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Augmented Reality, Virtual Reality & Semantic 3D Reconstruction
| Augmented Reality, Virtual Reality & Semantic 3D Reconstruction |
| Autore | Lv Zhihan |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (304 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
feature tracking
superpixel structure from motion three-dimensional reconstruction local feature multi-view stereo construction hazard safety education photoreality virtual reality anatomization audio classification olfactory display deep learning transfer learning inception model augmented reality higher education scientific production web of science bibliometric analysis scientific mapping applications in subject areas interactive learning environments 3P model primary education educational technology mobile lip reading system lightweight neural network face correction virtual reality (VR) computer vision projection mapping 3D face model super-resolution radial curve Dynamic Time Warping semantic 3D reconstruction eye-in-hand vision system robotic manipulator probabilistic fusion graph-based refinement 3D modelling 3D representation game engine laser scanning panoramic photography super-resolution reconstruction generative adversarial networks dense convolutional networks texture loss WGAN-GP orientation positioning viewpoint image matching algorithm transformation ADHD EDAH assessment continuous performance test Photometric Stereo (PS) 3D reconstruction fully convolutional network (FCN) semi-immersive virtual reality children cooperative games empowerment perception motor planning problem-solving area of interest wayfinding spatial information one-shot learning gesture recognition GREN skeleton-based 3D composition pre-visualization stereo vision 360° video |
| ISBN | 3-0365-6062-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910639985103321 |
Lv Zhihan
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Electrochemical Exfoliation of Graphene and Its Derivatives : Commercial Applications
| Electrochemical Exfoliation of Graphene and Its Derivatives : Commercial Applications |
| Autore | Khan Raju |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore Pte. Limited, , 2024 |
| Descrizione fisica | 1 online resource (350 pages) |
| Altri autori (Persone) |
KumarNeeraj
SadiqueMohd. Abubakar PariharArpana |
| Collana | Engineering Materials Series |
| ISBN | 981-9721-28-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- Editors and Contributors -- 1 Overview of Electrochemical Exfoliation Approaches -- 1.1 Introduction -- 1.2 Electrochemical Exfoliation -- 1.2.1 Liquid-Phase Exfoliation -- 1.2.2 Electrochemical Bubbling -- 1.2.3 Electrochemical Delamination -- 1.2.4 Pulse Electrochemical Exfoliation -- 1.2.5 Electrochemical Intercalation -- 1.3 Electrochemical Exfoliation Mechanism -- 1.3.1 Anode Exfoliation Mechanism -- 1.3.2 Cathodic Electrode Exfoliation Mechanism -- 1.3.3 Double Electrode Exfoliation Mechanism -- 1.3.4 Electrochemical Exfoliation via Alternative Intercalation -- 1.3.5 Electrochemical Exfoliation via Multistep Intercalation -- 1.4 Factors Affecting Electrochemical Exfoliation -- 1.4.1 Types and Nature of Electrode -- 1.4.2 Types of Electrolytes -- 1.4.3 Concentration of Electrolytes -- 1.4.4 Applied Working Bias/potential -- 1.4.5 Applied Electrical Current -- 1.4.6 Exfoliation Temperature and Amount of Hydrogen Peroxide -- 1.5 Influence of Electrolytes onto the 2D Materials -- 1.6 Influence of Intercalation on the 2D Materials -- 1.7 The Application of Electrochemically Exfoliated 2D Materials -- 1.7.1 2D Materials for Energy Storage -- 1.7.2 Energy Conversion Using 2D Materials -- 1.8 Summary and Future Prospect -- References -- 2 Advantages of Electrochemical Exfoliation Method Over Conventional Methods -- 2.1 Introduction -- 2.2 Synthesis Methods for Graphene and Its Derivatives -- 2.3 Top-Down Approaches -- 2.3.1 Mechanical Exfoliation -- 2.3.2 Chemical Exfoliation -- 2.3.3 Chemical Synthesis -- 2.3.4 Electrochemical Exfoliation -- 2.4 Bottom-Up Approaches -- 2.4.1 Epitaxial Growth -- 2.4.2 Pyrolysis -- 2.4.3 Chemical Vapor Deposition -- 2.4.4 Arc Discharge -- 2.5 Comparison of Electrochemical Exfoliation with Other Methods -- 2.6 Conclusion -- References.
3 Graphene and Its Derivatives: Various Routes of Synthesis -- 3.1 Introduction -- 3.2 Brief History of Graphene -- 3.3 Graphene Derivatives -- 3.3.1 Graphene Oxide (GO) -- 3.3.2 Reduced Graphene Oxide (r-GO) -- 3.3.3 Graphene Quantum Dots (GQDs) -- 3.3.4 Few-Layer Graphene -- 3.4 Various Routes of Synthesis of Graphene and Its Derivatives -- 3.4.1 Electrochemical Exfoliation -- 3.4.2 Mechanical Exfoliation -- 3.4.3 Arc Discharge -- 3.4.4 Unzipping of Carbon Nanotube -- 3.4.5 Chemical Oxidative-Exfoliation-Reduction -- 3.4.6 Chemical Vapor Deposition (CVD) -- 3.4.7 Pyrolysis -- 3.4.8 Plasma Synthesis -- 3.4.9 Epitaxial Growth on the Silicon Carbide -- 3.5 Advantages and Disadvantages of Various Synthesis Methods of Grapheme and Its Derivates -- 3.6 Conclusions -- 3.7 Promising Outlook and Challenges -- References -- 4 Structure and Electrochemical Properties of Graphene, Derivatives, and Its Nanocomposites -- 4.1 Introduction -- 4.2 Structure of Graphene -- 4.3 Electrochemical Properties -- 4.4 Derivatives of Graphene -- 4.4.1 Graphene Oxide -- 4.4.2 Graphene Quantum Dots -- 4.4.3 Reduced Graphene Oxide -- 4.5 Other Graphene Derivatives -- 4.5.1 Graphone -- 4.5.2 Graphyne -- 4.5.3 Graphdiyne -- 4.5.4 Graphane -- 4.6 Nanocomposites of Graphene -- 4.7 Conclusion -- References -- 5 Electrochemical Exfoliation a Green Approach: Waste to Wealth -- 5.1 Introduction -- 5.1.1 Graphene -- 5.2 Graphene-Based Materials -- 5.3 Graphene-Polymer Composites -- 5.4 Graphene-Semiconductor Nanomaterial Composites -- 5.5 Graphene Synthesis -- 5.5.1 Chemical Exfoliation -- 5.5.2 Mechanical Exfoliation -- 5.5.3 Electrochemical Exfoliation -- 5.5.4 Liquid Phase Exfoliation -- 5.5.5 Epitaxial Growth -- 5.5.6 Problems Associated with These Methods -- 5.6 Synthesis of Graphene Using Electrochemical Exfoliation -- 5.6.1 Using Pencil Electrode. 5.6.2 Using Direct Electrodeposition -- 5.7 Applications of Graphene in Various Fields -- 5.7.1 Drug/Gene Delivery and Cancer Therapy -- 5.7.2 Biosensing and Biomolecule Detection -- 5.7.3 In the Biological Imaging Field -- 5.8 Impact of Different Parameters on Graphene Oxide Yield -- 5.8.1 Types of Electrolytes -- 5.8.2 Electrolyte Concentration -- 5.8.3 Operating Temperature -- 5.9 Green Approaches of Graphene-Few Examples -- 5.9.1 Electrochemical Exfoliation of Pencil Core -- 5.9.2 Synthesis of Graphene Nanosheets -- 5.9.3 Palm Oil Leaves Extract -- 5.10 Graphene Glass Hybrid-A Reactive Barrier -- 5.11 Conclusion -- References -- 6 Mechanism of Synthesis for Graphene and Its Derivatives by Electrochemical Exfoliation -- 6.1 Introduction -- 6.2 Electrochemical Exfoliation of Graphene: Techniques and Mechanism -- 6.2.1 Experimental Setup and Mechanism of Exfoliation -- 6.3 Factors Affecting the Synthesis of Graphene and Its Derivative -- 6.3.1 Type of Graphite Material -- 6.3.2 Temperature -- 6.3.3 Type of Solvent -- 6.3.4 Voltage Bias -- 6.3.5 Role of Electrolyte Acid Concentration -- 6.3.6 PH -- 6.4 Synthesis of Graphene Materials by Electrochemical Exfoliation (EE) -- 6.4.1 Graphene -- 6.4.2 Graphene Oxide (GO) -- 6.4.3 Heteroatom-Doped Graphene -- 6.4.4 Other 2D Graphene Derivatives -- 6.4.5 Conclusion and Future Perspectives -- References -- 7 Unique Characteristics of Electrochemically Exfoliated Multidimensional Graphene and Its Derivatives -- 7.1 Introduction -- 7.2 Atomic Interactions in Diverse Types of Graphene -- 7.2.1 Graphene Oxide -- 7.2.2 Graphdiyne -- 7.2.3 Graphane -- 7.2.4 Graphyne -- 7.3 Properties of Graphene and Its Derivatives -- 7.3.1 Optical Properties -- 7.3.2 Mechanical Properties -- 7.3.3 Electronic Properties -- 7.3.4 Photochemistry -- 7.3.5 Large Surface Area -- 7.3.6 Easy Bio-Functionalization. 7.3.7 Availability of Delocalized π-Electrons -- 7.3.8 High Drug Loading Capacity -- 7.3.9 Affinity Toward Water -- 7.4 Unique Characteristics of Multidimensional Graphene Structures and Their Derivatives -- 7.4.1 Graphene Quantum Dots -- 7.4.2 Graphene Nanoribbons -- 7.4.3 Graphene Fibers -- 7.4.4 Graphene Nanomeshes -- 7.4.5 Rippled/Wrinkled Graphene -- 7.4.6 Graphene Membranes -- 7.4.7 3D Graphene Architectures -- 7.5 Properties of Electrochemically Exfoliated Graphene Doped with Heteroatoms -- 7.5.1 Nitrogen -- 7.5.2 Boron -- 7.5.3 Sulfur -- 7.5.4 Phosphorous -- 7.6 Conclusion -- References -- 8 Electrochemistry and Energy Storage Applications of Graphene and Its Derivatives -- 8.1 Introduction -- 8.2 Graphene and Its Derivatives -- 8.3 Graphene -- 8.4 Graphene Oxide -- 8.5 Reduced Graphene Oxide -- 8.6 Synthesis of Graphene and Its Derivatives -- 8.7 Top-Down Approach -- 8.8 Bottom-Up Approach -- 8.9 Electrochemistry and Electrochemical Properties -- 8.10 Composites of Graphene in Energy Storage Systems -- 8.11 Batteries -- 8.11.1 Lithium-Ion Batteries -- 8.11.2 Sodium-Ion Batteries -- 8.11.3 Other Types of Batteries -- 8.12 Supercapacitors -- 8.13 Some Other Energy Storage Systems -- 8.14 Conclusions -- References -- 9 Electrochemically Exfoliated Graphene and Its Derivatives: Applications in Biosensing and Bioimaging -- 9.1 Introduction -- 9.2 Electrochemical Exfoliation: A Facile Method for Synthesis of Graphene and Its Derivatives -- 9.3 Properties of Electrochemically Exfoliated Graphene and Its Derivatives -- 9.4 Application of Graphene and Its Derivatives in the Biosensing Field -- 9.5 Application of Graphene and Its Derivatives in the Bioimaging Field -- 9.6 Future Prospects and Challenges -- 9.7 Concluding Remark -- References -- 10 Electrochemical Exfoliation of Graphene and Its Derivatives and Its Extended Applications in Therapeutics. 10.1 Introduction -- 10.1.1 The Graphene Family of Nanomaterials -- 10.2 Structural Properties of Graphene and Its Derivatives Favouring Therapeutic Applications -- 10.3 Graphene-Based Therapeutics -- 10.4 Graphene and Its Derivatives as Therapeutics Drug Delivery Vehicle -- 10.4.1 Cargo Attachment -- 10.4.2 Cell Targeting -- 10.4.3 Cargo Delivery -- 10.5 Graphene and Its Derivatives for Gene Delivery -- 10.6 Derivatives of Graphene for Tissue Engineering -- 10.7 Graphene and Its Derivatives for Cancer Therapy -- 10.8 Future Prospects and Challenges -- References -- 11 Methodology Advancements for Bulk Production and Commercialization of Graphene and Its Derivatives -- 11.1 Introduction -- 11.2 Basic Methodologies for Bulk Production of Graphene and Its Derivatives -- 11.3 Top-Down Approach -- 11.3.1 Mechanical or Micromechanical Exfoliation Method -- 11.3.2 Liquid Phase Exfoliation Method -- 11.3.3 Electrochemical Exfoliation Method -- 11.4 Bottom-Up Approach -- 11.4.1 Chemical Vapour Deposition (CVD) -- 11.4.2 Epitaxial Technique -- 11.5 Challenges in the Bulk Synthesis of Graphene and Graphene Derivatives -- 11.5.1 Scalable Synthesis Methods -- 11.5.2 Cost of Production -- 11.5.3 Handling, Storage, and Transfer -- 11.5.4 Standardization and Quality Control -- 11.5.5 Impact on the Environment -- 11.6 Advancements in Methodologies for Bulk Production of Graphene -- 11.6.1 Improved CVD Techniques -- 11.6.2 PECVD -- 11.6.3 Thermal-Chemical Vapour Deposition (T-CVD) -- 11.6.4 Flash Graphene -- 11.6.5 Roll to Roll Production -- 11.6.6 Green Synthesis Methods -- 11.6.7 Hybrid Approaches -- 11.6.8 CVD (Chemical Vapor Deposition) Using Mechanical Techniques -- 11.6.9 Electrochemical Exfoliation Followed by Chemical Vapor Deposition -- 11.7 Global Graphene Market -- 11.8 Commercialization of Graphene Products and Their Applications -- 11.9 Conclusion. References. |
| Record Nr. | UNINA-9910861090303321 |
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Khan Raju
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| Singapore : , : Springer Singapore Pte. Limited, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Intelligent data analysis for COVID-19 pandemic / / M. Niranjanamurthy, Siddhartha Bhattacharyya, Neeraj Kumar, editors
| Intelligent data analysis for COVID-19 pandemic / / M. Niranjanamurthy, Siddhartha Bhattacharyya, Neeraj Kumar, editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (377 pages) |
| Disciplina | 362.1962414 |
| Collana | Algorithms for Intelligent Systems |
| Soggetto topico |
COVID-19 (Disease) - Economic aspects
COVID-19 (Disease) - Health aspects |
| ISBN | 981-16-1574-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Editors and Contributors -- Machine Learning-Based Ensemble Approach for Predicting the Mortality Risk of COVID-19 Patients: A Case Study -- 1 Introduction -- 2 Literature Review -- 3 Dataset and Methodology Used -- 3.1 Dataset Description and Preparation -- 3.2 Data Preprocessing -- 3.3 Classification and Ensembling Approaches -- 4 Ensembling Approaches -- 4.1 Boosting -- 4.2 Bagging -- 5 Experiments and Results -- 5.1 Feature Selection of Patient Attributes -- 5.2 Performance of Individual Classifiers -- 6 Conclusion -- References -- Role of Internet of Health Things (IoHTs) and Innovative Internet of 5G Medical Robotic Things (IIo-5GMRTs) in COVID-19 Global Health Risk Management and Logistics Planning -- 1 Introduction -- 1.1 Background of the Study -- 1.2 Aims and Objective of the Study -- 2 Literature Review -- 3 Research Design and Implementation -- 3.1 Research Analysis -- 3.2 Research Discussion -- 4 Future Research Focus -- 5 Recommendation -- 6 Conclusion -- References -- Battling COVID-19 with Process Model of Integrated Digital Technology: An Analysis of Qualitative Data -- 1 Introduction -- 2 Research Design and Structure -- 3 Digital Technology to Combat COVID-19 -- 3.1 Mobile Applications and COVID-19 -- 3.2 Artificial Intelligence, Internet of Things (IoT), Big Data Analytics, and COVID 19 -- 3.3 Social Media and COVID -- 4 Summarization of Digital Technology Applications and COVID-19 -- 5 Process Model of Integrated Digital Technology -- 6 Conclusion -- References -- High-Fidelity Intelligence Ventilator to Help Infect with COVID-19 Based on Artificial Intelligence -- 1 Introduction -- 2 Operating and Revision Modes -- 3 Design and Condition of the Instrument -- 4 Materials and Technology -- 4.1 Typical Parts Needed -- 4.2 Arduino Nano Compatible V3.0 ATmega328.
4.3 Electronic Motor Speed Controller -- 4.4 Wi-Fi Module -- 5 Results and Discussion -- 6 Conclusion -- References -- Boon of Artificial Intelligence in Diagnosis of COVID-19 -- 1 Introduction -- 2 Novel Coronavirus (SARS-CoV-2) -- 3 Evolution of Artificial Intelligence -- 3.1 Strong AI -- 3.2 Weak AI -- 4 Applications of Computational Techniques -- 4.1 Speed Up Diagnosis -- 4.2 Computerized Tracking -- 4.3 Tracking of Infected Individual -- 4.4 Prediction of Incidence Rate and Mortality Rate -- 4.5 Designing and Development of New Drugs and Vaccines -- 4.6 Lowering the Work Load -- 4.7 Prevention of Infectious Disease -- 5 Traditional Diagnostic Methodology -- 5.1 Lateral Flow Immunoassay (LFIA) -- 5.2 Chemiluminescent Immunoassay (CLIA) -- 5.3 Neutralization Assay -- 6 Machine Learning -- 6.1 Algorithms -- 6.2 Random Forest -- 7 Contact Tracing -- 8 Detection Through Smell -- 9 Conclusion -- References -- Artificial Intelligence and Big Data Solutions for COVID-19 -- 1 Introduction -- 2 The COVID-19 Pandemic -- 3 AI and Big Data Techniques for COVID-19 -- 4 AI and Big Data Applications for COVID-19 -- 4.1 Early Detecting and Finding COVID-19 Cases -- 4.2 Early Detecting and Finding COVID-19 Cases -- 4.3 Following Up Contacts -- 4.4 Projection of Cases and Moralities -- 4.5 Reducing the Workload on Healthcare Workers -- 4.6 Prevention of the Infections -- 5 A Proposed Model of AI and Big Data for COVID-19: Smartphone for Surveillance -- 6 Discussions -- 7 Future Insights -- 8 Conclusions -- References -- Emerging Trends in Higher Education During Pandemic Covid-19: An Impact Study from West Bengal -- 1 Introduction -- 2 Research Background Literature -- 3 Methodology -- 3.1 Research Gaps -- 3.2 Research Objectives -- 3.3 Sample Design -- 3.4 Research Approach -- 3.5 Research Tools Usage in Current Research -- 3.6 SXUK Case Study Process. 4 Research Findings and Discussion -- 4.1 Teaching-Learning Context During Covid-19 -- 4.2 Content Development Orientation -- 4.3 ICT Technology Strategies in HEIs -- 4.4 "Big Five" Strategies -- 4.5 Technology-CI Adapted Teaching-Learning Strategies -- 4.6 Higher Educational Institutes Wise -- 4.7 CI Awareness and Application in HEIs -- 4.8 Computational Intelligence-ICT Factors Confluence -- 4.9 CI-Based HEIs Cluster Membership -- 5 Case Organization: SXUK -- 5.1 Introduction -- 5.2 Historical Millstones of SXUK -- 5.3 SXUK Organogram -- 5.4 AI-CI Interface Strategies for SXUK -- 6 Conclusion -- References -- COVID-19: Virology, Epidemiology, Diagnostics and Predictive Modeling -- 1 Introduction -- 2 Virology of SARS-CoV-2 -- 3 Diagnostics and Current Line of Treatment of Coronavirus Disease-2019 (COVID-19) -- 4 Comparison of Population Distribution of India, USA and Spain -- 5 Mathematical Modeling -- 6 Concluding Remarks -- References -- Improved Estimation in Logistic Regression Through Quadratic Bootstrap Approach: An Application in Indian Agricultural E-learning System During COVID-19 Pandemic -- 1 Introduction -- 2 Logistic Regression Model -- 2.1 Preliminaries -- 2.2 Identification of the Most Influential Variable -- 2.3 Estimation in Logistic Regression Model -- 2.4 Goodness of Fit -- 2.5 Predictive Ability -- 2.6 Comparison Measures -- 3 Empirical Results -- 3.1 Data and Implementation -- 3.2 Comparative Assessment Between MLE and Quadratic Bootstrap Estimation -- 3.3 Outcomes of the Simulation Study -- 4 Conclusion -- References -- COVID-19 and Stock Markets: Deaths and Strict Policies -- 1 Introduction -- 2 COVID-19 and Its Macroeconomic Effects -- 3 COVID-19 and Stock Markets -- 4 Data and Econometric Model -- 4.1 Diagnostic Statistics and Correlation Analysis -- 4.2 Analysis Results -- 5 Conclusion -- References. Artificial Intelligence Techniques in Medical Imaging for Detection of Coronavirus (COVID-19/SARS-COV-2): A Brief Survey -- 1 Introduction -- 2 Literature Survey -- 3 Artificial Intelligence -- 4 Machine Learning -- 5 Neural Networks -- 5.1 Deep Learning -- 5.2 Transfer Learning -- 5.3 Convolutional Neural Networks -- 6 CNN Algorithms and Methods Used in the Survey -- 6.1 Inception V3 -- 6.2 ResNet-50 -- 6.3 Inception-ResNet-v2 -- 6.4 VGG-19 -- 6.5 MobileNet -- 7 Materials and Methods -- 7.1 Dataset -- 7.2 Performance Analysis Parameters -- 8 Results and Discussions -- 9 Conclusion and Future Challenges -- References -- A Travelling Disinfection-Man Problem (TDP) for COVID-19: A Nonlinear Binary Constrained Gaining-Sharing Knowledge-Based Optimization Algorithm -- 1 Coronavirus (COVID-19): An Overview -- 2 Coronavirus Decontamination Planning Process -- 3 Coronavirus Travelling Disinfection-Man Problem (TDP) -- 4 The Travelling Salesman Problem (TSP) and Its Variations -- 5 Mathematical Model Formulation for the Travelling Disinfection-Man Problem -- 6 Real Application Case Study Application: Ain Shams University, Cairo -- 7 Artificial Intelligence Techniques in Optimization -- 8 Proposed Methodology -- 8.1 Overview of Gaining-Sharing Knowledge-Based Optimization Algorithm (GSK) -- 8.2 Discrete Binary Gaining-Sharing Knowledge-Based Optimization Algorithm (DBGSK) -- 9 Experimental Results -- 10 Conclusions -- 11 Points for Future Researches -- References -- COVID-19 Lock Down Impact on Mental Health: A Cross-Sectional Online Survey from Kerala, India -- 1 Introduction -- 1.1 Motivation for Doing the Research -- 2 Review of Literature -- 3 Methods -- 4 Results and Discussions -- 4.1 Types of Activities -- 4.2 Mental Health Issues and Eating Behaviour -- 4.3 Awareness Among People -- 5 Conclusion -- References. Analysis, Modelling and Prediction of COVID-19 Outbreaks Using Machine Learning Algorithms -- 1 Introduction -- 2 COVID-19 Around the Global -- 3 Machine Learning and Its Types -- 3.1 Supervised Learning -- 4 Implementation -- 4.1 Evaluation Metrics -- 5 Time Series Data set -- 5.1 Analysis, Modelling and Prediction of COVID-19 -- 5.2 Confirmed Cases and Death Cases as on 20 July 2020-World -- 5.3 Confirmed Cases and Death Cases as on 20th July 2020-India -- 5.4 Model of Machine Learning Algorithm -- 5.5 Predicting the Outgrowth in the Next 3 Months-India -- 6 Conclusion -- References -- Tracking and Analysis of Corona Disease Using Intelligent Data Analysis -- 1 Introduction -- 2 AI Versus COVID-19 -- 2.1 Prediction and Data Sharing -- 2.2 R& -- D Sector -- 2.3 Deception -- 2.4 Monitoring -- 2.5 Data Overload -- 2.6 Arrangement of Automated Vehicles -- 2.7 Variances Between the AI Techniques [1] -- 3 Using AI to Detect, Respond, and Recover from COVID-19 -- 3.1 Computer-Based Intelligence for COVID-19 Medical Response -- 4 AI for COVID-19 Social Control -- 4.1 Man-Made Reasoning in the Battle Against COVID-19 -- 4.2 Information Access -- 4.3 Security Ensuring Applications -- 5 How Artificial Intelligence Applications can Contain Coronavirus COVID-19 -- 5.1 Man-Made Reasoning in the Battle Against COVID-19 -- 5.2 Information Access -- 5.3 Security Ensuring Applications -- 6 Conclusion -- References -- Index. |
| Record Nr. | UNINA-9910488705703321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Multimedia Big Data Computing for IoT Applications [[electronic resource] ] : Concepts, Paradigms and Solutions / / edited by Sudeep Tanwar, Sudhanshu Tyagi, Neeraj Kumar
| Multimedia Big Data Computing for IoT Applications [[electronic resource] ] : Concepts, Paradigms and Solutions / / edited by Sudeep Tanwar, Sudhanshu Tyagi, Neeraj Kumar |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xiv, 477 pages) |
| Disciplina | 005.7 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico |
Computational intelligence
Computer security Big data Multimedia information systems Application software Computational Intelligence Systems and Data Security Big Data Multimedia Information Systems Information Systems Applications (incl. Internet) |
| ISBN | 981-13-8759-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Multimedia Big data computing for IoT -- Energy Conservation in MMBD Computing and IoT – A Challenge -- An Architecture for the Real-Time Data Stream Monitoring in IoT -- Deep learning for Multimedia data in IoT -- Random Forest based Sarcastic Tweet Classification using multiple feature Collection -- Peak Average Power Ratio reduction in FBMC using SLM & PTS techniques -- Intelligent Personality Analysis on Indicators in IoT-MMBD Enabled Environment -- Data Reduction in MMBD Computing -- Large Scale MMBD Management and Retrieval -- Data Reduction Technique for Capsule Endoscopy -- Multimedia Social Big Data: Mining -- Advertisement prediction in social media environment using big data framework. |
| Record Nr. | UNINA-9910484861903321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Next Generation Computing Technologies on Computational Intelligence [[electronic resource] ] : 4th International Conference, NGCT 2018, Dehradun, India, November 21–22, 2018, Revised Selected Papers / / edited by Manish Prateek, Durgansh Sharma, Rajeev Tiwari, Rashmi Sharma, Kamal Kumar, Neeraj Kumar
| Next Generation Computing Technologies on Computational Intelligence [[electronic resource] ] : 4th International Conference, NGCT 2018, Dehradun, India, November 21–22, 2018, Revised Selected Papers / / edited by Manish Prateek, Durgansh Sharma, Rajeev Tiwari, Rashmi Sharma, Kamal Kumar, Neeraj Kumar |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (xix, 375 pages) : illustrations |
| Disciplina | 004.3 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Computer science
Application software Artificial intelligence Computer Science, general Information Systems Applications (incl. Internet) Artificial Intelligence |
| ISBN | 981-15-1718-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Image Processing, Pattern Analysis and Machine Vision -- Information and Data Convergence -- Disruptive Technologies for Future -- E-Governance and Smart World. |
| Record Nr. | UNINA-9910357842903321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019) [[electronic resource] /] / edited by Pradeep Kumar Singh, Wiesław Pawłowski, Sudeep Tanwar, Neeraj Kumar, Joel J. P. C. Rodrigues, Mohammad Salameh Obaidat
| Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019) [[electronic resource] /] / edited by Pradeep Kumar Singh, Wiesław Pawłowski, Sudeep Tanwar, Neeraj Kumar, Joel J. P. C. Rodrigues, Mohammad Salameh Obaidat |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (886 pages) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Computational intelligence
Machine learning Electrical engineering Data protection Computational Intelligence Machine Learning Communications Engineering, Networks Security |
| ISBN | 981-15-3369-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1: Communication & Network Technologies -- Chapter 1. State Of The Art: A Review on Vehicular Communications, Impact of 5g, Fractal Antennas for Future Communication -- Chapter 2. Energy Enhancement of TORA and DYMO by Optimization of Hello Messaging using BFO for MANET's -- Chapter 3. Horseshoe Shaped Multiband Antenna for Wireless Application -- Chapter 4. A Review Paper on Performance Analysis of IEEE 802.11e -- Chapter 5. Voice Control IOT Devices Framework for Smart Home -- Chapter 6. Comprehensive Analysis of Social based Opportunistic Routing Protocol: A Study -- Chapter 7. An Efficient Delay based Load Balancing using AOMDV in MANET -- Chapter 8. Metaheuristic-based Intelligent Solutions Searching Algorithms of Ant Colony Optimization and Backpropagation in Neural Networks -- Chapter 9. Evaluating Cohesion Score with Email Clustering.-Chapter 10. Congestion Control for Named Data Networking Based Wireless Ad-hoc Network.-Chapter 11. A Comparative review of various techniques for Image Splicing Detection and Localization..-Chapter 12. Analysis and Synthesis of Performance Parameter of Rectangular Patch Antenna -- Part 2: Advanced Computing Technologies & Latest Electrical & Electronics Trends -- Chapter 13. Fog Computing Research Opportunities & Challenges: A Comprehensive Survey -- Chapter 14. IIGPTS: IoT-based framework for Intelligent Green Public Transportation System -- Chapter 15. Integrating the AAL CasAware platform within an IoT ecosystem, leveraging the INTER-IoT approach. . |
| Record Nr. | UNINA-9910482986403321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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