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研究生:Shankari Priya Chakkaravarthi
研究生(外文):Shankari Priya Chakkaravarthi
論文名稱:Classification and Analysis of the Disaster Management Applications using Machine Learning
論文名稱(外文):Classification and Analysis of the Disaster Management Applications using Machine Learning
指導教授:莊秀文莊秀文引用關係
指導教授(外文):Chuang Sheu-Wen
口試委員:丁賢偉許明暉
口試委員(外文):Hsien-Wei TingHsu Min-Huei
口試日期:2020-07-08
學位類別:碩士
校院名稱:臺北醫學大學
系所名稱:大數據科技及管理研究所
學門:電算機學門
學類:電算機應用學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:53
中文關鍵詞:ClassificationDisaster Management ApplicationsMachine LearningK-Nearest NeighborsDecision TreeDisaster AppsApp ClassificationApp Analysis
外文關鍵詞:ClassificationDisaster Management ApplicationsMachine LearningK-Nearest NeighborsDecision TreeDisaster AppsApp ClassificationApp Analysis
相關次數:
  • 被引用被引用:0
  • 點閱點閱:68
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Table of Contents
Acknowledgements ii
Abstract iii
Table of Contents v
List of Tables vii
Table of Figures vii
CHAPTER 1 . INTRODUCTION 1
1.1 Background 1
1.2 Statement of the problem 2
1.3 Research objective 3
1.4 Significance of this research 3
CHAPTER 2 . LITERATURE REVIEW 4
2.1 Disaster management 4
2.2 Disaster management phases 4
2.2.1 Mitigation 4
2.2.2 Preparedness 5
2.2.3 Response 5
2.2.4 Recovery 6
2.3 Research on disaster management applications 7
2.4 Functional features of applications 9
2.5 Machine learning 9
CHAPTER 3: METHODS 11
3.1 Study design 11
3.2 Data collection 12
3.3 Supervised machine learning method 12
3.3.1 Data preprocessing 12
3.3.2 Labelling data for classification 14
3.3.3 Multi-label classification algorithms 17
3.4 Validation 17
3.5 Data analysis 18
3.5.1 Descriptive statistical analysis 18
3.5.2 Visualization 18
CHAPTER 4: RESULTS 19
4.1 Summary of preprocessed disaster management applications 19
4.1.1 Disaster type of the applications 19
4.1.2 Classification based on labels using python Programming 19
4.1.3 Visualization of classified applications 21
4.2 Identification of functional features of disaster management applications 22
4.3 Classification using machine learning models 24
4.4 Validation of results 25
4.4.1 Validation using machine learning evaluation metrics 25
4.4.2 Improvement of the machine learning model performance 25
4.4.3 Validation using manual investigation 26
CHAPTER 5: DISCUSSION 29
5.1 Results interpretation 29
5.2 Contribution to related products’ research 29
5.3 Limitations of the study 30
5.4 Conclusion 31
References 32
Appendix 1: Classified applications and their disaster type 34
Appendix 2: Functional features of the applications 37
Appendix 3: DT model classification result of the test data 41
Appendix 4: KNN model classification result of the test data 42
Appendix 5: DT vs KNN model classification result of 130 applications 43
Appendix 6: Classification using updated labels to input for improving ML model 51


List of Tables
Table 1. Sample dropped out irrelevant applications 14
Table 2. Descriptive statistics of the applications with their disaster types 19
Table 3. Classification combination of the 130 disaster applications 20
Table 4. Pivot table: Application count with disaster type and disaster phase 20
Table 5. Most common and least common functional features for each disaster phase 23
Table 6. ML Validation using evaluation metrics 25
Table 7. Validation of updated ML classification using evaluation metrics 26
Table 8. Comparison of app class between manual validation and ML prediction 28


Table of Figures
Figure 1. The four phases of disaster management system 4
Figure 2. Research framework 11
Figure 3. Data filtering 12
Figure 4. Lables of mitigation and preparedness functions 15
Figure 5. Lables of response and recovery functions 16
Figure 6. Line chart: Visualization of the classification results and disaster type 21
Figure 7. Sample obtained using random sampling 27
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