Kooperativer Bibliotheksverbund

UID:

Umfang: 1 Online-Ressource (XI, 385 p. 106 illus., 98 illus. in color)

Ausgabe: 1st ed. 2021

ISBN: 9783030677169

Serie: Studies in Systems, Decision and Control 348

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-67715-2

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-67717-6

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-67718-3

Sprache: Englisch

DOI: 10.1007/978-3-030-67716-9

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Umfang: 1 online resource (385 pages) : , illustrations

ISBN: 3-030-67716-8

Serie: Studies in Systems, Decision and Control ; v.348

Anmerkung: Intro -- Preface -- List of Reviewers -- Contents -- 1 A Survey of Using Machine Learning Algorithms During the COVID-19 Pandemic -- Abstract -- 1 Introduction -- 2 COVID-19 Infection Prediction -- 3 Survival Prediction of COVID-19 Patients -- 4 Vaccine Development -- 5 Drug Discovery -- 6 Critical Reflections -- 7 Conclusion and Future Insights -- References -- 2 Deep Learning Techniques and COVID-19 Drug Discovery: Fundamentals, State-of-the-Art and Future Directions -- Abstract -- 1 Introduction -- 2 Bioinformatics and Drug Discovery -- 3 DL-Based Drug Discovery: State-of-the-Art -- 4 COVID-19 Drug Discovery Strategy: A Viewpoint -- 5 Future Directions -- 6 Discussion -- 7 Conclusion -- Acknowledgements -- References -- 3 Covid-19 Detection Using Advanced CNN and X-rays -- Abstract -- 1 Introduction -- 2 Previous Works -- 3 Theory System Architecture -- 4 Proposed Model Building -- 5 Results and Observation -- 6 Conclusion -- References -- 4 Integration of Deep Learning Machine Models with Conventional Diagnostic Tools in Medical Image Analysis for Detection and Diagnosis of Novel Coronavirus (COVID-19) -- Abstract -- 1 Introduction -- 2 Role of Medical Imaging in COVID-19 Detection -- 3 Materials, Methods and Procedure -- 3.1 Creating a Systematic Search Strategy -- 4 Research Studies Related to DLM Applications in COVID-19 -- 5 Discussion -- 5.1 Interpretation -- 6 Advantages of DLM Applications -- 6.1 Rapid Screening -- 6.2 Segmentation -- 6.3 Detection -- 6.4 Classification -- 7 AI Used Techniques to Prevent the Spread of COVID-19 -- 8 Limitations -- 9 Conclusion -- References -- 5 Intelligent Systems and Novel Coronavirus (COVID-19): A Bibliometric Analysis -- Abstract -- 1 Introduction -- 2 Method -- 3 Results and Discussion -- 3.1 Most Used Keywords -- 3.2 Most Cited Articles and Journals. , 3.3 Most Productive Countries and Institutions -- 3.4 Most Cited Authors -- 3.5 Role of Intelligent Systems During COVID-19 Outbreaks -- 4 Conclusion -- References -- 6 Computational IT Tool Application for Modeling COVID-19 Outbreak -- Abstract -- 1 Introduction -- 2 Application of IT Technology for Infectious Disease Outbreak Management -- 3 Applied IT Technology for Modeling COVID-19 Outbreak -- 4 Examples of IT Application for Corresponding to COVID-19 Outbreak -- 5 Conclusion -- Conflict of interest -- References -- 7 Efficient Twitter Data Cleansing Model for Data Analysis of the Pandemic Tweets -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Twitter Data Cleansing Model -- 3.1.1 Extraction and Filtering Tweets -- 3.1.2 Noise Removal -- 3.1.3 Out of Vocabulary Cleansing -- 3.1.4 Tweet Transformations -- 3.2 Feature Extraction of the Model -- 3.3 Twitter Sentiment Classification Process -- 4 Experiments and Analysis -- 4.1 Experimental Setup -- 4.2 Data Collection -- 4.3 Performance Evaluation -- 4.4 Experimental Results -- 5 Conclusion -- References -- 8 Feature Based Automated Detection of COVID-19 from Chest X-Ray Images -- Abstract -- 1 Introduction -- 2 Literature Review and Contribution of the Study -- 3 Methodologies of COVID-19 Detection -- 3.1 Data Collection and System Requirement -- 3.2 Feature Extraction -- 3.3 Classification -- 3.4 Validation -- 4 Results -- 5 Discussion -- 6 Conclusion -- Acknowledgements -- References -- 9 Indoor Air Quality Monitoring Systems and COVID-19 -- Abstract -- 1 Introduction -- 1.1 COVID-19 and Underlying Illness -- 1.2 Indoor Air Pollution -- 2 Indoor Air Quality and COVID-19 -- 2.1 COVID-19: Association to Biomass Usage -- 2.2 COVID-19 and Ventilation Issues -- 3 IAQ Monitoring Systems: A Missed Opportunity -- 3.1 Existing Solutions for IAQ Monitoring -- 4 Conclusion. , References -- 10 Leveraging Digital Transformation Technologies to Tackle COVID-19: Proposing a Privacy-First Holistic Framework -- Abstract -- 1 Introduction -- 2 Background of the Study -- 3 Literature Review -- 3.1 Data-Driven Solutions -- 3.2 Digital Contact Tracing -- 3.3 Robotics -- 3.4 Virtual Clinics -- 4 The Proposed Integrated Digital Transformation Framework -- 4.1 Data Sources -- 4.2 Digital Transformation Technologies -- 4.3 Applications -- 4.3.1 Applications for Diagnosing COVID-19 -- 4.3.2 Applications for Treating COVID-19 -- 4.3.3 Applications for Preventing COVID-19 -- 4.4 Users -- 5 Benefits and Challenges of the Proposed System -- 5.1 Benefits of the Proposed Framework Architecture -- 5.1.1 Effective Strategic Management of COVID-19 Crisis -- 5.1.2 Reducing the Risk of Virus Transmission -- 5.1.3 Detecting COVID-19 Carriers as Early as Possible -- 5.1.4 Decreasing the Workload and the Stress Level of the Hospital Staff -- 5.1.5 Reducing the Risk of an Overwhelmed Healthcare System -- 5.1.6 Decreasing the Mortality Rates and Increasing the Treatment Success Rates -- 5.1.7 Reducing the Negative Impact of COVID-19 on the Economy -- 5.1.8 Decreasing the Stress Level of People -- 5.2 Challenges of the Proposed Framework Architecture -- 5.2.1 Data Acquisition and Integration -- 5.2.2 Privacy -- 5.2.3 The Lack of Historical Data -- 5.2.4 Governance -- 5.2.5 Expertise -- 5.2.6 Scalability -- 5.2.7 Lack of Legislation -- 5.2.8 The Lack of Infrastructure for 5G Network -- 5.2.9 Cost of Setup and Operation -- 5.2.10 Adoption and Trust -- 6 Conclusion -- References -- 11 Application of Modern Technologies on Fighting COVID-19: A Systematic and Bibliometric Analysis -- Abstract -- 1 Introduction -- 2 Methodology -- 3 Results -- 3.1 Telemedicine and Telehealth Service During COVID-19 -- 3.2 3D Printing Technology. , 3.3 Artificial Intelligence (AI) -- 3.4 Robotics -- 3.5 Mobile Data (5G, 6G) and Cloud Partnership -- 3.6 Cloud Partnership -- 3.7 Internet of Things (IoT) -- 3.8 Drone Technology -- 3.9 Solar-Powered Automated Handwashing Machine -- 3.10 GPS, WiFi and Bluetooth -- 4 Conclusion -- References -- 12 Mid-Term Forecasting of Fatalities Due to COVID-19 Pandemic: A Case Study in Nine Most Affected Countries -- Abstract -- 1 Introduction -- 2 Methodology -- 2.1 Holt's Exponential Smoothening Method -- 2.2 Polynomial Curve Fitting -- 2.3 Performance Parameters -- 3 Case Study -- 4 Results and Discussion -- 5 Conclusion -- Acknowledgements -- References -- 13 Problematic Use of Digital Technologies and Its Impact on Mental Health During COVID-19 Pandemic: Assessment Using Machine Learning -- Abstract -- 1 Introduction -- 2 Internet Addiction -- 2.1 Excessive Internet Usage During COVID-19 Pandemic -- 2.2 Assessment of Internet Addiction: Conventional Approach -- 2.3 Assessment of Internet Addiction: Machine Learning Based Approach -- 3 Social Media Addiction -- 3.1 Excessive Use of Social Media During Covid-19 Pandemic -- 3.2 Assessment of Social Media Addiction: Conventional Approach -- 3.3 Assessment of Social Media Addiction: Machine Learning Based Approach -- 3.4 Case Study I: Machine Learning for Analysis of Addictive Use of Twitter During COVID-19 Lockdown in India -- 4 Smartphone Addiction -- 4.1 Excessive Smartphone Usage During COVID-19 Pandemic -- 4.2 Assessment of Smartphone Addiction: Conventional Approach -- 4.3 Assessment of Smartphone Addiction: Machine Learning Based Approach -- 4.4 Case Study II: Assessment of Nomophobia Among University Students During COVID-19 Pandemic Using Machine Learning -- 5 Impact on Mental and Emotional Health and Sleep -- 5.1 Mental and Emotional Health -- 5.2 Sleep -- 6 Research Model -- 7 Discussion and Conclusion. , References -- 14 The Role of Technology Acceptance in Healthcare to Mitigate COVID-19 Outbreak -- Abstract -- 1 Introduction -- 2 Novel Coronavirus (COVID-19) -- 3 Research Methodology -- 3.1 Search Strategy -- 3.2 Selection Criteria -- 3.3 Data Abstraction and Analysis -- 4 Results and Discussion -- 5 Study Implications -- 6 Study Implications -- 7 Conclusion and Future Work -- Acknowledgements -- References -- 15 Psychological and Socio-Economic Effects of the COVID-19 Pandemic on Turkish Population -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Method -- 3.1 Population and Sample -- 4 Instruments -- 5 Procedure -- 6 Results -- 7 Discussion and Conclusion -- References -- 16 Behavioral Intention of Students in Higher Education Institutions Towards Online Learning During COVID-19 -- Abstract -- 1 Introduction -- 2 Model and Hypothesis Development -- 2.1 Perceived Enjoyment (PE) -- 2.2 Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) -- 2.3 Social Influence (SI) -- 3 Research Methodology -- 3.1 Context of Study -- 3.2 Measurement Development and Pilot Study -- 4 Finding -- 4.1 Common Method Bias (CBM) -- 4.2 Measurement Model Assessment -- 4.3 Structural Model Assessment -- 5 Discussion -- 6 Conclusion, Limitation, and Future Research -- References -- 17 Exploring the Main Determinants of Mobile Learning Application Usage During Covid-19 Pandemic in Jordanian Universities -- Abstract -- 1 Introduction -- 2 Literature Review -- 2.1 Online Learning and Covid-19 Pandemic in Jordanian Universities -- 3 Hypotheses and Research Model -- 3.1 Technological Factors -- 3.2 Individual Factors -- 3.3 Psychological Factors -- 4 Research Method -- 4.1 Data Collection -- 4.2 Participants -- 4.3 Research Instrument -- 4.4 Data Analysis Methods -- 5 Results -- 5.1 Results of Cronbach's Alpha -- 5.2 Results of Convergent and Discriminant Validity. , 5.3 Results of the Structural Equation Modelling (SEM).

Weitere Ausg.: ISBN 3-030-67715-X

Sprache: Englisch

Schlagwort(e): Llibres electrònics ; Llibres electrònics

UID:

Umfang: XI, 385 p. 106 illus., 98 illus. in color. , online resource.

Ausgabe: 1st ed. 2021.

ISBN: 9783030677169

Serie: Studies in Systems, Decision and Control, 348

Inhalt: This book tackles the recent research directions in using the newly emerged technologies during the era of COVID-19 pandemic. It mainly focuses on using emerging technologies and their impact on health care, education, and society. It also provides insights into the current challenges and constraints in using technologies during the era of COVID-19 pandemic and exposes new opportunities for future research in the domain.

In: Springer Nature eBook

Weitere Ausg.: Printed edition: ISBN 9783030677152

Weitere Ausg.: Printed edition: ISBN 9783030677176

Weitere Ausg.: Printed edition: ISBN 9783030677183

Sprache: Englisch

DOI: 10.1007/978-3-030-67716-9

URL: https://doi.org/10.1007/978-3-030-67716-9