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Advances in augmented reality and virtual reality / / Jitendra Kumar Verma, Sudip Paul, editors
| Advances in augmented reality and virtual reality / / Jitendra Kumar Verma, Sudip Paul, editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (220 pages) |
| Disciplina | 006.8 |
| Collana | Studies in computational intelligence |
| Soggetto topico |
Augmented reality
Virtual reality |
| ISBN |
981-16-7219-9
981-16-7220-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910743378303321 |
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Applications of Machine Learning / / edited by Prashant Johri, Jitendra Kumar Verma, Sudip Paul
| Applications of Machine Learning / / edited by Prashant Johri, Jitendra Kumar Verma, Sudip Paul |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (404 pages) |
| Disciplina | 006.31 |
| Collana | Algorithms for Intelligent Systems |
| Soggetto topico |
Applied mathematics
Engineering mathematics Signal processing Image processing Speech processing systems Machine learning Optical data processing Artificial intelligence Computer logic Mathematical statistics Mathematical and Computational Engineering Signal, Image and Speech Processing Machine Learning Image Processing and Computer Vision Logic in AI Probability and Statistics in Computer Science |
| ISBN |
981-15-3357-1
9781529688221 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Statistical Learning Process for the Reduction of Sample Collection Assuring a Desired Level of Confidence -- Sentiment Analysis on Google Play Store Data using Deep Learning -- Managing the Data Meaning in the Data Stream Processing: A Systematic Literature Mapping -- Tracking an Object using Traditional MS (Mean Shift) and CBWH MS (Mean Shift) Algorithm with Kalman Filter -- Transfer Learning and Domain Adaptation for Named Entity Recognition -- Knowledge Graph from Informal Text: Architecture, Components, Algorithms and Applications -- Neighborhood-based Collaborative Recommendations: An Introduction -- Classification of Arabic Texts Using Singular Value Decomposition and Fuzzy C-Means Algorithms -- Echo State Network Based Nonlinear Channel Equalization in Wireless Communication System -- Melody Extraction from Music: A Comprehensive Study -- Comparative Analysis of Combined Gas Turbine-Steam Turbine Power Cycle Performance by Using Entropy Generation and Statistical Methodology -- Data Mining - A Tool for Handling Huge Voluminous Data -- Improved Training Pattern in Back Propagation Neural Networks Using Holt-Winters' Seasonal Method and Gradient Boosting Model -- Ensemble of Multi-headed Machine Learning Architectures for Time-series Forecasting of Healthcare Expenditures -- Applying Soft Computing Approaches To Investigate Software Fault Proneness in Agile Software Development Environment. |
| Record Nr. | UNINA-9910403764103321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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IoT and cloud computing for societal good / / Jitendra Kumar Verma, Deepak Saxena, Vicente González-Prida, editors
| IoT and cloud computing for societal good / / Jitendra Kumar Verma, Deepak Saxena, Vicente González-Prida, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (331 pages) |
| Disciplina | 004.678 |
| Collana | EAI/Springer innovations in communication and computing |
| Soggetto topico |
Internet of things
Cloud computing |
| ISBN | 3-030-73885-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Technical Programme Committee -- Editorial Advisory Board -- Contents -- About the Editors -- Part I Tackling Climate Change -- 1 Towards Energy Efficient Cloud Computing: Research Directions and Methodological Approach -- 1.1 Introduction -- 1.2 Background Motivation -- 1.3 Power Consumption and Energy Efficient Dynamic VM Consolidation -- 1.4 Defining Objective and Setting Research Questions -- 1.5 Methodological Approach -- 1.6 Conclusion -- References -- 2 IoT-Based Smart Air Quality Control System: Prevention to COVID-19 -- 2.1 Introduction -- 2.1.1 Motivation -- 2.1.2 Contribution -- 2.1.3 Organization -- 2.2 Related Work -- 2.3 Proposed Model -- 2.4 Results and Discussion -- 2.5 Conclusion and Future Scope -- References -- 3 Forecasting of Air Pollution via a Low-Cost IoT-Based Monitoring System -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Methodology -- 3.3.1 Data -- 3.3.2 Models -- 3.3.3 Model Calibration -- 3.4 Result -- 3.5 Discussion and Future Work -- References -- 4 Internet of Things Based Best Fruit Segregation and Taxonomy System for Smart Agriculture -- 4.1 Introduction -- 4.2 Literature Review -- 4.3 Methodology -- 4.4 Software/Hardware Tool Used -- 4.4.1 Software Part -- 4.4.2 Hardware Part -- 4.5 Design and Implementation -- 4.5.1 Background Separation of Image -- 4.5.2 Calculating the Center of Blob -- 4.5.3 Calculating Black Dots -- 4.5.4 Fourier Transform -- 4.6 Conclusion -- References -- 5 Toward the Creation of a Web-Based Platform "Bike Sharing" in the Local Transport System -- 5.1 General Formulation of the Problem -- 5.2 Analysis of Recent Research and Publications -- 5.3 Selection of Previously Unsolved Parts of the Overall Problem -- 5.4 Statement of Research Tasks -- 5.5 Material and Research Results -- 5.6 Conclusions -- References -- Part II Digital Health, Learning and Industry.
6 A Survey of Societal Applications of IOT -- 6.1 Introduction -- 6.2 Motivation -- 6.3 IOT -- 6.4 Trends in the Internet of Things -- 6.5 Comparison with the Existing System -- 6.6 Agriculture Applications of IoT -- 6.6.1 Monitoring of Irrigation in Agriculture with IoT -- 6.6.2 Smart Agriculture -- 6.6.3 Intelligent Irrigation -- 6.6.4 Automated Weather Report Causation for Smart Irrigation -- 6.6.5 IoT Based Disease Analysis in Agriculture -- 6.6.6 Flood Prevention Using IoT -- 6.7 Healthcare Applications of IoT -- 6.7.1 IoT in Healthcare -- 6.7.2 Challenges in Health Care with IoT -- 6.7.3 Multidisciplinary Health Care System -- 6.7.4 Survey on Internet of Things Based on Health Care -- 6.7.5 IoT Incorporate Attention Monitoring to Intercept Incursion -- 6.7.6 IoT Based Healthcare with Body Sensor Network (BSN) -- 6.8 IoT in Military -- 6.8.1 Smart Sniper with IoT -- 6.8.2 Military Applications in Smart City with IoT -- 6.8.3 IoT Based Military Federation -- 6.8.4 Fault Tolerant Techniques on the Internet of Military Things -- 6.8.5 IoT Based Military Applications -- 6.9 IoT in Railways -- 6.10 Smart Train Detector Using IoT Approach -- 6.11 IoT in Smart Cities -- 6.12 Smart Home -- 6.12.1 Energy Competent Home Automation Using IoT -- 6.12.2 Home Gadgets Control with IoT -- 6.12.3 Performance Analysis on Wireless Smart Home Automation -- 6.13 Industrial IoT -- 6.13.1 The Internet of Robotic Things -- 6.14 Recognition Proficiency -- 6.15 Fluctuation Proficiency -- 6.16 Conclusion -- References -- 7 Simplify the Difficult: Artificial Intelligence and Cloud Computing in Healthcare -- 7.1 Introduction -- 7.2 Motivation -- 7.3 Cloud Computing -- 7.3.1 Software as a Service (SaaS) -- 7.3.2 Platform as a Service (PaaS) -- 7.3.3 Infrastructure as a Service (IaaS) -- 7.3.4 Internet of Things (IoT) -- 7.4 Artificial Intelligence Tools -- 7.4.1 Neuron. 7.4.2 Neural Networks -- 7.4.2.1 Deep Neural Network -- 7.4.2.2 Natural Language Processing -- 7.4.2.3 Recurrent Neural Networks (RNN) -- 7.4.2.4 Convolutional Neural Network (CNN) -- 7.5 Application Areas -- 7.5.1 Information Tools -- 7.5.1.1 Applications in Health Care Administration -- 7.5.1.2 Cloud Computing Systems for Healthcare Management -- 7.5.1.3 Health Monitoring with IoT -- 7.5.2 Disease Management -- 7.5.2.1 Diabetes Management Through Artificial Intelligence -- 7.5.2.2 Monitoring Mental Health Through IoT -- 7.5.3 Preemptive Measures -- 7.5.3.1 Early Detection Through Biomarkers -- 7.5.3.2 Diagnosis Through Image Recognition -- 7.5.4 Pathological Diagnosis -- 7.5.4.1 Rheumatoid Arthritis -- 7.5.4.2 Epileptic Seizures -- 7.5.4.3 Alzheimer's Disease -- 7.5.4.4 Diabetic Retinopathy -- 7.5.4.5 Breast Cancer -- 7.5.4.6 Obstructive Lung Disease -- 7.5.4.7 Cardiovascular Diseases (CVDs) -- 7.5.5 Applications in Mental Healthcare -- 7.5.5.1 Mental Health Detection Through Sentimental Analysis on Tweets -- 7.5.5.2 Suicide Prevention -- 7.6 Current Limitations -- 7.7 Conclusions and Future Prospects -- References -- 8 NOS Personal Assistant to Engage Elderly People withSmart Home -- 8.1 Introduction -- 8.2 Background -- 8.2.1 Internet of Things -- 8.2.2 Ambient Assisted Living Market -- 8.2.3 Natural Interfaces Landscape -- 8.3 NOS Technology -- 8.3.1 The Challenges of NOS Personal Assistant -- 8.3.2 The Overall Solution -- 8.3.3 A Bottom-Up Approach -- 8.4 Future Research and Innovation Directions -- 8.5 Conclusion -- References -- 9 Digital Technologies Changing the Landscape of Corporate Learning and Development -- 9.1 Introduction -- 9.1.1 Rise of Digital Learning -- 9.2 Digital Learning Framework -- 9.2.1 Start with the End in Mind -- 9.2.2 Assess the Digital Fluency of Your Target Audience -- 9.2.3 Design the Content. 9.2.4 Select Appropriate Digital Learning Platforms and Tools -- 9.2.5 Implement the Digital Learning Program -- 9.2.6 Measure the Impact and Foster Continuous Improvement -- 9.3 Creating the Culture of Learning -- 9.4 Upskilling L& -- D Personnel -- 9.5 Conclusion -- References -- 10 An Assessment of the Behavioral Intention of Generation Z Toward the Adoption of Digital Learning Applications -- 10.1 Introduction -- 10.2 Literature Review -- 10.2.1 Digital Learning -- 10.2.2 Internet of Things (IoT) and Cloud Computing -- 10.2.3 Digital Learning App -- 10.2.4 Actual Use (AU) -- 10.2.5 Behavioral Intention to Use (BITU) -- 10.2.6 Attitude Toward Use (ATU) -- 10.2.7 Perceived Usefulness (PU) -- 10.2.8 Perceived Ease of Use (PEOU) -- 10.2.9 Utility, Learning, and Perceived Usefulness -- 10.2.10 Student Engagement and Perceived Usefulness -- 10.2.11 Data Security and Perceived Usefulness -- 10.2.12 Feedback and Rating and Perceived Usefulness -- 10.2.13 Accessibility and Perceived Usefulness -- 10.2.14 User Interface and Perceived Usefulness -- 10.2.15 Entertainment and Perceived Ease of Use -- 10.2.16 Accessibility and Perceived Ease of Use -- 10.2.17 User Interface and Perceived Ease of Use -- 10.2.18 Exiting TAM Models -- 10.3 Research Methodology -- 10.4 Data Analysis and Interpretation -- 10.4.1 Sample Characteristics and Distributions (N == 490) -- 10.4.2 Measurement Model: Reliability and Validity -- 10.4.3 Structural Equation Model -- 10.5 Discussion -- 10.6 Practical Implications -- 10.7 Theoretical Implications -- 10.8 Conclusion -- References -- 11 A Literature Review on Lean Manufacturing in the Industry 4.0: From Integrated Systems to IoT and Smart Factories -- 11.1 Introduction -- 11.2 The Term Industry 4.0 -- 11.3 From Integrated System to IoT -- 11.4 Smart Factory: Future of Automated Production -- 11.5 Industry 4.0 Characteristics. 11.6 Enabling Technologies of Industry 4.0 -- 11.6.1 Big Data and Analytics -- 11.6.2 Industrial Cloud and Cloud Computing -- 11.6.3 Internet of Things (IoT) -- 11.6.4 Augmented Reality -- 11.6.5 Simulation -- 11.6.6 Autonomous Robot -- 11.6.7 Horizontal and Vertical Integration -- 11.6.8 Additive Manufacturing -- 11.6.9 Cybersecurity -- 11.7 Conclusions -- References -- Part III Improving the Technology -- 12 Multimodal Feature Analysis for Precise Human Hand Gesture Recognition -- 12.1 Introduction and Background -- 12.2 Multimodal Feature Analysis for Gesture Recognition -- 12.2.1 Multi-model Feature Extraction -- 12.2.2 Multi-model Feature Recognition -- 12.3 Results and Discussion -- 12.3.1 Success Ratio -- 12.3.2 Recognition Time -- 12.4 Conclusion -- References -- 13 Calculating the Optimal Frequency of Maintenance for the Improvement of Risk Management: Plausible Models for the Integration of Cloud and IoT -- 13.1 Introduction -- 13.2 Variables Used in the Maintenance Frequency Optimization Process -- 13.2.1 Reliability and Risk -- 13.2.2 Operational Costs -- 13.2.3 Loss of Performance -- 13.2.4 Extension of Equipment Life -- 13.3 Mathematical Models Used for Preventive Replacement -- 13.3.1 Optimal Replacement Model with Use -- 13.3.2 Optimal Interval of Preventive Replacement -- 13.4 Case Study -- 13.5 Conclusions -- References -- 14 Interceptor Pattern-Based Middleware for IoT Protocol Interoperability -- 14.1 Introduction -- 14.2 Related Works -- 14.3 Related Works -- 14.4 Related Works -- 14.5 Specific Use Case -- 14.6 Test Results and Discussion -- 14.7 Conclusions -- References -- 15 Mining Active Influential Nodes for Finding Information Diffusion in Social Networks -- 15.1 Introduction -- 15.2 Literature Review -- 15.2.1 Influential Nodes Identification in Static Network. 15.2.2 Influential Nodes Identification in Dynamic/Temporal Networks. |
| Record Nr. | UNINA-9910523754603321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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