book

Computational Intelligence for Managing Pandemics

Name: Computational Intelligence for Managing Pandemics
ISBN: 9783110712278

by Aditya Khamparia, Rubaiyat Hossain Mondal, Prajoy Podder, Bharat Bhushan, Victor de Albuquerque, Sachin Kumar

September 2021

Intermediate to advanced

340 pages

11h 30m

English

De Gruyter

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Leena Sharma 1 Application of rough set theory to analyze primary parameters causing death in COVID-19 patients
1.1 Introduction1.2 Literature survey1.3 Mathematical background1.4 Proposed work1.5 Results and discussion1.5.1 Reducts and core1.5.2 Rules1.6 Conclusion and future scope
Khadim Kébé 2 From Wuhan to Africa: insights into the genome of the SARS-CoV-2
2.1 Introduction2.2 Molecular biology of SARS-CoV-22.3 Genomic and technology2.3.1 Next-generation sequencing (NGS)2.3.2 Genome sequencing of SARS-CoV-22.4 Genomic epidemiology of SARS-CoV-2 in Africa2.4.1 Introduction sources of SARS-CoV-22.4.2 Analysis of the spike (S) glycoprotein of SARS-CoV-22.5 Conclusion
Shawni Dutta, Samir Kumar Bandyopadhyay 3 Analysis of divergence aspects of breast in breast cancer patients that change due to COVID-19
3.1 Introduction3.2 Related work3.3 Background3.3.1 ML-based classifier models3.3.2 Deep learning techniques3.3.3 Evaluating models3.4 Dataset description3.4.1 Dataset 13.4.2 Dataset 23.5 Development of proposed systems3.5.1 Proposed method 13.5.2 Proposed method 23.5.3 Proposed method 33.6 Result3.6.1 Analysis of proposed method 13.6.2 Analysis of proposed method 23.6.3 Analysis of proposed method 33.7 Conclusion
Mutiu Iyanda Lasisi, Umar Olansile Ajetunmobi, Abdul Ganiyy Gbolagade Adiamoh 4 The correlates among containment, management and public interest indicators of COVID-19 in Nigeria: managerial and policy implications for stakeholders
4.1 Introduction and theoretical underpinning4.2 Existing studies and emerging gap4.3 Measures and data4.4 Analysis and discussion4.5 Conclusion4.6 Managerial and policy recommendations
Anupriya Pandey, Pragya Katyayan, Nisheeth Joshi 5 Effect of lockdown during the COVID-19 pandemic using mathematical modeling: a quantitative study
5.1 Introduction5.2 Literature review5.3 Methodology5.4 Results and discussion5.5 Conclusion and future work
Arushi Uppal, Utkarsh Sharma, Bharat Bhushan 6 Flattening the curve of COVID-19 outbreak by early forecasting
6.1 Introduction6.2 Literature reviews6.3 Kalman filter: a mathematical overview6.4 Proposed methodology6.5 Experiments and results6.6 Conclusion
Deepak Pachpatte 7 Analysis and forecasting of COVID-19 infections in India using ARIMA model
7.1 Introduction7.2 Autoregressive-integrated moving average model7.3 Results7.4 Discussions and conclusions
Nure Naushin Sharmy 8 Effectiveness of machine learning in predicting the spread of COVID-19
8.1 Introduction8.2 Methodology8.2.1 Polynomial regression8.2.2 Autoregression model8.2.3 Moving average models8.2.4 ARIMA model8.2.5 Holt’s linear method8.2.6 Support vector machine8.3 Analysis8.3.1 Prediction for India8.3.2 Prediction for the USA8.4 Conclusion
Anupriya Pandey, Pragya Katyayan, Nisheeth Joshi 9 Mathematical modeling of the transmission dynamics of novel coronavirus: an India-specific study
9.1 Introduction9.2 Literature reviews9.3 Methodology9.4 Results and discussions9.5 Conclusion and future work
Pinto Kumar Paul, Subrato Bharati, Prajoy Podder, M. Rubaiyat Hossain Mondal 10 The role of IoMT during pandemics
10.1 Introduction10.2 Applications of IoMT10.3 Architecture of IoMT10.3.1 Device integration layer10.3.2 Data integration layer10.3.3 Data visualization layer10.4 Broad area networks10.5 Security in IoMT10.5.1 Characteristics of security10.5.2 Threats and attacks10.6 Design factor and its related challenges10.7 Conclusion

Sunanda Dixit, J. Mahalakshmi, Riya Mahesh 11 Internet of medical things approach to COVID-19
11.1 Introduction11.2 Need and importance11.3 The IoMT framework11.4 IoMT applicationsWellness and preventive care11.5 COVID-19 challenges: public versus medical fraternity11.6 IoMT’s approach to COVID-19 cure11.6.1 COVID-19 health monitoring technologies11.6.2 COVID-19 lifestyle assessment technologies11.6.3 COVID-19 drug management – dispense and delivery11.6.4 Stationary medical devices for COVID-19 cure11.7 Advancements in COVID-19 cure11.7.1 MedIoTek’s VinCense11.7.2 Italy’s heat-sensor-based drones11.7.3 Karnataka’s smart kiosks11.8 Merits of IoMT in COVID-19 cure11.8.1 Tracing11.8.2 Diagnosis11.8.3 Remote healthcare11.8.4 Effectual social distancing11.9 IoMT vulnerabilities: challenges and solutions11.9.1 Challenges11.9.2 Solutions11.10 Security and privacy threat: challenging aspect of IoMT11.10.1 User privacy and data protection11.10.2 Authentication and identity management11.10.3 Trust management and policy integration11.10.4 End-to-end security11.10.5 Impervious security solution11.11 Conclusion
Subrato Bharati, M. Rubaiyat Hossain Mondal 12 Applications and challenges of AI-driven IoHT for combating pandemics: a review
12.1 Introduction12.2 Crucial roles of IoHT in COVID-1912.2.1 Smartphone applications of IoHT12.2.2 IoHT buttons12.2.3 Wearable technologies in IoHT12.2.4 Role of robots in IoHT12.2.5 Role of drones in IoHT12.3 Challenging aspects and expected future applications of IoHT12.4 Conclusion
Rutwik Patel, Ninad Mehendale 13 Deep learning on medical images to combat a pandemic
13.1 Introduction13.1.1 Medical images13.1.2 Issues during pandemics13.1.3 Deep learning techniques13.1.4 Different neural networks13.2 Literature review13.2.1 Classification of X-rays13.2.2 Computer tomography (CT) using deep learning13.2.3 Classification of magnetic resonance imaging (MRI)13.2.4 Ultrasound image analysis using deep learning13.3 Comparison of different techniques reported in the literature13.4 Designing your own deep learning model based on medical images13.5 Proposed models13.6 Applications of deep learning in medical image diagnosis13.7 Future scope13.8 Summary
Sohaib Asif, Wenhui Yi, Jin Hou, Tao Yi, Jinhai Si 14 Deep convolutional neural network for the classification of COVID-19 from chest X-ray images
14.1 Introduction14.2 Problem statement14.3 Technical background14.3.1 Deep learning14.3.2 Neural networks14.3.3 Type of neural networks and machine learning techniques14.3.4 Deep neural networks14.3.5 Convolution neural networks14.4 Materials and proposed methodology14.4.1 Collection of dataset14.4.2 Data preprocessing14.4.3 Image augmentation14.4.4 Transfer learning14.4.5 TensorFlow14.4.6 Proposed architecture14.4.7 Setting hyperparameters14.5 Results and discussion14.5.1 Training accuracy and cross-entropy14.6 Conclusion
Sunita S. Chauhan, Sweta A. Dargad 15 Deep learning for analysis of COVID-19 electronic health records
15.1 Introduction15.1.1 Artificial intelligence15.1.2 Machine learning15.1.3 Supervised learning15.1.4 Unsupervised learning15.1.5 Reinforcement learning15.2 Deep feedforward network15.2.1 Architecture of DFN15.2.2 Training15.2.3 Classification loss15.2.4 Gradient descent15.2.5 Backpropagation15.3 Convolutional neural network15.3.1 Problem with deep feedforward networks15.3.2 Layers of convolutional neural network15.4 Image classification of COVID-19 electronic health records with CNNs15.4.1 Dataset description15.5 Conclusion15.6 Future scope and limitations
Index
Computational Intelligence for Machine Learning and Healthcare Informatics

Overview

This book uncovers the stakes and possibilities of handling pandemic diseases with the help of Computational Intelligence, using cases and applications from the current Covid-19 pandemic. The book chapters will focus on the application of CI and its related fields in managing different aspects of Covid-19, including modelling of the disease spread, data-driven prediction, identification of disease hotspots, and medical decision support.

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