Bioinformatics and Medical Applications. Группа авторов

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      1  Cover

      2  Title Page

      3  Copyright

      4  Preface

      5  1 Probabilistic Optimization of Machine Learning Algorithms for Heart Disease Prediction 1.1 Introduction 1.2 Literature Review 1.3 Tools and Techniques 1.4 Proposed Method 1.5 Conclusion References

      6  2 Cancerous Cells Detection in Lung Organs of Human Body: IoT-Based Healthcare 4.0 Approach 2.1 Introduction 2.2 Literature Review 2.3 Proposed Systems 2.4 Experimental Results and Analysis 2.5 Novelties 2.6 Future Scope, Limitations, and Possible Applications 2.7 Recommendations and Consideration 2.8 Conclusions References

      7  3 Computational Predictors of the Predominant Protein Function: SARS-CoV-2 Case 3.1 Introduction 3.2 Human Coronavirus Types 3.3 The SARS-CoV-2 Pandemic Impact 3.4 Computational Predictors 3.6 Future Implications 3.7 Acknowledgments References

      8  4 Deep Learning in Gait Abnormality Detection: Principles and Illustrations 4.1 Introduction 4.2 Background 4.3 Related Works 4.4 Methods 4.5 Conclusion and Future Work 4.6 Acknowledgments References

      9  5 Broad Applications of Network Embeddings in Computational Biology, Genomics, Medicine, and Health 5.1 Introduction 5.2 Types of Biological Networks 5.3 Methodologies in Network Embedding 5.4 Attributed and Non-Attributed Network Embedding 5.5 Applications of Network Embedding in Computational Biology 5.6 Limitations of Network Embedding in Biology 5.7 Conclusion and Outlook References

      10  6 Heart Disease Classification Using Regional Wall Thickness by Ensemble Classifier 6.1 Introduction 6.2 Related Study 6.3 Methodology 6.4 Implementation and Result Analysis 6.5 Conclusion References

      11  7 Deep Learning for Medical Informatics and Public Health 7.1 Introduction 7.2 Deep Learning Techniques in Medical Informatics and Public Health 7.3 Applications of Deep Learning in Medical Informatics and Public Health 7.4 Open Issues Concerning DL in Medical Informatics and Public Health 7.5 Conclusion References

      12  8 An Insight Into Human Pose Estimation and Its Applications 8.1 Foundations of Human Pose Estimation 8.2 Challenges to Human Pose Estimation 8.3 Analyzing the Dimensions 8.4 Standard Datasets for Human Pose Estimation 8.5 Deep Learning Revolutionizing Pose Estimation 8.6 СКАЧАТЬ