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• 1.
• Deep Learning for Innovative Medical Decision Support
• 2.
• Deep Learning and Image Analysis for Medical Decision Support
• 3.
• Deep Learning Oriented Systems for Medical Education
• 4.
• Hybrid Deep Systems for Medical Education and Decision Support
• 5.
• Deep Learning and Optimization for Medical Education and Decision Support 6.
• Deep Learning and Multimedia for Medical Education and Decision Support
• 7.
• Deep Learning and Traditional Methods for Medical Education and Decision Support.
• (source: Nielsen Book Data)

This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today's problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation. .
(source: Nielsen Book Data)

• 1. Predictive models to the COVID-19
• 2. An AI Based Decision Support and Resource Management System for COVID-19 Pandemic
• 3. Normalizing Images is Good to Improve Computer-Assisted COVID-19 Diagnosis
• 4. Detection and Screening of COVID-19 Through Chest CT Radiographs Using Deep Neural Networks
• 5. Differential Evolution to improve the effectiveness of the epidemiological SEIR model enhanced with dynamic social distancing: the case of COVID-19 and Italy
• 6. Limitations and Challenges on the Diagnosis of COVID-19 Using Radiology Images and Deep Learning
• 7. Deep Convolutional Neural Network Based Image Classification for Covid-19 Diagnosis
• 8. Statistical Machine Learning Forecasting Simulation for Discipline Prediction and Cost Estimation of COVID-19 Pandemic
• 9. Application of Machine Learning for the Diagnosis of COVID-19
• 10. PwCOV in Cluster Based Web Server: An Assessment of Service Oriented Computing for COVID-19 Disease Processing System
• 11. COVID-19-affected Medical Image Analysis using Denser Net
• 12. uTakeCare: unlock full decentralization of personal data for a respectful decontainment in the context of COVID
• -19: toward a digitally empowered anonymous citizenship
• 13. COVID-19 Detection from Chest X-Rays Using Transfer Learning with Deep CNN
• 14. Lexicon Based Sentiment Analysis Using Twitter Data: A Case of COVID-19 Outbreak in India and Abroad
• 15. Real time social distancing alerting and contact tracing using image processing
• 16. Machine Learning Models for Predicting Survivability in COVID-19 Patients
• 17. Robust and Secured Telehealth System for COVID-19 Patients
• 18. A Novel Approach to Predict COVID-19 Using Support Vector Machine
• 19. An Ensemble Predictive Analytics of Covid-19 Infodemic Tweets Using Bag of Words
• 20. Forecast & Prediction of Covid-19 Using Machine Learning
• 21. Time Series Analysis of the COVID-19 Pandemic in Australia using Genetic Programming
• 22. Image Analysis and Data Processing for COVID-19
• 23. A Demystifying Convolutional Neural Networks using Gradcam for Prediction of Coronavirus Disease (Covid-19) On X-Ray Images
• 24. Transfer Learning Based Convolutional Neural Network for Covid-19 Detection with X-Ray Images
• 25. Computational Modelling of the Pharmacological actions of some anti-viral agents against SARS-CoV-2
• 26. Mobile Technology Solutions for COVID
• -19: RSSI-based COVID-19 mobile app to comply with social distancing using bluetooth signals from smartphones
• 27. COVID-19 Pandemic in India: Forecasting Using Machine Learning Techniques
• 28. Mathematical Recipe for Curbing Corona Virus (Covid-19) Transmition Dynamics
• 29. Sliding Window Time Series Forecasting with Multi-Layer Perceptron and Multi Regression of COVID-19 outbreak in Malaysia
• 30. A Two-Level Deterministic Reasoning Pattern to Curb the Spread of Covid-19 in Africa
• 31. Data-driven approach to covid-19 infection forecast in Nigeria using negative binomial regression model
• 32. A Novel Machine Learning Based Detection and Diagnosis Model for Corona Virus Disease (Covid-19) using Discrete Wavelet Transform (DWT) with Rough Neural Network (RNN)
• 33. Artificial Intelligence Based Solutions for Early Identification and Classification of COVID-19 and Acute Respiratory Distress Syndrome
• 34. Internet of Medical Things (IoMT) with Machine Learning based COVID-19 Diagnosis Model using Chest X-Ray Images
• 35. The growth of COVID-19 in Spain. A view based on time-series forecasting methods
• 36. On Privacy Enhancement Using u-Indistinguishability to COVID19 Contact Tracing Approach in Korea
• 37. Scheduling Shuttle Ambulance Vehicles for COVID-19 Quarantine Cases, A Multi-objective Multiple 0-1 Knapsack Model with A Novel Discrete Binary Gaining-Sharing knowledge-based Optimization Algorithm.
• (source: Nielsen Book Data)

Data Science for COVID-19 presents leading-edge research on data science techniques for the detection, mitigation, treatment and elimination of COVID-19. Sections provide an introduction to data science for COVID-19 research, considering past and future pandemics, as well as related Coronavirus variations. Other chapters cover a wide range of Data Science applications concerning COVID-19 research, including Image Analysis and Data Processing, Geoprocessing and tracking, Predictive Systems, Design Cognition, mobile technology, and telemedicine solutions. The book then covers Artificial Intelligence-based solutions, innovative treatment methods, and public safety. Finally, readers will learn about applications of Big Data and new data models for mitigation.
(source: Nielsen Book Data)

• Fusion of Deep Learning and Image Processing Techniques for Breast Cancer Diagnosis
• Performance Evaluation of Classification Algorithms on Diagnosis of Breast Cancer and Skin Disease
• Deep Learning Algorithms in Medical Image Processing for Cancer Diagnosis: Overview, Challenges and Future
• Classification of Canine Fibroma and Fibrosarcoma Histopathological Images Using Convolutional Neural Networks
• Evaluation of Big Data Based CNN Models in Classification of Skin Lesions with Melanoma
• Combined Radiology and Pathology Based Classification of Tumor Types
• Improved Deep Learning Techniques for Better Cancer Diagnosis
• Using Deep Learning Techniques in Detecting Lung Cancer
• Effective Use of Deep Learning and Image Processing for Cancer Diagnosis
• A Deep Learning Architecture for Identification of Breast Cancer on Mammography by Learning Various Representations of Cancerous Mass
• Deep learning for brain tumor segmentation
• Convolutional Neural Network Approach for the Detection of Lung Cancers in Chest X-Ray Images
• Future of Deep Learning for Cancer Diagnosis
• Brain Tumor Segmentation Using 2D-UNET Convolutional Neural Network
• Use of Deep Learning Approaches in Cancer Diagnosis
• Deep Learning for Magnetic Resonance Images of Gliomas.

This book explores various applications of deep learning to the diagnosis of cancer, while also outlining the future face of deep learning-assisted cancer diagnostics. As is commonly known, artificial intelligence has paved the way for countless new solutions in the field of medicine. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more accurate results. As a vital research area, medical diagnosis is among those in which deep learning-oriented solutions are often employed. Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation.
(source: Nielsen Book Data)

• State and Trends of Machine Learning Approaches in Business: An Empirical Review.- Piecewise Demodulation based on Combined Artificial Neural Network for Quadrate Frequency Shift Keying Communication Signals.- A New Variable Ordering Method for the K2 Algorithm.- A Benefit Optimization Approach to the Evaluation of Classification Algorithms.- Financial Fraud Detection through Artificial Intelligence.- Deep Learning-based Software Energy Consumption Profiling.- Prediction of Potential Bank Customers: Application on Data Mining.- The Model Selection Methods for Sparse Biological Networks.- A Hybrid Approach for the Sentiment Analysis of Turkish Twitter Data.- Text Mining and Statistical Learning for the Analysis of the Voice of the Customer.- Effect the Number of Reservations on Implementation of Operating Room Scheduling with Genetic Algorithm.- Statistical Learning Applied to Malware Detection.- Improved Social Spider Algorithm via Differential Evolution.- On the Prediction of Possibly Forgotten Shopping Basket items.- Selection and Training of School Administrators in Different Countries.- A Walking and Balance Analysis Based on Pedobarography.- On the Notion of Structure Species in the Bourbaki's Sense.
• (source: Nielsen Book Data)

This book features research presented at the 1st International Conference on Artificial Intelligence and Applied Mathematics in Engineering, held on 20-22 April 2019 at Antalya, Manavgat (Turkey). In today's world, various engineering areas are essential components of technological innovations and effective real-world solutions for a better future. In this context, the book focuses on problems in engineering and discusses research using artificial intelligence and applied mathematics. Intended for scientists, experts, M.Sc. and Ph.D. students, postdocs and anyone interested in the subjects covered, the book can also be used as a reference resource for courses related to artificial intelligence and applied mathematics.
(source: Nielsen Book Data)

• On the intersection of artificial intelligence and distance education / Utku Kose
• Global knowledge networking (GKN) with utilizing of digital libraries: a proposed model / Azadeh Heidari, Leila Nemati-Anaraki
• Intelligent educational support system / Duygu Mutlu-Bayraktar
• Designing a multi-agent system for improving the accounting e-learning / Bogdan Patrut
• Intelligent questioning system based on fuzzy logic / Omer Deperlioglu, Guray Sonugur, Kadir Suzme
• Review and proposal for intelligent distance education / Ali Hakan Isik, Göksel Aslan
• A web-based intelligent educational laboratory system for forecasting chaotic time series / Utku Kose, Ahmet Arslan
• Expert systems in distance education / Duygu Mutlu-Bayraktar, Esad Esgin
• For an intelligent e-learning: a managerial model suggestion for artificial intelligence supported e-learning content flow / Utku Kose
• Resolving the paradox of overconfident students with intelligent methods / Denis Smolin, Sergey Butakov
• Design and development of an expert system shell program and evaluation of its effectiveness / Aslihan Tufekci
• An example application of an artificial intelligence-supported blended learning education program in computer engineering / Tuncay Yigit, Arif Koyun, Asim Sinan Yuksel, Ibrahim Arda Cankaya, Utku Kose
• Agent-based distributed intelligent tutoring system using case-based reasoning / Shweta, Praveen Dhyani, O. P. Rishi
• Evaluation of clustering methods for adaptive learning systems / Wilhelmiina Hämäläinen, Ville Kumpulainen, Maxim Mozgovoy
• Evolutionary approach for automatic and dynamic modeling of students' learning styles / Fabiano Azevedo Dorça
• Ideas on the future of intelligent web-based e-learning / Utku Kose.

Though educational methods such as distance and e-learning have addressed our modern, knowledge-based society's requirement for innovative approaches to performing educational activities, room for improvement still exists.Artificial Intelligence Applications in Distance Education seeks to examine the efforts made to bridge the gap between student and educator with computer applications. Through an in-depth discussion of applications employed to overcome the problems encountered during educational processes, this premier reference collection aims to enhance teachers' and students' educational experiences and improve their knowledge of the literature and the latest developments in educational technologies.
(source: Nielsen Book Data)

• Sentiment Analysis in Turkish using Transformer-based Deep Learning Models
• Mathematical Modeling of an Antenna Device Based on a T-shaped Waveguide of the Microwave Range
• Higher-order and stable numerical scheme for nonlinear diffusion system via compact finite difference and adaptive step-size Runge-Kutta methods
• Explainable Artificial Intelligence (XAI) for Deep Learning Based Intrusion Detection Systems
• A Color Channel Based Analysis on Image Tessellation
• Modeling of Shear Strength of Basalt Fiber Reinforced Clay (BFRC) Soil Using Artificial Neural Network (ANN)
• Change of the Internet with Blockchain and Metaverse Technologies
• A Language-Free Hate Speech Identification on Code-Mixed Conversational Tweets
• Decision Trees in Causal Inference
• The Resilience of Unmanned Aerial Vehicles to Cyberattacks and Assessment of Potential Threats.

As general, this book is a collection of the most recent, quality research papers regarding applications of Artificial Intelligence and Applied Mathematics for engineering problems. The papers included in the book were accepted and presented in the 4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2022), which was held in Baku, Azerbaijan (Azerbaijan Technical University) between May 20 and 22, 2022. Objective of the book content is to inform the international audience about the cutting-edge, effective developments and improvements in different engineering fields. As a collection of the ICAIAME 2022 event, the book gives consideration for the results by especially intelligent system formations and the associated applications. The target audience of the book is international researchers, degree students, practitioners from industry, and experts from different engineering disciplines.

• Classification and Regression Problems for Communication Paradigms
• Deep Learning and Vision Computing
• Deep- Recurrent Neural network (RNN) for Industrial informatics
• Extended AI for heterogeneous Edge.

This book constitutes selected papers presented during the First International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2022, held in Chennai, India, in March 2022. The 27 papers presented were thoroughly reviewed and selected from 97 submissions. They are organized in topical sections as follows: classification and regression problems for communication paradigms; deep learning and vision computing; deep- recurrent neural network (RNN) for industrial informatics; extended AI for heterogeneous edge.

• Chapter 1: COVID-19 pandemic analysis using application of AI
• Chapter 2: M-health: a revolution due to technology in healthcare sector
• Chapter 3: Analysis of Big Data in electroencephalography (EEG)
• Chapter 4: An analytical study of COVID-19 outbreak
• Chapter 5: IoT-based smart healthcare monitoring system
• Chapter 6: Development of a secured IoMT device with prioritized medical information for tracking and monitoring COVID patients in rural areas
• Chapter 7: An IoT-based system for a volumetric estimation of human brain morphological features from magnetic resonance images
• Chapter 8: Healthcare monitoring through IoT: security challenges and privacy issues
• Chapter 9: E-health natural language processing
• Chapter 10: Blockchain of things for healthcare asset management
• Chapter 11: Artificial intelligence: practical primer for clinical research in cardiovascular disease
• Chapter 12: Deep data analysis for COVID-19 outbreak
• Chapter 13: Healthcare system using deep learning
• Chapter 14: Intelligent classification of ECG signals using machine learning techniques
• Chapter 15: A survey and taxonomy on mutual interference mitigation techniques in wireless body area networks
• Chapter 16: Predicting COVID cases using machine learning, android, and firebase cloud storage
• Chapter 17: Technological advancement with artificial intelligence in healthcare
• Chapter 18: Changing dynamics on the Internet of Medical Things: challenges and opportunities
• Chapter 19: Internet of Drones (IOD) in medical transport application
• Chapter 20: Blockchain-based Internet of Things (IoT) for healthcare systems: COVID-19 perspective
• Chapter 21: Artificial intelligence-based diseases detection and diagnosis in healthcare.
• (source: Nielsen Book Data)

IoT-enabled healthcare technologies can be used for remote health monitoring, rehabilitation assessment and assisted ambient living. Healthcare analytics can be applied to the data gathered from these different areas to improve healthcare outcomes by providing clinicians with real-world, real-time data so they can more easily support and advise their patients. The book explores the application of AI systems to analyse patient data and guide interventions. IoT-based monitoring systems and their security challenges are also discussed. The book is designed to be a reference for healthcare informatics researchers, developers, practitioners, and people who are interested in the personalised healthcare sector. The book will be a valuable reference tool for those who identify and develop methodologies, frameworks, tools, and applications for working with medical big data and researchers in computer engineering, healthcare electronics, device design and related fields.
(source: Nielsen Book Data)

• Intro
• General Committees
• Honorary Chairs
• General Chair
• Conference Chairs
• Organizing Committee
• Secretary and Social Media
• Accommodation and Registration/Venue Desk
• Travel/Transportation
• Web/Design/Conference Session
• Scientific Committee
• Keynote Speaks
• ICAIAME 2021 Keynote Speakers
• Foreword
• Preface
• Contents
• About the Conference
• Scope/Topics
• Conference Scope/Topics (as not limited to)
• Conference Posters
• 1 Implementation of Basic Math Processing Skills with Neural Arithmetic Expressions in One and Two Stage Numbers
• 1.1 Introduction

• 1.1.1 Neural Arithmetic Expressions and Logic Units
• 1.1.2 Long Short-Term Memory Algorithm
• 1.2 Related Work
• 1.3 Proposed Method
• 1.4 Experimental Findings
• 1.5 Conclusions
• References
• 2 An Example Application for Early Diagnosis of Retinal Diseases Using Deep Learning Methods
• 2.1 Introduction
• 2.2 Material and Method
• 2.2.1 Material
• 2.2.2 Method
• 2.3 Research Findings
• 2.4 Discussion
• 2.5 Results
• References
• 3 Autonomous Parking with Continuous Reinforcement Learning
• 3.1 Introduction
• 3.2 Related Works
• 3.2.1 Deep Q Networks

• 3.2.2 Deep Deterministic Policy Gradient Algorithm
• 3.2.3 Twin Delayed Temporal Difference Algorithm
• 3.2.4 Soft Actor Critic Algorithm
• 3.2.5 Hindsight Experience Replay Algorithm
• 3.2.6 Parking Environment Simulation Model
• 3.3 Experiments and Results
• 3.4 Conclusions and Future Work
• References
• 4 Design and Manufacturing of a 3 DOF Robot with Additive Manufacturing Methods
• 4.1 Introduction
• 4.2 Material and Method
• 4.2.1 Material
• 4.3 Method
• 4.4 Findings and Discussion
• 4.5 Conclusion
• References

• 5 Real-Time Mask Detection Based on Artificial Intelligence Using Renewable Energy System Unmanned Aerial Vehicle
• 5.1 Introduction
• 5.2 Related Studies
• 5.3 Material and Method
• 5.3.1 Material
• 5.3.2 Material and Method
• 5.4 Research Findings
• 5.5 Conclusion
• References
• 6 Investigation of Effect of Wrapping Length on the Flexural Properties of Wooden Material in Reinforcement with Aramid FRP
• 6.1 Introduction
• 6.2 Material and Method
• 6.3 Results
• 6.4 Conclusions
• References
• 7 Deep Learning-Based Air Defense System for Unmanned Aerial Vehicles
• 7.1 Introduction

• 7.2 Material and Method
• 7.2.1 Material
• 7.2.2 Method
• 7.3 Research Findings
• 7.3.1 MobileNetV2 Training Results
• 7.3.2 Xception Training Results
• 7.3.3 InceptionV3 Training Results
• 7.4 Results
• References
• 8 Strategic Framework for ANFIS and BIM Use on Risk Management at Natural Gas Pipeline Project
• 8.1 Introductıon
• 8.2 Literature
• 8.3 Materials and Methods
• 8.3.1 Artificial Neural Networks (ANN)
• 8.3.2 Structure of Artificial Neural Network
• 8.3.3 Fuzzy Inference System
• 8.3.4 Adaptive Neuro-Fuzzy Inference System-ANFIS

This book brings together the most recent, quality research papers accepted and presented in the 3rd International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2021) held in Antalya, Turkey between 1-3 October 2021. Objective of the content is to provide important and innovative research for developments-improvements within different engineering fields, which are highly interested in using artificial intelligence and applied mathematics. As a collection of the outputs from the ICAIAME 2021, the book is specifically considering research outcomes including advanced use of machine learning and careful problem designs on human-centred aspects. In this context, it aims to provide recent applications for real-world improvements making life easier and more sustainable for especially humans. The book targets the researchers, degree students, and practitioners from both academia and the industry.
(source: Nielsen Book Data)

• Image Processing for Computational Intelligence against COVID-19.- Design of Computational Intelligence against COVID-19.- Data Processing for Computational Intelligence against COVID-19.- Geoprocessing/Tracking based Computational Intelligence for COVID-19.- Computational Intelligence based Diagnosis for COVID-19.- Predictive Computational Intelligence Systems for COVID-19.- Computational Intelligence based mobile technology solutions for COVID-19.- Computational Intelligence based treatment for COVID-19.- Public Safety oriented Computational Intelligence applications for COVID-19.- Internet of Health Things (IoHT) against COVID-19.- Big Data and Computational Intelligence for COVID-19.- New Data Models to be used by Computational Intelligence against COVID-19.- Predictive Computational Intelligence for future pandemics.- Future pandemics analyze with Computational Intelligence.- Vaccine / drug discovery with Computational Intelligence against COVID-19.- Intelligent optimization and Computational Intelligence for pandemics.- Reinforcement Computational Intelligence for pandemics precautions.- Managerial applications by Computational Intelligence for COVID-19 and pandemics.
• (source: Nielsen Book Data)

The book covers a wide topic collection starting from essentials of Computational Intelligence to advance, and possible application types against COVID-19 as well as its effects on the field of medical, social, and different data-oriented research scopes. Among these topics, the book also covers very recently, vital topics in terms of fighting against COVID-19 and solutions for future pandemics. The book includes the use of computational intelligence for especially medical diagnosis and treatment, and also data-oriented tracking-predictive solutions, which are key components currently for fighting against COVID-19. In this way, the book will be a key reference work for understanding how computational intelligence and the most recent technologies (i.e. Internet of Healthcare Thing, big data, and data science techniques) can be employed in solution phases and how they change the way of future solutions. The book also covers research works with negative results so that possible disadvantages of using computational intelligence solutions and/or experienced side-effects can be known widely for better future of medical solutions and use of intelligent systems against COVID-19 and pandemics. The book is considering both theoretical and applied views to enable readers to be informed about not only research works but also theoretical views about essentials/components of intelligent systems against COVID-19/pandemics, possible modeling scenarios with current and future perspective as well as solution strategies thought by researchers all over the world.
(source: Nielsen Book Data)

• 1. Essentials of COVID-19 Coronaviruses
• 2. Molecular Docking Study of Transmembrane serine protease type-2 inhibitors for the treatment of covid-19
• 3. Gut-lung crosstalk in COVID-19 pathology and fatality rate
• 4. Data Sharing and Privacy Issues Arising with COVID-19 Data and Applications
• 5. COVID-19 Outlook in the United States of America: A Data Driven Thematic Approach
• 6. Artificial Intelligence and COVID
• -19: Fighting Pandemics
• 7. Data Science A Survey on Statistical Analysis of the Latest Outbreak of 2019 Pandemic Novel Corona virus Disease (COVID-19) using ANOVA
• 8. Application of Big Data in the COVID-19 Pandemic
• 9. Artificial Intelligence based Solutions for COVID-19
• 10. Telemedicine applications for pandemic diseases with a focus on COVID-19
• 11. Impact of COVID-19 and Lockdown Policies on Farming, Food Security and Agribusiness in West Africa
• 12. Study and Impact Analysis of COVID-19 Pandemic Clinical Data on Infection Spreading
• 13. Towards Analyzing the Impact of HealthCare Treatments in Industry 4.0 Environment - A self-care case study during Covid-19 Outbreak
• 14. Big Data Processing and Analysis on the Impact of COVID-19 on Public Transport Delay
• 15. The Role of Societal Research and Development Center in Analyzing Society in Pandemic Times
• 16. Modelling and Predicting the Spread of COVID
• -19: A Continental Analysis
• 17. Applications of BIM for Disease Spread Assessment due to the Organisation of Building Artefacts
• 18. COVID-19 DIAGNOSIS-MYTHS AND PROTOCOLS
• 19. Quarantine within Quarantine: COVID-19 and GIS Scenario Dynamics Modelling in Tasmania, Australia
• 20. Essentials of COVID-19 and Treatment Approaches
• 21. Coronavirus Epidemic and Its Social / Mental Dimensions
• 22. Coronavirus: A Scientometric Study of World Research Publications
• 23. The Effects of COVID-19 Pandemic on Western Balkan Financial Markets
• 24. Prioritization of health emergency research and disaster preparedness: a systematic assessment of corona virus disease 2019 (COVID-19) pandemic
• 25. A Review on Epidemiology, Genomic Characteristics, Spread and Treatments of COVID-19
• 26. Control of antibiotic resistance and super infections as a strategy to manage COVID-19 deaths
• 27. Assessment of global research trends in the application of data science, deep and machine learning to COVID-19 pandemic
• 28. Identification of lead inhibitors of TMPRSS2 isoform 1 of SARS-CoV-2 target using Neural Network, Random Forest and molecular docking
• 29. The linkage between epidemic of COVID-19 and oil prices: Case of Saudi Arabia, January 22 - April 17
• 30. Role of Big Geospatial Data in the COVID-19 Crisis
• 31. COVID
• -19: Will it be a Game Changer in Higher Education in India?
• 32. Are Northern and Southern Regions equally affected by the COVID-19 Pandemic? Empirical Evidence from Nigeria
• 33. Covid-19 lethality reduction using Artificial Intelligence Solutions derived from Telecommunications Systems
• 34. The significance of Daily Increase and Mortality Cases due to COVID-19 in some African Countries
• 35. Data Interpretation Leading to Image Processing: A Hybrid Perspective to A Global Pandemic- COVID19
• 36. COVID
• -19: Monitoring the pandemic in India
• 37. Potential Antiviral Therapies for Corona Virus Disease (COVID-19).
• (source: Nielsen Book Data)

• 1. Predictive models to the COVID-19
• 2. An AI Based Decision Support and Resource Management System for COVID-19 Pandemic
• 3. Normalizing Images is Good to Improve Computer-Assisted COVID-19 Diagnosis
• 4. Detection and Screening of COVID-19 Through Chest CT Radiographs Using Deep Neural Networks
• 5. Differential Evolution to improve the effectiveness of the epidemiological SEIR model enhanced with dynamic social distancing: the case of COVID-19 and Italy
• 6. Limitations and Challenges on the Diagnosis of COVID-19 Using Radiology Images and Deep Learning
• 7. Deep Convolutional Neural Network Based Image Classification for Covid-19 Diagnosis
• 8. Statistical Machine Learning Forecasting Simulation for Discipline Prediction and Cost Estimation of COVID-19 Pandemic
• 9. Application of Machine Learning for the Diagnosis of COVID-19
• 10. PwCOV in Cluster Based Web Server: An Assessment of Service Oriented Computing for COVID-19 Disease Processing System
• 11. COVID-19-affected Medical Image Analysis using Denser Net
• 12. uTakeCare: unlock full decentralization of personal data for a respectful decontainment in the context of COVID
• -19: toward a digitally empowered anonymous citizenship
• 13. COVID-19 Detection from Chest X-Rays Using Transfer Learning with Deep CNN
• 14. Lexicon Based Sentiment Analysis Using Twitter Data: A Case of COVID-19 Outbreak in India and Abroad
• 15. Real time social distancing alerting and contact tracing using image processing
• 16. Machine Learning Models for Predicting Survivability in COVID-19 Patients
• 17. Robust and Secured Telehealth System for COVID-19 Patients
• 18. A Novel Approach to Predict COVID-19 Using Support Vector Machine
• 19. An Ensemble Predictive Analytics of Covid-19 Infodemic Tweets Using Bag of Words
• 20. Forecast & Prediction of Covid-19 Using Machine Learning
• 21. Time Series Analysis of the COVID-19 Pandemic in Australia using Genetic Programming
• 22. Image Analysis and Data Processing for COVID-19
• 23. A Demystifying Convolutional Neural Networks using Gradcam for Prediction of Coronavirus Disease (Covid-19) On X-Ray Images
• 24. Transfer Learning Based Convolutional Neural Network for Covid-19 Detection with X-Ray Images
• 25. Computational Modelling of the Pharmacological actions of some anti-viral agents against SARS-CoV-2
• 26. Mobile Technology Solutions for COVID
• -19: RSSI-based COVID-19 mobile app to comply with social distancing using bluetooth signals from smartphones
• 27. COVID-19 Pandemic in India: Forecasting Using Machine Learning Techniques
• 28. Mathematical Recipe for Curbing Corona Virus (Covid-19) Transmition Dynamics
• 29. Sliding Window Time Series Forecasting with Multi-Layer Perceptron and Multi Regression of COVID-19 outbreak in Malaysia
• 30. A Two-Level Deterministic Reasoning Pattern to Curb the Spread of Covid-19 in Africa
• 31. Data-driven approach to covid-19 infection forecast in Nigeria using negative binomial regression model
• 32. A Novel Machine Learning Based Detection and Diagnosis Model for Corona Virus Disease (Covid-19) using Discrete Wavelet Transform (DWT) with Rough Neural Network (RNN)
• 33. Artificial Intelligence Based Solutions for Early Identification and Classification of COVID-19 and Acute Respiratory Distress Syndrome
• 34. Internet of Medical Things (IoMT) with Machine Learning based COVID-19 Diagnosis Model using Chest X-Ray Images
• 35. The growth of COVID-19 in Spain. A view based on time-series forecasting methods
• 36. On Privacy Enhancement Using u-Indistinguishability to COVID19 Contact Tracing Approach in Korea
• 37. Scheduling Shuttle Ambulance Vehicles for COVID-19 Quarantine Cases, A Multi-objective Multiple 0-1 Knapsack Model with A Novel Discrete Binary Gaining-Sharing knowledge-based Optimization Algorithm.
• (source: Nielsen Book Data)

Data Science for COVID-19 presents leading-edge research on data science techniques for the detection, mitigation, treatment and elimination of COVID-19. Sections provide an introduction to data science for COVID-19 research, considering past and future pandemics, as well as related Coronavirus variations. Other chapters cover a wide range of Data Science applications concerning COVID-19 research, including Image Analysis and Data Processing, Geoprocessing and tracking, Predictive Systems, Design Cognition, mobile technology, and telemedicine solutions. The book then covers Artificial Intelligence-based solutions, innovative treatment methods, and public safety. Finally, readers will learn about applications of Big Data and new data models for mitigation.
(source: Nielsen Book Data)
Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques for the detection, mitigation, treatment and elimination of the COVID-19 virus. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques, including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes is now possible.
(source: Nielsen Book Data)

• Front Cover
• Data Science for COVID-19
• Data Science for COVID-19: Volume Two: Societal and Medical Perspectives
• Copyright
• Contents
• Contributors
• Foreword
• Preface
• 1
• Essentials of the COVID-19 coronavirus
• 1. Introduction
• 1.1 Background
• 1.2 Rationale
• 2. Materials and methods
• 2.1 Retrieval of nucleotides and amino acid sequences
• 2.2 Determination of physiochemical properties of the novel coronavirus gene sequences
• 2.3 Determination of guanine-cytosine content of SARS-CoV-2 sequences from 12 endemic countries

• 2.4 Determination of evolutionary distance, mutation pathway, time of gene divergence, and phylogenetic analysis of the novel S ...
• 2.5 Prediction of secondary and tertiary protein folding RNA structure of coronavirus sequences
• 3. Revealed essential features of COVID-19 coronavirus
• 3.1 Essential physical attributes of COVID-19 coronavirus
• 3.1.1 Molecular weight/size of COVID-19 coronavirus
• 3.1.2 Total number of atoms of the COVID-19 coronavirus
• 3.1.3 Amino acid side chain constituents of the COVID-19 coronavirus
• 3.1.4 Aliphatic (side chain) index of the COVID-19 coronavirus

• 3.1.5 Instability index of the COVID-19 coronavirus
• 3.1.6 Guanine-cytosine content of the COVID-19 coronavirus
• 3.1.7 Half-life of the COVID-19 coronavirus in human reticulocytes
• 3.2 Essential chemical features of the COVID-19 coronavirus
• 3.2.1 Grand hydropathicity of the COVID-19 coronavirus
• 3.2.2 Theoretic isoelectric point (pl) of the COVID-19 coronavirus
• 3.2.3 Extinction/attenuation coefficients of the COVID-19 coronavirus
• 3.2.4 Total number of negatively charged amino acid residues
• 3.2.5 Total number of positively charged amino acid residues

• 3.2.6 Coding regions for COVID-19 viruses
• 3.3 Biological characteristics of the COVID-19 virus
• 3.3.1 Protein coat structure of the COVID-19 coronavirus
• 3.3.2 Primary protein structures of the COVID-19 coronavirus
• 3.3.3 Secondary protein folding structures of the COVID-19 coronavirus
• 3.3.4 Tertiary protein folding structures of the COVID-19 coronavirus
• 3.3.5 Domain architectural composition of the COVID-19 coronavirus
• 3.3.6 Antigen epitope characteristics of the COVID-19 coronavirus
• 3.4 Phylogenetic characterization of the COVID-19 coronavirus

• 3.4.1 Evolutionary distance of the COVID-19 coronavirus
• 3.4.2 Time of genetic divergence and group consensus of the COVID-19 coronavirus
• 3.4.3 Mutation and evolutionary pathway of the COVID-19 coronavirus
• 3.4.4 Genetic polymorphism/single nucleotide polymorphisms
• 3.5 General essential features of the COVID-19 coronavirus
• 3.5.1 Common symptoms of COVID-19
• 3.5.2 Mode of spread of SARS-CoV-2 infection
• 3.5.3 Diagnosis of SARS-CoV-2 infection
• 3.5.4 Prevailing prophylactic measures
• 3.5.5 Therapeutics/vaccine trials available at the moment

• Introduction
• Essentials of Cognitive IoT for Healthcare
• Interpretability Problem in IoT for Healthcare
• Transparent and Black-Box IoT Systems for Healthcare
• Evaluation of Cognitive Capabilities of IoT in Healthcare
• Massive Health Data Analytics for Cognitive IoT
• Usability Evaluation of Cognitive IoT for Healthcare
• Hybrid Methods for Interpretable IoT for Healthcare
• Interpretable Cognitive IoT for Pandemics
• Interpretable Cognitive IoT for Cancer
• Interpretable Cognitive IoT for Sustainable Massive Health
• Interpretable Cognitive IoT for Health Robotics
• Interpretable Cognitive IoT for Personal Healthcare
• Wearables in the Context of IoT for Massive Healthcare
• Security for Massive Health Data Used by Cognitive IoT
• Future Insights for Interpretable Cognitive IoT in Healthcare
• Conclusion

This book presents research on how interpretable cognitive IoT can work to help with the massive amount of data in the healthcare industry. The authors give importance to IoT systems with intense machine learning features; this ensures the scope corresponds to use of cognitive IoT for understanding, reasoning, and learning from medical data. The authors discuss the interpretability of an intelligent system and its trustworthiness as a smart tool in the context of massive healthcare applications. As a whole, book combines three important topics: massive data, cognitive IoT, and interpretability. Topics include health data analytics for cognitive IoT, usability evaluation of cognitive IoT for healthcare, interpretable cognitive IoT for health robotics, and wearables in the context of IoT for healthcare. The book acts as a useful reference work for a wide audience including academicians, scientists, students, and professionals. Presents research and application of cognitive interpretable data for healthcare; Includes both positive outcomes and negative results of ongoing research into IoT and healthcare; Encourages readers to submit their data or open source software, to create a repository for ongoing study