Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Approximation theory, sequence spaces and applications / / S. A. Mohiuddine, Bipan Hazarika, and Hemant Kumar Nashine
| Approximation theory, sequence spaces and applications / / S. A. Mohiuddine, Bipan Hazarika, and Hemant Kumar Nashine |
| Autore | Mohiuddine S. A. |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (277 pages) |
| Disciplina | 511.4 |
| Collana | Industrial and Applied Mathematics |
| Soggetto topico |
Approximation theory
Approximation theory - Data processing Teoria de l'aproximació Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-19-6116-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Editors -- 1 Topology on Geometric Sequence Spaces -- 1.1 Introduction -- 1.1.1 α-Generator and Geometric Complex Field -- 1.1.2 Some Useful Relations Between Geometric Operations and Ordinary Arithmetic Operations -- 1.1.3 G-Limit -- 1.1.4 G-Continuity -- 1.2 Geometric Vector Spaces -- 1.2.1 Geometric Vector Space -- 1.2.2 Dual System -- 1.3 Topology on Geometric Sequence Spaces -- 1.3.1 Normal Topology -- 1.3.2 Perfect Sequence Space -- 1.3.3 Simple Space -- 1.3.4 Symmetric Sequence Spaces -- References -- 2 Composition Operators on Second-Order Cesàro Function Spaces -- 2.1 Introduction -- 2.2 Examining the Boundedness -- 2.3 Compactness and Essential Norm of Composition Operators -- 2.4 Fredholm Composition Operators -- 2.5 Conclusion -- References -- 3 Generalized Deferred Statistical Convergence -- 3.1 Definitions and Preliminaries -- 3.2 Deferred Statistical Convergence of Order αβ -- 3.3 Strong s-Deferred Cesàro Summability of Order αβ -- 3.4 Inclusion Theorems -- 3.5 Special Cases -- References -- 4 Approximation by Generalized Lupaş-Pǎltǎnea Operators -- 4.1 Introduction -- 4.2 Basic Results -- 4.3 Main Results -- 4.3.1 Weighted Approximation -- 4.3.2 Quantitative Voronoskaja-Type Approximation Theorem -- 4.3.3 Grüss Voronovskaya-Type Theorem -- 4.3.4 Approximation Properties of DBV[0,infty) -- References -- 5 Zachary Spaces mathcalZp[mathbbRinfty] and Separable Banach Spaces -- 5.1 Introduction -- 5.1.1 Preliminaries -- 5.1.2 Basis for a Banach Spaces -- 5.2 Space of Functions of Bounded Mean Oscillation (BMO[mathbbRIinfty]) -- 5.3 Zachary Space mathcalZp[mathbbRIinfty] -- 5.4 Zachary Space mathcalZp[mathfrakB], Where mathfrakB is Separable Banach Space -- References -- 6 New Generalization of the Power Summability Methods for Dunkl Generalization of Szász Operators via q-Calculus.
6.1 Introduction -- 6.2 Dunkl Generalization of the Szász Operators Obtained by q-Calculus -- 6.3 Preliminary Results -- 6.4 Direct Estimates -- 6.5 Weighted Approximation -- 6.6 Statistical Approximation Properties for Dunkl Generalization of Szász Operators via q-Calculus -- 6.7 Rate of Convergence of the Dunkl Generalization of Szász Operators via q-Calculus -- 6.8 Conclusion -- References -- 7 Approximation by Generalized Szász-Jakimovski-Leviatan Type Operators -- 7.1 Introduction -- 7.2 Construction of Operators and Estimation of Moments -- 7.3 Approximation in Weighted Spaces -- 7.4 Some Direct Approximation Theorems -- 7.5 A-Statistical Convergence -- 7.6 Conclusion -- References -- 8 On Approximation of Signals -- 8.1 Introduction -- 8.2 Known Results -- 8.3 Main Theorems -- 8.4 Lemmas -- 8.5 Proof of the Lemmas -- 8.6 Proof of Main Theorems -- 8.7 Conclusion -- References -- 9 Numerical Solution for Nonlinear Problems -- 9.1 Introduction -- 9.2 Introducing Some Nonlinear Functional and Fractional Equations -- 9.3 A Coupled Semi-analytic Method to Find the Solution of Equation (9.1) -- 9.3.1 Constructing Some Iterative Algorithms to Approximate the Solution of Equations (9.2)-(9.5) -- 9.4 Convergence of the Algorithms -- 9.5 Constructing an Iterative Algorithm by Sinc Function -- 9.5.1 One-Dimensional Functional Integral Equation -- 9.5.2 Convergence of Algorithm (9.62) -- 9.5.3 Two-Dimensional Functional Integral Equation -- References -- 10 Szász-Type Operators Involving q-Appell Polynomials -- 10.1 Introduction -- 10.2 Construction of the Operators and Basic Estimates -- 10.3 Some Basic Results -- 10.4 Pointwise Approximation Results -- 10.5 Weighted Approximation -- 10.6 A-Statistical Approximation -- References -- 11 Commutants of the Infinite Hilbert Operators -- 11.1 Introduction -- 11.2 Main Results. 11.3 Norm of Operators on Sequence Spaces Φn(p) and Ψn(p) -- References -- 12 On Complex Uncertain Sequences Defined by Orlicz Function -- 12.1 Introduction -- 12.2 Preliminaries -- 12.3 Complex Uncertain Sequence Spaces -- 12.4 Statistical Convergence of Complex Uncertain Sequences -- 12.5 Complex Uncertain Sequence Spaces Defined by Orlicz Function -- 12.6 Statistical Convergence of Complex Uncertain Sequences Defined by Orlicz Function -- 12.7 On Paranormed Type p-Absolutely Summable Uncertain Sequence Spaces Defined by Orlicz Functions -- 12.8 Lacunary Convergence Concepts of Complex Uncertain Sequences with Respect to Orlicz Function -- 12.9 Conclusion -- References -- 13 Ulam-Hyers Stability of Mixed Type Functional Equation Deriving From Additive and Quadratic Mappings in Intuitionistic Random Normed Spaces -- 13.1 Introduction -- 13.2 Preliminaries -- 13.3 Ulam-Hyers Stability for Odd Case -- 13.4 Ulam-Hyers Stability for Even Case -- 13.5 Ulam-Hyers Stability for Mixed Case -- 13.6 Conclusion -- References -- 14 A Study on q-Euler Difference Sequence Spaces -- 14.1 Introduction, Preliminaries, and Notations -- 14.1.1 Euler Matrix of Order 1 and Sequence Spaces -- 14.1.2 q-Calculus -- 14.2 q-Euler Difference Sequence Spaces -- 14.3 Alpha-, Beta-, and Gamma-Duals of q-Euler Difference Sequence Spaces -- 14.4 Matrix Transformations -- 14.5 Compact Operators and Hausdorff Measure of Non-compactness (Hmnc) -- References. |
| Record Nr. | UNINA-9910634045303321 |
Mohiuddine S. A.
|
||
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Argument & computation
| Argument & computation |
| Pubbl/distr/stampa | London, UK : , : Taylor & Francis, , ©2010- |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Computational linguistics Computer science Informàtica Processament de dades Tecnologia |
| Soggetto genere / forma |
Periodicals.
Revistes electròniques. |
| ISSN | 1946-2174 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Argument and computation |
| Record Nr. | UNINA-9910170160103321 |
| London, UK : , : Taylor & Francis, , ©2010- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Argument & computation
| Argument & computation |
| Pubbl/distr/stampa | London, UK : , : Taylor & Francis, , ©2010- |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Computational linguistics Computer science Informàtica Processament de dades Tecnologia |
| Soggetto genere / forma |
Periodicals.
Revistes electròniques. |
| ISSN | 1946-2174 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Argument and computation |
| Record Nr. | UNISA-996510069903316 |
| London, UK : , : Taylor & Francis, , ©2010- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
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] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence for information management : a healthcare perspective / / K.G. Srinivasa, Siddesh G.M., S.R. Mani Sekhar, editors
| Artificial intelligence for information management : a healthcare perspective / / K.G. Srinivasa, Siddesh G.M., S.R. Mani Sekhar, editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (332 pages) |
| Disciplina | 610.285 |
| Collana | Studies in big data |
| Soggetto topico |
Artificial intelligence - Medical applications
Intel·ligència artificial en medicina Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-0415-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910483957203321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence in higher education and scientific research : future development / / Fatima Roumate, editor
| Artificial intelligence in higher education and scientific research : future development / / Fatima Roumate, editor |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Singapore : , : Springer, , 2023 |
| Descrizione fisica | 1 online resource (152 pages) : illustrations |
| Disciplina | 371.334 |
| Collana | Bridging Human and Machine |
| Soggetto topico |
Artificial intelligence - Educational applications
Education, Higher - Data processing Educational technology Intel·ligència artificial Educació superior Processament de dades Tecnologia educativa |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9789811986413
9789811986406 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910672446403321 |
| Singapore : , : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence in PET/CT oncologic imaging / / edited by John A. Andreou, Paris A. Kosmidis, and Athanasios D. Gouliamos
| Artificial intelligence in PET/CT oncologic imaging / / edited by John A. Andreou, Paris A. Kosmidis, and Athanasios D. Gouliamos |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (156 pages) |
| Disciplina | 610.285 |
| Soggetto topico |
Tomography, Emission
Artificial intelligence - Medical applications Processament de dades Tomografia Intel·ligència artificial en medicina |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-10090-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Acknowledgements -- Contents -- 1: Introduction: Artificial Intelligence (AI) Systems for Oncology -- 1.1 Introduction -- 1.2 Applications -- 1.3 Challenges -- References -- 2: Positron Emission Tomography in Bone and Soft Tissue Tumors -- 2.1 Introduction -- 2.2 Positron Emission Tomography in Sarcomas -- 2.3 Positron Emission Tomography in Gastrointestinal Stromal Tumors -- 2.4 Artificial Intelligence -- 2.5 Conclusion -- References -- 3: PET/CT in Brain Tumors: Current Artificial Intelligence Applications -- 3.1 Introduction -- 3.2 Radiopharmaceuticals -- 3.3 Radiomics in the Study of Brain Malignancies -- 3.4 Identification of Brain Tumors, Molecular Markers, Grading and Prognosis -- 3.4.1 FDG PET -- 3.4.2 MET PET -- 3.4.3 FDOPA PET -- 3.4.4 FET PET -- 3.4.5 FLT PET and Other Tracers -- 3.5 Biopsy Guiding -- 3.6 Radiation Therapy Planning -- 3.7 Treatment Monitoring -- 3.8 Role of PET/CT in Brain Metastases -- References -- 4: Artificial Intelligence in Head and Neck Cancer Patients -- 4.1 Introduction -- 4.2 Artificial Intelligence: Performing Tasks Requiring Human Intelligence -- 4.3 Artificial Intelligence in Medicine -- 4.4 Artificial Intelligence in Oncology: Head and Neck Cancer -- 4.5 Conclusions -- References -- 5: PET-CT in Lung Cancer -- References -- 6: Breast Cancer: PET/CT Imaging -- References -- 7: PET/CT in Gynecologic Cancer -- 7.1 PET/CT with [18F]FDG in Cervical Cancer -- 7.1.1 Initial Diagnosis and Prognosis -- 7.1.2 Initial Staging -- 7.1.3 Radiotherapy Planning -- 7.1.4 Restaging after Treatment -- 7.1.5 Tumor Recurrence -- 7.1.6 Conclusion -- 7.2 PET/CT with [18F]FDG in Endometrial Cancer -- 7.2.1 Initial Diagnosis and Prognosis -- 7.2.2 Initial Staging -- 7.2.3 Tumor Recurrence -- 7.2.4 Conclusion -- 7.3 PET/CT with [18F]FDG in Ovarian Cancer.
7.3.1 Initial Diagnosis: Differentiation Between Malignant and Benign Ovarian Tumors and Prognosis -- 7.3.2 Initial Staging -- 7.3.3 Radiotherapy Planning -- 7.3.4 Restaging After Treatment -- 7.3.5 Tumor Recurrence -- 7.3.6 Conclusion -- References -- 8: PET-CT Staging of Rectal Carcinoma -- 8.1 Introduction -- 8.2 Diagnosis and Initial Staging -- 8.3 Detection and Staging of Recurrent Disease -- 8.4 Monitoring Treatment Response and Planning of Radiation Therapy -- 8.5 PET/CT Radiomics in Rectal Cancer -- 8.6 Conclusions -- References -- 9: Advances in Neuroendocrine Tumor Imaging, Including PET and Artificial Intelligence (AI) -- 9.1 Introduction -- 9.2 SSTR-Based Imaging -- 9.3 Ga-68 SSTR-vs. F18-FDG -- 9.4 Theragnostics in Neuroendocrine Tumors -- 9.5 Tentative Approach to AI in PET/CT Regarding Neuroendocrine Tumors -- References -- 10: PET/CT in the Evaluation of Adrenal Gland Mass -- 10.1 Introduction -- 10.2 PET/CT in Evaluation of Adrenal Masses in Cancer and Noncancer Patients -- 10.3 PET/CT in Primary Tumors' Evaluation -- 10.4 Towards Artificial Intelligence -- 10.5 Conclusion -- References -- 11: PET/CT in Renal Cancer -- 11.1 Introduction -- 11.2 18F-FDG-PET for Renal Cancer Investigation -- 11.2.1 Renal Mass Characterization and Initial Staging -- 11.2.2 Relapse and Evaluation of Treatment Response -- 11.3 Non-FDG Radiopharmaceutical for RCC Imaging -- 11.4 Towards Artificial Intelligence -- References -- 12: PET/CT Findings in Testicular Cancer -- 12.1 Initial Staging: Early Detection of Micrometastases -- 12.2 Response to Treatment Assessment: Residual Mass Characterization -- 12.3 Seminomatous GCTs -- 12.4 Nonseminomatous Germ Cell Tumors -- References -- 13: PET/CT in Prostate Cancer -- 13.1 Introduction -- 13.2 Imaging of Prostate Cancer with PET/CT. 13.3 Artificial Intelligence in the Service of Prostate Cancer Patients -- References -- 14: The Role of 18FDG-PET/CT in Malignant Lymphomas Clinical Implications -- 14.1 Introduction -- 14.2 PET/CT in Initial Staging -- 14.2.1 Role of PET in the Initial Staging of Lymphomas -- 14.2.2 PET in the Assessment of Bone Marrow Involvement -- 14.2.2.1 Hodgkin Lymphoma -- 14.2.2.2 Diffuse Large B Cell and Primary Mediastinal Large B Cell Lymphoma [24-33] -- 14.2.2.3 Other Lymphoma Subtypes -- 14.2.3 Potential Prognostic Impact of Baseline PET Parameters -- 14.3 PET/CT in Response Assessment After Completion of Therapy -- 14.3.1 Criteria for Response Assessment and Definitions of PET Positivity -- 14.3.2 Who Should Have an EOT-PET-Based Response Assessment and When? -- 14.3.3 Clinical Data in Individual Lymphoma Subtypes -- 14.3.3.1 Hodgkin Lymphoma -- 14.3.3.2 Primary Mediastinal Large B Cell Lymphoma -- 14.3.3.3 Diffuse Large B Cell Lymphoma -- 14.3.3.4 Follicular Lymphoma -- 14.3.3.5 Mantle Cell Lymphoma -- 14.3.3.6 T Cell Lymphomas -- 14.4 Interim Response Assessment -- 14.4.1 Who Might Benefit from Interim PET-Based Early Response Assessment? -- 14.4.2 Clinical Data in Individual Lymphoma Subtypes -- 14.4.2.1 Hodgkin Lymphoma -- 14.4.3 Is It Reasonable to Modify Treatment of HL in Response to Interim PET Results? -- 14.4.3.1 Diffuse Large B Cell Lymphoma -- 14.4.3.2 Primary Mediastinal Large B Cell Lymphoma -- 14.4.3.3 T Cell Lymphomas -- 14.5 Impact of Interim and EOT-PET on Clinical Practice: Randomized Trials -- 14.5.1 Hodgkin Lymphoma -- 14.5.1.1 Radiotherapy Questions -- 14.5.2 Chemotherapy Questions -- 14.5.3 Aggressive B Cell Lymphomas -- 14.5.3.1 Radiotherapy Questions -- 14.5.3.2 Chemotherapy Questions -- 14.6 PET in the Setting of Autologous Stem Cell Transplantation (ASCT). 14.7 PET in the Era of Novel Agents -- 14.7.1 Programmed Death-1 (PD-1) Inhibitors -- 14.7.2 Chimeric Antigen Receptor (CAR) T cells -- 14.8 Artificial Intelligence in F-FDG-PET/CT Scan -- 14.9 The Role of PET/CT in the Follow-Up of Lymphomas -- 14.10 Conclusions -- References. |
| Record Nr. | UNINA-9910620200803321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence, big data and data science in statistics : challenges and solutions in environmetrics, the natural sciences and technology / / Ansgar Steland, Kwok-Leung Tsui, editors
| Artificial intelligence, big data and data science in statistics : challenges and solutions in environmetrics, the natural sciences and technology / / Ansgar Steland, Kwok-Leung Tsui, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (378 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Big data Mathematical statistics - Data processing Intel·ligència artificial Dades massives Estadística matemàtica Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-07155-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910631085803321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence, big data and data science in statistics : challenges and solutions in environmetrics, the natural sciences and technology / / Ansgar Steland, Kwok-Leung Tsui, editors
| Artificial intelligence, big data and data science in statistics : challenges and solutions in environmetrics, the natural sciences and technology / / Ansgar Steland, Kwok-Leung Tsui, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (378 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Big data Mathematical statistics - Data processing Intel·ligència artificial Dades massives Estadística matemàtica Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-07155-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996499870303316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Soggetto topico
-
Processament de dades
(155)
- Statistics (19)
- Machine learning (17)
- Aprenentatge automàtic (16)
- Estadística matemàtica (16)