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
• 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.
• Sommario/riassunto: "The cognitive approach to the IoT provides connectivity to everyone and everything since IoT connected devices are known to increase rapidly. When the IoT is integrated with cognitive technology, performance is improved, and smart intelligence is obtained. Discussed in this book are different types of datasets with structured content based on cognitive systems. The IoT gathers the information from the real time datasets through the internet, where the IoT network connects with multiple devices. This book mainly concentrates on providing the best solutions to existing real-time issues in the cognitive domain. Healthcare-based, cloud-based and smart transportation-based applications in the cognitive domain are addressed. The data integrity and security aspects of the cognitive computing main are also thoroughly discussed along with validated results."--
• 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.: 9910677030603321
• Lo trovi qui: Univ. Federico II