Cognitive engineering for next generation computing : a practical analytical approach / / edited by Kolla Bhanu Prakash [and three others]

• Pubblicazione: Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021]; ©2021
• Descrizione fisica: 1 online resource (368 pages) : illustrations
• Disciplina: 004.21
• Soggetto topico: Soft computing; User-centered system design; Internet of things
• Soggetto genere / forma: Electronic books.
• Persona (resp. second.): PrakashKolla Bhanu
• Note generali: Includes index.
• Nota di contenuto: Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgment -- 1 Introduction to Cognitive Computing -- 1.1 Introduction: Definition of Cognition, Cognitive Computing -- 1.2 Defining and Understanding Cognitive Computing -- 1.3 Cognitive Computing Evolution and Importance -- 1.4 Difference Between Cognitive Computing and Artificial Intelligence -- 1.5 The Elements of a Cognitive System -- 1.5.1 Infrastructure and Deployment Modalities -- 1.5.2 Data Access, Metadata, and Management Services -- 1.5.3 The Corpus, Taxonomies, and Data Catalogs -- 1.5.4 Data Analytics Services -- 1.5.5 Constant Machine Learning -- 1.5.6 Components of a Cognitive System -- 1.5.7 Building the Corpus -- 1.5.8 Corpus Administration Governing and Protection Factors -- 1.6 Ingesting Data Into Cognitive System -- 1.6.1 Leveraging Interior and Exterior Data Sources -- 1.6.2 Data Access and Feature Extraction -- 1.7 Analytics Services -- 1.8 Machine Learning -- 1.9 Machine Learning Process -- 1.9.1 Data Collection -- 1.9.2 Data Preparation -- 1.9.3 Choosing a Model -- 1.9.4 Training the Model -- 1.9.5 Evaluate the Model -- 1.9.6 Parameter Tuning -- 1.9.7 Make Predictions -- 1.10 Machine Learning Techniques -- 1.10.1 Supervised Learning -- 1.10.2 Unsupervised Learning -- 1.10.3 Reinforcement Learning -- 1.10.4 The Significant Challenges in Machine Learning -- 1.11 Hypothesis Space -- 1.11.1 Hypothesis Generation -- 1.11.2 Hypotheses Score -- 1.12 Developing a Cognitive Computing Application -- 1.13 Building a Health Care Application -- 1.13.1 Healthcare Ecosystem Constituents -- 1.13.2 Beginning With a Cognitive Healthcare Application -- 1.13.3 Characterize the Questions Asked by the Clients -- 1.13.4 Creating a Corpus and Ingesting the Content -- 1.13.5 Training the System. ; 1.13.6 Applying Cognition to Develop Health and Wellness -- 1.13.7 Welltok -- 1.13.8 CaféWell Concierge in Action -- 1.14 Advantages of Cognitive Computing -- 1.15 Features of Cognitive Computing -- 1.16 Limitations of Cognitive Computing -- 1.17 Conclusion -- References -- 2 Machine Learning and Big Data in Cyber-Physical System: Methods, Applications and Challenges -- 2.1 Introduction -- 2.2 Cyber-Physical System Architecture -- 2.3 Human-in-the-Loop Cyber-Physical Systems (HiLCPS) -- 2.4 Machine Learning Applications in CPS -- 2.4.1 K-Nearest Neighbors (K-NN) in CPS -- 2.4.2 Support Vector Machine (SVM) in CPS -- 2.4.3 Random Forest (RF) in CPS -- 2.4.4 Decision Trees (DT) in CPS -- 2.4.5 Linear Regression (LR) in CPS -- 2.4.6 Multi-Layer Perceptron (MLP) in CPS -- 2.4.7 Naive Bayes (NB) in CPS -- 2.5 Use of IoT in CPS -- 2.6 Use of Big Data in CPS -- 2.7 Critical Analysis -- 2.8 Conclusion -- References -- 3 HemoSmart: A Non-Invasive Device and Mobile App for Anemia Detection -- 3.1 Introduction -- 3.1.1 Background -- 3.1.2 Research Objectives -- 3.1.3 Research Approach -- 3.1.4 Limitations -- 3.2 Literature Review -- 3.3 Methodology -- 3.3.1 Methodological Approach -- 3.3.2 Methods of Analysis -- 3.4 Results -- 3.4.1 Impact of Project Outcomes -- 3.4.2 Results Obtained During the Methodology -- 3.5 Discussion -- 3.6 Originality and Innovativeness of the Research -- 3.6.1 Validation and Quality Control of Methods -- 3.6.2 Cost-Effectiveness of the Research -- 3.7 Conclusion -- References -- 4 Advanced Cognitive Models and Algorithms -- 4.1 Introduction -- 4.2 Microsoft Azure Cognitive Model -- 4.2.1 AI Services Broaden in Microsoft Azure -- 4.3 IBM Watson Cognitive Analytics -- 4.3.1 Cognitive Computing -- 4.3.2 Defining Cognitive Computing via IBM Watson Interface -- 4.3.3 IBM Watson Analytics -- 4.4 Natural Language Modeling. ; 4.4.1 NLP Mainstream -- 4.4.2 Natural Language Based on Cognitive Computation -- 4.5 Representation of Knowledge Models -- 4.6 Conclusion -- References -- 5 iParking-Smart Way to Automate the Management of the Parking System for a Smart City -- 5.1 Introduction -- 5.2 Background & -- Literature Review -- 5.2.1 Background -- 5.2.2 Review of Literature -- 5.3 Research Gap -- 5.4 Research Problem -- 5.5 Objectives -- 5.6 Methodology -- 5.6.1 Lot Availability and Occupancy Detection -- 5.6.2 Error Analysis for GPS (Global Positioning System) -- 5.6.3 Vehicle License Plate Detection System -- 5.6.4 Analyze Differential Parking Behaviors and Pricing -- 5.6.5 Targeted Digital Advertising -- 5.6.6 Used Technologies -- 5.6.7 Specific Tools and Libraries -- 5.7 Testing and Evaluation -- 5.8 Results -- 5.9 Discussion -- 5.10 Conclusion -- References -- 6 Cognitive Cyber-Physical System Applications -- 6.1 Introduction -- 6.2 Properties of Cognitive Cyber-Physical System -- 6.3 Components of Cognitive Cyber-Physical System -- 6.4 Relationship Between Cyber-Physical System for Human-Robot -- 6.5 Applications of Cognitive Cyber-Physical System -- 6.5.1 Transportation -- 6.5.2 Industrial Automation -- 6.5.3 Healthcare and Biomedical -- 6.5.4 Clinical Infrastructure -- 6.5.5 Agriculture -- 6.6 Case Study: Road Management System Using CPS -- 6.6.1 Smart Accident Response System for Indian City -- 6.7 Conclusion -- References -- 7 Cognitive Computing -- 7.1 Introduction -- 7.2 Evolution of Cognitive System -- 7.3 Cognitive Computing Architecture -- 7.3.1 Cognitive Computing and Internet of Things -- 7.3.2 Cognitive Computing and Big Data Analysis -- 7.3.3 Cognitive Computing and Cloud Computing -- 7.4 Enabling Technologies in Cognitive Computing -- 7.4.1 Cognitive Computing and Reinforcement Learning -- 7.4.2 Cognitive Computing and Deep Learning. ; 7.5 Applications of Cognitive Computing -- 7.5.1 Chatbots -- 7.5.2 Sentiment Analysis -- 7.5.3 Face Detection -- 7.5.4 Risk Assessment -- 7.6 Future of Cognitive Computing -- 7.7 Conclusion -- References -- 8 Tools Used for Research in Cognitive Engineering and Cyber Physical Systems -- 8.1 Cyber Physical Systems -- 8.2 Introduction: The Four Phases of Industrial Revolution -- 8.3 System -- 8.4 Autonomous Automobile System -- 8.4.1 The Timeline -- 8.5 Robotic System -- 8.6 Mechatronics -- References -- 9 Role of Recent Technologies in Cognitive Systems -- 9.1 Introduction -- 9.1.1 Definition and Scope of Cognitive Computing -- 9.1.2 Architecture of Cognitive Computing -- 9.1.3 Features and Limitations of Cognitive Systems -- 9.2 Natural Language Processing for Cognitive Systems -- 9.2.1 Role of NLP in Cognitive Systems -- 9.2.2 Linguistic Analysis -- 9.2.3 Example Applications Using NLP With Cognitive Systems -- 9.3 Taxonomies and Ontologies of Knowledge Representation for Cognitive Systems -- 9.3.1 Taxonomies and Ontologies and Their Importance in Knowledge Representation -- 9.3.2 How to Represent Knowledge in Cognitive Systems? -- 9.3.3 Methodologies Used for Knowledge Representation in Cognitive Systems -- 9.4 Support of Cloud Computing for Cognitive Systems -- 9.4.1 Importance of Shared Resources of Distributed Computing in Developing Cognitive Systems -- 9.4.2 Fundamental Concepts of Cloud Used in Building Cognitive Systems -- 9.5 Cognitive Analytics for Automatic Fraud Detection Using Machine Learning and Fuzzy Systems -- 9.5.1 Role of Machine Learning Concepts in Building Cognitive Analytics -- 9.5.2 Building Automated Patterns for Cognitive Analytics Using Fuzzy Systems -- 9.6 Design of Cognitive System for Healthcare Monitoring in Detecting Diseases -- 9.6.1 Role of Cognitive System in Building Clinical Decision System. ; 9.7 Advanced High Standard Applications Using Cognitive Computing -- 9.8 Conclusion -- References -- 10 Quantum Meta-Heuristics and Applications -- 10.1 Introduction -- 10.2 What is Quantum Computing? -- 10.3 Quantum Computing Challenges -- 10.4 Meta-Heuristics and Quantum Meta-Heuristics Solution Approaches -- 10.5 Quantum Meta-Heuristics Algorithms With Application Areas -- 10.5.1 Quantum Meta-Heuristics Applications for Power Systems -- 10.5.2 Quantum Meta-Heuristics Applications for Image Analysis -- 10.5.3 Quantum Meta-Heuristics Applications for Big Data or Data Mining -- 10.5.4 Quantum Meta-Heuristics Applications for Vehicular Trafficking -- 10.5.5 Quantum Meta-Heuristics Applications for Cloud Computing -- 10.5.6 Quantum Meta-Heuristics Applications for Bioenergy or Biomedical Systems -- 10.5.7 Quantum Meta-Heuristics Applications for Cryptography or Cyber Security -- 10.5.8 Quantum Meta-Heuristics Applications for Miscellaneous Domain -- References -- 11 Ensuring Security and Privacy in IoT for Healthcare Applications -- 11.1 Introduction -- 11.2 Need of IoT in Healthcare -- 11.2.1 Available Internet of Things Devices for Healthcare -- 11.3 Literature Survey on an IoT-Aware Architecture for Smart Healthcare Systems -- 11.3.1 Cyber-Physical System (CPS) for e-Healthcare -- 11.3.2 IoT-Enabled Healthcare With REST-Based Services -- 11.3.3 Smart Hospital System -- 11.3.4 Freescale Home Health Hub Reference Platform -- 11.3.5 A Smart System Connecting e-Health Sensors and Cloud -- 11.3.6 Customizing 6LoWPAN Networks Towards IoT-Based Ubiquitous Healthcare Systems -- 11.4 IoT in Healthcare: Challenges and Issues -- 11.4.1 Challenges of the Internet of Things for Healthcare -- 11.4.2 IoT Interoperability Issues -- 11.4.3 IoT Security Issues. ; 11.5 Proposed System: 6LoWPAN and COAP Protocol-Based IoT System for Medical Data Transfer by Preserving Privacy of Patient.
• Titolo autorizzato: Cognitive engineering for next generation computing
• ISBN: 1-119-71129-0; 1-119-71130-4; 1-119-71128-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910555003003321
• Lo trovi qui: Univ. Federico II