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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
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| Stevenage : , : Institution of Engineering & Technology, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Intelligent computing and applications : proceedings of ICDIC 2020 / / edited by B. Narendra Kumar Rao [and three others]
| Intelligent computing and applications : proceedings of ICDIC 2020 / / edited by B. Narendra Kumar Rao [and three others] |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (489 pages) |
| Disciplina | 006.3 |
| Collana | Smart Innovation, Systems and Technologies |
| Soggetto topico |
Artificial intelligence
Big data |
| ISBN | 981-19-4162-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Conference Committee -- Preface -- Contents -- About the Editors -- 1 Prediction of Depression-Related Posts in Instagram Social Media Platform -- 1.1 Introduction -- 1.2 Literature Survey -- 1.3 Proposed Work -- 1.4 Implementation -- 1.5 Results and Discussions -- References -- 2 Classification of Credit Card Frauds Using Autoencoded Features -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Autoencoders -- 2.3.1 Basic Architecture -- 2.3.2 Mathematical Model for Autoencoder -- 2.4 Proposed Method -- 2.4.1 Feature Extraction Phase -- 2.4.2 Classification Phase -- 2.5 Results and Discussion -- 2.5.1 Dataset -- 2.5.2 Experimental Setup -- 2.5.3 Evaluation Metrics -- 2.5.4 Results and Discussion -- 2.6 Conclusion -- References -- 3 BIVFN: Blockchain-Enabled Intelligent Vehicular Fog Networks -- 3.1 Introduction -- 3.2 Vehicular Fog Networking -- 3.3 BIVFN: Blockchain-Enabled Intelligent Vehicular Fog Network for Connected Vehicles -- 3.3.1 Architecture -- 3.3.2 Phases of BIVFN -- 3.4 Security Requirements Attainment in BIVFN -- 3.5 Conclusion -- References -- 4 Deep Learning Approach for Pedestrian Detection, Tracking, and Suspicious Activity Recognition in Academic Environment -- 4.1 Introduction -- 4.2 Literature Survey -- 4.2.1 Pedestrian Dataset -- 4.2.2 Proposed Deep Learning Architecture and Academic Environment Pedestrian Dataset -- 4.3 Recent Deep Learning Architecture -- 4.4 Experimental Results -- 4.5 Conclusions -- References -- 5 Data-Driven Approach to Deflate Consumption in Delay Tolerant Networks -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 DTN Features -- 5.2.2 Irregular Interfacing -- 5.2.3 Low Speed, Inefficiency, and High Queue Interruption -- 5.2.4 Limited Resource -- 5.2.5 Node Life Time -- 5.2.6 Dynamic Topology -- 5.2.7 Performance Issues -- 5.3 Proposed Method -- 5.4 Algorithm -- 5.5 Conclusion -- References.
6 Code-Level Self-adaptive Approach for Building Reusable Software Components -- 6.1 Introduction -- 6.2 Literature Survey -- 6.2.1 Adaptive Reuse -- 6.2.2 Adaptable Software System Versus Traditional Software System -- 6.2.3 Adaptive Methods to Improve the Software System -- 6.3 Proposed Work -- 6.3.1 Software Engineering with Reusable Components -- 6.3.2 Strategy Development Reuse -- 6.3.3 The Incorporation Process to Reuse Project -- 6.3.4 Process Measurement and Evolution -- 6.3.5 Management Reuse -- 6.4 Results and Discussions -- 6.5 Conclusion and Future Work -- References -- 7 Design of a Deep Network Model for Weed Classification -- 7.1 Introduction -- 7.2 Related Works -- 7.3 Methodology -- 7.3.1 Dataset Description -- 7.3.2 Dense Convolutional Neural Networks (DCNN) -- 7.4 Numerical Results and Discussion -- 7.5 Conclusion -- References -- 8 E-Voting System Using U-Net Architecture with Blockchain Technology -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 Methodology -- 8.4 Results and Discussion -- 8.5 Conclusion -- References -- 9 Multi-layered Architecture to Monitor and Control the Energy Management in Smart Cities -- 9.1 Introduction -- 9.1.1 Proposed Questions -- 9.1.2 Methodology -- 9.2 Literature -- 9.2.1 Smart Meters -- 9.2.2 Smart City -- 9.2.3 Internet of Things (IoT) -- 9.2.4 Cloud-Fog-IoT Environment -- 9.2.5 A Network Architecture -- 9.2.6 Drones for Smart Cities -- 9.2.7 Network Architectures for Smart Cities -- 9.2.8 Objectives of Energy Meter Monitoring Device System in -- 9.3 A Novel Approach to Data Acquisition for Energy Monitoring System -- 9.3.1 Proposed Architecture for Cloud-Fog-IoT Environment-Framework -- 9.3.2 The Activities in Three-Layer Network -- 9.3.3 Arduino-Based Energy Meter Control and Management -- 9.3.4 Components of IoT-Based Energy Meter Control and Management. 9.3.5 Features of Proposed 'Three-Layered Architecture' for Smart Meter Monitoring and Management with Integration of Cloud, Fog, IoT and Wireless Sensor Network Model as Shown in Fig. 9.3 Are as Follows -- 9.3.6 Threat Landscape in Three Layer Architecture -- 9.4 Conclusions -- References -- 10 Bio-Inspired Firefly Algorithm for Polygonal Approximation on Various Shapes -- 10.1 Introduction -- 10.2 Problem Formulation -- 10.2.1 Basic Pre-processing -- 10.3 Firefly Algorithm -- 10.3.1 Firefly for Polygonal Approximation -- 10.4 Experimental Results and Discussion -- 10.5 Conclusion -- References -- 11 An Efficient IoT Security Solution Using Deep Learning Mechanisms -- 11.1 Introduction -- 11.2 Related Work -- 11.2.1 Safety in IoT Operation -- 11.2.2 Gaps in the Presented Security Resolutions for IoT Networks -- 11.2.3 Machine Learning: A Solution to Iot Security Challenges -- 11.3 Methodology -- 11.4 Results and Discussion -- 11.4.1 Dataset Used -- 11.4.2 Training and Test Data -- 11.5 Conclusion -- References -- 12 Intelligent Disease Analysis Using Machine Learning -- 12.1 Introduction -- 12.2 Literature Survey -- 12.3 Proposed Work -- 12.4 Results and Analysis -- 12.5 Conclusion -- References -- 13 Automated Detection of Skin Lesions Using Back Propagation Neural Network -- 13.1 Introduction -- 13.2 Literature Survey and Proposed Work -- 13.2.1 Image Segmentation -- 13.2.2 Morphological Image Processing -- 13.3 Results -- 13.4 Conclusion and Future Enhancement -- References -- 14 Detection of COVID-19 Using CNN and ML Algorithms -- 14.1 Introduction -- 14.2 Literature Survey -- 14.2.1 Pneumonia and COVID-19 Detection Using Convolution Neural Network -- 14.2.2 COVID-19 Detection Using Recurrent Neural Network -- 14.2.3 Transfer Learning to Detect COVID-19 Automatically from X-ray Images Using Convolutional Neural Networks. 14.2.4 Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks -- 14.2.5 Use of Fuzzy Soft Set in Decision Making of COVID-19 Risks -- 14.2.6 CNN-LSTM Combination for COVID-19 Prediction -- 14.2.7 Statistical Techniques for Combating COVID-19 -- 14.2.8 Computational Intelligence Techniques for Combating COVID-19: A Survey -- 14.2.9 Toward Using Recurrent Neural Networks for Predicting Influenza-Like Illness: Case Study of COVID-19 in Morocco -- 14.2.10 The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischer's Society -- 14.3 Proposed Methodology -- 14.3.1 Detecting COVID-19 Using CNN -- 14.3.2 Detecting COVID-19 Using Random Forest -- 14.4 Experimental Results -- 14.5 Conclusion -- References -- 15 Prioritization of Watersheds Using GIS and Fuzzy Analytical Hierarchy (FAHP) Method -- 15.1 Introduction -- 15.2 Relevant Study -- 15.3 Methodology -- 15.3.1 Topographical Maps -- 15.3.2 Watershed Atlas of India (WAI) -- 15.3.3 Fuzzy Analytical Hierarchy Program (FAHP) -- 15.4 Results and Discussion -- 15.4.1 Morphometric Analysis -- 15.4.2 Watershed Prioritization Utilizing the Fuzzy Analytical Hierarchy Process (FAHP) Technique -- 15.5 Conclusion -- References -- 16 A Narrative Framework with Ensemble Learning for Face Emotion Recognition -- 16.1 Introduction -- 16.2 Related Work -- 16.3 Proposed Work -- 16.4 Results and Discussion -- 16.5 Conclusion -- References -- 17 Modified Cloud-Based Malware Identification Technique Using Machine Learning Approach -- 17.1 Introduction -- 17.2 Literature Survey -- 17.3 Proposed Methodology -- 17.4 Conclusion -- References -- 18 Design and Deployment of the Road Safety System in Vehicular Network Based on a Distance and Speed -- 18.1 Introduction -- 18.2 Literature Review. 18.3 Proposed System -- 18.3.1 Experimental Methodology -- 18.3.2 Flow of Execution of an Application -- 18.3.3 Modules of the Application -- 18.4 Conclusion -- References -- 19 Diagnosis of COVID-19 Using Artificial Intelligence Techniques -- 19.1 Introduction -- 19.2 Materials and Frameworks -- 19.2.1 Materials and Datasets -- 19.2.2 Frameworks -- 19.3 Approach -- 19.4 Conclusion and Future -- References -- 20 Location Tracking via Bluetooth -- 20.1 Introduction -- 20.2 Related Work -- 20.3 Methodology -- 20.4 Implementation -- 20.4.1 System Components -- 20.4.2 Application Implementation -- 20.5 Conclusion -- References -- 21 Shrimp Surfacing Recognition System in the Pond Using Deep Computer Vision -- 21.1 Introduction -- 21.2 Literature Survey -- 21.3 Proposed Work -- 21.3.1 Image Data Augmentation -- 21.3.2 Horizontal and Vertical Shift Augmentation -- 21.3.3 Horizontal and Vertical Flip Augmentation -- 21.3.4 Random Rotation Augmentation -- 21.4 Results and Analysis -- 21.5 Conclusion -- References -- 22 Sign Language Recognition for Needy People Using Machine Learning Model -- 22.1 Introduction -- 22.2 Relevant Study -- 22.2.1 Pros and Cons of the Existing and Planned Models -- 22.3 Proposed Method -- 22.3.1 Data Preparation -- 22.4 Results and Discussion -- 22.5 Conclusion -- References -- 23 Efficient Usage of Spectrum by Using Joint Optimization Channel Allocation Method -- 23.1 Introduction -- 23.2 Literature Survey -- 23.3 Proposed Method -- 23.4 Experimental Results -- 23.5 Comparison of Results -- 23.6 Conclusion and Future Scope -- References -- 24 An Intelligent Energy-Efficient Routing Protocol for Wearable Body Area Networks -- 24.1 Introduction -- 24.2 Issues in Designing Routing Protocols -- 24.2.1 Existing Energy-Efficient Routing Protocols in WBAN -- 24.3 Proposed Approach -- 24.3.1 Intelligent Energy-Efficient Routing Protocol. 24.4 Simulation Setup. |
| Record Nr. | UNINA-9910624302803321 |
| Gateway East, Singapore : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||