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Biblioteca

MARC Lista (tabellare)
3D Printing in Medicine : A Practical Guide for Medical Professionals / / edited by Frank J. Rybicki, Gerald T. Grant
3D Printing in Medicine : A Practical Guide for Medical Professionals / / edited by Frank J. Rybicki, Gerald T. Grant
Edizione [1st ed. 2017.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
Descrizione fisica 1 online resource (VIII, 138 p. 78 illus., 77 illus. in color.)
Disciplina 616.0757
Soggetto topico Radiology
Surgery
Neurosurgery
Personal Protective Equipment
Printing, Three-Dimensional
COVID-19
Imaging / Radiology
ISBN 3-319-61924-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction.- 3D printing technologies --  Post-processing of DICOM images --  Establishing a 3D Service in your medical facility.- The 3D printing lab in a radiology practice --  Training and education of a 3D medical printing technologist --  Cranio-Maxillofacial 3D printing --  3D printing in Neurosurgery and Neurointervention.- Cardiovascular 3D printing.- Musculoskeletal 3D printing.- Virtual Surgical applications.- Design and Fabrication of Customized Patient Devices.- 3D printing in Radiation Oncology.- FDA interests in the 3D printing of medical models and devices.- Bioprinting.
Record Nr. UNINA-9910254494703321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced machine learning technologies and applications : proceedings of AMLTA 2021 / / edited by Aboul-Ella Hassanien, Kuo-Chi Chang, Tang Mincong
Advanced machine learning technologies and applications : proceedings of AMLTA 2021 / / edited by Aboul-Ella Hassanien, Kuo-Chi Chang, Tang Mincong
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (1,144 pages) : illustrations
Disciplina 006.31
Collana Advances in Intelligent Systems and Computing
Soggetto topico Machine learning
Aprenentatge automàtic
COVID-19
Intel·ligència artificial en medicina
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-69717-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910484064003321
Gateway East, Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advances in intelligent computing and communication : proceedings of ICAC 2020 ; Bhubaneswar, Odisha, India, November 2020 / / editors, Swagatam Das, Mihir Narayan Mohanty
Advances in intelligent computing and communication : proceedings of ICAC 2020 ; Bhubaneswar, Odisha, India, November 2020 / / editors, Swagatam Das, Mihir Narayan Mohanty
Pubbl/distr/stampa Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (713 pages) : illustrations (chiefly color)
Disciplina 621.382
Collana Lecture notes in networks and systems
Soggetto topico Digital communications
Image processing - Digital techniques
Soft computing
Processament digital d'imatges
Intel·ligència artificial en medicina
Informàtica mèdica
COVID-19
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 981-16-0695-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. Classification and Detection of Leaves using Different Image processing Techniques Chapter 2. Covid-19 Detection :An Approach Using X-Ray Images and Deep Learning Techniques Chapter 3. Covid-19 Detection :An Approach Using X-Ray Images and Deep Learning Techniques Chapter 4. Realization of a vehicular robotic system using the principle of photonics Chapter 5. A Modified Hybrid Planar Antenna for Cognitive Radio Application Chapter 6. Detection of Broken and Good Medical Tablets Using Various Machine Learning Models Chapter 7. Lungs Nodule Prediction using Convolutional Neural Network and K-Nearest Neighbor Chapter 8. Quantitative Structure Activity Relationships (QSARs) Study for KCNQ Genes(Kv7) and Drug discovery Chapter 9. Apple fruit disease detection and classification using k-means clustering method Chapter 10. A Detailed Review of the Optimal Distributed Generation Placement in Smart Power Distribution Systems
Record Nr. UNINA-9910483988903321
Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
After the storm : Post-pandemic trends in the Southern Mediterranean / edited by Andrey Kortunov and Paolo Magri
After the storm : Post-pandemic trends in the Southern Mediterranean / edited by Andrey Kortunov and Paolo Magri
Pubbl/distr/stampa Milano, : ISPI, : Ledizioni, 2020
Descrizione fisica 112 p. ; 21 cm
Disciplina 327.561
Soggetto topico COVID-19
ISBN 978-88-5526389-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996573270703316
Milano, : ISPI, : Ledizioni, 2020
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Alternative medicine interventions for COVID-19 / / Muhammad Zia-Ul-Haq [and three others] editors
Alternative medicine interventions for COVID-19 / / Muhammad Zia-Ul-Haq [and three others] editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (291 pages)
Disciplina 614.592414
Soggetto topico COVID-19 (Disease) - Alternative treatment
COVID-19
Medicina alternativa
Soggetto genere / forma Llibres electrònics
ISBN 3-030-67989-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- About the Editors -- Chapter 1: Introduction to COVID-19 -- Introduction -- Origin of COVID -- Emergence and Epidemiology -- Human-Animal Interaction as Risk Factor -- Best Suitable Animal Model -- COVID and Animal Welfare -- Molecular Differences Between Different Animal Species -- Vaccines for COVID from Animals -- Conclusion -- References -- Chapter 2: Medicinal Plants as COVID-19 Remedy -- Introduction -- Existing Plants with Potential Therapeutic Applications for Coronavirus Family (SARS-CoV) -- Plants as Specific Inhibitors of HCoV Target Proteins -- Bupleurum Species -- Artemisia annua L. -- Isatis indigotica Fortune ex Lindl. -- Alcea digitata (Boiss.) Alef. -- Lycoris radiata (L'Hér.) Herb. -- Pyrrosia lingua (Thunb.) Farw. -- Houttuynia cordata Thunb. -- Torreya nucifera L. -- Lindera aggregata (Sims) Kosterm. -- Rheum palmatum L. -- Cerasus avium (L.) Moench. -- Polygonum multiflorum Thunb. -- Citrus aurantium L. -- Rubia tinctorum L. -- Onopordum acanthium L. -- Quercus infectoria G. Olivier -- Crataegus microphylla C. Koch -- Berberis integerrima Bunge -- Alnus japonica (Thunb.) Steud. -- Paulownia tomentosa (Thunb.) Steud. -- Psoralea corylifolia L. -- Tribulus terrestris L. -- Medicinal Plants as Potential Source of Natural Antiviral Agents Against COVID -19 -- Allium Sativum (Garlic) -- Azadirachta indica (Neem) -- Curcuma domestica (Turmeric, Haldi) -- Echinacea purpurea L. (Echinacea) -- Foeniculum vulgare Mill. (Fennel) -- Glycyrrhiza glabra (Licorice) -- Melissa officinalis L. (Lemon Balm) -- Mentha piperita L. (Peppermint) -- Nigella sativa L. (Black Seeds) -- Origanum vulgare L. (Oregano) -- Ocimum bacilicum L. (Basil) -- Rosmarinus officinalis L. (Rosemary) -- Salvia officinalis L. (Sage) -- Senna alexandrina Mill. (Senna Makki) -- Zingiber officinale Rosc. (Ginger) -- Limitations.
Conclusions -- References -- Chapter 3: Traditional Chinese Medicines as Possible Remedy Against SARS-CoV-2 -- Introduction -- Traditional Chinese Medical Practices -- Laws for TCM Formulation -- Antiviral Potential of Plant Metabolites -- Structure of SARS-CoV-2 -- Relevance of SARS-CoV with SARS-CoV-2 -- Entry of SARS-CoV vs. SARS-CoV-2 into Host Cell -- TCM and SARS-CoV -- TCM and SARS-CoV-2 (COVID-19) -- Most Practiced Herbs in Formulations Against COVID-19 -- Utilization Frequency of Herbs in the Formulation of (COVID-19) Decoctions -- Approved/Proposed Decoctions with Successful Clinical Trials -- Qingfei Paidu Decoction (QFPD) (Approved) -- Huashi Baidu Decoction (HBD) (Approved) -- Xiaochaihu Decoction (XCHD) -- Xuanfei Baidu Decoction (XFBD) (Approved) -- Jinhua Qinggan (JQ) (Approved) -- Huoxiang Zhengqi (HZ) Capsule -- Lianhua Qingwen (LQ) -- Xuebijing Injection (XbI) (Approved) -- Reduning Injection (RdI) -- Shufeng Jiedu Capsule (SFJC) -- Ma Xing Shi Gan Decoction (MXSGD) (Approved) -- Yin Qiao San (YQS) -- Yupingfeng San (YPFS) -- Therapeutic Logic of TCM as COVID-19 Inhibitor -- Mechanism of Direct Inhibition -- TCM Targeting ACE2 -- TCM Targeting Protease -- TCM Directly Targeting DNA Via Intercalation -- TCM Directly Targeting DNA via JAK-STAT Signaling Pathway -- TCM Targeting sEH -- Targeting VEGF -- Targeting DPP4 -- Targeting JUN -- Targeting EGFR -- Targeting IL1B -- Mechanism of Indirect Mechanisms -- Anti-inflammation and Immune Regulation to Avoid Cytokine Storm -- TCM Protect Organ Damage in COVID-19 Patients -- Regulation of Renin-Angiotensin-Aldosterone System (RAAS) and Potential TCM Targets -- Cytokine-Mediated Positive and Negative Immunity -- TCM Targeting MAPK (Cytokine) -- TCM Targeting Platinum Drug Resistance (Cytokine) -- TCM Targeting Tyrosine Kinase Resistance (Cytokine).
TCM Targeting Interleukins (ILs) (Cytokine) -- TCM Targeting TNF (Cytokines) -- TCM Targeting Lymphocyte-Mediated Adaptive Immune System -- Regulation of Apoptosis and Potential TCM Targets -- TCM Targeting BCL Proteins -- TCM Targeting CASP -- 7TCM Targeting Akt or Protein Kinase B (PKB) -- Regulation of NF-κB Pathway and Potential TCM Targets -- TCM Targeting TP53 -- TCM Targeting ICAM1 -- TCM Targeting RELA -- Regulation of Arachidonic Acid Metabolism and Potential TCM Targets -- TCM Targeting cPLA2α of Arachidonic Acid Metabolism -- TCM Targeting Cyclooxygenase-2 of Arachidonic Acid Metabolism -- TCM Targeting LOX of Arachidonic Acid Metabolism -- TCM Targeting Cytochrome P450 -- TCM Targeting Leukotrienes (LTs) -- TCM Targeting CALM -- TCM Targeting HIF-1 Signaling Pathways -- TCM Targeting Endocrine Resistance -- TCM Targeting FOS -- TCM Targeting PTGS2 -- Regulation of Polyamine Metabolism and Potential TCM Targets -- TCM Targeting NOS2 and AOC1 -- TCM Targeting CCL2 -- Meta-analysis -- TCM from Chinese Combat Zone to World Combat Zone -- Shortcomings of Antiviral Medicine on COVID-19 -- Conclusions -- References -- Chapter 4: Plant-Based Natural Products: Potential Anti-COVID-19 Agents -- Introduction -- Antiviral Mechanistic Aspects of Phytochemicals -- Mechanism -- Plant Selection for Antiviral Screening -- Different Classes of Phytochemicals as Antiviral Agents -- Alkaloids -- Flavonoids -- Classification of Flavonoids -- Chalcones -- Dihydrochalcones -- Flavones -- Flavonones -- Dihydroflavonols -- Flavonol -- Isoflavonoids -- Isoflavones -- Isoflavanones -- Neoflavonoids -- Terpenoids -- Tannins -- Vitamins -- Chromones and Coumarins -- Organosulfur Compounds -- Selenium Compounds -- Miscellaneous Antiviral Phytochemicals -- Curcumin and Its Derivative -- Chlorophyllin -- Gingerols -- Chitin and Chitosan -- Anthraquinone -- Conclusion.
References -- Chapter 5: Foods as First Defense Against COVID-19 -- Introduction -- Role of Nutrition and Diet in Prevention and Management of COVID-19 -- Prioritizing Nutrition Interventions During COVID-19 -- Grains -- Vegetables and Fruits -- Nuts -- Legumes -- Meat -- Milk and Milk Products -- Herbs -- Role of Vitamins in COVID-19 -- Vitamin A -- Vitamin B -- Vitamin C -- Vitamin D -- Vitamin E -- Role of Minerals in COVID-19 -- Iron -- Selenium -- Zinc -- Magnesium -- Role of Omega-3 Polyunsaturated Fatty Acids -- Nutrients for Immune System and Resilience in COVID-19 -- Nutrition in Lowering Stress and Depression in COVID-19 -- Food Choices for Diabetic Patients During COVID-19 -- Food Choices for Cardiovascular Patients During COVID-19 -- Food Choices for Cancer Patients During COVID-19 -- Food Choices for Patients with Digestive Disorders During COVID-19 -- Food Choices for Pulmonary Disease Patients During COVID-19 -- Malnutrition May Exacerbate COVID-19 -- Nutrition and Post-COVID Recovery -- Future Perspectives -- Conclusion -- References -- Chapter 6: Drugs for the Treatment of COVID-19 -- Introduction -- Dexamethasone -- Chloroquine -- Remdesivir -- Angiotensin-Converting Enzyme Inhibitors -- Spike Protein Inhibitors -- Conclusions -- References -- Chapter 7: COVID-19 Pandemic and Vaccines -- Introduction -- COVID-19 Vaccines: A Need of Time -- Coronavirus Vaccine Development: Challenges of the Past -- Vaccine Development Strategies -- Nucleic Acid Vaccines -- Protein Subunit Vaccines -- Inactivated or Live-Attenuated Virus Vaccines -- Virus Vector-Based Vaccines -- Repurposed Vaccines for COVID-19 and Off-Target Effects of Other Vaccines -- Plant-Based Vaccines -- Animal Models for SARS-CoV-2 Studies -- Cell Culture Systems for SARS-CoV-2 Studies -- Clinical and Immunological Endpoints -- Vaccine Development Landscape.
Global and Equitable Distribution of Vaccines -- Vaccine Developers and Geographical Distribution -- Conclusions -- References -- Chapter 8: Updates in Vaccine Development Against COVID-19 -- Introduction -- Sinovac Biotech, China -- Conclusions -- Bibliography -- Chapter 9: COVID-19: Recent Developments in Therapeutic Approaches -- Introduction -- Morphology of SARS-CoV-2 -- Genome Organization of SARS-CoV-2 -- Replication of SARS-CoV-2 -- Transmission and Pathogenesis of SARS-CoV-2 -- Clinical Characterization of SARS-CoV-2 -- Clinical and Laboratory Diagnosis of SARS-CoV-2 -- Therapeutic Options for the Treatment of COVID-19 -- Antivirals or Immunomodulatory Drugs for COVID-19 -- Vaccine for COVID-19 -- Plasma/Serotherapy for COVID-19 -- Alternative Therapies -- Anticoagulant Therapy -- Glucocorticoids (GC) -- Stem Cell-Based Therapy -- ACE-2-Mediated Therapies -- CRISPR/Cas System -- Conclusion -- References -- Index.
Record Nr. UNINA-9910483536303321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analysis of infectious disease problems (Covid-19) and their global impact / / Praveen Agarwal [and three others], editors
Analysis of infectious disease problems (Covid-19) and their global impact / / Praveen Agarwal [and three others], editors
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (635 pages)
Disciplina 616.241400285
Collana Infosys Science Foundation series in mathematical sciences
Soggetto topico COVID-19
Models matemàtics
COVID-19 (Disease) - Mathematical models
Soggetto genere / forma Llibres electrònics
ISBN 981-16-2450-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgements -- Contents -- About the Editors -- General Analysis -- Continued and Serious Lockdown Could Have Minimized Many Newly Transmitted Cases of Covid-19 in the U.S.: Wavelets, Deterministic Models, and Data -- 1 Introduction -- 2 Methods, Models and Data -- 3 Data -- 4 Results -- 5 Concluding Remarks -- References -- Dynamical Analysis of a Caputo Fractional Order SIR Epidemic Model with a General Treatment Function -- 1 Introduction -- 2 Mathematical Model and Preliminaries -- 3 Preliminaries -- 4 The Well-Posedness of the Model and Equilibria -- 4.1 Existence of Endemic Equilibrium -- 5 Local Stability Analysis -- 6 Global Stability Analysis -- 6.1 Infection-Free Equilibrium -- 6.2 Endemic Equilibrium -- 7 Numerical Simulations -- 8 Concluding Remarks -- References -- Protective Face Shield Effectiveness: Mathematical Modelling -- 1 Introduction -- 2 Practical Application of Face Shields -- 3 Mathematical Modelling -- 3.1 Euler Form of Equations -- 3.2 Lagrangian Form of Equations -- 3.3 Model Description -- 3.4 Numerical Methods -- 3.5 Computer Simulation -- 4 Full-Scale Experiment -- 5 Conclusion -- References -- On the Evolution Equation for Modelling the Covid-19 Pandemic -- 1 Introduction -- 2 The Evolution Equation -- 2.1 The Classical Kolmogorov-Feller Equation -- 2.2 The Generalised Kolmogorov-Feller Equation -- 2.3 Orthonormal Memory Functions -- 2.4 Time Series Models -- 2.5 Logarithmic Scale Analysis -- 3 Random Walk Fields -- 4 Self-Affine Random Walk Fields -- 4.1 Solution for Eq. (7) -- 4.2 Solution for Eq. (8) -- 4.3 Random Walk Analysis -- 4.4 Example Results -- 5 The Bio-Dynamics Hypothesis -- 5.1 Self-affine Structures of a Virus -- 5.2 A Parametric Self-affine Model -- 5.3 Discussion -- 6 Summary, Conclusions and Future Research -- 6.1 Summary -- 6.2 Conclusions -- 6.3 Future Research.
References -- Modelling the Dynamics of Fake News Spreading Transmission During Covid-19 Through Social Media -- 1 Introduction -- 2 Methodology/Proposal -- 2.1 SIR Model for Fake News Transmission -- 2.2 Fake News Transmission Rate Through Different Social Media Platforms -- 2.3 Fake News Transmission Rate Among Users of Different Age Groups -- 2.4 Fake News Transmission Rate Among Users of Facebook from Different Countries -- 3 Results, Interpretation and Discussion -- 4 Conclusion -- References -- Generalized Logistic Equations in Covid-Related Epidemic Models -- 1 Introduction -- 2 Logistic Coefficients Models -- 2.1 Computable Examples -- 3 Carrying Capacity Periodically Variable -- 3.1 Existence of Periodic Solution -- 3.2 Cosinusoidal Carrying Capacity -- 4 Periodic Harvesting -- 4.1 Global Features of the Solution -- 4.2 Closed-Form Integration and Examples -- 4.3 A Sample Problem -- References -- A Transition of Shared Mobility in Metro Cities-A Challenge Post-Lockdown Covid-19 -- 1 Introduction -- 2 BPR Model -- 3 Data Analysis & -- Implementation -- 3.1 Data Description -- 3.2 Model Application & -- Results -- 3.3 Prediction of Traffic Scenarios Post-Lockdown -- 4 India's Transport Growth Journey and Its Effect on Energy and Environment -- 4.1 Transport and Environment -- 4.2 Health and Social Issues -- 4.3 Personal Vehicles and Their Impact -- 4.4 Measures to Curb the Traffic Upsurge -- References -- Analysis of Covid-19 Virus Spreading Statistics by the Use of a New Modified Weibull Distribution -- 1 Introduction and Preliminaries -- 1.1 The New Model NMWB Distribution -- 1.2 The Reliability Function -- 1.3 Moments of the Distribution -- 1.4 Order Statistics -- 1.5 Parameter Estimation -- 1.6 Relationship with Weibull-Related Results -- 2 Main Results -- 2.1 Statistical Properties -- 2.2 Least Square Estimates (LSES).
2.3 Order Statistics -- 2.4 Parameter Estimation -- 3 Applications -- 4 Conclusion -- References -- Lifting Lockdown Control Measure Assessment: From Finite-to Infinite-Dimensional Epidemic Models for Covid-19 -- 1 Introduction -- 2 Data Collection -- 3 Basic Covid-19 Model -- 3.1 Reproduction Numbers -- 3.2 Parameter and Initial Data Estimation -- 4 Discrete Age-Structured Covid-19 Model -- 4.1 Reproduction Numbers -- 4.2 Parameter and Initial Data Estimation -- 5 Covid-19 Model with Constant Delay -- 5.1 Reproduction Numbers -- 5.2 Parameter and Initial Data Estimation -- 6 Covid-19 Model with Threshold-Type Delay -- 7 Models with Demographic Effects -- 7.1 Covid-19 Model with Constant Delay -- 7.2 Covid-19 Model with Threshold-Type Delay -- 8 Discussion -- References -- Introduction to the Grey Systems Theory and Its Application in Mathematical Modeling and Pandemic Prediction of Covid-19 -- 1 A Brief Introduction to the Grey Systems Theory -- 2 Description of the Traditional Linear and Nonlinear Univariate Grey Models GM(1, 1) and NGBM(1, 1) -- 2.1 Building the Traditional Grey Model GM(1, 1) -- 2.2 The Nonlinear Grey Bernoulli Model NGBM(1, 1) -- 3 Optimization of the Univariate Grey Models -- 3.1 Optimization of Hyper-parameters -- 3.2 Rolling Mechanism -- 3.3 Optimization of the Initial Condition -- 4 Applications of Univariate Grey Models in Predicting Total Covid-19 Infected Cases -- 5 Description of the Existing GM(1, N) and GMC(1, N) Models -- 5.1 The Traditional GM(1, N) Model -- 5.2 The Grey Model with Convolution Integral GMC(1, N) -- 5.3 Variations of the Current GMC(1, N) and GMC(1, N) Models -- 5.4 Representation of the Nonlinear Grey Model with Convolution Integral NGMC(1, N) -- 6 Grey System Models with Fractional Order Accumulation -- 6.1 Definition of the Fractional Order Accumulation -- 6.2 The Fractional GMpq(1, 1) Model.
6.3 The Fractional Multivariate Grey Model with Convolutional Integral GMC pq(1, N) -- 6.4 Optimization of the Fractional Order r -- 7 Introduction to the Grey Relational Analysis -- 7.1 Data Preprocessing -- 7.2 Grey Relational Coefficient and Grey Relational Grade -- 8 Applications of Grey Relational Analysis In medicine -- 8.1 General Applications of Grey Relational Analysis in Medical Data Analysis -- 8.2 Application in Telecare -- 8.3 Grey Data Management in Medicine -- References -- Mathematical Analysis of Diagnosis Rate Effects in Covid-19 Transmission Dynamics with Optimal Control -- 1 Introduction -- 2 Model Formulation -- 3 Mathematical Analysis -- 3.1 The Disease-Free Equilibrium and Control Reproduction Number -- 3.2 Global Stability of DFE -- 3.3 Existence and Local Stability of the Endemic Equilibrium -- 3.4 Sensitivity Analysis -- 3.5 Numerical Simulation -- 4 Optimal Control -- 4.1 Building the Optimal Control Problem -- 4.2 Characterization of the Optimal Control -- 4.3 Numerical Simulation of the Optimal Control Problem -- 5 Conclusion -- References -- Development of Epidemiological Modeling RD-Covid-19 of Coronavirus Infectious Disease and Its Numerical Simulation -- 1 Introduction -- 2 Infectious Disease Epidemiology Components -- 2.1 Timelines of Infection -- 2.2 Estimation of Transmission Probability -- 2.3 The SAR is a Proportion, Not a Rate -- 3 Estimation of Basic Reproduction Number/ Proliferation Number -- 3.1 Estimation of R0 -- 3.2 Virulence of R0 and the Case Fatality Ratio (CFR) -- 4 Incidence Rate as a Function of Prevalence and Contact Rate -- 5 Dynamic Epidemic Process in a Closed Population -- 6 RD-Covid-19 Epidemiological Model -- 7 Numerical Simulation of RD-Covid-19 Model -- 7.1 PART-1: Numerical Outcome of RD-Covid-19 Model Outcome for INDIA.
7.2 PART-2: Numerical Outcome of RD-Covid-19 Model Outcome for CHINA -- 7.3 PART-3: Numerical Outcome of RD-Covid-19 Model Outcome for BRAZIL -- 7.4 PART-4: Numerical Outcome of RD-Covid-19 Model Outcome for RUSSIA -- 8 Conclusions -- References -- Mediterranean Diet-A Healthy Dietary Pattern and Lifestyle for Strong Immunity -- 1 Introduction -- 2 Mediterranean Lifestyle -- 3 Benefits of Mediterranean Diet -- 4 Mediterranean Diet for a Healthy Gut -- 5 Conclusion -- References -- Rate-Induced Tipping Phenomena in Compartment Models of Epidemics -- 1 Introduction -- 1.1 Outline -- 2 Preliminaries -- 2.1 Compartment Models with Time-Dependent Parameters -- 2.2 Autonomous SIR Model -- 2.3 Autonomous SIRS Model -- 3 Linear Compartment Models -- 3.1 Artifacts of Rate-Induced Tipping -- 4 Nonlinear Compartment Models -- 4.1 Local Normal Form for a Bifurcation of Codimension Two -- 4.2 Idealized Models -- 5 Irreducible Rate-Induced Tipping in Non-autonomous Models -- 5.1 Artifacts of Rate-Induced Tipping -- 6 Conclusion -- References -- Analysis of Impact of Covid-19 Pandemic on Financial Markets -- 1 Introduction -- 2 Market Behaviour During Initial and Intermediate Pandemic Phases -- 2.1 Covid-19 Market Crash (2020/02/19-2020/03/19) -- 2.2 Market Recovery After Covid-19 Crash (2020/03/20 - 2020/03/26) -- 2.3 Pandemic Growth After 2020/03/18 -- 3 Framework for Modelling Pandemic Impact -- 3.1 Susceptible, Infected, Recovered and Death (SIRD) Model with Time-Dependent Parameters and Social Distancing -- 3.2 Calibration Algorithm -- 3.3 Phenomenological Pandemic Model (PPM) -- 3.4 The Process N(t) in an Intermediate Phase -- 3.5 Approximation to PPM -- 3.6 Calibration of PPM -- 3.7 Mapping Epidemic Variables to Financial Risk Factors -- 4 Simulation of Stress Scenarios -- 4.1 Simulation of Risk Drivers Under the SIRD Model -- 4.2 PPM Simulation.
5 Conclusion.
Record Nr. UNINA-9910502987903321
Gateway East, Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analysis of infectious disease problems (Covid-19) and their global impact / / Praveen Agarwal [and three others], editors
Analysis of infectious disease problems (Covid-19) and their global impact / / Praveen Agarwal [and three others], editors
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (635 pages)
Disciplina 616.241400285
Collana Infosys Science Foundation series in mathematical sciences
Soggetto topico COVID-19
Models matemàtics
COVID-19 (Disease) - Mathematical models
Soggetto genere / forma Llibres electrònics
ISBN 981-16-2450-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgements -- Contents -- About the Editors -- General Analysis -- Continued and Serious Lockdown Could Have Minimized Many Newly Transmitted Cases of Covid-19 in the U.S.: Wavelets, Deterministic Models, and Data -- 1 Introduction -- 2 Methods, Models and Data -- 3 Data -- 4 Results -- 5 Concluding Remarks -- References -- Dynamical Analysis of a Caputo Fractional Order SIR Epidemic Model with a General Treatment Function -- 1 Introduction -- 2 Mathematical Model and Preliminaries -- 3 Preliminaries -- 4 The Well-Posedness of the Model and Equilibria -- 4.1 Existence of Endemic Equilibrium -- 5 Local Stability Analysis -- 6 Global Stability Analysis -- 6.1 Infection-Free Equilibrium -- 6.2 Endemic Equilibrium -- 7 Numerical Simulations -- 8 Concluding Remarks -- References -- Protective Face Shield Effectiveness: Mathematical Modelling -- 1 Introduction -- 2 Practical Application of Face Shields -- 3 Mathematical Modelling -- 3.1 Euler Form of Equations -- 3.2 Lagrangian Form of Equations -- 3.3 Model Description -- 3.4 Numerical Methods -- 3.5 Computer Simulation -- 4 Full-Scale Experiment -- 5 Conclusion -- References -- On the Evolution Equation for Modelling the Covid-19 Pandemic -- 1 Introduction -- 2 The Evolution Equation -- 2.1 The Classical Kolmogorov-Feller Equation -- 2.2 The Generalised Kolmogorov-Feller Equation -- 2.3 Orthonormal Memory Functions -- 2.4 Time Series Models -- 2.5 Logarithmic Scale Analysis -- 3 Random Walk Fields -- 4 Self-Affine Random Walk Fields -- 4.1 Solution for Eq. (7) -- 4.2 Solution for Eq. (8) -- 4.3 Random Walk Analysis -- 4.4 Example Results -- 5 The Bio-Dynamics Hypothesis -- 5.1 Self-affine Structures of a Virus -- 5.2 A Parametric Self-affine Model -- 5.3 Discussion -- 6 Summary, Conclusions and Future Research -- 6.1 Summary -- 6.2 Conclusions -- 6.3 Future Research.
References -- Modelling the Dynamics of Fake News Spreading Transmission During Covid-19 Through Social Media -- 1 Introduction -- 2 Methodology/Proposal -- 2.1 SIR Model for Fake News Transmission -- 2.2 Fake News Transmission Rate Through Different Social Media Platforms -- 2.3 Fake News Transmission Rate Among Users of Different Age Groups -- 2.4 Fake News Transmission Rate Among Users of Facebook from Different Countries -- 3 Results, Interpretation and Discussion -- 4 Conclusion -- References -- Generalized Logistic Equations in Covid-Related Epidemic Models -- 1 Introduction -- 2 Logistic Coefficients Models -- 2.1 Computable Examples -- 3 Carrying Capacity Periodically Variable -- 3.1 Existence of Periodic Solution -- 3.2 Cosinusoidal Carrying Capacity -- 4 Periodic Harvesting -- 4.1 Global Features of the Solution -- 4.2 Closed-Form Integration and Examples -- 4.3 A Sample Problem -- References -- A Transition of Shared Mobility in Metro Cities-A Challenge Post-Lockdown Covid-19 -- 1 Introduction -- 2 BPR Model -- 3 Data Analysis & -- Implementation -- 3.1 Data Description -- 3.2 Model Application & -- Results -- 3.3 Prediction of Traffic Scenarios Post-Lockdown -- 4 India's Transport Growth Journey and Its Effect on Energy and Environment -- 4.1 Transport and Environment -- 4.2 Health and Social Issues -- 4.3 Personal Vehicles and Their Impact -- 4.4 Measures to Curb the Traffic Upsurge -- References -- Analysis of Covid-19 Virus Spreading Statistics by the Use of a New Modified Weibull Distribution -- 1 Introduction and Preliminaries -- 1.1 The New Model NMWB Distribution -- 1.2 The Reliability Function -- 1.3 Moments of the Distribution -- 1.4 Order Statistics -- 1.5 Parameter Estimation -- 1.6 Relationship with Weibull-Related Results -- 2 Main Results -- 2.1 Statistical Properties -- 2.2 Least Square Estimates (LSES).
2.3 Order Statistics -- 2.4 Parameter Estimation -- 3 Applications -- 4 Conclusion -- References -- Lifting Lockdown Control Measure Assessment: From Finite-to Infinite-Dimensional Epidemic Models for Covid-19 -- 1 Introduction -- 2 Data Collection -- 3 Basic Covid-19 Model -- 3.1 Reproduction Numbers -- 3.2 Parameter and Initial Data Estimation -- 4 Discrete Age-Structured Covid-19 Model -- 4.1 Reproduction Numbers -- 4.2 Parameter and Initial Data Estimation -- 5 Covid-19 Model with Constant Delay -- 5.1 Reproduction Numbers -- 5.2 Parameter and Initial Data Estimation -- 6 Covid-19 Model with Threshold-Type Delay -- 7 Models with Demographic Effects -- 7.1 Covid-19 Model with Constant Delay -- 7.2 Covid-19 Model with Threshold-Type Delay -- 8 Discussion -- References -- Introduction to the Grey Systems Theory and Its Application in Mathematical Modeling and Pandemic Prediction of Covid-19 -- 1 A Brief Introduction to the Grey Systems Theory -- 2 Description of the Traditional Linear and Nonlinear Univariate Grey Models GM(1, 1) and NGBM(1, 1) -- 2.1 Building the Traditional Grey Model GM(1, 1) -- 2.2 The Nonlinear Grey Bernoulli Model NGBM(1, 1) -- 3 Optimization of the Univariate Grey Models -- 3.1 Optimization of Hyper-parameters -- 3.2 Rolling Mechanism -- 3.3 Optimization of the Initial Condition -- 4 Applications of Univariate Grey Models in Predicting Total Covid-19 Infected Cases -- 5 Description of the Existing GM(1, N) and GMC(1, N) Models -- 5.1 The Traditional GM(1, N) Model -- 5.2 The Grey Model with Convolution Integral GMC(1, N) -- 5.3 Variations of the Current GMC(1, N) and GMC(1, N) Models -- 5.4 Representation of the Nonlinear Grey Model with Convolution Integral NGMC(1, N) -- 6 Grey System Models with Fractional Order Accumulation -- 6.1 Definition of the Fractional Order Accumulation -- 6.2 The Fractional GMpq(1, 1) Model.
6.3 The Fractional Multivariate Grey Model with Convolutional Integral GMC pq(1, N) -- 6.4 Optimization of the Fractional Order r -- 7 Introduction to the Grey Relational Analysis -- 7.1 Data Preprocessing -- 7.2 Grey Relational Coefficient and Grey Relational Grade -- 8 Applications of Grey Relational Analysis In medicine -- 8.1 General Applications of Grey Relational Analysis in Medical Data Analysis -- 8.2 Application in Telecare -- 8.3 Grey Data Management in Medicine -- References -- Mathematical Analysis of Diagnosis Rate Effects in Covid-19 Transmission Dynamics with Optimal Control -- 1 Introduction -- 2 Model Formulation -- 3 Mathematical Analysis -- 3.1 The Disease-Free Equilibrium and Control Reproduction Number -- 3.2 Global Stability of DFE -- 3.3 Existence and Local Stability of the Endemic Equilibrium -- 3.4 Sensitivity Analysis -- 3.5 Numerical Simulation -- 4 Optimal Control -- 4.1 Building the Optimal Control Problem -- 4.2 Characterization of the Optimal Control -- 4.3 Numerical Simulation of the Optimal Control Problem -- 5 Conclusion -- References -- Development of Epidemiological Modeling RD-Covid-19 of Coronavirus Infectious Disease and Its Numerical Simulation -- 1 Introduction -- 2 Infectious Disease Epidemiology Components -- 2.1 Timelines of Infection -- 2.2 Estimation of Transmission Probability -- 2.3 The SAR is a Proportion, Not a Rate -- 3 Estimation of Basic Reproduction Number/ Proliferation Number -- 3.1 Estimation of R0 -- 3.2 Virulence of R0 and the Case Fatality Ratio (CFR) -- 4 Incidence Rate as a Function of Prevalence and Contact Rate -- 5 Dynamic Epidemic Process in a Closed Population -- 6 RD-Covid-19 Epidemiological Model -- 7 Numerical Simulation of RD-Covid-19 Model -- 7.1 PART-1: Numerical Outcome of RD-Covid-19 Model Outcome for INDIA.
7.2 PART-2: Numerical Outcome of RD-Covid-19 Model Outcome for CHINA -- 7.3 PART-3: Numerical Outcome of RD-Covid-19 Model Outcome for BRAZIL -- 7.4 PART-4: Numerical Outcome of RD-Covid-19 Model Outcome for RUSSIA -- 8 Conclusions -- References -- Mediterranean Diet-A Healthy Dietary Pattern and Lifestyle for Strong Immunity -- 1 Introduction -- 2 Mediterranean Lifestyle -- 3 Benefits of Mediterranean Diet -- 4 Mediterranean Diet for a Healthy Gut -- 5 Conclusion -- References -- Rate-Induced Tipping Phenomena in Compartment Models of Epidemics -- 1 Introduction -- 1.1 Outline -- 2 Preliminaries -- 2.1 Compartment Models with Time-Dependent Parameters -- 2.2 Autonomous SIR Model -- 2.3 Autonomous SIRS Model -- 3 Linear Compartment Models -- 3.1 Artifacts of Rate-Induced Tipping -- 4 Nonlinear Compartment Models -- 4.1 Local Normal Form for a Bifurcation of Codimension Two -- 4.2 Idealized Models -- 5 Irreducible Rate-Induced Tipping in Non-autonomous Models -- 5.1 Artifacts of Rate-Induced Tipping -- 6 Conclusion -- References -- Analysis of Impact of Covid-19 Pandemic on Financial Markets -- 1 Introduction -- 2 Market Behaviour During Initial and Intermediate Pandemic Phases -- 2.1 Covid-19 Market Crash (2020/02/19-2020/03/19) -- 2.2 Market Recovery After Covid-19 Crash (2020/03/20 - 2020/03/26) -- 2.3 Pandemic Growth After 2020/03/18 -- 3 Framework for Modelling Pandemic Impact -- 3.1 Susceptible, Infected, Recovered and Death (SIRD) Model with Time-Dependent Parameters and Social Distancing -- 3.2 Calibration Algorithm -- 3.3 Phenomenological Pandemic Model (PPM) -- 3.4 The Process N(t) in an Intermediate Phase -- 3.5 Approximation to PPM -- 3.6 Calibration of PPM -- 3.7 Mapping Epidemic Variables to Financial Risk Factors -- 4 Simulation of Stress Scenarios -- 4.1 Simulation of Risk Drivers Under the SIRD Model -- 4.2 PPM Simulation.
5 Conclusion.
Record Nr. UNISA-996466401503316
Gateway East, Singapore : , : Springer, , [2021]
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Application of artificial intelligence in Covid-19 / / Sachi Nandan Mohanty [and three others], editors
Application of artificial intelligence in Covid-19 / / Sachi Nandan Mohanty [and three others], editors
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (593 pages)
Disciplina 610.285
Collana Medical virology: from pathogenesis to disease control series
Soggetto topico Artificial intelligence - Medical applications
COVID-19
Intel·ligència artificial en medicina
Soggetto genere / forma Llibres electrònics
ISBN 981-15-7317-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword 1 -- Foreword 2 -- Preface -- Acknowledgements -- Contents -- About the Editors -- Part I: AI as a Source of Prides for Healthcare -- 1: Comprehensive Claims of AI for Healthcare Applications-Coherence Towards COVID-19 -- 1.1 Orientation of Artificial Intelligence in Healthcare Research -- 1.2 Correlated Investigational Analysis of AI Appliances in Healthcare System and Various Clinical Diseases -- 1.2.1 Disease Detection and Diagnosis -- 1.2.2 Automated Robert Treatment and Drug Design, Discovery -- 1.2.3 Healthcare Data Management Supported by Digital Managerial Application -- 1.2.4 AI in Public and Clinical Health -- 1.3 Motivational AI Devices for Healthcare -- 1.3.1 AI-Administered Devices with Machine Learning and Deep Learning -- 1.3.2 AI Attributed Devices with IOT -- 1.3.3 AI Supervised Devices with Big Data and Data Science -- 1.3.4 AI-Based Mining and NLP -- 1.3.5 AI-Enabled Expert System -- 1.4 Demand of AI for COVID-19 -- 1.4.1 Prior Alert Generation -- 1.4.2 Continuous Tracing and Following COVID-19 Symptoms -- 1.4.3 Diagnosis and Prognosis -- 1.4.4 Treatment and Possible Drug Design and Discovery -- 1.4.5 Control over Society and People with Guidelines -- 1.5 Conclusions and Future Work -- 1.6 Executive Summary -- References -- 2: Artificial Intelligence-Based Systems for Combating COVID-19 -- 2.1 Introduction -- 2.2 How Technology Can Help in Containing the Pandemic? -- 2.3 Technological Approach Vs Non-technological Approach of Treatment of COVID-19 -- 2.4 Existing Technologies to Detect/Diagnose the Virus -- 2.4.1 Non-contact Infrared Thermometers -- 2.5 Thermal Screening via Thermal Cameras -- 2.5.1 Symptom-Based Diagnosis -- 2.5.2 Ventilators -- 2.6 Means of Prevention from COVID-19 -- 2.6.1 Masks -- 2.6.2 Sanitizers/Hand Rub -- 2.6.3 Sanitizing Tunnels for Public Areas.
2.6.4 Washing Hands with Soap for 20s -- 2.6.5 Avoiding Handshakes -- 2.7 Use of Modern Technologies for Making Diagnosis Faster, Easier, and Effective -- 2.8 Proposed Techniques to Effectively Control the Rise in Cases of COVID-19 -- 2.8.1 Crowdsource-Based Applications -- 2.9 Conclusion -- References -- Part II: AI Warfare in COVID-19 Diagnosis, Detection, Prediction, Prognosis and Knowledge Representation -- 3: Artificial Intelligence-Mediated Medical Diagnosis of COVID-19 -- 3.1 Introduction -- 3.2 Pathogenesis and Diagnostic Windows -- 3.3 AI Assisted COVID-19 Diagnosis -- 3.3.1 Potential Application for Infection Detection -- 3.3.2 Application of AI on `Omics´ Big-Data -- 3.3.3 Use of AI on Radiology Data -- 3.4 Future Directions -- References -- 4: Artificial Intelligence (AI) Combined with Medical Imaging Enables Rapid Diagnosis for Covid-19 -- 4.1 Introduction -- 4.1.1 Reverse Transcription-Polymerase Chain Reaction -- 4.1.2 Isothermal Amplification Assays -- 4.1.3 Antigen Tests -- 4.1.4 Serological Tests -- 4.1.5 Rapid Diagnostic Tests (RDT) -- 4.1.6 Enzyme-Linked ImmunoSorbent Assay (ELISA) -- 4.1.7 Neutralization Assay -- 4.1.8 Chemiluminescent Immunoassay -- 4.2 AI-Based Diagnosis -- 4.2.1 Chest CT or X-ray CT Scans -- 4.2.2 Chest Radiography -- 4.2.2.1 Limitation -- 4.3 Other Predictive Measures for Covid-19 Diagnosis -- 4.3.1 Pulse Oximetry -- 4.3.2 Thermal Screening -- 4.4 Conclusions -- References -- 5: Role of Artificial Intelligence in COVID-19 Prediction Based on Statistical Methods -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Dataset Description -- 5.4 Experimental Results -- 5.4.1 Combinatorial (Quick) Approach -- 5.4.2 Stepwise Forward Selection Approach -- 5.4.3 Stepwise Mixed Selection Approach -- 5.4.4 GMDH Neural Network Approach -- 5.5 Comparison Between the Algorithms Based on MAE, RMSE, SD, Correlation.
5.6 Conclusion -- References -- 6: Data-Driven Symptom Analysis and Location Prediction Model for Clinical Health Data Processing and Knowledgebase Developmen... -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Rudiments of Random Forest Machine Learning Algorithm -- 6.4 Case Study for Symptom Analysis and Its Prediction with Random Forest Using COVID-19 WHO Data Set -- 6.4.1 Step Wise Experimental Result Analysis and Discussions -- 6.4.2 Calculation of Average Baseline Error -- 6.4.3 Classifying Into Zones -- 6.4.3.1 Setting Threshold Value -- 6.4.4 Color Attribute of Map with Zones (Green, Orange, and Red) -- 6.5 Augmented Enhancements to the Detection and Prediction Analysis for COVID 19 -- 6.5.1 Appending a New Drop-Down Menu in the Detection Page -- 6.6 Aligning Output of This Research as a Supplement to Heighten Up Healthcare and Public Health -- 6.7 Conclusions -- 6.8 Future Work -- References -- 7: A Decision Support System Using Rule-Based Expert System for COVID-19 Prediction and Diagnosis -- 7.1 Introduction -- 7.2 Background -- 7.2.1 Machine Learning-Based Data-Oriented Approach -- 7.2.2 Expert System-Based Knowledge-Oriented Approach -- 7.3 Overview of Expert System -- 7.3.1 Fundamentals -- 7.3.2 Expert System Architecture -- 7.3.3 Expert System Design Issues -- 7.4 Case Study: COVID-19 -- 7.4.1 Feasibility of Expert System on COVID-19 -- 7.4.2 Problem Description -- 7.4.3 Proposed Expert System: ESCOVID -- 7.4.3.1 Rule Set and Knowledgebase -- 7.4.3.2 Inference Mechanism -- 7.5 Implementation and Testing -- 7.6 Conclusion -- References -- 8: A Predictive Mechanism to Intimate the Danger of Infection via nCOVID-19 Through Unsupervised Learning -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 Methodology -- 8.3.1 Data Collection -- 8.3.2 Relevant Dataset -- 8.3.3 Data Processing -- 8.3.3.1 Algorithm of Clustering -- 8.4 Result Analysis.
8.4.1 Overall Behavior of All Unsupervised Learning Model (Figs. 8.9 and 8.10) -- 8.5 Conclusion -- References -- 9: Artificial Intelligence-Enabled Prognosis Technologies for SARS-CoV-2/COVID-19 -- 9.1 Introduction -- 9.1.1 Epidemiology and Phylogeography of Pathogen -- 9.1.2 Human-to-Human Transmission -- 9.1.3 Clinical Phenotype Variations and Pathogenesis -- 9.2 Current Prognosis Practices -- 9.2.1 Diagnosis Services -- 9.2.2 Control Practices -- 9.2.2.1 Sanitization -- 9.2.2.2 Treatment -- 9.3 Challenges of SARS-CoV-2 -- 9.3.1 Phylogeography and Clinical Features -- 9.3.2 Mass Community and Healthcare Management -- 9.3.3 Transmission and Distancing -- 9.3.4 Diagnosis and Treatment -- 9.3.5 Disease Modeling Approaches -- 9.3.6 Data Security Concerns -- 9.4 Advanced Technologies -- 9.4.1 Internet of Things (IoT) -- 9.4.2 Artificial Intelligence (AI) -- 9.4.3 Databases and Analytics -- 9.4.4 Advanced Genomics and proteomics -- 9.4.5 Cloud Computing and Optimization -- 9.4.6 Digital Medicine and Healthcare -- 9.4.7 Biosensor and Bioelectronics -- 9.5 Integrated Technology and Logical Products -- 9.5.1 AI, Cloud, Sensor and IoT -- 9.6 AI-Enabled Prognosis Technology, Product, and Model Description -- 9.6.1 Technology and Product: AI Analysis and Program in Healthcare -- 9.6.2 Product and Technology: AI-Based sanitization Machine Using Cloud computing and Optimization -- 9.6.3 Product and Technology: IOT-Based AI-Enabled Touchless Hand Sanitizer Machine -- 9.6.4 Technology and Model: Prognosis Healthcare Model for Mass Community -- 9.6.4.1 Standard Prognosis Practices -- 9.7 Adaptation of AI-Enabled Technology and Disease Research -- 9.7.1 Hygiene, Distancing, and Virus Control -- 9.7.2 Understanding of Pathogenic Consequences -- 9.8 Conclusion -- 9.9 Future Prospects -- References.
10: Intelligent Agent Based Case Base Reasoning Systems Build Knowledge Representation in COVID-19 Analysis of Recovery of Inf... -- 10.1 Introduction -- 10.2 Related Work -- 10.3 COVID-19 -- 10.4 Symptom of COVID-19 -- 10.5 Artificial Intelligence -- 10.6 Machine Learning -- 10.7 Natural Language Processing -- 10.8 Robotics -- 10.9 Autonomous Vehicles -- 10.10 Vision -- 10.11 Clinical Artificial Intelligence -- 10.12 Expert System -- 10.13 Machine Learning -- 10.14 Intelligent Agent -- 10.15 Characteristic Agents -- 10.16 Clinical Intelligent Agent -- 10.17 Multi-Agent System -- 10.18 Java Agent Framework (JADE) -- 10.19 Clinical Multi-Agents -- 10.20 Case Base Reasoning -- 10.21 The CBR Cycle -- 10.22 JCOLIBRI -- 10.23 Clinical Case Base Reasoning Systems -- 10.24 Knowledge Base System -- 10.25 Clinical Knowledge Base System -- 10.26 Amalgamation OF CAI, CIA, CMAS, CCBR Using in KBSCOVID-19 Model -- 10.27 Implementation of MASCBR-Based Knowledge Base Patients Recovery from COVID-19 Pandemic -- 10.28 Conclusion -- 10.29 Future Work -- References -- Part III: Machine Learning Solicitation for COVID 19 -- 11: Epidemic Analysis of COVID-19 Using Machine Learning Techniques -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Pattern Identification for COVID-19 -- 11.4 Experiment Analysis -- 11.4.1 Dataset 1: Based on Geographic Distribution (https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geogr... -- 11.4.1.1 Description of the Dataset -- 11.4.1.2 Correlation Between the Variables -- 11.4.1.3 Generating Heat Map of the Correlation -- 11.4.2 Dataset 2 (https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv) -- 11.4.2.1 Snapshot of the dataset -- 11.4.2.2 Generating Pair Plot -- 11.5 Pattern Prediction of Covid-19 Using Machine Learning Approaches -- 11.6 Conclusions -- References.
12: Machine Learning Application in COVID-19 Drug Development.
Record Nr. UNINA-9910502987803321
Gateway East, Singapore : , : Springer, , [2021]
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Artificial intelligence for COVID-19 / / Diego Oliva, Said Ali Hassan, Ali Mohamed, editors
Artificial intelligence for COVID-19 / / Diego Oliva, Said Ali Hassan, Ali Mohamed, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (585 pages)
Disciplina 610.285
Collana Studies in systems, decision and control
Soggetto topico COVID-19
Intel·ligència artificial en medicina
Processament de dades
Artificial intelligence - Medical applications
Soggetto genere / forma Llibres electrònics
ISBN 3-030-69744-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Simulation of the Relation Between the Number of COVID-19 Death Cases as a Result of the Number of Handwashing Facilities by Using Artificial Intelligence -- 1 Literature Review -- 2 Aim of the Research -- 2.1 Statement of the Problem -- 2.2 Research Methodology -- 2.3 Research Setting and Research Paradigm -- 2.4 Limitations of the Method -- 3 Results and Discussion -- 4 Conclusion -- References -- Big Data and Data Analytics for an Enhanced COVID-19 Epidemic Management -- 1 Introduction -- 2 Big Data and Big Data Analytics for COVID-19 -- 2.1 Big Data Analytics Life Cycle -- 3 The Opportunities of Big Data and Big Data Analytics in COVID-19 Pandemic -- 4 Challenges of Big Data and Big Data Analytics During COVID-19 Pandemic -- 5 Conclusion -- References -- Application of COVID-19 Pandemic Using Artificial Intelligence -- 1 Introduction -- 2 Premature Detection of the Coronavirus (COVID-19) -- 3 Succinct Review on Transferable Syndrome Outburst in the Year 2020 -- 4 Applications of Artificial Intelligence in COVID-19 Pandemic -- 4.1 Premature Detection and Diagnosis of Infection -- 4.2 Protrusion of Suitcases and Transience -- 4.3 Progress of Drugs and Vaccines -- 4.4 Tumbling the Work of Healthcare Employees -- 5 The Original AI Capability of Bluedot and Metabiota -- 5.1 Bluedot -- 5.2 Metabiota -- 6 Conclusion -- References -- Application of Artificial Intelligence for COVID-19 Epidemic: An Exploratory Study, Opportunities, Challenges, and Future Prospects -- 1 Introduction -- 2 Artificial Intelligence (AI) Techniques in COVID-19 Outbreak -- 3 The Applicability of Artificial Intelligence During COVID-19 Pandemic -- 4 The Challenges Applying Artificial Intelligence During COVID-19 Pandemic -- 5 Conclusion -- References -- Diagnosing COVID-19 Virus in the Cardiovascular System Using ANN -- 1 Introduction.
1.1 Electrocardiography -- 1.2 Cardiovascular Risk Factors Associated with the Worse Outcomes of COVID-19 -- 2 COVID-19 Cardiovascular Manifestations -- 2.1 COVID-19 and Cardiac Arrhythmia -- 2.2 COVID-19 Myocardial Injury and Heart Failure -- 2.3 COVID-19 and Myocarditis -- 2.4 Variability in Heart Rate -- 2.5 COVID-19 and Ischemic Heart Disease -- 3 Results and Discussion -- 4 Results from Artificial Neural Network -- 5 Conclusion -- References -- An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings -- 1 Introduction -- 2 Methodology -- 2.1 COVID-19 5-Class Balanced Dataset -- 2.2 The Pipeline of Deep Feature Extraction from Pretrained Networks and Machine Learning Classification -- 2.3 Performance Evaluation of the Proposed COVID-19 Detection Pipeline -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Understanding Role of Information and Communication Technology Application in Vietnam's Prevention and Control of COVID-19 Pandemic -- 1 Introduction -- 2 Literature Review -- 3 The Role of the Communication in Propagating Against COVID-19 in Vietnam -- 4 The Role of Information Technology Application in Warning and Detecting COVID-19 Patients -- 4.1 The Role of Information Technology in Digitizing Public Services Towards Reducing Risk of Infection -- 5 Discussion and Conclusion -- 6 Study Limitations and Contributions -- References -- Robotics and Automation: The Rescuers of COVID Era -- 1 Introduction -- 2 Importance of Robotics and Automation During Coronavirus -- 3 Applications of Robotics & -- Automation -- 3.1 Applications (Pre- COVID) -- 3.2 Applications (Post- COVID) -- 4 Problems Associated with Robotics & -- Automation -- 5 Types of Robotics and Automation.
6 Different Applications for Which Robots Are Used All Over World During Coronavirus -- 7 Future Development and Investment on Robotics -- 8 Conclusions and Suggestions -- References -- Nonparametric Tests for Comparing COVID-19 Machine Learning Forecasting Models -- 1 Introduction -- 2 Challenges of Forecasting of COVID-19 Outbreak -- 2.1 Forecasting Models for COVID-19 Outbreak -- 2.2 Nonparametric Methods for Comparing Forecast Models -- 3 Klyushin-Petunin Nonparametric Test for Homogeneity -- 4 Estimation of Forecast Model Using the P-Statistics -- 5 Conclusion and Scope for the Future Work -- References -- Artificial Intelligence and the Control of COVID-19: A Review of Machine and Deep Learning Approaches -- 1 Introduction -- 2 Knowledge Areas of AI in COVID-19 Control -- 2.1 Prediction, Tracking and Social Control -- 2.2 Cures and Treatments -- 2.3 Prognosis and Diagnosis -- 2.4 Data Dashboards -- 3 Generalized AI Response to COVID-19 -- 4 Challenges -- 4.1 Small Data Sample Size -- 4.2 Daily Case of Occurrences -- 4.3 Overlapping of Disease Symptoms -- 4.4 Need for Robust and Effective for COVID-19 -- 5 Conclusion -- References -- Optimization of the International Trade Activities in the Period of COVID-19 by Proposing an Algorithm -- 1 Introduction -- 2 Application Model in International Exchange for the Proper Management of the COVID-19 Containment Period -- 3 Principle of Operation of the Optimization Model in International Trade -- 3.1 Taking Quality into Account in Flows -- 3.2 Deterministic Model-Wait and See (WS) -- 4 The Application's Algorithm for Optimizing the Operations of International Trade Companies -- 4.1 The Algorithm -- 4.2 Discussion and Interpretation -- 5 Conclusion -- References -- Internet of Things (IoT) and Real Time Applications -- 1 Introduction and Key Discoveries.
1.1 Year-on-Year Utilization of IoT Stages by Industry -- 1.2 IoT Safekeeping and Information Secrecy -- 2 Malware Recognition Methods -- 2.1 IoT Precautions Code of Behaviors -- 2.2 Confidentiality in IoT -- 3 Real Time Application of IoT -- 3.1 A Real Time IoT-Based Wearable Communication Enabled Jacket to Monitor and Analyze the COVID-19 -- 3.2 Microstrip Patch Antennas -- 3.3 Block Diagram of Navigation System -- 4 Conclusion -- References -- Optimum Scheduling of the Disinfection Process for COVID-19 in Public Places with a Case Study from Egypt, a Novel Discrete Binary Gaining-Sharing Knowledge-Based Metaheuristic Algorithm -- 1 Introduction -- 2 Disinfection Scheduling Strategies for Public Places -- 3 Mathematical Model Formulation for the Disinfection Scheduling Strategies -- 3.1 Mathematical Model -- 4 A Real Application Case Study: Educational Institutions, Cairo, Egypt -- 5 The Proposed Methodology -- 5.1 Gaining Sharing Knowledge-Based Optimization Algorithm (GSK) -- 5.2 Discrete Binary Gaining Sharing Knowledge-Based Optimization Algorithm (DBGSK) -- 6 Experimental Results -- 7 Conclusions and Points for Future Researches -- References -- Predicating COVID19 Epidemic in Nepal Using the SIR Model -- 1 Introduction -- 2 Materials and Methods -- 2.1 Study Area -- 2.2 Data Collection -- 2.3 Methods -- 3 Results -- 4 Discussion -- 5 Conclusions -- References -- Feature Extraction of Coronavirus X-Ray Images by RNN, Correlational Networks, and PNN -- 1 Introduction -- 2 Related Work -- 3 About Coronavirus X-Ray Images -- 3.1 A Common Finding of CT Images -- 3.2 Changes in Chronological CT -- 4 Methodology -- 5 Implementation -- 5.1 Dataset -- 5.2 Evaluation Metrics -- 6 Discussion -- 7 Conclusion -- 8 Future Enhancement -- References -- Text Mining for Covid-19 Analysis in Latin America -- 1 Introduction -- 1.1 Covid-19 Outbreak in the World.
1.2 Covid-19 in Latin America -- 1.3 Text Mining Applications -- 1.4 Research in Latin America -- 1.5 PubMed -- 2 NLP Applied to South America -- 2.1 March -- 2.2 April -- 2.3 May -- 2.4 June -- 2.5 July -- 3 NLP Applied to Central America -- 4 NLP Applied to North America -- 5 Conclusions -- 6 Future Work -- References -- Spread of COVID-19 in Odisha (India) Due to Influx of Migrants and Stability Analysis Using Mathematical Modeling -- 1 Introduction -- 2 Mathematical Modeling and Basic Assumptions -- 2.1 Basic Assumptions -- 2.2 Existence of Boundedness and Positive Invariant of the Solutions -- 2.3 Basic Reproduction Number and Existence of Equilibrium -- 2.4 Local Stability Analysis -- 2.5 Global Stability for the Endemic Equilibrium -- 3 Interpretation of the Numerical Results -- 4 Conclusion -- References -- COVID-19 Epidemic Analysis and Prediction in Virudhunagar District Using Machine Learning -- 1 Introduction -- 1.1 Preventive Measures -- 2 Coronavirus Pandemic in Virudhunagar District, Tamilnadu Literature -- 2.1 History of Virudhunagar -- 2.2 Significance of the Study -- 2.3 Review of Literature -- 3 Materials and Methods -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Machine Learning Classifiers -- 4 Results and Discussions -- 4.1 Data Visualization of Sivakasi and Virudhunagar -- 4.2 Infection Rate Growth Phase -- 4.3 Reasons and Outbreaks -- 5 Conclusion -- References -- Internet of Things and Covid-19 Safety Precautions: Roles of Information Communication Technology in Health Emergency Control for Global Development -- 1 Introduction -- 2 Methodology -- 3 Review of Literature -- 4 History of IoT and IoMT -- 5 IoT and IoMT as a Technology-Based Tool for Treatment -- 5.1 Internet of Medical Things (IoMT) -- 6 Usefulness of IoT -- 7 Problems Faced by IoMT -- 8 Corona Virus or Covid-19 -- 9 Effects of Corona Virus.
10 Safety Precautions During Covid-19.
Record Nr. UNINA-9910495348203321
Cham, Switzerland : , : Springer, , [2021]
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Artificial intelligence in COVID-19 / / Niklas Lidströmer and Yonina C. Eldar, editors
Artificial intelligence in COVID-19 / / Niklas Lidströmer and Yonina C. Eldar, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (346 pages)
Disciplina 362.1962414
Soggetto topico COVID-19 (Disease) - Data processing
Medical informatics
Pandemics - Economic aspects
COVID-19
Epidèmies
Processament de dades
Informàtica mèdica
Soggetto genere / forma Llibres electrònics
ISBN 3-031-08506-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Preface -- Contents -- About the Editors -- Chapter 1: Introduction to Artificial Intelligence in COVID-19 -- Pandemics -- History of Pandemics -- The COVID-19 Pandemic -- Origins of the COVID-19 Pandemic -- Continuous Fight for Science and Reason -- Modern Tools for Pandemic Control -- A Brief Chronology of the Chapters of This Book -- Power of Science -- References -- Chapter 2: AI for Pooled Testing of COVID-19 Samples -- Introduction -- System Model -- The PCR Process -- Mathematical Model -- Pooled COVID-19 Tests -- Recovery from Pooled Tests -- Group Testing Methods for COVID-19 -- Adaptive GT Methods -- Non-Adaptive GT Methods -- Pooling Matrix -- Noiseless Linear Non-Adaptive Recovery -- Noisy Non-Linear Non-Adaptive Recovery -- Summary -- Compressed Sensing for Pooled Testing for COVID-19 -- Compressed Sensing Forward Model for Pooled RT-PCR -- CS Algorithms for Recovery -- Details of Algorithms -- Assessment of Algorithm Performance and Experimental Protocols -- Choice of Pooling Matrices -- Choice of Number of Pools -- Use of Side Information in Pooled Inference -- Comparative Discussion and Summary -- References -- Chapter 3: AI for Drug Repurposing in the Pandemic Response -- Introduction -- Desirable Features of AI for Drug Repurposing in Pandemic Response -- Technical Flexibility and Efficiency -- Clinical Applicability and Acceptability -- Major AI Applications for Drug Repurposing in Response to COVID-19 -- Knowledge Mining -- Network-Based Analysis -- In Silico Modelling -- IDentif.AI Platform for Rapid Identification of Drug Combinations -- Project IDentif.AI -- IDentif.AI for Drug Optimization Against SARS-CoV-2 -- IDentif.AI 2.0 Platform in an Evolving Pandemic -- IDentif.AI as a Pandemic Preparedness Platform -- Use of Real-World Data to Identify Potential Targets for Drug Repurposing.
Future Directions -- References -- Chapter 4: AI and Point of Care Image Analysis for COVID-19 -- Introduction -- Motivation for Using Imaging -- Motivation for Using AI with Imaging -- Integration of Imaging with Other Modalities -- Literature Overview -- Chest X-Ray Imaging -- Diagnosis Models -- Prognosis Models -- Use of Longitudinal Imaging -- Fusion with Other Data Modalities -- Common Issues with AI and Chest X-Ray Imaging -- Duplication and Quality Issues -- Source Issues -- Frankenstein Datasets -- Implicit Biases in the Source Data -- Artificial Limitations Due to Transfer Learning -- Computed Tomography Imaging -- Diagnosis Models -- Prognosis Models -- Applications to Regions Away from the Lungs -- Use of Longitudinal Imaging -- Fusion with Other Data Modalities -- Common Issues with AI and Computed Tomography Imaging -- Ultrasound Imaging -- What Can be Observed in LUS -- Models Assisting in Interpreting LUS -- Diagnosis Models -- Prognosis Models -- Use of Longitudinal Imaging -- Common Issues with AI and Ultrasound Imaging -- Conclusions -- Success Stories -- Pitfalls to Focus On -- Lessons Learned and Recommendations -- The Next Pandemic -- References -- Chapter 5: Machine Learning and Laboratory Values in the Diagnosis, Prognosis and Vaccination Strategy of COVID-19 -- Introduction -- COVID-19, Machine Learning and Laboratory Values: The State of the Art -- Literature Search Results -- Diagnostic Studies -- Prognostic Studies -- Considerations on the Literature Reviewed -- Heterogeneity in Patient Selection -- Laboratory Parameters Used by Machine Learning Models -- Types of Models and Their Validation -- Model Implementation -- The Role of Artificial Intelligence in the Vaccination Strategy Against SARS-COV-2 Through Laboratory Tests -- Real-World Vaccination Strategies -- Artificial Intelligence Potentialities -- Conclusions.
Appendix 1 -- Diagnostic Papers (D) -- Prognostic Papers (P) -- Appendix 2: Tool Online -- References -- Chapter 6: AI and the Infectious Medicine of COVID-19 -- Introduction -- AI and ML for SARS-CoV-2 Early Research Using Pathogen Sequence Data -- AI and ML for Research of SARS-CoV-2 Antivirals -- AI and ML for COVID-19 Infectious Medicine Early Research Using Language Data -- AI and ML in Real World Data Analysis of COVID-19 -- AI and ML in Molecular Diagnostics of COVID-19 -- AI and ML in Image-Based Diagnostics of COVID-19 and Clinical Decision Support -- AI and ML in COVID-19 Medical Care -- Prevention, Infection Risk and Epidemiology -- Treatment and Prognosis -- Conclusions -- References -- Chapter 7: AI and ICU Monitoring on the Wake of the COVID-19 Pandemic -- Introduction -- ICU Monitoring Through AI -- ICU Monitoring and AI in Pre-pandemic Times -- The Impact of the COVID-19 Pandemic on the ICU and the Role of AI -- Conclusions -- References -- Chapter 8: Symptom Based Models of COVID-19 Infection Using AI -- Introduction -- Using Machine Learning Methods to Determine Mortality of Patient with COVID-19 -- Using Machine Learning Methods to Detect the Presence of COVID-19 Infection -- Using Machine Learning Methods to Differentiate COVID-19 and Influenza/Common Cold Infections -- Summary, Limitations, Challenges, and Future Applications -- References -- Chapter 9: AI Techniques for Forecasting Epidemic Dynamics: Theory and Practice -- Introduction -- A Review of Model Types and Limits to Forecasting -- Preliminaries -- Model Details -- Metrics for Forecast Evaluations -- AI-Driven Engineering -- An Example of a Real-time Forecasting Model -- Results -- A GNN-Based Spatio-Temporal Model -- Additional Details Regarding the Framework -- Forecasting Performance -- Theoretical Foundations for Forecasting in Network Models -- Overview.
Some Short-Term Forecasting Problems and Their Computational Intractability -- Discussion -- References -- Chapter 10: Regulatory Aspects on AI and Pharmacovigilance for COVID-19 -- What Does Artificial Intelligence Mean According to Legal Definition? -- AI and Health -- The European Union Legal Framework: A Work in Progress -- The Proposed EU Regulation (Artificial Intelligence Act) -- The Use of AI in Research and Developing Medicinal Products and Monitoring Their Quality, Safety and Efficacy -- The Added Value Brought Using Artificial Intelligence in Performing Pharmacovigilance Activities in General and During the COVID-19 Pandemic -- Ethical Issues: A Few Caveats -- The Personal Data Protection Implications -- Provisional Conclusions -- Suggested Reading -- Chapter 11: AI and the Clinical Immunology/Immunoinformatics for COVID-19 -- Introduction -- Challenge for Traditional Vaccines in COVID-19 -- Long Development and Design Period -- Difficulties in Knowing and Optimizing the Efficacy and Side Effects -- Uncertainties with the Development and Other Costs During Production, Storage, and Transportation -- Hard to Tackle Unknown and Emerging Mutations of Viruses -- Existing AI Techniques Help the Traditional Vaccine Development in COVID-19 -- AI Makes the Practical Experimental Results Computational -- AI-Based Computational Tools Can Help the Traditional Vaccine Design -- AI-Based In Silico Vaccine Design -- Our Recently Proposed DeepVacPred Vaccine Design Framework -- Artificial Intelligence for Investigating Viral Evolution and Mutations -- An Algorithmic Information Theoretic Approach to Discover the State Machine Generator Governing the Viral Sequence Structure and Enabling AI Strategies for Viral Mutation Prediction -- Characterizing the Temporal Evolution of SARS-CoV-2 in a Continuous Manner.
Detecting Regions Within Viral Sequences Likely to Exhibit Mutations -- Summary -- References -- Chapter 12: AI and Dynamic Prediction of Deterioration in Covid-19 -- Introduction -- COVID-19: A Novel Disease-Usage of Newer or Older Clinical Decisions Support Systems? -- Clinical Decisions Support System Stable Parameters/Features Using Threshold Values -- Patient Deterioration -- General Prediction Scores -- Early Warning Systems (EWS) -- AI for Prediction of Deterioration -- AI Assisted Patient-Specific Risk Prediction -- AI Assisted Prediction of Critical Illness and Deterioration in COVID-19 Patients -- Mortality Prediction Models for Covid-19 -- Mortality Prediction Models Using High-Frequency Data -- Prediction Models for Sepsis -- Explainable and Interpretable Machine Learning Methods for Clinical Decision Support Systems -- References -- Chapter 13: AI, Epidemiology and Public Health in the Covid Pandemic -- Introduction -- Epidemiology: Definition and Purposes -- Epidemiology and Public Health: How They Relate to Each Other and the Concept of One Health -- Individual Health and Population Health -- The Articulation Between Individual and Population Level -- Biomedical and Biopsychosocial Models of Health: Individual, Environmental and Social Determinants of Health -- From Precision Medicine to Precision Public Health -- Epidemiology and Public Health in the Digital Era: Prerequisites -- A Ubiquitous Digitization -- The Evolutions of the Regulatory Framework on Personal Data -- Connected Devices and Equipment Rates -- Digital and E-health Literacy -- Towards a Real Life Use of AI in Epidemiology and Public Health: Some First Examples -- No Data Means No Artificial Intelligence: A Few Words About Data Federation and "New" Types of Data -- Citizens and Patients as Producers, Actor and Manager of Their Own Health.
At the Population Level, Health Surveillance Systems and AI.
Record Nr. UNINA-9910624309003321
Cham, Switzerland : , : Springer, , [2022]
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