Machine Learning Algorithms and Applications. Группа авторов
n id="u444492b3-f377-5626-a0f7-63f36341f187">
Table of Contents
1 Cover
5 Preface
6 Part 1: Machine Learning for Industrial Applications 1 A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services 1.1 Introduction 1.2 Literature Survey 1.3 Implementation Details 1.4 Results and Discussions 1.5 Conclusion References 2 Automatic Counting and Classification of Silkworm Eggs Using Deep Learning 2.1 Introduction 2.2 Conventional Silkworm Egg Detection Approaches 2.3 Proposed Method 2.4 Dataset Generation 2.5 Results 2.6 Conclusion Acknowledgment References 3 A Wind Speed Prediction System Using Deep Neural Networks 3.1 Introduction 3.2 Methodology 3.3 Results and Discussions 3.4 Conclusion References 4 Res-SE-Net: Boosting Performance of ResNets by Enhancing Bridge Connections 4.1 Introduction 4.2 Related Work 4.3 Preliminaries 4.4 Proposed Model 4.5 Experiments 4.6 Results 4.7 Conclusion References 5 Sakshi Aggarwal, Navjot Singh and K.K. Mishra 5.1 Genesis 5.2 The Big Picture: Artificial Neural Network 5.3 Delineating the Cornerstones 5.4 Deep Learning Architectures 5.5 Why is CNN Preferred for Computer Vision Applications? 5.6 Unravel Deep Learning in Medical Diagnostic Systems 5.7 Challenges and Future Expectations 5.8 Conclusion References 6 Two-Stage Credit Scoring Model Based on Evolutionary Feature Selection and Ensemble Neural Networks 6.1 Introduction 6.2 Literature Survey 6.3 Proposed Model for Credit Scoring 6.4 Results and Discussion 6.5 Conclusion References 7 Enhanced Block-Based Feature Agglomeration Clustering for Video Summarization 7.1 Introduction 7.2 Related Works 7.3 Feature Agglomeration Clustering 7.4 Proposed Methodology 7.5 Results and Analysis 7.6 Conclusion References
7 Part 2: Machine Learning for Healthcare Systems 8 Cardiac Arrhythmia Detection and Classification From ECG Signals Using XGBoost Classifier 8.1 Introduction 8.2 Materials and Methods 8.3 Results and Discussion 8.4 Conclusion References 9 GSA-Based Approach for Gene Selection from Microarray Gene Expression Data 9.1 Introduction 9.2 Related Works 9.3 An Overview of Gravitational Search Algorithm