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[null,"1.","Record Nr.",null,"UNINA9910484109303321"]
[null,null,"Titolo",null,"Emerging technologies during the era of covid-19 pandemic / / edited by Ibrahim Arpaci, 3 others"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"3-030-67716-8"]
[null,null,"Descrizione fisica",null,"1 online resource (385 pages) : illustrations"]
[null,null,"Collana",null,"Studies in Systems, Decision and Control ; ; v.348"]
[null,null,"Disciplina",null,"601"]
[null,null,"Soggetti",null,"Technological innovations"]
[{"$nulls":4},"COVID-19 Pandemic, 2020-"]
[{"$nulls":4},"Innovacions tecnològiques"]
[{"$nulls":4},"Pandèmia de COVID-19, 2020-"]
[{"$nulls":4},"Llibres electrònics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- List of Reviewers -- Contents -- 1 A Survey of Using Machine Learning Algorithms During the COVID-19 Pandemic -- Abstract -- 1 Introduction -- 2 COVID-19 Infection Prediction -- 3 Survival Prediction of COVID-19 Patients -- 4 Vaccine Development -- 5 Drug Discovery -- 6 Critical Reflections -- 7 Conclusion and Future Insights -- References -- 2 Deep Learning Techniques and COVID-19 Drug Discovery: Fundamentals, State-of-the-Art and Future Directions -- Abstract -- 1 Introduction -- 2 Bioinformatics and Drug Discovery -- 3 DL-Based Drug Discovery: State-of-the-Art -- 4 COVID-19 Drug Discovery Strategy: A Viewpoint -- 5 Future Directions -- 6 Discussion -- 7 Conclusion -- Acknowledgements -- References -- 3 Covid-19 Detection Using Advanced CNN and X-rays -- Abstract -- 1 Introduction -- 2 Previous Works -- 3 Theory System Architecture -- 4 Proposed Model Building -- 5 Results and Observation -- 6 Conclusion -- References -- 4 Integration of Deep Learning Machine Models with Conventional Diagnostic Tools in Medical Image Analysis for Detection and Diagnosis of Novel Coronavirus (COVID-19) -- Abstract -- 1 Introduction -- 2 Role of Medical Imaging in COVID-19 Detection -- 3 Materials, Methods and Procedure -- 3.1 Creating a Systematic Search Strategy -- 4 Research Studies Related to DLM Applications in COVID-19 -- 5 Discussion -- 5.1 Interpretation -- 6 Advantages of DLM "]
[{"$nulls":4},"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."]
[{"$nulls":4},"3.3 Most Productive Countries and Institutions -- 3.4 Most Cited Authors -- 3.5 Role of Intelligent Systems During COVID-19 Outbreaks -- 4 Conclusion -- References -- 6 Computational IT Tool Application for Modeling COVID-19 Outbreak -- Abstract -- 1 Introduction -- 2 Application of IT Technology for Infectious Disease Outbreak Management -- 3 Applied IT Technology for Modeling COVID-19 Outbreak -- 4 Examples of IT Application for Corresponding to COVID-19 Outbreak -- 5 Conclusion -- Conflict of interest -- References -- 7 Efficient Twitter Data Cleansing Model for Data Analysis of the Pandemic Tweets -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Twitter Data Cleansing Model -- 3.1.1 Extraction and Filtering Tweets -- 3.1.2 Noise Removal -- 3.1.3 Out of Vocabulary Cleansing -- 3.1.4 Tweet Transformations -- 3.2 Feature Extraction of the Model -- 3.3 Twitter Sentiment Classification Process -- 4 Experiments and Analysis -- 4.1 Experimental Setup -- 4.2 Data Collection -- 4.3 Performance Evaluation -- 4.4 Experimental Results -- 5 Conclusion -- References -- 8 Feature Based Automated Detection of COVID-19 from Chest X-Ray Images -- Abstract -- 1 Introduction -- 2 Literature Review and Contribution of the Study -- 3 Methodologies of COVID-19 Detection -- 3.1 Data Collection and System Requirement -- 3.2 Feature Extraction -- 3.3 Classification -- 3.4 Validation -- 4 Results -- 5 Discussion -- 6 Conclusion -- Acknowledgements -- References -- 9 Indoor Air Quality Monitoring Systems and COVID-19 -- Abstract -- 1 Introduction -- 1.1 COVID-19 and Underlying Illness -- 1.2 Indoor Air Pollution -- 2 Indoor Air Quality and COVID-19 -- 2.1 COVID-19: Association to Biomass Usage -- 2.2 COVID-19 and Ventilation Issues -- 3 IAQ Monitoring Systems: A Missed Opportunity -- 3.1 Existing Solutions for IAQ Monitoring -- 4 Conclusion."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"Infrastructure for 5G Network -- 5.2.9 Cost of Setup and Operation -- 5.2.10 Adoption and Trust -- 6 Conclusion -- References -- 11 Application of Modern Technologies on Fighting COVID-19: A Systematic and Bibliometric Analysis -- Abstract -- 1 Introduction -- 2 Methodology -- 3 Results -- 3.1 Telemedicine and Telehealth Service During COVID-19 -- 3.2 3D Printing Technology."]
[{"$nulls":4},"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."]
[{"$nulls":4},"References -- 14 The Role of Technology Acceptance in Healthcare to Mitigate COVID-19 Outbreak -- Abstract -- 1 Introduction -- 2 Novel Coronavirus (COVID-19) -- 3 Research Methodology -- 3.1 Search Strategy -- 3.2 Selection Criteria -- 3.3 Data Abstraction and Analysis -- 4 Results and Discussion -- 5 Study Implications -- 6 Study Implications -- 7 Conclusion and Future Work -- Acknowledgements -- References -- 15 Psychological and Socio-Economic Effects of the COVID-19 Pandemic on Turkish Population -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Method -- 3.1 Population and Sample -- 4 Instruments -- 5 Procedure -- 6 Results -- 7 Discussion and Conclusion -- References -- 16 Behavioral Intention of Students in Higher Education Institutions Towards Online Learning During COVID-19 -- Abstract -- 1 Introduction -- 2 Model and Hypothesis Development -- 2.1 Perceived Enjoyment (PE) -- 2.2 Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) -- 2.3 Social Influence (SI) -- 3 Research Methodology -- 3.1 Context of Study -- 3.2 Measurement Development and Pilot Study -- 4 Finding -- 4.1 Common Method Bias (CBM) -- 4.2 Measurement Model Assessment -- 4.3 Structural Model Assessment -- 5 Discussion -- 6 Conclusion, Limitation, and Future Research -- References -- 17 Exploring the Main Determinants of Mobile Learning Application Usage During Covid-19 Pandemic in Jordanian Universities -- Abstract -- 1 Introduction -- 2 Literature Review -- 2.1 Online Learning and Covid-19 Pandemic in Jordanian "]
[{"$nulls":4},"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."]
[{"$nulls":4},"5.3 Results of the Structural Equation Modelling (SEM)."]