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|a 9780323903783
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|a Q335
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|a Chang, Victor
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|a Novel AI and data science advancements for sustainability in the era of COVID-19
|h [electronic resource]
|c edited by Victor Chang, Mohamed Abdel-Basset, Muthu Ramachandran, Nicolas Green, Gary Wills
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| 260 |
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|a London
|b Academic Press
|c 2022
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| 300 |
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|a 1 online resource
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|a 6.1. International Classification of Diseases (ICD) -- 6.2. Digital Imaging and Communications in Medicine (DICOM) -- 6.3. International Committee on the Taxonomy of Viruses (ICTV) -- 7. Role of core team -- 7.1. Medical research activities -- 7.2. Virtual medical research center -- 8. Overview of viruses -- 8.1. Viruses -- 8.2. Spreading vectors -- 8.3. Human immunodeficiency viruses -- 8.4. Role of immune system -- 8.5. Parts of immune system -- 8.6. Characteristics of immune system -- 8.6.1. White blood cells -- 8.6.2. Antibodies -- 8.6.3. Complement system -- 8.6.4. Lymphatic system
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|a Intro -- Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 -- Copyright -- Contents -- Contributors -- Chapter 1: Deep learning-based hybrid models for prediction of COVID-19 using chest X-ray -- 1. Introduction -- 2. Related work -- 3. Modeling -- 3.1. PCA-feature ensembles -- 3.2. Optimally weighted majority voting -- 3.3. Feature extraction -- 3.4. Layer modification -- 4. Experimental setup -- 4.1. Baseline models -- 4.1.1. VGG-16 (Simonyan & Zisserman, 2015) -- 4.1.2. ResNet 50 (He et al., 2016) -- 4.1.3. Inception V3 (Szegedy et al., 2015) -- 4.2. Dataset
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|a 3.2. Big data analytics as a tool for fighting pandemics: A systematic review of literature -- 4. Review of big data analytics, artificial intelligence and nature-inspired computing models towards accurate detection ... -- 5. Significant applications of big data in COVID-19 pandemic -- 6. Research problem -- 7. Research questions -- 8. Objectives -- 9. Methodology -- 9.1. Techniques -- 10. Algorithm -- 11. Conclusion -- 11.1. Big data -- 11.2. Machine learning -- 11.3. COVID-19 -- Acknowledgment -- References
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|a 4.3. Data augmentation -- 4.4. Other preprocessing -- 4.5. Evaluation metrics -- 4.5.1. Accuracy -- 4.5.2. Precision -- 4.5.3. Recall -- 4.5.4. F-1 score -- 4.6. Experimental details -- 5. Results and discussion -- 6. Conclusions -- References -- Chapter 2: Investigation of COVID-19 and scientific analysis big data analytics with the help of machine learning -- 1. Introduction and background -- 2. Literature review -- 3. COVID-19 pandemic in the new era of big data analytics: Methodological innovations and future research directions -- 3.1. Deep learning applications for COVID-19
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|a Chapter 3: Designing a conceptual model in the artificial intelligence environment for the health care sector -- 1. Introduction -- 2. Background -- 3. Literature review -- 4. Approach suggested for designing a conceptual model -- 5. Selection of concepts in information and communication technology -- 5.1. Artificial intelligence -- 5.2. Role of artificial intelligence -- 5.3. Machine learning -- 5.4. Algorithms -- 5.5. Data warehouse -- 5.6. Virtual reality -- 5.7. Cloud computing -- 6. Databases related to classification of diseases, digital image code, and viruses taxonomy
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|a Electronic data processing / fast
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|a Big data / fast
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|a Big data / http://id.loc.gov/authorities/subjects/sh2012003227
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|a Pandémie de COVID-19, 2020- / Informatique
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|a Artificial intelligence / fast
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|a COVID-19 Pandemic, 2020- / Data processing
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|a Artificial intelligence / http://id.loc.gov/authorities/subjects/sh85008180
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|a Artificial Intelligence
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|a Données volumineuses
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|a Intelligence artificielle
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|a artificial intelligence / aat
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|a Abdel-Basset, Mohamed
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|a Ramachandran, Muthu
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|a Green, Nicolas
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|a eng
|2 ISO 639-2
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|b OREILLY
|a O'Reilly
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| 776 |
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|z 9780323903783
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|u https://learning.oreilly.com/library/view/~/9780323903783/?ar
|x Verlag
|3 Volltext
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|a Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 discusses how the role of recent technologies applied to health settings can help fight virus outbreaks. Moreover, it provides guidelines on how governments and institutions should prepare and quickly respond to drastic situations using technology to support their communities in order to maintain life and functional as efficiently as possible. The book discusses topics such as AI-driven histopathology analysis for COVID-19 diagnosis, bioinformatics for subtype rational drug design, deep learning-based treatment evaluation and outcome prediction, sensor informatics for monitoring infected patients, and machine learning for tracking and prediction models. In addition, the book presents AI solutions for hospital management during an epidemic or pandemic, along with real-world solutions and case studies of successful measures to support different types of communities. This is a valuable source for medical informaticians, bioinformaticians, clinicians and other healthcare workers and researchers who are interested in learning more on how recently developed technologies can help us fight and minimize the effects of global pandemics. Discusses AI advancements in predictive and decision modeling and how to design mobile apps to track contagion spread Presents the smart contract concept in blockchain and cryptography technology to guarantee security and privacy of people's data once their information has been used to fight the pandemic Encompasses guidelines for emergency preparedness, planning, recovery and continuity management of communities to support people in emergencies like a virus outbreak
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