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.
Advanced machine learning approaches in cancer prognosis : challenges and applications / / Janmenjoy Nayak [and four others] editors
| Advanced machine learning approaches in cancer prognosis : challenges and applications / / Janmenjoy Nayak [and four others] editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (461 pages) |
| Disciplina | 006.31 |
| Collana | Intelligent systems reference library ; Volume 204. |
| Soggetto topico |
Cancer - Prognosis - Technological innovations
Machine learning Artificial intelligence - Medical applications Càncer Pronòstic mèdic Innovacions tecnològiques Intel·ligència artificial en medicina Aprenentatge automàtic |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-71975-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910482988603321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 | ||
| ||
Advanced prognostic predictive modelling in healthcare data analytics / / Sudipta Roy, Lalit Mohan Goyal, Mamta Mittal, editors
| Advanced prognostic predictive modelling in healthcare data analytics / / Sudipta Roy, Lalit Mohan Goyal, Mamta Mittal, editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (317 pages) |
| Disciplina | 610.28563 |
| Collana | Lecture Notes on Data Engineering and Communications Technologies |
| Soggetto topico |
Artificial intelligence - Medical applications
Medical informatics Information visualization Pronòstic mèdic Simulació per ordinador Intel·ligència artificial en medicina Informàtica mèdica Mineria de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-0538-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910483684603321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Artificial Intelligence, Computation, and Data Science : For Medicine and Life Science / / edited by Tuan D. Pham, Hong Yan, Muhammad W. Ashraf, Folke Sjöberg
| Advances in Artificial Intelligence, Computation, and Data Science : For Medicine and Life Science / / edited by Tuan D. Pham, Hong Yan, Muhammad W. Ashraf, Folke Sjöberg |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (373 pages) |
| Disciplina | 610.285 |
| Collana | Computational Biology |
| Soggetto topico |
Bioinformatics
Artificial intelligence Artificial intelligence - Data processing Computer science Biomathematics Image processing - Digital techniques Computer vision Computational and Systems Biology Artificial Intelligence Data Science Theory of Computation Mathematical and Computational Biology Computer Imaging, Vision, Pattern Recognition and Graphics Intel·ligència artificial en medicina Investigació mèdica Ciències de la vida Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-69951-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I: Review of Recent Developments in AI, Computational Models for Complex Data Analysis, and Data Science -- 1. Recent Developments in AI -- 2. Recent Developments in Computational Models for Data Analysis -- 3. Recent Developments in Data Science -- Part II: Applications in Medicine and Physiology -- 4. Cancer -- 5. Neuroscience -- 6. Cardiology -- 7. Critical Care -- 8. Health Care -- 9. Digital Pathology -- Part III: Applications in Life Science -- 10. Systems Biology -- 11. Cell Biology -- 12. Biochemistry -- 13. Chemo-metrics -- 14. Food Technology. |
| Record Nr. | UNINA-9910492152303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Artificial Intelligence, Computation, and Data Science : For Medicine and Life Science / / edited by Tuan D. Pham, Hong Yan, Muhammad W. Ashraf, Folke Sjöberg
| Advances in Artificial Intelligence, Computation, and Data Science : For Medicine and Life Science / / edited by Tuan D. Pham, Hong Yan, Muhammad W. Ashraf, Folke Sjöberg |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (373 pages) |
| Disciplina | 610.285 |
| Collana | Computational Biology |
| Soggetto topico |
Bioinformatics
Artificial intelligence Artificial intelligence - Data processing Computer science Biomathematics Image processing - Digital techniques Computer vision Computational and Systems Biology Artificial Intelligence Data Science Theory of Computation Mathematical and Computational Biology Computer Imaging, Vision, Pattern Recognition and Graphics Intel·ligència artificial en medicina Investigació mèdica Ciències de la vida Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-69951-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I: Review of Recent Developments in AI, Computational Models for Complex Data Analysis, and Data Science -- 1. Recent Developments in AI -- 2. Recent Developments in Computational Models for Data Analysis -- 3. Recent Developments in Data Science -- Part II: Applications in Medicine and Physiology -- 4. Cancer -- 5. Neuroscience -- 6. Cardiology -- 7. Critical Care -- 8. Health Care -- 9. Digital Pathology -- Part III: Applications in Life Science -- 10. Systems Biology -- 11. Cell Biology -- 12. Biochemistry -- 13. Chemo-metrics -- 14. Food Technology. |
| Record Nr. | UNISA-996464404503316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in cognitive research, artificial intelligence and neuroinformatics : proceedings of the 9th International Conference on Cognitive Sciences, Intercognsci-2020, October 10-16, 2020, Moscow, Russia / / edited by Boris M. Velichkovsky, Pavel M. Balaban, Vadim L. Ushakov
| Advances in cognitive research, artificial intelligence and neuroinformatics : proceedings of the 9th International Conference on Cognitive Sciences, Intercognsci-2020, October 10-16, 2020, Moscow, Russia / / edited by Boris M. Velichkovsky, Pavel M. Balaban, Vadim L. Ushakov |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (741 pages) |
| Disciplina | 153 |
| Collana | Advances in Intelligent Systems and Computing |
| Soggetto topico |
Cognitive science
Neurociència cognitiva Ciència cognitiva Intel·ligència artificial en medicina |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-71637-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484525603321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 | ||
| ||
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] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence and machine learning in healthcare / / Ankur Saxena, Shivani Chandra
| Artificial intelligence and machine learning in healthcare / / Ankur Saxena, Shivani Chandra |
| Autore | Saxena Ankur |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (XIX, 228 p. 119 illus., 88 illus. in color.) |
| Disciplina | 610.285 |
| Soggetto topico |
Artificial intelligence - Medical applications
Intel·ligència artificial en medicina Aprenentatge automàtic |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-0811-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1_Big Data Analytics and AI for Healthcare -- Chapter 2_Genetics with Big Data and AI -- Chapter 3_AI and Big Data for next-generation sequencing -- Chapter 4_Artificial Intelligence for Computational Biology -- Chapter 5_Artificial intelligence and machine learning in clinical development -- Chapter 6_Big data analytics for personalized medicine -- Chapter 7_Generating and Managing Healthcare data with AI -- Chapter 8_Big Data and Artificial Intelligence for diseases -- Chapter 9_Artificial Intelligence and Big Data for Public Health -- Chapter 10_Biasness in Healthcare Big Data and Computational Algorithms -- Chapter 11_AI and ML in Healthcare: An Ethical perspective. |
| Record Nr. | UNINA-9910484050503321 |
Saxena Ankur
|
||
| Gateway East, Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence and ophthalmology : perks, perils and pitfalls / / Parul Ichhpujani, Sahil Thakur, editors
| Artificial intelligence and ophthalmology : perks, perils and pitfalls / / Parul Ichhpujani, Sahil Thakur, editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (149 pages) |
| Disciplina | 617.700285 |
| Collana | Current Practices in Ophthalmology |
| Soggetto topico |
Ophthalmology - Data processing
Artificial intelligence - Medical applications Oftalmologia Intel·ligència artificial en medicina |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-0634-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484596003321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
