دانلود کتاب Artificial Intelligence in Medical Virology (Medical Virology: From Pathogenesis to Disease Control)

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Foreword\nPreface\nAcknowledgements\nContents\nEditors and Contributors\n1: Artificial Intelligence for Global Healthcare\n 1.1 Introduction\n 1.2 Role of Artificial Intelligence (AI) in Public Health\n 1.2.1 Health Protection\n 1.2.1.1 Disease Detection\n 1.2.1.2 Data Pattern Analysis for Near-Real-Time Surveillance\n 1.2.2 Health Promotion\n 1.2.3 Improving the Efficiency of Healthcare Services\n 1.2.3.1 Detecting Diabetic Retinopathy\n 1.2.3.2 Health Informatics and Electronic Medical Records\n 1.2.3.3 Booking Appointments\n 1.2.3.4 Surgical Assistance\n 1.3 Global Health Challenges\n 1.3.1 Impact of Data Biases\n 1.3.2 Absence or Insufficiency of IT Infrastructure\n 1.3.3 Trustworthiness of AI\n 1.3.4 Importance and Impact of AI Laws\n 1.3.5 Societal Acceptance\n 1.4 Artificial Intelligence and Its Opportunities in Global Health\n 1.4.1 AI-Interventions and Application Areas\n 1.4.2 Artificial Intelligence Opportunities in Global Health\n 1.5 Demand and Drawbacks of AI in the Context of Global Health\n 1.6 Conclusion\n References\n2: Artificial Intelligence for Epidemiology COVID-19: Quick Assessment\n 2.1 Introduction\n 2.1.1 In the Future We Will Link Our Brains with Artificial Intelligence Systems\n 2.1.2 Terrible COVID-19\n 2.2 Related Works\n 2.2.1 Limitations in the Literature Study Are As Follows\n 2.3 Artificial Intelligence Methods and Applications for Healthcare\n 2.3.1 Artificial Intelligence Methods for Healthcare\n 2.3.2 Artificial Intelligence Applications for HealthCare\n 2.3.3 Some More AI Applications for Healthcare\n 2.4 Summary\n References\n3: Artificial Intelligence in Rural Health in Developing Countries\n 3.1 Healthcare Delivery in Rural Areas\n 3.2 AI in Resource-Limited Settings\n 3.3 Six Principles of AI by the World Health Organization\n 3.4 Challenges in Development and Implementation of AI\n 3.5 Telemedicine and Data Collection\n 3.6 Processing of Data\n 3.7 Devices and Instruments that May Help in Developing AI in Rural Area\n 3.8 Example of AI Application in a Rural Areas\n 3.9 Summary\n References\n4: The Role of Artificial Intelligence to Track COVID-19 Disease\n 4.1 Introduction\n 4.2 Literature Review\n 4.3 Diagnosis and Tracking\n 4.4 Treatment and Vaccination\n 4.5 Precautions and Social Control\n 4.6 Conclusion\n References\n5: Artificial Intelligence Techniques Based on K-MeansTwo Way Clustering and Greedy Triclustering Approach for 3D Gene Express...\n 5.1 Introduction\n 5.2 Background Study\n 5.2.1 Issues in the Literature\n 5.3 Proposed Work\n 5.3.1 Mean Correlation Value Equation for Tricluster (TriMCV)\n 5.3.2 Function for Fitness\n 5.3.3 Description of K-MeansTwo Way\n 5.3.3.1 Tricluster Generation Using K-MeansTwo Way Clustering\n Algorithm 5.1 Tricluster Seed Formation Step Using K-MeansTwo Way Clustering\n 5.3.4 Specification of Greedy Triclustering\n Algorithm 5.2 Greedy Triclustering (GreedyTri)\n 5.4 Result and Analysis\n 5.4.1 CDC15 Experiment Using 3D GED\n 5.4.2 Elutriation Experiment Using 3D GED\n 5.4.3 Pheromone Experiment Using 3D GED\n 5.5 Summary\n References\n6: Detection of COVID-19 Cases from X-Ray and CT Images Using Transfer Learning and Deep Convolution Neural Networks\n 6.1 Introduction\n 6.2 Related Works\n 6.3 Experiment Setup\n 6.3.1 Dataset Description\n 6.3.2 DCNN\n 6.3.2.1 Inception V3\n 6.3.2.2 VGG-16\n 6.3.2.3 VGG-19\n 6.3.3 Parameters Information\n 6.3.3.1 Adam\n 6.3.3.2 Stochastic Gradient Descent (SGD)\n 6.3.3.3 Limited Memory: Broyden-Fletcher-Goldfarb-Shanno Algorithm (L-BFGS-B)\n 6.3.3.4 tanh\n 6.3.3.5 Identity\n 6.3.3.6 Logistic\n 6.3.3.7 ReLu\n 6.3.4 Evaluation and Classification\n 6.4 Conclusion and Future Work\n References\n7: Computer Vision: A Detailed Review onAugmented Reality (AR), Virtual Reality (VR), Telehealth, and Digital Radiology\n 7.1 Introduction\n 7.2 Literature Review\n 7.3 Applications of Computer Vision\n 7.3.1 Computer Vision in Sports\n 7.3.1.1 Sports Production\n 7.3.1.2 Player Tracking\n 7.3.1.3 Ball Tracking\n 7.3.2 Computer Vision in Health and Medicine\n 7.3.2.1 Cancer Detection\n 7.3.2.2 Cell Classification\n 7.3.2.3 Tumor Detection\n 7.3.2.4 Development Analysis\n 7.3.2.5 Cover Detection\n 7.3.3 Computer Vision in Agriculture and Farming\n 7.3.3.1 Absconds in Agriculture\n 7.3.3.2 Counting\n 7.3.3.3 Plant Recognition (Fig. 7.3)\n 7.3.3.4 Animal Monitoring\n 7.3.3.5 Farm Automation\n 7.3.4 Computer Vision in Retail and Manufacturing (Fig. 7.4)\n 7.3.4.1 Customer Tracking\n 7.3.4.2 Individuals Counting\n 7.3.4.3 Thief Detection\n 7.3.4.4 Waiting Time Analytic\n 7.3.4.5 Social Distance\n 7.3.4.6 Productivity Analytics\n 7.3.4.7 Quality Management\n 7.4 Advantages\n 7.4.1 Impact of Computer Vision\n 7.4.1.1 Financial Services (Fig. 7.5)\n 7.4.2 Insurance\n 7.4.3 Capital Markets\n 7.4.4 Commerce\n 7.4.5 Banking\n 7.5 Future of Computer Vision\n 7.6 AR, VR and What CV Means to Them\n 7.7 Computer Vision and Augmented Reality for E-Commerce\n 7.8 Conclusion\n References\n8: Stroke Disease Prediction Model Using ANOVA with Classification Algorithms\n 8.1 Background of the Study\n 8.2 Related Works\n 8.3 Materials and Methods\n 8.3.1 Classification\n 8.4 Results and Discussions\n 8.5 Conclusion\n Appendix\n References\n9: A Concise Review on Developmental and Evaluation Methods of Artificial Intelligence on COVID-19 Detection\n 9.1 Introduction\n 9.2 Diagnosis of COVID-19 with Machine Learning and Deep Learning Approaches\n 9.3 Review on Benchmark Dataset Utilized for the Assessment of Prevailing Artificial Intelligence Approaches\n 9.4 Comparative Analysis of Various Nature Inspired and Other Deep Leaning Algorithms on the Detection of COVID-19 Detection\n 9.5 Dealing with COVID-19 Application of Artificial Intelligence and Machine Learning\n 9.5.1 Early Detection and Prompt Diagnosis\n 9.5.2 Treatment Monitoring\n 9.5.3 Contact Tracing\n 9.5.4 Mortality Rate\n 9.5.5 Vaccine and Drug Development\n 9.5.6 Work Load Reduction of Healthcare Experts\n 9.5.7 Disease Prevention\n 9.6 Shortcomings of Current Methods\n 9.7 Critical Analysis\n 9.8 Conclusion\n References\n10: Artificial Intelligence-Based Healthcare Industry 4.0 for Disease Detection Using Machine Learning Techniques\n 10.1 Introduction\n 10.2 Framework to Apply Machine Learning for Disease Detection\n 10.3 Current Trends in Disease Detection: Machine Learning perspective\n 10.4 The Case Studies\n 10.4.1 Predicting the Heart Disease: Case Study I\n 10.4.2 COVID-19 Detection-Case Study II\n 10.5 Conclusion and Future Scope\n References\n11: Deep Autoencoder Neural Networks for Heart Sound Classification\n 11.1 Introduction\n 11.2 Literature Survey\n 11.3 Proposed Algorithm\n 11.3.1 PCG Signal Acquisition\n 11.3.2 Pre-processing Block\n 11.3.3 Time-Frequency Image Representation\n 11.3.4 Stacked Autoencoder (SAEN) Neural Network and Softmax Classifier\n Algorithm 11.1 The proposed algorithm of heart sound classification method\n 11.4 Pre-processing and Time-Frequency Image Generation\n 11.4.1 Infinite Impulse Response-Constant-Q Transform (IIR-CQT)\n 11.5 Deep Autoencoder Neural Network-Based Classification\n 11.6 Experimental Results and Discussions\n 11.6.1 IoT-Based Application of the Proposed Method\n 11.7 Conclusion\n References