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  • 1
    Online-Ressource
    Online-Ressource
    Milton :Auerbach Publishers, Incorporated,
    Schlagwort(e): Artificial intelligence. ; Electronic books.
    Materialart: Online-Ressource
    Seiten: 1 online resource (326 pages)
    Ausgabe: 1st ed.
    ISBN: 9781000547269
    DDC: 006.3
    Sprache: Englisch
    Anmerkung: 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.
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  • 2
    Schlagwort(e): Big Data ; Hochleistungsrechnen ; Datenanalyse
    Materialart: Buch
    Seiten: xxii, 428 Seiten , Diagramme
    ISBN: 9783319207438
    Serie: Computer communications and networks
    RVK:
    Sprache: Englisch
    Anmerkung: Literaturangaben
    Standort Signatur Einschränkungen Verfügbarkeit
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