 Conclusions -- References -- Chapter 7 Deep Learning Approach to Industrial Energy Sector and Energy Forecasting with Prophet -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Methodology -- 7.3.1 Splitting of Data (Test/Train) -- 7.3.2 Prophet Model -- 7.3.3 Data Cleaning -- 7.3.4 Model Implementation -- 7.4 Results -- 7.4.1 Comparing Forecast to Actuals -- 7.4.2 Adding Holidays -- 7.4.3 Comparing Forecast to Actuals with the Cleaned Data -- 7.5 Conclusion and Future Scope -- References -- Chapter 8 Application of Novel AI Mechanism for Minimizing Private Data Release in Cyber-Physical Systems -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Proposed Mechanism -- 8.4 Experimental Results -- 8.5 Future Directions -- 8.6 Conclusion -- References -- Chapter 9 Environmental and Industrial Applications Using Internet of Things (IoT) -- 9.1 Introduction -- 9.2 IoT-Based Environmental Applications -- 9.3 Smart Environmental Monitoring -- 9.3.1 Air Quality Assessment -- 9.3.2 Water Quality Assessment -- 9.3.3 Soil Quality Assessment -- 9.3.4 Environmental Health-Related to COVID-19 Monitoring -- 9.4 Applications of Sensors Network in Agro-Industrial System -- 9.5 Applications of IoT in Industry -- 9.5.1 Application of IoT in the Autonomous Field -- 9.5.2 Applications of IoT in Software Industries -- 9.5.3 Sensors in Industry -- 9.6 Challenges of IoT Applications in Environmental and Industrial Applications -- 9.7 Conclusions and Recommendations -- Acknowledgments -- References. Chapter 10 An Introduction to Security in Internet of Things (IoT) and Big Data -- 10.1 Introduction -- 10.2 Allusion Design of IoT -- 10.2.1 Stage 1-Edge Tool -- 10.2.2 Stage 2-Connectivity -- 10.2.3 Stage 3-Fog Computing -- 10.2.4 Stage 4-Data Collection -- 10.2.5 Stage 5-Data Abstraction -- 10.2.6 Stage 6-Applications -- 10.2.7 Stage 7-Cooperation and Processes -- 10.3 Vulnerabilities of IoT -- 10.3.1 The Properties and Relationships of Various IoT Networks -- 10.3.2 Device Attacks -- 10.3.3 Attacks on Network -- 10.3.4 Some Other Issues -- 10.3.4.1 Customer Delivery Value -- 10.3.4.2 Compatibility Problems With Equipment -- 10.3.4.3 Compatibility and Maintenance -- 10.3.4.4 Connectivity Issues in the Field of Data -- 10.3.4.5 Incorrect Data Collection and Difficulties -- 10.3.4.6 Security Concern -- 10.3.4.7 Problems in Computer Confidentiality -- 10.4 Challenges in Technology -- 10.4.1 Skepticism of Consumers -- 10.5 Analysis of IoT Security -- 10.5.1 Sensing Layer Security Threats -- 10.5.1.1 Node Capturing -- 10.5.1.2 Malicious Attack by Code Injection -- 10.5.1.3 Attack by Fake Data Injection -- 10.5.1.4 Sidelines Assaults -- 10.5.1.5 Attacks During Booting Process -- 10.5.2 Network Layer Safety Issues -- 10.5.2.1 Attack on Phishing Page -- 10.5.2.2 Attacks on Access -- 10.5.2.3 Attacks on Data Transmission -- 10.5.2.4 Attacks on Routing -- 10.5.3 Middleware Layer Safety Issues -- 10.5.3.1 Attack by SQL Injection -- 10.5.3.2 Attack by Signature Wrapping -- 10.5.3.3 Cloud Attack Injection with Malware -- 10.5.3.4 Cloud Flooding Attack -- 10.5.4 Gateways Safety Issues -- 10.5.4.1 On-Boarding Safely -- 10.5.4.2 Additional Interfaces -- 10.5.4.3 Encrypting End-to-End -- 10.5.5 Application Layer Safety Issues -- 10.5.5.1 Theft of Data -- 10.5.5.2 Attacks at Interruption in Service -- 10.5.5.3 Malicious Code Injection Attack. 10.6 Improvements and Enhancements Needed for IoT Applications in the Future -- 10.7 Upcoming Future Research Challenges with Intrusion Detection Systems (IDS) -- 10.8 Conclusion -- References -- Chapter 11 Potential, Scope, and Challenges of Industry 4.0 -- 11.1 Introduction -- 11.2 Key Aspects for a Successful Production -- 11.3 Opportunities with Industry 4.0 -- 11.4 Issues in Implementation of Industry 4.0 -- 11.5 Potential Tools Utilized in Industry 4.0 -- 11.6 Conclusion -- References -- Chapter 12 Industry 4.0 and Manufacturing Techniques: Opportunities and Challenges -- 12.1 Introduction -- 12.2 Changing Market Demands -- 12.2.1 Individualization -- 12.2.2 Volatility -- 12.2.3 Efficiency in Terms of Energy Resources -- 12.3 Recent Technological Advancements -- 12.4 Industrial Revolution 4.0 -- 12.5 Challenges to Industry 4.0 -- 12.6 Conclusion -- References -- Chapter 13 The Role of Multiagent System in Industry 4.0 -- 13.1 Introduction -- 13.2 Characteristics and Goals of Industry 4.0 Conception -- 13.3 Artificial Intelligence -- 13.3.1 Knowledge-Based Systems -- 13.4 Multiagent Systems -- 13.4.1 Agent Architectures -- 13.4.2 JADE -- 13.4.3 System Requirements Definition -- 13.4.4 HMI Development -- 13.5 Developing Software of Controllers Multiagent Environment Behavior Patterns -- 13.5.1 Agent Supervision -- 13.5.2 Documents Dispatching Agents -- 13.5.3 Agent Rescheduling -- 13.5.4 Agent of Executive -- 13.5.5 Primary Roles of High-Availability Agent -- 13.6 Conclusion -- References -- Chapter 14 An Overview of Enhancing Encryption Standards for Multimedia in Explainable Artificial Intelligence Using Residue Number Systems for Security -- 14.1 Introduction -- 14.2 Reviews of Related Works -- 14.3 Materials and Methods -- 14.3.1 Multimedia -- 14.3.2 Artificial Intelligence and Explainable Artificial Intelligence -- 14.3.3 Cryptography. 14.3.4 Encryption and Decryption. |
Record Nr. | UNINA-9910829846403321 |
Hoboken, NJ : , : Wiley, , [2023] | ||
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Lo trovi qui: Univ. Federico II | ||
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