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Biblioteca

MARC Lista (tabellare)
Blockchain, Artificial Intelligence, and the Internet of Things : Possibilities and Opportunities
Blockchain, Artificial Intelligence, and the Internet of Things : Possibilities and Opportunities
Autore Raj Pethuru
Pubbl/distr/stampa Cham : , : Springer International Publishing AG, , 2021
Descrizione fisica 1 online resource (218 pages)
Altri autori (Persone) DubeyAshutosh Kumar
KumarAbhishek
RathorePramod Singh
Collana EAI/Springer Innovations in Communication and Computing Ser.
Soggetto genere / forma Electronic books.
ISBN 9783030776374
9783030776367
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910510565503321
Raj Pethuru  
Cham : , : Springer International Publishing AG, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Building Embodied AI Systems: The Agents, the Architecture Principles, Challenges, and Application Domains / / edited by Pethuru Raj, Alvaro Rocha, Simar Preet Singh, Pushan Kumar Dutta, B. Sundaravadivazhagan
Building Embodied AI Systems: The Agents, the Architecture Principles, Challenges, and Application Domains / / edited by Pethuru Raj, Alvaro Rocha, Simar Preet Singh, Pushan Kumar Dutta, B. Sundaravadivazhagan
Autore Raj Pethuru
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (526 pages)
Disciplina 006.3
Altri autori (Persone) RochaAlvaro
SinghSimar Preet
DuttaPushan Kumar
SundaravadivazhaganB
Collana Information Systems Engineering and Management
Soggetto topico Automatic control
Robotics
Automation
Computational intelligence
Artificial intelligence
Control, Robotics, Automation
Computational Intelligence
Artificial Intelligence
ISBN 9783031682568
9783031682551
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. Building Embodied AI Systems: The Agents, the Architectural Principles, Challenges and Application Domains -- Chapter 2. Demystifying Embodied AI -- Chapter 3. Navigating the Nexus: Unravelling Challenges, Ethics, and Applications of Embodied AI in Drone Technology through the Lens of Computer Vision -- Chapter 4. Artificial Intelligence Algorithm Models For Agents Of Embodiment For Drone Applications -- Chapter 5. Artificial intelligence algorithms and models for embodied agents: enhancing autonomy in drones and robots -- Chapter 6. Enhanced Security and Privacy from Industry 4.0 and 5.0 Vision -- Chapter 7. Exploring applications: intelligent drones and robots in industrial settings -- Chapter 8. The Industrial Revolution: Harnessing Embodied AI Systems -- Chapter 9. Synergistic Fusion: Vision-Language Models in Advancing Autonomous Driving and Intelligent Transportation Systems -- Chapter 10. Health Care Industry Use Cases of Embodied AI -- Chapter 11. Computing, clouds, analytics and artificial intelligence at the edge.-...Etc.
Record Nr. UNINA-9910951802703321
Raj Pethuru  
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Cloud enterprise architecture / / Pethuru Raj
Cloud enterprise architecture / / Pethuru Raj
Autore Raj Pethuru
Edizione [1st edition]
Pubbl/distr/stampa Boca Raton, Fla. : , : CRC Press, , 2013
Descrizione fisica 1 online resource (511 p.)
Disciplina 004.67/82
004.6782
Soggetto topico Cloud computing
Software architecture
Computer software - Development
Soggetto genere / forma Electronic books.
ISBN 1-4665-8907-8
0-429-06717-8
1-4665-0233-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Contents; Foreword; Preface; Acknowledgments; Author; CEA Book Audience and Key Takeaways; Chapter 1 - Cloud-Enabled Smart Enterprises!; Chapter 2 - Cloud-Inspired Enterprise Transformations!; Chapter 3 - Cloud-Instigated IT Transformations!; Chapter 4 - Cloud EA: Frameworks and Platforms; Chapter 5 - Cloud Application Architecture; Chapter 6 - Cloud Data Architecture; Chapter 7 - Cloud Technology Architecture; Chapter 8 - Cloud Integration Architecture; Chapter 9 - Cloud Management Architecture; Chapter 10 - Cloud Security Architecture (CSA)
Chapter 11 - Cloud Governance ArchitectureChapter 12 - Cloud Onboarding Best Practices; Back Cover
Record Nr. UNINA-9910462548503321
Raj Pethuru  
Boca Raton, Fla. : , : CRC Press, , 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Cloud enterprise architecture / / Pethuru Raj
Cloud enterprise architecture / / Pethuru Raj
Autore Raj Pethuru
Edizione [1st edition]
Pubbl/distr/stampa Boca Raton, Fla. : , : CRC Press, , 2013
Descrizione fisica 1 online resource (511 p.)
Disciplina 004.67/82
004.6782
Soggetto topico Cloud computing
Software architecture
Computer software - Development
ISBN 1-138-37465-2
1-4665-8907-8
0-429-06717-8
1-4665-0233-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Contents; Foreword; Preface; Acknowledgments; Author; CEA Book Audience and Key Takeaways; Chapter 1 - Cloud-Enabled Smart Enterprises!; Chapter 2 - Cloud-Inspired Enterprise Transformations!; Chapter 3 - Cloud-Instigated IT Transformations!; Chapter 4 - Cloud EA: Frameworks and Platforms; Chapter 5 - Cloud Application Architecture; Chapter 6 - Cloud Data Architecture; Chapter 7 - Cloud Technology Architecture; Chapter 8 - Cloud Integration Architecture; Chapter 9 - Cloud Management Architecture; Chapter 10 - Cloud Security Architecture (CSA)
Chapter 11 - Cloud Governance ArchitectureChapter 12 - Cloud Onboarding Best Practices; Back Cover
Record Nr. UNINA-9910785715603321
Raj Pethuru  
Boca Raton, Fla. : , : CRC Press, , 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Cloud enterprise architecture / / Pethuru Raj
Cloud enterprise architecture / / Pethuru Raj
Autore Raj Pethuru
Edizione [1st edition]
Pubbl/distr/stampa Boca Raton, Fla. : , : CRC Press, , 2013
Descrizione fisica 1 online resource (511 p.)
Disciplina 004.67/82
004.6782
Soggetto topico Cloud computing
Software architecture
Computer software - Development
ISBN 1-138-37465-2
1-4665-8907-8
0-429-06717-8
1-4665-0233-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Contents; Foreword; Preface; Acknowledgments; Author; CEA Book Audience and Key Takeaways; Chapter 1 - Cloud-Enabled Smart Enterprises!; Chapter 2 - Cloud-Inspired Enterprise Transformations!; Chapter 3 - Cloud-Instigated IT Transformations!; Chapter 4 - Cloud EA: Frameworks and Platforms; Chapter 5 - Cloud Application Architecture; Chapter 6 - Cloud Data Architecture; Chapter 7 - Cloud Technology Architecture; Chapter 8 - Cloud Integration Architecture; Chapter 9 - Cloud Management Architecture; Chapter 10 - Cloud Security Architecture (CSA)
Chapter 11 - Cloud Governance ArchitectureChapter 12 - Cloud Onboarding Best Practices; Back Cover
Record Nr. UNINA-9910800034703321
Raj Pethuru  
Boca Raton, Fla. : , : CRC Press, , 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Cloud-native computing : how to design, develop, and secure microservices and event-driven applications / / Pethuru Raj, Skylab Vanga and Akshita Chaudhary
Cloud-native computing : how to design, develop, and secure microservices and event-driven applications / / Pethuru Raj, Skylab Vanga and Akshita Chaudhary
Autore Raj Pethuru
Pubbl/distr/stampa Piscataway, New Jersey ; ; Hoboken, New Jersey : , : IEEE Press : , : Wiley, , [2023]
Descrizione fisica 1 online resource (354 pages)
Disciplina 005.3
Soggetto topico Application software - Development
Cloud computing
Application software
ISBN 1-119-81479-0
1-119-81477-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- About the Authors -- Preface -- Acknowledgments -- Chapter 1 The Dawning of the Digital Era -- Demystifying the Digitization Paradigm -- Delineating the Digitalization Technologies -- Trendsetting Technologies for the Digital Era -- Why Digitization Is Indispensable -- The Connectivity and Integration Options -- The Promising Digital Intelligence Methods -- The Technological Approaches Toward Smarter Environments -- Briefing the Brewing Idea of Digital Twin -- Envisioning the Digital Universe -- Cloud-Native Applications (CNAs) -- Conclusion -- References -- Chapter 2 The Cloud-Native Computing Paradigm for the Digital Era -- Introduction -- The Onset of the Digital Era -- The Maturity of Software-defined Cloud Environments -- The Hybrid Model of Microservices Architecture (MSA) and Event-driven Architecture (EDA) -- The Aspect of Containerization -- The Emergence of Container Lifecycle Management Platforms -- Tending Toward Cloud-Native Computing -- Demystifying the Cloud-Native Architecture Style -- Distinguishing Cloud-Native Infrastructure -- Cloud-Native Security -- Cloud-Native Computing Advantages -- Conclusion -- References -- Chapter 3 Kubernetes Architecture, Best Practices, and Patterns -- Introduction -- The Emergence of Containerized Applications for IT Portability -- Microservices Architecture (MSA) Applications for IT Agility and Adaptivity -- The Onset of Containerized Cloud Environments -- The Need for Container Orchestration Platform Solutions -- The Significance of Kubernetes for Cloud-Native Systems -- Kubernetes for Edge Cloud Environments -- Kubernetes for Multi-Cloud Implementations -- Delineating the Kubernetes' Master-Slave Architecture -- The Special Features of the Kubernetes Platform -- Best Practices for Efficient and Effective Kubernetes -- Kubernetes Patterns.
Conclusion -- References -- Chapter 4 The Observability, Chaos Engineering, and Remediation for Cloud-Native Reliability -- Introduction -- The Emergence of Cloud-Native Observability -- The Key Motivations for Cloud-Native Applications -- Cloud-Native Applications: The Realization Technologies -- DevOps for Cloud-Native Applications (CNAs) -- Container Orchestration Platforms -- The Cloud-Native Application Challenges [1, 2] -- Cloud-Native Resiliency -- Cloud-Native Chaos Engineering -- Cloud-Native Observability [3-5] -- Cloud-Native Observability: The Benefits -- Cloud-Native Observability for Chaos Engineering -- AIOps-Enabled Cloud-Native Observability -- Building System Resilience Through AIOps -- Cloud-Native Remediation -- Conclusion -- References -- Chapter 5 Creating Kubernetes Clusters on Private Cloud (VMware vSphere) -- Introduction -- Purpose -- Scope -- Deployment Pre-requirements -- Prerequisites -- vCenter Requirements -- Cluster Resources -- Required IP Addresses -- DNS Records -- Create Local Linux Installer VM on VMware vSphere -- Generating an SSH Private Key and Adding it to the Agent -- Create DHCP Server -- Download OpenShift Installation for vSphere -- Procedure -- Adding vCenter Root CA Certificates to your Installer VM -- Deploying the OCP Cluster on VMware vSphere -- Installing the CLI on Linux -- Uninstall OpenShift Cluster -- Considerations When you Delete OpenShift for VMware (https://cloud.ibm.com/docs/vmwaresolutions) -- Conclusion -- Further Reading -- Chapter 6 Creating Kubernetes Clusters on Public Cloud (Microsoft Azure) -- Introduction -- Prerequisites -- Configuring a Public DNS Zone in Azure -- DNS Creation -- Prerequisites -- Create a DNS Zone -- Required Azure Roles -- Creating a Service Principal -- Azure CLI Setup -- Manually Create IAM -- Start Installation of OCP -- Uninstall Cluster -- Conclusion.
Further Reading -- Chapter 7 Design, Development, and Deployment of Event-Driven Microservices Practically -- Introduction -- Technology Stack to Build Microservices -- Express Framework -- Steps to Set Up Your Project -- Blog Post Microservice -- Comments Microservice -- Implementation of Event-Driven Model -- Event Bus -- Deployment Strategies -- Conclusion -- Chapter 8 Serverless Computing for the Cloud-Native Era -- Introduction -- The Key Motivations for Serverless Computing [1, 2] -- Briefing Serverless Computing -- The Serverless Implications -- The Evolution of Serverless Computing [3, 4] -- Serverless Application Patterns -- Containers as the Function Runtime -- Serverless Computing Components [5, 6] -- Advantages of Using a Serverless Database -- Disadvantages of Using Serverless Databases -- Top Benefits of Serverless Computing -- Overcoming Serverless Obstacles -- The Future of Serverless Computing -- Conclusion -- Appendix -- Knative for Serverless Computing -- References -- Chapter 9 Instaling Knative on a Kubernetes Cluster -- Introduction -- Knative Serving Resources -- Further Reading -- Chapter 10 Delineating Cloud-Native Edge Computing -- Introduction -- Briefing Cloud-Native Computing -- Technical and Business Cases for Cloud-Native Computing [3, 4] -- The Emergence of Edge Computing -- Cloud-Native Technologies for Edge Computing -- Benefits of Bringing the Cloud-Native Principles to the Edge -- The Deployment Scenarios at the Edge -- Kubernetes Deployment Options for Edge Computing -- Cloud-Native at the Edge: The Use Cases -- Navigating Heterogeneous Environments at the Edge -- Monitoring Kubernetes-Enabled Edge Environments -- Edge Analytics for Real-Time Video Surveillance -- Describing Edge AI -- Conclusion -- References -- Chapter 11 Setting up a Kubernetes Cluster using Azure Kubernetes Service -- Introduction.
Benefits of Azure Kubernetes Service -- Purpose -- Scope -- An Introduction to Azure Kubernetes Service -- Features of Azure Kubernetes Services -- Azure Kubernetes Service Use Cases -- Common Uses for Azure Kubernetes Service -- High-Level Architecture -- Architecture Design -- Deployment Pre-Requisites -- Conclusion -- Further Reading -- Chapter 12 Reliable Cloud-Native Applications through Service Mesh -- Introduction -- Delineating the Containerization Paradigm -- Demystifying Microservices Architecture -- Decoding the Growing Role of Kubernetes for the Container Era -- Describing the Service Mesh Concept [1-3] -- Demystifying Service Mesh -- The Service Mesh Contributions -- The Leading Service Mesh Solutions -- Why Service Mesh is Paramount? -- Service Mesh Architectures -- Monitoring the Service Mesh -- Service Mesh Deployment Models -- Conclusion -- Appendix -- Deploying the Red Hat OpenShift Service Mesh Control Plane -- References -- Chapter 13 Cloud-Native Computing: The Security Challenges and the Solution Approaches -- Introduction to Cloud Capabilities -- Delineating the Cloud-Native Paradigm -- Why Cloud-Native Computing -- About Cloud-Native Applications -- Beginning of Cloud-Native Application Security -- Cloud-Native Security Challenges -- Capabilities of Cloud-Native Security Solutions -- Cloud-Native Application Security Procedures -- Securing Cloud-Native Applications -- Pillars of Cloud-Native Security -- Cloud-Native Security: Best Practices -- Kubernetes Security Best Practices -- Container Security Best Practices -- Cloud-Native Security Best Practices -- The Emergence of Cloud-Native Security Products and Platforms -- Key Properties of Cloud-Native Security Platforms -- Cloud Workload Protection Platforms -- Kubernetes Security Products -- AIOps for Cloud-Native Security -- Conclusion -- Reference.
Chapter 14 Microservices Security: The Concerns and the Solution Approaches -- Microservice Security Challenges and Concerns -- Best Practices to Secure Microservices -- How to Implement Fundamental Authentication and Authorization Strategies -- Dive Deeper into API Gateway -- APACHE APISIX -- Configuring APISIX -- Conclusion -- Further Reading -- Chapter 15 Setting Up Apache Kafka Clusters in a Cloud Environment and Secure Monitoring -- Introduction -- Introspecting Kafka -- Kafka Component Overview -- Guide to Set Up a Kafka Cluster -- Prerequisites -- Steps to Install -- Step 1: Setup virtual machines for Kafka -- Step 2: Configure Zookeeper and Kafka on both the machines -- zookeeper.service -- kafka.service -- Step 3: Test Zookeeper and Kafka installation -- Kafka Command Line Features -- Set Up Your Monitoring Tools for Your Cluster: Prometheus and Grafana -- Fetch Metrics Using Prometheus -- Install JMX Exporter Agent on Kafka broker -- Create Prometheus as a service on the Admin machine -- Visualize using Grafana -- Secure your cluster -- Encryption -- Authentication -- Authorization -- Conclusion -- Further Reading -- Chapter 16 Installing Knative Serving On EKS -- Prerequisites -- EKS Installation Procedure -- Installing Knative Serving Using YAML Files -- Prerequisites -- System Requirements -- Install the Serving component -- Verify the Installation -- Configure DNS -- Install kn Using a Binary -- Install kn Using Go -- Sample Application -- Creating Your Deployment with the Knative CLI -- Interacting with Your App -- Index -- EULA.
Record Nr. UNINA-9910830945803321
Raj Pethuru  
Piscataway, New Jersey ; ; Hoboken, New Jersey : , : IEEE Press : , : Wiley, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Cloud-Native Computing : How to Design, Develop, and Secure Microservices and Event-Driven Applications
Cloud-Native Computing : How to Design, Develop, and Secure Microservices and Event-Driven Applications
Autore Raj Pethuru
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2022
Descrizione fisica 1 online resource (354 pages)
Altri autori (Persone) VangaSkylab
ChaudharyAkshita
ISBN 1-119-81479-0
1-119-81477-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- About the Authors -- Preface -- Acknowledgments -- Chapter 1 The Dawning of the Digital Era -- Demystifying the Digitization Paradigm -- Delineating the Digitalization Technologies -- Trendsetting Technologies for the Digital Era -- Why Digitization Is Indispensable -- The Connectivity and Integration Options -- The Promising Digital Intelligence Methods -- The Technological Approaches Toward Smarter Environments -- Briefing the Brewing Idea of Digital Twin -- Envisioning the Digital Universe -- Cloud-Native Applications (CNAs) -- Conclusion -- References -- Chapter 2 The Cloud-Native Computing Paradigm for the Digital Era -- Introduction -- The Onset of the Digital Era -- The Maturity of Software-defined Cloud Environments -- The Hybrid Model of Microservices Architecture (MSA) and Event-driven Architecture (EDA) -- The Aspect of Containerization -- The Emergence of Container Lifecycle Management Platforms -- Tending Toward Cloud-Native Computing -- Demystifying the Cloud-Native Architecture Style -- Distinguishing Cloud-Native Infrastructure -- Cloud-Native Security -- Cloud-Native Computing Advantages -- Conclusion -- References -- Chapter 3 Kubernetes Architecture, Best Practices, and Patterns -- Introduction -- The Emergence of Containerized Applications for IT Portability -- Microservices Architecture (MSA) Applications for IT Agility and Adaptivity -- The Onset of Containerized Cloud Environments -- The Need for Container Orchestration Platform Solutions -- The Significance of Kubernetes for Cloud-Native Systems -- Kubernetes for Edge Cloud Environments -- Kubernetes for Multi-Cloud Implementations -- Delineating the Kubernetes' Master-Slave Architecture -- The Special Features of the Kubernetes Platform -- Best Practices for Efficient and Effective Kubernetes -- Kubernetes Patterns.
Conclusion -- References -- Chapter 4 The Observability, Chaos Engineering, and Remediation for Cloud-Native Reliability -- Introduction -- The Emergence of Cloud-Native Observability -- The Key Motivations for Cloud-Native Applications -- Cloud-Native Applications: The Realization Technologies -- DevOps for Cloud-Native Applications (CNAs) -- Container Orchestration Platforms -- The Cloud-Native Application Challenges [1, 2] -- Cloud-Native Resiliency -- Cloud-Native Chaos Engineering -- Cloud-Native Observability [3-5] -- Cloud-Native Observability: The Benefits -- Cloud-Native Observability for Chaos Engineering -- AIOps-Enabled Cloud-Native Observability -- Building System Resilience Through AIOps -- Cloud-Native Remediation -- Conclusion -- References -- Chapter 5 Creating Kubernetes Clusters on Private Cloud (VMware vSphere) -- Introduction -- Purpose -- Scope -- Deployment Pre-requirements -- Prerequisites -- vCenter Requirements -- Cluster Resources -- Required IP Addresses -- DNS Records -- Create Local Linux Installer VM on VMware vSphere -- Generating an SSH Private Key and Adding it to the Agent -- Create DHCP Server -- Download OpenShift Installation for vSphere -- Procedure -- Adding vCenter Root CA Certificates to your Installer VM -- Deploying the OCP Cluster on VMware vSphere -- Installing the CLI on Linux -- Uninstall OpenShift Cluster -- Considerations When you Delete OpenShift for VMware (https://cloud.ibm.com/docs/vmwaresolutions) -- Conclusion -- Further Reading -- Chapter 6 Creating Kubernetes Clusters on Public Cloud (Microsoft Azure) -- Introduction -- Prerequisites -- Configuring a Public DNS Zone in Azure -- DNS Creation -- Prerequisites -- Create a DNS Zone -- Required Azure Roles -- Creating a Service Principal -- Azure CLI Setup -- Manually Create IAM -- Start Installation of OCP -- Uninstall Cluster -- Conclusion.
Further Reading -- Chapter 7 Design, Development, and Deployment of Event-Driven Microservices Practically -- Introduction -- Technology Stack to Build Microservices -- Express Framework -- Steps to Set Up Your Project -- Blog Post Microservice -- Comments Microservice -- Implementation of Event-Driven Model -- Event Bus -- Deployment Strategies -- Conclusion -- Chapter 8 Serverless Computing for the Cloud-Native Era -- Introduction -- The Key Motivations for Serverless Computing [1, 2] -- Briefing Serverless Computing -- The Serverless Implications -- The Evolution of Serverless Computing [3, 4] -- Serverless Application Patterns -- Containers as the Function Runtime -- Serverless Computing Components [5, 6] -- Advantages of Using a Serverless Database -- Disadvantages of Using Serverless Databases -- Top Benefits of Serverless Computing -- Overcoming Serverless Obstacles -- The Future of Serverless Computing -- Conclusion -- Appendix -- Knative for Serverless Computing -- References -- Chapter 9 Instaling Knative on a Kubernetes Cluster -- Introduction -- Knative Serving Resources -- Further Reading -- Chapter 10 Delineating Cloud-Native Edge Computing -- Introduction -- Briefing Cloud-Native Computing -- Technical and Business Cases for Cloud-Native Computing [3, 4] -- The Emergence of Edge Computing -- Cloud-Native Technologies for Edge Computing -- Benefits of Bringing the Cloud-Native Principles to the Edge -- The Deployment Scenarios at the Edge -- Kubernetes Deployment Options for Edge Computing -- Cloud-Native at the Edge: The Use Cases -- Navigating Heterogeneous Environments at the Edge -- Monitoring Kubernetes-Enabled Edge Environments -- Edge Analytics for Real-Time Video Surveillance -- Describing Edge AI -- Conclusion -- References -- Chapter 11 Setting up a Kubernetes Cluster using Azure Kubernetes Service -- Introduction.
Benefits of Azure Kubernetes Service -- Purpose -- Scope -- An Introduction to Azure Kubernetes Service -- Features of Azure Kubernetes Services -- Azure Kubernetes Service Use Cases -- Common Uses for Azure Kubernetes Service -- High-Level Architecture -- Architecture Design -- Deployment Pre-Requisites -- Conclusion -- Further Reading -- Chapter 12 Reliable Cloud-Native Applications through Service Mesh -- Introduction -- Delineating the Containerization Paradigm -- Demystifying Microservices Architecture -- Decoding the Growing Role of Kubernetes for the Container Era -- Describing the Service Mesh Concept [1-3] -- Demystifying Service Mesh -- The Service Mesh Contributions -- The Leading Service Mesh Solutions -- Why Service Mesh is Paramount? -- Service Mesh Architectures -- Monitoring the Service Mesh -- Service Mesh Deployment Models -- Conclusion -- Appendix -- Deploying the Red Hat OpenShift Service Mesh Control Plane -- References -- Chapter 13 Cloud-Native Computing: The Security Challenges and the Solution Approaches -- Introduction to Cloud Capabilities -- Delineating the Cloud-Native Paradigm -- Why Cloud-Native Computing -- About Cloud-Native Applications -- Beginning of Cloud-Native Application Security -- Cloud-Native Security Challenges -- Capabilities of Cloud-Native Security Solutions -- Cloud-Native Application Security Procedures -- Securing Cloud-Native Applications -- Pillars of Cloud-Native Security -- Cloud-Native Security: Best Practices -- Kubernetes Security Best Practices -- Container Security Best Practices -- Cloud-Native Security Best Practices -- The Emergence of Cloud-Native Security Products and Platforms -- Key Properties of Cloud-Native Security Platforms -- Cloud Workload Protection Platforms -- Kubernetes Security Products -- AIOps for Cloud-Native Security -- Conclusion -- Reference.
Chapter 14 Microservices Security: The Concerns and the Solution Approaches -- Microservice Security Challenges and Concerns -- Best Practices to Secure Microservices -- How to Implement Fundamental Authentication and Authorization Strategies -- Dive Deeper into API Gateway -- APACHE APISIX -- Configuring APISIX -- Conclusion -- Further Reading -- Chapter 15 Setting Up Apache Kafka Clusters in a Cloud Environment and Secure Monitoring -- Introduction -- Introspecting Kafka -- Kafka Component Overview -- Guide to Set Up a Kafka Cluster -- Prerequisites -- Steps to Install -- Step 1: Setup virtual machines for Kafka -- Step 2: Configure Zookeeper and Kafka on both the machines -- zookeeper.service -- kafka.service -- Step 3: Test Zookeeper and Kafka installation -- Kafka Command Line Features -- Set Up Your Monitoring Tools for Your Cluster: Prometheus and Grafana -- Fetch Metrics Using Prometheus -- Install JMX Exporter Agent on Kafka broker -- Create Prometheus as a service on the Admin machine -- Visualize using Grafana -- Secure your cluster -- Encryption -- Authentication -- Authorization -- Conclusion -- Further Reading -- Chapter 16 Installing Knative Serving On EKS -- Prerequisites -- EKS Installation Procedure -- Installing Knative Serving Using YAML Files -- Prerequisites -- System Requirements -- Install the Serving component -- Verify the Installation -- Configure DNS -- Install kn Using a Binary -- Install kn Using Go -- Sample Application -- Creating Your Deployment with the Knative CLI -- Interacting with Your App -- Index -- EULA.
Record Nr. UNINA-9910623986603321
Raj Pethuru  
Newark : , : John Wiley & Sons, Incorporated, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Demystifying Graph Data Science : Graph Algorithms, Analytics Methods, Platforms, Databases, and Use Cases
Demystifying Graph Data Science : Graph Algorithms, Analytics Methods, Platforms, Databases, and Use Cases
Autore Raj Pethuru
Edizione [1st ed.]
Pubbl/distr/stampa Stevenage : , : Institution of Engineering & Technology, , 2022
Descrizione fisica 1 online resource (363 pages)
Disciplina 511.5
Altri autori (Persone) KumarAbhishek
García DíazVicente
Muthuraman SundarNachamai
Collana Computing and Networks
Soggetto topico Graph algorithms
ISBN 1-83724-484-7
1-5231-5341-5
1-83953-489-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Title -- Copyright -- Contents -- About the Editors -- Book preface -- 1 Toward graph data science -- 1.1 Introduction -- 1.2 Concept of graph -- 1.3 Graph travels on analysis -- 1.4 Graph plotting -- 1.5 Network graph of an ETFL ARK Funds -- 1.6 Twitch verse -- 1.6.1 Use of graph theory mechanisms for solving data science problems -- 1.7 Data visualization techniques -- 1.8 Present research ongoing -- 1.9 Next 60 years of data science -- 1.10 Scientific data analytics tested empirically -- 1.11 Conclusion -- 1.12 Future work -- References -- 2 Data science: the Artificial Intelligence (AI) algorithms-inspired use cases -- 2.1 Introduction -- 2.2 The evolution and elevation of data science -- 2.3 Anomaly detection -- 2.3.1 Binary and multiclass classification -- 2.3.2 Personalization -- 2.4 Fraud detection -- 2.4.1 Challenges to fraud detection -- 2.4.2 Best practices for observability with fraud models -- 2.4.3 Important metrics -- 2.4.4 Performance degradation -- 2.4.5 Overcoming the drift problem -- 2.5 AI-enabled fake news detection -- 2.6 AI-inspired credit card fraud detection -- 2.7 AI-empowered forest fire prediction -- 2.8 AI-induced breast cancer (BC) detection -- 2.8.1 Phase 0 - preparation of data -- 2.8.2 Phase 1: data investigation -- 2.8.3 Phase 2: data categories -- 2.8.4 Phase 3: feature scaling -- 2.8.5 Phase 4: ML model selection -- 2.8.6 Phase 5 - model evaluation -- 2.8.7 Phase 6 - model optimization -- 2.9 Stopping cyber attacks by AI algorithms -- 2.10 ML for cyber security -- 2.11 Network protection -- 2.12 Endpoint detection and response (EDR) -- 2.13 Threat detection by EDR -- 2.14 Containment -- 2.15 Application security -- 2.16 User behavior -- 2.17 Process behavior -- 2.18 The modern data architecture (MDA) -- 2.18.1 Smart applications -- 2.18.2 Smarter edge.
2.18.3 Faster, more accurate, and easier management -- 2.19 The Kafka platform for data scientists -- 2.20 Kafka APIs -- 2.21 Conclusion -- References -- 3 Accelerating graph analytics -- 3.1 Introduction -- 3.2 Graph analytics methods to deliver smarter AI -- 3.2.1 Semi supervised learning with graph algorithms -- 3.3 Data preparation -- 3.4 Steps to get started with graph machine learning model -- 3.4.1 Structured query-oriented knowledge graphs -- 3.4.2 Query-based feature engineering -- 3.4.3 Extending the use of graph algorithms -- 3.4.4 Approaches based on local similarity -- 3.4.5 Approaches that are based on global similarity -- 3.4.6 Approaches based on quasi-local similarity -- 3.5 Graph embeddings -- 3.5.1 Why graph embeddings are needed? -- 3.5.2 GNN and native learning -- 3.5.3 Based on the graph type -- 3.6 Applications -- 3.6.1 Classification of text -- 3.6.2 Translation by a neural computer -- 3.6.3 Image classification is a technique used in the field of image manipulation -- 3.6.4 Object detection is a feature that allows detecting of objects in the environment -- 3.6.5 Semantic segmentation is the process of separating words based on their semantic meaning -- 3.6.6 Combinatorial optimization is a technique for maximizing the number of options -- 3.7 Conclusion -- References -- 4 Introduction to IoT data analytics and its use cases -- 4.1 Background and context -- 4.1.1 Cost of compute -- 4.1.2 AI/ML frameworks and optimizations -- 4.1.3 Dedicated hardware components -- 4.1.4 Sensors and data -- 4.1.5 Pre-trained models -- 4.1.6 Retail -- 4.1.7 Medical -- 4.1.8 Industrial -- 4.1.9 Automotive -- 4.1.10 Education -- 4.1.11 Conclusion -- 4.2 IoT analytics system and concepts -- 4.2.1 Overview -- 4.2.2 Data ingestion -- 4.2.3 Analytics pipeline -- 4.3 Network -- 4.3.1 Wired network technologies -- 4.3.2 Wireless network technologies.
4.4 AI -- 4.4.1 Training strategies -- 4.4.2 ML -- 4.4.3 Decision tree (DT) and random forest (RF) -- 4.4.4 Support vector machine (SVM) -- 4.4.5 DL -- 4.4.6 Convolutional neural networks (CNN) -- 4.4.7 Architectures and implementations -- 4.4.8 Other neural networks -- 4.5 Orchestration -- 4.5.1 Container technologies -- 4.5.2 Overview -- 4.5.3 Architecture -- 4.5.4 Cluster constructs -- 4.5.5 Deploying a service -- 4.5.6 Containerizing the application -- 4.5.7 Deployment -- 4.5.8 Microservices -- 4.6 IoT deployments -- 4.6.1 Edge deployments -- 4.6.2 Hybrid cloud edge deployments -- References -- 5 Demystifying digital transformation technologies in healthcare -- 5.1 Introduction -- 5.2 Primal elements driving the medical industry -- 5.2.1 Recent research in healthcare -- 5.2.2 Medical expenses and their surge -- 5.2.3 Improvement in mortality rate of older people -- 5.2.4 Modulating relationship -- 5.2.5 Eccentric frameworks -- 5.3 Technology trends in healthcare -- 5.3.1 Smart watches and clinical device network -- 5.3.2 Intelligence and data analytics -- 5.3.3 Augmented reality (AR) and virtual reality (VR) -- 5.3.4 Telemedicine -- 5.4 Technology challenges in healthcare -- 5.4.1 Data processing -- 5.4.2 Cybersecurity -- 5.4.3 Digital user experience -- 5.5 Big Data in healthcare -- 5.6 Big Data in healthcare applications -- 5.7 Challenges for Big Data in healthcare -- 5.7.1 Data collection challenges -- 5.7.2 Procedure and method challenges -- 5.7.3 Data management challenges -- 5.7.4 Significant factors that support health plan agencies in enhancing quality measurement results -- 5.8 Case study -- 5.9 Proposed method -- 5.10 Experimental results and discussion -- 5.10.1 Accuracy analysis -- 5.10.2 Average performance analysis -- 5.10.3 Average response time (ART) -- 5.10.4 Pattern classification time -- 5.10.5 Error rate -- 5.10.6 Conclusion.
References -- 6 Semantic knowledge graph technologies in data science -- 6.1 Introduction -- 6.2 Knowledge extraction and information extraction -- 6.2.1 Professional-based systems -- 6.2.2 Construction of knowledge graph -- 6.2.3 Conceptualization -- 6.3 Creating knowledge graphs using semantic models -- 6.3.1 System construction -- 6.3.2 Synopsis mapping -- 6.3.3 Semantic model -- 6.4 Semantic graph infrastructure -- 6.4.1 Sources of knowledge -- 6.4.2 Extraction of knowledge -- 6.4.3 Convergence of knowledge -- 6.4.4 Processing, collection, and graphical demonstration of knowledge graphs -- 6.5 Semantic knowledge graph -- 6.5.1 Architecture -- 6.5.2 Characteristics -- 6.5.3 Evaluations -- 6.6 Finance industry - a case study for knowledge graph -- 6.6.1 Data authority -- 6.6.2 Automated fraud detection -- 6.6.3 Knowledge management -- 6.6.4 Insider trading -- 6.6.5 AI in capital funding -- 6.6.6 Enabling venture capitalization -- 6.6.7 Analyzing credentials -- 6.6.8 Product-based community analysis -- 6.6.9 Challenges -- 6.7 Conclusion -- References -- 7 Why graph analytics? -- 7.1 Introduction -- 7.1.1 Types of graphs -- 7.1.2 Difference between relational analytics and graph analytics -- 7.2 Big graph analytics -- 7.2.1 Vs of big graph -- 7.3 The basics of graph analytics -- 7.4 Graph analytic techniques -- 7.4.1 Path analytic -- 7.4.2 Analytical connectivity -- 7.4.3 Community analytic -- 7.4.4 Centrality analytic -- 7.5 Big graph analytics algorithms -- 7.5.1 PageRank -- 7.5.2 Connected component -- 7.5.3 Distributed minimum spanning tree -- 7.5.4 Graph search -- 7.5.5 Clustering -- 7.6 Big graph analytics framework for healthcare -- 7.6.1 Big graph characteristics -- 7.6.2 Impact of big graph analytics in healthcare -- 7.6.3 Proposed framework for patient data analytics -- 7.6.4 Key players of proposed model -- 7.7 Implementation and results.
7.8 Conclusion -- References -- 8 Graph technology: a detailed study of trending techniques and technologies of graph analytics -- 8.1 Introduction -- 8.1.1 Centrality -- 8.1.2 Degree centrality -- 8.1.3 Types of graphs -- 8.1.4 Graph algorithms and graph analytics implementations -- 8.1.5 Page rank -- 8.1.6 Graph database -- 8.1.7 Applications -- 8.1.8 Major graph analytics applications -- 8.2 Technology for graph databases -- 8.2.1 Enactment -- 8.2.2 Flexibility -- 8.2.3 Agility -- 8.3 Graph data science? -- 8.3.1 Graph data science applications -- 8.3.2 Graph data science library -- 8.4 Defined graph database -- 8.4.1 Forms of graph databases -- 8.4.2 Graphs of properties -- 8.4.3 Graphs in RDF -- 8.4.4 What are graphs and graph libraries and how do they work? -- 8.4.5 Benefits in graph databases -- 8.4.6 What is the role of graph databases and graph analytics? -- 8.4.7 Graph database use case: money laundering -- 8.4.8 Social network review is a good example of how to use a graph database -- 8.4.9 Credit card theft is an example of how a graph database can be used -- 8.4.10 The evolution of graph databases -- 8.5 Graph analytics trending techniques and technologies -- 8.5.1 Trend 1: Artificial intelligence (AI) that is smarter, quicker, and more accountable -- 8.5.2 Trend 2 -- 8.5.3 The third trend is decision intelligence -- 8.5.4 Trend 4th: X analytics -- 8.5.5 Trend 5th: enhanced data protection -- 8.6 Conclusion -- References -- 9 A holistic analysis to identify the efficiency of data growth using a standardized method of non-functional requirements in graph applications -- 9.1 Introduction -- 9.1.1 Key features of graph database -- 9.1.2 Process related to graph database -- 9.2 Literature survey -- 9.2.1 Graph data modeling -- 9.2.2 Non-functional requirements -- 9.2.3 Building of graph-based application model.
9.3 Various working techniques in graph database.
Record Nr. UNINA-9911004860003321
Raj Pethuru  
Stevenage : , : Institution of Engineering & Technology, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Engineering the Metaverse : Enabling Technologies, Platforms and Use Cases
Engineering the Metaverse : Enabling Technologies, Platforms and Use Cases
Autore Raj Pethuru
Edizione [1st ed.]
Pubbl/distr/stampa Stevenage : , : Institution of Engineering & Technology, , 2024
Descrizione fisica 1 online resource (394 pages)
Disciplina 006.8
Altri autori (Persone) KumarPrasanna
SharmaD. P
SainiKavita
RaoB. Narendra Kumar
KosuriHarshavardhan
ChallaNagendra Panini
RanjanaR
Collana Computing and Networks Series
Soggetto topico Metaverse
Virtual reality
ISBN 1-83724-351-4
1-83953-881-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Contents -- About the editor -- 1. Demystifying metaverse system engineering | Pethuru Raj -- 2. Metaverse enabling technologies and industry use cases | Pethuru Raj -- 3. Virtual, augmented, mixed, and extended reality for the metaverse | Prasanna Kumar -- 4. Immersive technologies for the metaverse | Harshavardhan Kosuri -- 5. 3D modeling for the metaverse | Harshavardhan Kosuri -- 6. Generative AI for the enterprise metaverse system engineering | Pethuru Raj -- 7. Digital twin and metaverse | Kavita Saini and Ritu Gupta -- 8. Metaverse world and seven layers | Kavita Saini -- 9. The convergence of spatial computing, edge computing, neuromorphic computing, HPC, quantum computing, and data analytics for the metaverse | D.P. Sharma -- 10. The convergence of Web 3.0 with AR, VR, IoT, and AI for shaping metaverse engineering | D.P. Sharma -- 11. Detailing the unique power of the blockchain technology for the metaverse world | Kavita Saini, K. Sivakumar, B.S. Vidhyasagar and H. Anwar Basha -- 12. Metaverse use cases (individual and industrial) and application domains | Harshavardhan Kosuri -- 13. Application domains for the metaverse | Prasanna Kumar -- 14. Illustrating metaverse-driven smart manufacturing | Pethuru Raj -- 15. The metaverse as an educational tool: enhancing learning with blockchain technology | B. Narendra Kumar Rao and Shaik Shameen Taz -- 16. IoT, AI, and metaverse in smart healthcare systems: a review of recent advances and future trends | B. Narendra Kumar Rao, N. Bala Krishna and Chinthapatla Pranay Varna -- 17. Application of metaverse in regenerative medicine: applications and challenges | R. Ranjana, R.K. Sahana and B. Narendra Kumar Rao.
18. Metaverse for heart illness prediction and analysis | Bollapalli Althaph, Nagendra Panini Challa, Narendra Kumar Rao, Kamepalli SL Prasanna, Nagaraju Jajam, Venkata Sasi Deepthi Ch and Beebi Naseeba -- 19. Metaverse for Indian palm leaf manuscripts | Basaraboyina Yohoshiva, Nagendra Panini Challa, Narendra Kumar Rao, Beebi Naseeba and Venkata Sasi Deepthi Ch -- The conclusion and the future of the metaverse -- Index.
Record Nr. UNINA-9911006946903321
Raj Pethuru  
Stevenage : , : Institution of Engineering & Technology, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Explainable Artificial Intelligence (XAI) : Concepts, Enabling Tools, Technologies and Applications
Explainable Artificial Intelligence (XAI) : Concepts, Enabling Tools, Technologies and Applications
Autore Raj Pethuru
Edizione [1st ed.]
Pubbl/distr/stampa Stevenage : , : Institution of Engineering & Technology, , 2023
Descrizione fisica 1 online resource (465 pages)
Disciplina 006.3
Altri autori (Persone) KöseUtku
SakthivelUsha
NagarajanSusila
AsirvadamVijanth Sagayan
Collana Computing and Networks Series
Soggetto topico Artificial intelligence
Machine learning
ISBN 1-83724-425-1
1-5231-6305-4
1-83953-696-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Title -- Copyright -- Contents -- About the editors -- Preface -- 1 An overview of past and present progressions in XAI -- 1.1 Introduction -- 1.2 Background study -- 1.2.1 Key-related ideas of XAI -- 1.3 Overview of XAI -- 1.4 History of XAI -- 1.5 Top AI patterns -- 1.6 Conclusion -- References -- 2 Demystifying explainable artificial intelligence (EAI) -- 2.1 Introduction -- 2.1.1 An overview of artificial intelligence -- 2.1.2 Introduction to explainable AI -- 2.2 Concept of XAI -- 2.3 Explainable AI (EAI) architecture -- 2.4 Learning techniques -- 2.5 Demystifying EAI methods -- 2.5.1 Clever Hans -- 2.5.2 Different users and goals in EAI -- 2.5.3 EAI as quality assurance -- 2.6 Implementation: how to create explainable solutions -- 2.6.1 Method taxonomy -- 2.6.2 Rules - intrinsic local explanations -- 2.6.3 Prototypes -- 2.6.4 Learned representation -- 2.6.5 Partial dependence plot - global post-hoc explanations -- 2.6.6 Feature attribution (importance) -- 2.7 Applications -- 2.8 Conclusion -- References -- 3 Illustrating the significance of explainable artificial intelligence (XAI) -- 3.1 Introduction -- 3.2 The growing power of AI -- 3.3 The challenges and concerns of AI -- 3.4 About the need for AI explainability -- 3.5 The importance of XAI -- 3.6 The importance of model interpretation -- 3.6.1 Model transparency -- 3.6.2 Start with interpretable algorithms -- 3.6.3 Standard techniques for model interpretation -- 3.6.4 ROC curve -- 3.6.5 Focus on feature importance -- 3.6.6 Partial dependence plots (PDPs) -- 3.6.7 Global surrogate models -- 3.6.8 Criteria for ML model interpretation methods -- 3.7 Briefing feature importance scoring methods -- 3.8 Local interpretable model-agnostic explanations (LIMEs) -- 3.9 SHAP explainability algorithm -- 3.9.1 AI trust with symbolic AI.
3.10 The growing scope of XAI for the oil and gas industry -- 3.10.1 XAI for the oil and gas industry -- 3.11 Conclusion -- Bibliography -- 4 Inclusion of XAI in artificial intelligence and deep learning technologies -- 4.1 Introduction -- 4.2 What is XAI? -- 4.3 Why is XAI important? -- 4.4 How does XAI work? -- 4.5 Role of XAI in machine learning and deep learning algorithm -- 4.6 Applications of XAI in machine learning in deep learning -- 4.7 Difference between XAI and AI -- 4.8 Challenges in XAI -- 4.9 Advantages of XAI -- 4.10 Disadvantages of XAI -- 4.11 Future scope of XAI -- 4.12 Conclusion -- References -- 5 Explainable artificial intelligence: tools, platforms, and new taxonomies -- 5.1 Introduction -- 5.2 ML-based systems and awareness -- 5.3 Challenges of the time -- 5.3.1 Requirement of explainability -- 5.3.2 Impact of high-stake decisions -- 5.3.3 Concerns of society -- 5.3.4 Regulations and interpretability issue -- 5.4 State-of-the-art approaches -- 5.5 Assessment approaches -- 5.6 Drivers for XAI -- 5.6.1 Tools and frameworks -- 5.7 Discussion -- 5.7.1 For researchers outside of computer science: taxonomies -- 5.7.2 Taxonomies and reviews focusing on specific aspects -- 5.7.3 Fresh perspectives on taxonomy -- 5.7.4 Taxonomy levels at new levels -- 5.8 Conclusion -- References -- 6 An overview of AI platforms, frameworks, libraries, and processes -- 6.1 Introduction to AI -- 6.2 Role of AI in the 21st century -- 6.2.1 The 2000s -- 6.2.2 The 2010s -- 6.2.3 The future -- 6.3 How AI transformed the world -- 6.3.1 Transportation -- 6.3.2 Finance -- 6.3.3 Healthcare -- 6.3.4 Intelligent cities -- 6.3.5 Security -- 6.4 AI process -- 6.5 TensorFlow -- 6.5.1 Installation -- 6.5.2 TensorFlow basics -- 6.6 Scikit learn -- 6.6.1 Features -- 6.6.2 Installation -- 6.6.3 Scikit modeling -- 6.6.4 Data representation in scikit -- 6.7 Keras.
6.7.1 Features -- 6.7.2 Building a model in Keras -- 6.7.3 Applications of Keras -- 6.8 Open NN -- 6.8.1 Application -- 6.8.2 RNN -- 6.9 Theano -- 6.9.1 An overview -- 6.10 Why go for Theano Python library? -- 6.10.1 PROS -- 6.10.2 CONS -- 6.11 Basics of Theano -- 6.11.1 Subtracting two scalars -- 6.11.2 Adding two scalars -- 6.11.3 Adding two matrices -- 6.11.4 Logistic function -- References -- 7 Quality framework for explainable artificial intelligence (XAI) and machine learning applications -- 7.1 Introduction -- 7.2 Background -- 7.3 Integrated framework for AI applications development -- 7.4 AI systems characteristics vs. SE best practices -- 7.4.1 Explainable AI characteristics -- 7.5 ML lifecycle (model, data-oriented, and data analytics-oriented lifecycle) -- 7.6 AI/ML requirements engineering -- 7.7 Software effort estimation for AMD, RL, and NLP systems -- 7.7.1 Modified COCOMO model for AI, ML, and NLP applications and apps -- 7.8 Software engineering framework for AI and ML (SEF4 AI and ML) applications -- 7.9 Reference Architecture for AI & -- ML -- 7.10 Evaluation of Reference Architecture (REF) for AI & -- ML: explainable Chatbot case study -- 7.11 Conclusions and further research -- References -- 8 Methods for explainable artificial intelligence -- 8.1 Preliminarily study -- 8.2 Importance of XAI for human-interpretable models -- 8.3 Overview of XAI techniques -- 8.4 Taxonomy of popular XAI methods -- 8.4.1 Backpropagation-based methods -- 8.4.2 Perturbation methods -- 8.4.3 Influence methods -- 8.4.4 Knowledge extraction -- 8.4.5 Concept methods -- 8.4.6 Visualization methods -- 8.4.7 Example-based explanation -- 8.5 Conclusion -- References -- 9 Knowledge representation and reasoning (KRR) -- 9.1 Introduction -- 9.2 Methodology -- 9.2.1 Reference model -- 9.2.2 Ontologies -- 9.2.3 Knowledge graphs.
9.2.4 Semantic web technologies -- 9.2.5 ML -- 9.2.6 Tools and techniques -- 9.3 Results and discussion -- 9.3.1 Case study: using different techniques for representing medical knowledge [7] -- 9.3.2 Case study: using different techniques for representing academic knowledge [8] -- 9.3.3 Case study: using different techniques for representing farmer knowledge [9] -- 9.3.4 Case study: social media knowledge representation techniques [10] -- 9.3.5 Case study: using different techniques for representing cyber security knowledge [11] -- 9.4 Conclusion and future work -- References -- 10 Knowledge visualization: AI integration with 360-degree dashboards -- 10.1 Introduction -- 10.2 Information visualization vs. knowledge visualization -- 10.3 Knowledge visualization in design thinking -- 10.4 Visualization in transferring knowledge -- 10.5 The knowledge visualization model -- 10.5.1 Knowledge visualization framework -- 10.6 Formats and examples of knowledge visualization -- 10.6.1 Conceptual diagrams -- 10.6.2 Visual metaphors -- 10.6.3 Knowledge animation -- 10.6.4 Knowledge maps -- 10.6.5 Knowledge domain visualization -- 10.7 Types and usage of knowledge visualization tools -- 10.8 Knowledge visualization templates -- 10.8.1 Mind maps -- 10.8.2 Swimlane diagrams -- 10.8.3 Matrix diagrams -- 10.8.4 Flowcharts -- 10.8.5 Concept maps -- 10.8.6 Funnel charts or diagrams -- 10.9 Visualization in machine learning -- 10.9.1 Decision trees -- 10.9.2 Decision graph -- 10.10 Conclusion -- References -- 11 Empowering machine learning with knowledge graphs for the semantic era -- 11.1 Introduction -- 11.2 Tending towards digitally transformed enterprises -- 11.3 The emergence of KGs -- 11.4 Briefing the concept of KGs -- 11.5 Formalizing KGs -- 11.6 Creating custom KGs -- 11.7 Characterizing KGs -- 11.8 Use cases of KGs -- 11.9 ML and KGs.
11.10 KGs for explainable and responsible AI -- 11.11 Stardog enterprise KG platform -- 11.12 What CANNOT be considered a KG? -- 11.13 Conclusion -- Bibliography -- 12 Enterprise knowledge graphs using ensemble learning and data management -- 12.1 Introduction -- 12.2 Current ensemble model learning -- 12.2.1 Bagging -- 12.2.2 Boosting -- 12.2.3 Random Forest -- 12.3 Related work and literature review -- 12.4 Methodology -- 12.4.1 Enhanced ensemble model framework -- 12.4.2 Training and testing datasets -- 12.4.3 Enhanced ensemble model and algorithm -- 12.5 Experimental setup and enterprise dataset -- 12.5.1 Ensemble models performance evaluation using enterprise knowledge graph -- 12.5.2 Tree classification as knowledge graph -- 12.6 Result and discussion -- 12.7 Conclusion -- References -- 13 Illustrating graph neural networks (GNNs) and the distinct applications -- 13.1 Introduction -- 13.2 Briefing the distinctions of graphs -- 13.3 The challenges -- 13.4 ML algorithms -- 13.5 DL algorithms -- 13.6 The emergence of GNNs -- 13.7 Demystifying DNNs on graph data -- 13.8 GNNs: the applications -- 13.9 The challenges for GNNs -- 13.10 Conclusion -- Bibliography -- 14 AI applications-computer vision and natural language processing -- 14.1 Object recognition -- 14.2 AI-powered video analytics -- 14.3 Contactless payments -- 14.4 Foot tracking -- 14.5 Animal detection -- 14.6 Airport facial recognition -- 14.7 Autonomous driving -- 14.8 Video surveillance -- 14.9 Healthcare medical detection -- 14.10 Computer vision in agriculture -- 14.10.1 Drone-based crop monitoring -- 14.10.2 Yield analysis -- 14.10.3 Smart systems for crop grading and sorting -- 14.10.4 Automated pesticide spraying -- 14.10.5 Phenotyping -- 14.10.6 Forest information -- 14.11 Computer vision in transportation -- 14.11.1 Safety and driver assistance -- 14.11.2 Traffic control.
14.11.3 Driving autonomous vehicles.
Record Nr. UNINA-9911007174203321
Raj Pethuru  
Stevenage : , : Institution of Engineering & Technology, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui