Note: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Authors -- Chapter 1: Describing the Cognitive IoT Paradigm -- Introduction -- About the Internet of Things (IoT) Conundrum -- The Trends and Transitions towards the IoT Era -- Deeper Digitization towards Smart Objects -- The Growing Device Ecosystem -- Device-to-Device Integration -- Fog/Edge Device Computing -- Software-defined Cloud Infrastructures for the IoT Era -- Cloud Infrastructures for Real-time Big Data Analytics -- Cloud Infrastructures for IoT Devices -- The IoT Integration Types -- Cloud-to-Cloud (C2C) Integration -- Sensor-to-Cloud (S2C) Integration -- The IoT Reference Architectures -- The IoT Realization Technologies -- The IoT Implications -- The IoT-inspired Industrial Applications -- Connected Applications -- Structural Health of Buildings -- Smartness Overloaded Industries -- Smart Energy -- Smart Healthcare -- Smarter Homes -- Smart Cargo Handling -- Smart Traffic Management -- Smart Inventory and Replenishment Management -- Smart Cash Payments -- Smart Tracking -- Smart Displays -- Smart Asset Management -- Air Quality -- Noise Monitoring -- Smart Parking -- How to Make IoT Environments Intelligent? -- Connectivity + Cognition Leads to Smarter Environments -- Characterizing Cognitive Systems and Environments -- Building Cognitive Systems and Services -- Envisioning Cognitive Edge Devices -- Conclusion -- References -- Chapter 2: Demystifying the Cognitive Computing Paradigm -- Introduction -- Briefing the Next-generation Technologies -- Artificial Intelligence (AI) -- Software-defined Cloud Environments -- Defining Cognitive Computing -- The Distinct Attributes of Cognitive Systems -- Cognitive Computing Technologies -- Cognitive Computing: The Industry Use Cases -- Products and Services Become Smarter with Cognitive Technologies. , Creating New Product Categories -- Automation and Orchestration of Processes through Cognitive Technologies -- Cognitive Technologies Enable the Transition of Data to Information and Knowledge -- Real-world Cognitive Computing Applications -- The Strategic Implications of Cognitive Computing -- The Widespread Usage of Cognitive Systems -- Edge Computing -- Serverless Computing -- Enhanced Cognitive Capability with Big Data -- Cognitive Intelligence (CI) -- Cognitive Computing Strategy: The Best Practices -- Cognitive Application Platforms -- Artificial Intelligence (AI) vs Cognitive Computing -- Advantages of Cognitive Computing -- Conclusion -- References -- Chapter 3: The Cognitive IoT: The Platforms, Technologies, and Their Use Cases -- Introduction -- Chapter Organization -- Generic Architecture of IoT Platform -- Generic Architecture of Cognitive IoT Platform (CIoT) -- Data Flow in a Cognitive IoT-Based Architecture -- Cognitive Capabilities Offered by IoT Platforms -- Classification of Cognitive Capabilities Offered by IoT Platforms -- Cognitive Capabilities for Consumer Market -- Cognitive Capabilities for Enterprise Market -- Prominent Cognitive IoT Platforms -- Google Cloud IoT Platform -- Google Cloud IoT Core -- Cloud IoT Edge -- Reference Architecture for Google Cloud IoT Platform -- IBM Watson IoT Platform -- Architecture of Amazon Web Services IoT (AWS IoT) -- Working of AWS IoT -- Microsoft Azure IoT Architecture -- Microsoft Azure IoT Reference Architecture -- OpenMTC -- How to Get Started with OpenMTC -- Summary -- References -- Chapter 4: Delineating the Key Capabilities of Cognitive Cloud Environments -- Introduction -- AI for Deeper Data Analytics and Decisive Automation -- The Significance of the Cloud Paradigm -- Briefing Cognitive Computing -- Characterizing Cognitive Systems -- The Key Drivers of Cognitive Computing. , The Distinct Attributes of Cognitive Computing -- The Potentials of Cognitive Technologies -- Real-life Examples of Cognitive Systems -- VantagePoint AI -- Welltok -- SparkCognition -- Expert System -- Microsoft Cognitive Services -- DeepMind -- Cognitive Computing: The Benefits -- Automated Data Analytics -- Process Optimization -- Better Level of Customer Interactions -- Cognitive Assistants Automate Customer Care -- Deeper Human Engagement and Personalization -- Enhanced Expertise and Knowledge Processing -- Products and Services to Sense and Think -- The Prominent Use Cases of Cognitive Computing -- Tending towards Cognitive Analytics -- Miscellaneous Applications -- Cognitive Clouds -- Describing the Cloud Journey -- Virtualized and Managed Clouds -- Software-defined Clouds -- Containerized Clouds -- Envisioning Cognitive Clouds -- The Need for Integrated Cognitive Platforms -- Illustrating Cognitive Capabilities for Next-generation Clouds -- Cognitive Cloud Autoscaling -- Cognitive Cryptography -- Conclusion -- References -- Chapter 5: Machine Learning (ML) Algorithms for Enabling the Cognitive Internet of Things (CIoT) -- Introduction -- The Emergence of Cognitive IoT Systems -- The Cognitive IoT Systems: A Few Use Cases for ML Practice -- Machine Learning for Cognitive IoT Systems -- About Machine Learning (ML) Algorithms -- Supervised ML Algorithms -- Types of Supervised ML Algorithms -- The Regression and Classification ML Algorithms -- Linear Regression -- Logistic Regression -- Linear Discriminant Analysis (LDA) -- Decision Trees -- Support Vector Machine (SVM) -- Hyperplanes -- Logistic Regression and the Large Margin Intuition -- Naive Bayes Algorithm -- K-Nearest Neighbours (KNNs) -- Learning Vector Quantization (LVQ) -- Random Forest (RF) -- Ensembling Methods -- Bagging -- Boosting -- Stacking -- Hands-On Lab. , Use Cases of Machine Learning towards Cognitive Systems -- Code Sample and Explanation -- Linear Discriminant Analysis (LDA) Classification Example -- Conclusion -- References -- Chapter 6: Unsupervised and Semi-supervised Machine Learning Algorithms for Cognitive IoT Systems -- Introduction -- Data-driven Insights -- Enhanced User Experience -- Process Automation -- The Emergence of Cognitive Systems -- Heading towards the Cognitive Era -- The Realization of Digital Entities -- Digital Entities Can Form Localized, Ad Hoc, and Dynamic Networks -- The Explosion of Digital Data -- Data to Information and Knowledge -- The Future Internet -- Intelligent and Real-time Applications -- Edge AI -- The Marriage of IoT and AI Paradigms -- Setting Up and Sustaining Smart Environments -- Machine Learning (ML) Algorithms for the Cognitive World -- Unsupervised Learning Algorithms -- Why Unsupervised Learning? -- Types of Unsupervised Learning -- Clustering -- The Clustering Types -- Briefing the K-means Algorithm -- Hands-On Lab -- How Does K-means Clustering Work? -- Hierarchical Clustering - Agglomerative and Divisive Clustering -- Association -- The Key Use Cases of Unsupervised Learning -- Data Compression -- Dimensionality Reduction -- Generative Models -- Unsupervised Deep Learning -- Applications of Unsupervised Machine Learning -- Disadvantages of Unsupervised Learning -- Semi-supervised Learning Algorithms -- Why Is Semi-supervised ML Important? -- Reinforcement Learning -- Machine Learning Use Cases -- Conclusion -- References -- Chapter 7: Deep Learning Algorithms for Cognitive IoT Solutions -- Briefing Deep Learning -- The Origin and Mesmerizing Journey of Deep Learning -- How Deep Learning Works? -- The Deep Learning Use Cases -- The Significance of Deep Learning Algorithms -- Classification -- Object Detection -- Segmentation. , The Top Deep Learning Algorithms -- Multilayer Perceptron Neural Network (MLPNN) -- Backpropagation -- Convolutional Neural Network (CNN) -- Recurrent Neural Network (RNN) -- Long Short-Term Memory (LSTM) -- Generative Adversarial Network (GAN) -- Deep Belief Network (DBN) -- Hands-On Lab -- A Sample Implementation of CNN Image Classification with Python -- Save the Model -- Evaluate and Test -- Summary -- References -- Chapter 8: Computer Vision (CV) Technologies and Tools for Vision-based Cognitive IoT Systems -- Introduction -- How Does Computer Vision Work? -- Computer Vision Applications -- A New Era of Cancer Treatment -- Autopiloting Cars -- Face Recognition -- Computer Vision in Healthcare -- Computer Vision for Defect detection -- Computer Vision for Assembly Verification -- Computer Vision and Robotics for Bin Picking -- Assisting in Diagnostics -- Personalized Experience -- Reducing In-store Theft -- Industrial Applications of Computer Vision -- Predictive Maintenance -- Computer Vision for Automating Inventory Management -- Warehouse Management -- Helping Business to Optimize Marketing -- Branded Object Recognition -- Optimizing Agriculture with Computer Vision -- Encouraging Citizen Scientists -- Remote Visual Assistance & -- Self-service -- Vision-enabled Machines -- Deep Learning for Machine Vision -- How Does Deep Learning Contribute for and Complement Machine Vision? -- The Limitations of Computer Vision -- Hands-On Lab -- Computer Vision Sample Code Implementation -- Image Detection with OpenCV -- OpenCV -- Haar Cascade Classifier -- Sample Code Walk Thru -- Canny Edge Detection Algorithm with OpenCV -- Conclusion -- References -- Chapter 9: Natural Language Processing (NLP) Methods for Cognitive IoT Systems -- Prominent NLP Use Cases -- Other Use Cases -- Natural Language Processing (NLP) towards Cognitive IoT Solutions. , NLP Can Improve Many Different Processes.

Note: Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- List of Figures -- List of Tables -- List of Contributors -- List of Abbreviations -- Chapter 1: An Analytical Framework for the Industrial Internet of Things (IIoT): Importance, Recent Challenges, and Enabling Technologies -- 1.1: Introduction -- 1.1.1: Industrial automation with IoT -- 1.1.2: Objective -- 1.2: Literature Survey -- 1.2.1: Industry 4.0 -- 1.3: Enabling Technologies for IIoT -- 1.3.1: Blockchain technology -- 1.3.2: Cloud computing -- 1.3.3: Big data analytics -- 1.3.4: Artificial intelligence and cyber-physical systems -- 1.3.5: Augmented and virtual reality -- 1.4: Framework and Case Studies -- 1.4.1: SnappyData -- 1.4.2: Fault detection classification -- 1.5: Challenges in IIoT -- 1.5.1: Schemes for efficient data storage -- 1.5.2: IoT systems from different vendors working together -- 1.5.3: Adaptable and resilient technologies for analyzing large datasets -- 1.5.4: Trust in IIoT systems -- 1.5.5: Integration of wireless technologies and protocols in the Internet of Things (IIoT) -- 1.5.6: The edge of decentralization -- 1.5.7: New operating systems for the Internet of Things -- 1.5.8: Public safety in IIoT -- 1.6: Application for IIoT Framework -- 1.7: Conclusion and Future Scope -- Chapter 2: Industry Automation: The Contributions of Artificial Intelligence (AI) -- 2.1: Introduction -- 2.2: Automation Systems Potential -- 2.3: Application Landscape and Production-related Scenarios -- 2.3.1: Autonomy-level classification of industrial AI applications -- 2.4: Impact of Artificial Intelligence in Industry4.0: (I4.0) -- 2.4.1: Order-controlled production (OCP) -- 2.4.2: Smart production (SP2) -- 2.4.3: Innovative product development (IPD) -- 2.4.4: Seamless and dynamic engineering of plants (SDP) -- 2.4.5: Circular economy (CRE). , 2.4.6: 5G for digital factories - mobile controlled production (MCP) -- 2.5: Industry Use Cases for AI-enabled Collaboration -- 2.5.1: Artificial intelligence in healthcare industry -- 2.5.1.1: The use of predictive analytics to confirm the need for surgery -- 2.5.1.2: Intelligent surgical robots -- 2.5.2: Artificial intelligence in manufacturing and factories -- 2.5.2.1: Analytical services for advanced data -- 2.5.2.2: Predictive maintenance -- 2.5.2.3: Automation of robotic processes -- 2.5.3: Artificial intelligence in automobile -- 2.5.3.1: The use of artificial intelligence to improve design -- 2.5.3.2: AI application in manufacturing -- 2.5.3.3: Examples of AI in manufacturing - inspiring changes -- 2.5.4: Application of artificial intelligence in quality control -- 2.5.5: Manufacturing industry trends with emerging AI -- 2.5.6: The Internet of Things is emerging as Industry 4.0's future -- 2.5.7: Future scope of research -- 2.6: Conclusion -- Chapter 3: Industry Automation: The Contributions of Artificial Intelligence -- 3.1: Introduction -- 3.2: Literature Review -- 3.3: Industry 5.0: and AI -- 3.4: Problems with Human-Robot Collaboration -- 3.4.1: Issues with law and regulation -- 3.4.2: Subjective opinion for using robots at work -- 3.4.3: Psychosocial problems caused by human-robot collaboration -- 3.4.4: Changes that result from human-robot collaboration -- 3.4.5: The shifting functions of human resources divisions -- 3.5: Wafer Fabrication Automation -- 3.6: AI as a Vital Technology in Industry 5.0 -- 3.6.1: Impact of AI on different industries -- 3.7: Artificial General Intelligence (AGI) -- 3.8: The Scenario of AI in the Focus of Manufacturing -- 3.9: Automation based on AI (ABAI) -- 3.9.1: Computerized root cause analysis using AI -- 3.9.2: Intelligent computing in product matching -- 3.10: Robotic Process Automation. , 3.11: The Digital Solutions Entangled in Industry 5.0 -- 3.12: AMS for Industry 5.0: Advanced Manufacturing System -- 3.13: Methods, Data, and Results -- 3.14: Conclusion -- Chapter 4: Artificial Intelligence (AI) Driven Industrial Automation -- 4.1: Introduction -- 4.2: Evolution of Artificial Intelligence -- 4.3: Industry 4.0: Technologies -- 4.4: Development in AI -- 4.5: AI Future Perception -- 4.6: Digital Transformation -- 4.7: Components of AI in Automation -- 4.8: Artificial Intelligence Applications in Automation -- 4.9: Automation and AI -- 4.10: Conclusion -- Chapter 5: Quantum Machine and Deep Learning Models for Industry Automation -- 5.1: Introduction -- 5.2: Difference Between Classical and Quantum Data -- 5.3: Quantum Computing -- 5.3.1: Qubit -- 5.3.2: Superposition -- 5.3.3: Entanglement -- 5.4: Quantum Machine Learning (QML) -- 5.5: Classical Machine Learning vs. Quantum Computing -- 5.5.1: Linear algebra problems have been solved via quantum machine learning -- 5.6: Quantum Thinking in Depth -- 5.6.1: Principal component analysis in quantum -- 5.6.2: Support vector quantum machines -- 5.6.3: Optimization -- 5.7: Quantum Learning in Depth -- 5.7.1: Why is quantum machine learning so exciting? -- 5.8: The Essence of Quantum Computing -- 5.8.1: Taking the initiative to manage uncertainty -- 5.8.2: Welcoming a new AI era -- 5.8.3: Cybersecurity advancement -- 5.8.4: Accuracy of weather predictions -- 5.8.5: A signal to develop better life-saving drugs -- 5.9: A Portal to Exciting Future Technology -- 5.9.1: How AI will change thanks to quantum computing -- 5.9.2: Processes for making better business decisions -- 5.9.3: Quantum security and artificial intelligence -- 5.9.4: AI and quantum computing complement DevOps -- 5.9.5: Where are our IT systems vulnerable? -- 5.9.6: Limitation of quantum machine learning. , 5.9.7: Hardware constraints -- 5.9.8: Program restrictions -- 5.10: More on Quantum Computing and Machine Learning Connections -- 5.10.1: Wavefunction -- 5.10.2: The significance of accuracy -- 5.10.3: Data power and quantum machine learning -- 5.11: Case Study -- 5.11.1: Q-SVM (quantum support vector machine algorithm) -- 5.11.2: Why did they need Q-SVM? -- 5.11.3: Import the library -- 5.11.4: Install the dataset -- 5.12: Quantum Computing and Machine Learning for Industry Automation -- 5.12.1: Discover -- 5.12.2: Design -- 5.12.3: Control -- 5.12.4: Supply chains -- 5.12.5: How does manufacturing begin? -- 5.13: Conclusion and Future Scope -- Chapter 6: The Contribution of Computer Vision in the Manufacturing Industries and the Scope for Further Excellence -- 6.1: Introduction -- 6.2: Components of a Machine Vision Systems -- 6.3: Image Formation -- 6.4: Computer vision algorithms -- 6.5: Use Case of the Computer Vision in Industries -- 6.5.1: Product assembly -- 6.5.2: Defect detection -- 6.5.3: 3D Vision system -- 6.5.4: Vision-guided robots -- 6.5.5: Predictive maintenance -- 6.6: Safety and Security Standards -- 6.7: Packaging Standards -- 6.8: Barcode Analysis -- 6.9: Inventory Management -- 6.10: Optimizing Supply Chains -- 6.11: Quality Inspection with Computer Vision -- 6.12: Computer Vision during the Covid-19: Pandemic -- 6.13: Computer Vision in the Automotive Industry -- 6.13.1: Press shop -- 6.13.2: Body shop -- 6.13.3: Paint shop -- 6.13.4: Final assembly shop -- 6.14: Computer Vision Performance Metrics -- 6.14.1: Intersection over union (IoU) -- 6.14.2: Precision -- 6.14.3: Recall -- 6.14.4: F1: score -- 6.15: Conclusion -- Chapter 7: Waste Management 4.0: An Industry Automation Approach to the FutureWaste Management System -- 7.1: Introduction -- 7.2: Exploring CPS -- 7.2.1: CPS -- 7.2.2: Drawbacks of CPS. , 7.3: Industry 4.0: Environment -- 7.4: Challenges in the Waste Management Industry -- 7.5: Applications of CPS in the Waste Management Industry -- 7.6: Influence of Industry 4.0: on the Waste Management Industry -- 7.7: Barriers to Implementing Industry 4.0: in the Waste Management Industry -- 7.8: Case Study: Machine Learning for Waste Management -- 7.9: Conclusion -- Chapter 8: Industrial Internet of Things (IIoT) for E-waste Recycling System -- 8.1: Introduction -- 8.2: Background Study -- 8.3: IIoT Working -- 8.4: IIoT Security -- 8.4.1: Risks and challenges of IIoT -- 8.4.2: Difference between IoT and IIoT -- 8.4.3: IIoT applications and examples -- 8.5: Industries using IIoT -- 8.6: Advantages and Disadvantages of IIoT -- 8.6.1: Hindrances of IIoT -- 8.7: Case Study - IoT for E-waste Recycling System -- 8.8: Future Trends of IIoT -- 8.9: Conclusion -- Chapter 9: A Multi-hazard Industry Assessment System Based on Unmanned Aerial Vehicles (UAVs) for Bridges Crossing Seasonal Rivers -- 9.1: Introduction -- 9.2: Literature Survey -- 9.3: Methodology -- 9.3.1: UAV-derived DEM generation by 3D style -- 9.3.2: Hydrodynamic analyses -- 9.3.3: 3D FEM generation -- 9.3.4: Tectonic evaluation -- 9.3.5: Soil modeling -- 9.3.6: Bridge modeling -- 9.4: Result and Discussion -- 9.4.1: Scour depth and flood load calculations by hydraulic modeling -- 9.5: Conclusion -- Chapter 10: Air Quality Prediction using Machine Learning Techniques for Intelligent Monitoring Systems -- 10.1: Introduction -- 10.2: Materials and Methods -- 10.3: Results and Discussion -- 10.4: Conclusion -- Chapter 11: Facial Emotion Classification for Industry Automation using Convolutional Neural Networks -- 11.1: Introduction -- 11.2: Related Works -- 11.3: Dataset Description -- 11.4: Model Architecture -- 11.5: Model Training -- 11.6: Model Metrics -- 11.7: Activation Maps. , 11.8: Implementation Workflow.