Descripción del título
Cognitive computing for Int...
"Cognitive Computing for Internet of Medical Things (IoMT) offers a complete assessment of the present scenario, role, challenges, technologies, and impact of IoMT-enabled smart healthcare systems. It contains chapters discussing various biomedical applications under the umbrella of the IoMT"--
Monografía
monografia Rebiun36540889 https://catalogo.rebiun.org/rebiun/record/Rebiun36540889 m o d | cr cnu|||||||| 230318s2023 flu ob 001 0 eng d 1-00-325624-4 1-003-25624-4 1-000-80600-6 UPVA 998815141703706 MiAaPQ eng rda pn MiAaPQ MiAaPQ 004.678 23 Cognitive computing for Internet of Medical Things edited by A Prasanth [and four others] Boca Raton, Florida London New York CRC Press [2023] Boca Raton, Florida London New York Boca Raton, Florida London New York CRC Press 2023 1 online resource (231 pages) 1 online resource (231 pages) Text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- List of Contributors -- 1. Toward the Internet of Things and Its Applications: A Review on Recent Innovations and Challenges -- 1.1 Introduction -- 1.1.1 Sensor Layer -- 1.1.2 Communication Layer -- 1.1.3 Network Device Layer -- 1.1.4 Data Center Layer -- 1.1.5 Application and Analytics Layer -- 1.2 IoT and Transportation -- 1.2.1 Smart Parking -- 1.2.2 Peer-to-Peer Ridesharing -- 1.2.3 Self-Driving Cars -- 1.3 IoT and Smart Cities -- 1.3.1 Telemedicine -- 1.3.2 Smart Security -- 1.3.3 Real-Time Environment Monitoring System -- 1.4 Precision Agriculture -- 1.5 Conclusions -- References -- 2. Introduction to Cognitive Computing -- 2.1 Introduction -- 2.2 Evolution of Cognitive Computing -- 2.3 Comparison -- 2.3.1 Example Case -- 2.4 Dimensions of Cognitive Computing -- 2.4.1 Reasoning -- 2.4.2 Relating -- 2.4.3 Perception -- 2.4.4 Learning -- 2.5 Architecture of Cognitive Computing -- 2.5.1 IoT in Cognitive Computing -- 2.5.2 Big Data Analysis in Cognitive Computing -- 2.5.3 Cloud and Cognitive Computing -- 2.6 Supporting Technologies for Cognitive Computing -- 2.6.1 Reinforcement Learning -- 2.6.1.1 Components -- 2.6.1.2 Approaches -- 2.6.2 Cognitive Computing and Deep Learning -- 2.6.3 Cognitive Computing and Image Processing -- 2.7 Cognitive Analytics (Coganalytics) -- 2.8 Applications of Cognitive Computing -- 2.8.1 Cognitive Computing in Robotic Industry -- 2.8.2 Cognitive Computing in Emotion Communication -- 2.8.3 Cognitive Computing in Retail and Logistics Industry -- 2.8.4 Cognitive Computing in Banking and Finance -- 2.8.5 Cognitive Computing in the Power and Energy Sector -- 2.8.6 Cognitive Computing in Cybersecurity -- 2.8.7 Cognitive Computing in Healthcare -- 2.8.7.1 Impacts of Cognitive Computing in Healthcare -- 2.9 Conclusions References -- 3. IoT with 5G in Healthcare Systems -- 3.1 Introduction -- 3.1.1 Examples of IoT in Healthcare -- 3.1.2 Working Principles of IoT in Healthcare -- 3.1.3 Advantages of IoT in Healthcare -- 3.1.4 Obstacles of IoT in Healthcare -- 3.2 5G Connectivity -- 3.2.1 5G-IoT Healthcare Applications -- 3.2.1.1 From Vision to Reality -- 3.2.1.2 Abilities of VOLANSYS 5G -- 3.2.2 5G Connectivity, e-Health, and m-Health -- 3.2.3 5G in Revolutionized Healthcare -- 3.2.3.1 Fast Transmission of Huge Imaging Documents -- 3.2.3.2 Broadening Telemedicine Administrations -- 3.2.3.3 Enhancing Augmented Reality, Virtual Reality, and Spatial Computing -- 3.2.3.4 Predictable, Constant Remote Observing -- 3.2.3.5 ML & -- AI -- 3.3 Internet of Medical Things (IoMT) -- 3.3.1 IoT Security -- 3.3.2 IoMT Obstacles -- 3.3.3 Opportunities in Healthcare Information Technology -- 3.3.4 Computerized Therapeutics (DTx) -- 3.4 Conclusions -- References -- 4. Communication Protocols for IoMT-Based Healthcare Systems -- 4.1 Introduction -- 4.2 Architecture of IoT -- 4.2.1 Three-Layer Architecture -- 4.2.2 Middleware Architecture -- 4.2.3 Service-Oriented Architecture (SOA) -- 4.2.4 Five-Layer Architecture -- 4.2.5 Cloud-Specific Architecture -- 4.3 Architecture of IoMT -- 4.4 IoMT Communication Protocols -- 4.4.1 Zigbee Protocol -- 4.4.2 Radio-Frequency Identification (RFID) -- 4.4.3 Time Synchronized Mesh Protocol (TSMP) -- 4.4.4 Near-Field Communication (NFC) -- 4.4.5 Bluetooth IEEE 802.15.1 -- 4.4.6 Wireless-HART -- 4.4.7 Weightless -- 4.4.8 Wireless Fidelity (Wi-Fi) -- 4.4.9 Constrained Application Protocol (CoAP) -- 4.4.10 Message Queuing Telemetry Transport (MQTT) -- 4.4.11 Advanced Message Queuing Protocol (AMQP) -- 4.4.12 Data Distribution Service (DDS) -- 4.4.13 Extensible Messaging and Presence Protocol (XMPP) -- 4.4.14 WebSocket -- 4.5 Conclusions -- References 5. Security and Privacy of Biomedical Data in IoMT -- 5.1 Introduction -- 5.1.1 IoMT Architecture -- 5.1.1.1 Application Layer -- 5.1.1.2 Network Layer -- 5.1.1.3 Perception Layer -- 5.2 Background -- 5.2.1 Privacy and Security Needs for IHS -- 5.2.1.1 Requirements at Data Level -- 5.2.1.2 Requirements at Sensor Level -- 5.2.1.3 Requirements at Personal Server Level -- 5.2.1.4 Requirements at Medical Server Level -- 5.2.2 IHS Security Schemes -- 5.2.3 The Relationship between IoMT Vulnerabilities and Threats -- 5.3 Findings -- 5.3.1 Security Plans for Installable IoMT Devices -- 5.3.2 Targeted Security and Privacy Aspects in IoMT -- 5.3.2.1 Breach of Data Confidentiality -- 5.3.2.2 Attacks Employing Social Engineering (SE) -- 5.3.2.3 Privacy Invasion -- 5.3.2.4 Message Validation and Data Security Threats -- 5.3.2.5 Tool/User Authentication Threats -- 5.3.2.6 Malware Attacks -- 5.3.3 IoMT Security Measures -- 5.3.3.1 Non-Technical Safety Precautions -- 5.3.3.2 Technological Security Procedures -- 5.4 Discussion -- 5.4.1 Inexpensive Cryptographic Algorithms -- 5.4.2 Inexpensive Authentication Protocols -- 5.4.3 Security Architecture with Layer -- 5.4.4 Detecting Sensor Anomalies in Medical Devices -- 5.5 Conclusions and Future Work -- 5.5.1 Future Research Directions -- References -- 6. Cyber-Security Threats to IoMT-Enabled Healthcare Systems -- 6.1 Introduction -- 6.1.1 Where Did IoMT Comes from -- 6.1.2 Heart of the Problem -- 6.1.3 Motivations -- 6.1.4 Contributions -- 6.2 IoMT Framework, Perception, and Future -- 6.2.1 Devices of IoMT -- 6.2.2 Application and Service Domains of IoMT -- 6.3 IoMT Challenges, Risks, and Concerns -- 6.3.1 IoMT Challenges -- 6.3.2 Risks within IoMT -- 6.3.3 IoMT Concerns -- 6.4 Cyber-Attacks Aligned with IoMT -- 6.4.1 Features of Cyber-Attacks -- 6.4.2 IoMT Targeted Security Aspects -- 6.5 Methodologies 6.5.1 Dataset Description -- 6.5.1.1 Characteristic Details -- 6.5.2 Existing Method -- 6.5.3 Proposed Method -- 6.5.3.1 Model Design -- 6.5.3.2 Experiment Setup -- 6.5.4 Performance Metrics and Result Evaluation -- 6.5.4.1 Evaluation Metrics -- 6.5.4.2 Result Evaluation -- 6.5.4.3 Model Structure and Performance -- 6.6 Conclusions -- References -- 7. Using Self-Organizing Map to Find Cardiac Risk Based on Body Mass Index -- 7.1 Introduction -- 7.2 Literature Survey -- 7.3 Methodology -- 7.3.1 BMI Scales -- 7.4 Results and Discussion -- 7.5 Conclusions -- References -- 8. Embedded Medical IoT Devices for Monitoring and Diagnosing Patient Health in Rural Areas Peoples Using IoMT Technology -- 8.1 Introduction -- 8.2 HIoT Technology -- 8.2.1 Location Technology -- 8.2.2 Technology of Identification -- 8.2.3 Technology of Communication -- 8.3 Applications and Services of IoMT -- 8.3.1 Services -- 8.3.1.1 Ambient Assisted Living -- 8.3.1.2 Mobile IoT -- 8.3.1.3 Wearable Devices -- 8.3.1.4 Cognitive Computing -- 8.3.1.5 Reaction of Drug -- 8.3.1.6 Blockchain -- 8.3.1.7 Child Health Information -- 8.3.2 Applications -- 8.3.2.1 ECG Monitoring -- 8.3.2.2 Monitoring the Glucose Level -- 8.3.2.3 Temperature Monitoring -- 8.3.2.4 Blood Pressure Monitoring -- 8.3.2.5 Measuring Oxygen Saturation -- 8.3.2.6 Measuring and Monitoring Asthma -- 8.3.2.7 Monitoring the Mood -- 8.3.2.8 Management of Medication -- 8.3.2.9 Management of Wheelchair -- 8.3.2.10 Rehabilitation System -- 8.3.2.11 Other Notable Applications -- 8.4 Limitations, Challenges, and Opportunities -- 8.4.1 Servicing and Maintenance Cost -- 8.4.2 Power Usage -- 8.4.3 Standardization -- 8.4.4 Data Privacy and Security -- 8.4.5 Scalability -- 8.4.6 Identification -- 8.4.7 Self-/Automatic Configuration -- 8.4.8 Continuous Monitoring -- 8.4.9 Investigation of New Diseases -- 8.4.10 Impact on Environment 8.5 Conclusions -- References -- 9. Case Studies: Cancer Prediction and Diagnosis in the IoMT Environment -- 9.1 Introduction -- 9.1.1 Types of Cancerous Tumors -- 9.1.2 Cogitation on Cancer Prediction -- 9.2 ML and IoMT -- 9.3 Best Cancer Prediction and Diagnosis Using Supervised Algorithm -- 9.4 Early Detection of Tumor Cells and Symptoms of Breast Cancer -- 9.5 Various ML-Based Breast Cancer Classification -- 9.5.1 LR Classifier -- 9.5.2 SVM in Cancer Detection -- 9.5.3 DT Classifier -- 9.6 Performance Evaluation -- 9.6.1 Result Analysis of LR Classifier -- 9.6.1.1 Confusion Matrix for LR Classifier -- 9.6.2 Analysis of SVM Classifier -- 9.6.2.1 Confusion Matrix for SVM Classifier -- 9.6.3 Analysis of DT Classifier -- 9.6.3.1 Confusion Matrix for DT Classifier -- 9.7 Comparative Analysis of Various Classifiers -- 9.8 Conclusions -- References -- 10. A Deep Exploration of Imaging Diagnosis Approaches for IoMT-Based Coronavirus Disease of 2019 Diagnosis System - A Case Study -- 10.1 Introduction -- 10.2 Backgrounds of Imaging Techniques -- 10.2.1 PET -- 10.2.2 Lung Ultrasound -- 10.2.3 MRI -- 10.3 Features to Be Pondered of COVID -- 10.4 Algorithms Deployed for Imaging Techniques -- 10.4.1 Visual Geometry Group Net -- 10.4.2 Inception V3 Designs -- 10.4.2.1 ResNet -- 10.4.2.2 DenseNet -- 10.4.2.3 Inf-Net -- 10.4.2.4 UNet -- 10.5 Machine Learning Techniques -- 10.6 Coronavirus Dataset -- 10.7 Related Works of Coronavirus Disease of 2019 and IoT -- 10.7.1 Coronavirus Disease of 2019 Symptoms Imaging System -- 10.8 Conclusions -- References -- Index "Cognitive Computing for Internet of Medical Things (IoMT) offers a complete assessment of the present scenario, role, challenges, technologies, and impact of IoMT-enabled smart healthcare systems. It contains chapters discussing various biomedical applications under the umbrella of the IoMT"-- Provided by publisher Internet of things Medical informatics Soft Computing Prasanth, A. editor 1-03-218788-3