Pub Date : 2022-07-01DOI: 10.1109/CCAT56798.2022.00016
Haowen Chen
Under-five Mortality Rate (U5MR), as one of the 17 Sustainable Development Goals established by United Nations, reveals the social commitment on children's health and international humanitarian development progress. In addition to traditional regression analysis and dimension-reduction factor analysis regarding the determinants of child mortality, this paper takes a step further and conducts cluster analysis using data mining and machine learning techniques with Python to better visualize and demonstrate the geospatial traits of global development progress on certain topic. The result of stepwise multivariate regression analysis suggests that the average life expectancy, female fertility rates and GDP per person of the area are the top three factors that affect U5MR. Factor analysis is then applied to reduce the variables into four dimensions, demographic factor, individual financial factor, national trade factor and Heath spending & Income factor. With the outcomes of the principal component analysis, Python is adopted to perform K-Means cluster analysis. Four classes, determined by elbow method and Silhouette experiment, are clustered to represent levels of development of countries. The results are visualized on a world map for intuitive interpretation. Supported and cross-verified by existing studies, sub-Saharan African countries require immediate attention and international assistance as the new-born and the mothers fall victims of inadequate fundamental, feasible and deliverable resources such as immunization, skilled attendant, early breastfeeding, and warmth. Through scientific and statistic methods, this paper is dedicated for international organizations, governments, and NGOs to optimize and facilitate recourses given the geospatial and unbalanced socioeconomic and health resources worldwide.
{"title":"Machine Learning and Statistics Analysis of Socioeconomic and Health Factors Impact on the Progress of Countries' Humanitarian Commitments","authors":"Haowen Chen","doi":"10.1109/CCAT56798.2022.00016","DOIUrl":"https://doi.org/10.1109/CCAT56798.2022.00016","url":null,"abstract":"Under-five Mortality Rate (U5MR), as one of the 17 Sustainable Development Goals established by United Nations, reveals the social commitment on children's health and international humanitarian development progress. In addition to traditional regression analysis and dimension-reduction factor analysis regarding the determinants of child mortality, this paper takes a step further and conducts cluster analysis using data mining and machine learning techniques with Python to better visualize and demonstrate the geospatial traits of global development progress on certain topic. The result of stepwise multivariate regression analysis suggests that the average life expectancy, female fertility rates and GDP per person of the area are the top three factors that affect U5MR. Factor analysis is then applied to reduce the variables into four dimensions, demographic factor, individual financial factor, national trade factor and Heath spending & Income factor. With the outcomes of the principal component analysis, Python is adopted to perform K-Means cluster analysis. Four classes, determined by elbow method and Silhouette experiment, are clustered to represent levels of development of countries. The results are visualized on a world map for intuitive interpretation. Supported and cross-verified by existing studies, sub-Saharan African countries require immediate attention and international assistance as the new-born and the mothers fall victims of inadequate fundamental, feasible and deliverable resources such as immunization, skilled attendant, early breastfeeding, and warmth. Through scientific and statistic methods, this paper is dedicated for international organizations, governments, and NGOs to optimize and facilitate recourses given the geospatial and unbalanced socioeconomic and health resources worldwide.","PeriodicalId":423535,"journal":{"name":"2022 International Conference on Computer Applications Technology (CCAT)","volume":"519 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123121988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/CCAT56798.2022.00013
Rogel M. Labanan, Bernie S. Fabito, Rodolfo C. Raga
The Philippines, an archipelago situated along the Pacific Ring of Fire, has recorded active seismic activities over the recent years. Earthquakes ranked third in terms of occurrence and first in terms of death tolls over the past 20 years with any other types of natural hazards globally. Throughout the history of natural disaster occurrences experienced by Metropolitan Manila, earthquakes pose the greatest threat to life, property, and the economy. Thus, a significant earthquake event in the Capital of the Philippines will greatly affect the nation's economy. Due to earthquake hazards' frequency of occurrence, intensity, and variability in Metropolitan Manila, the government is compelled to adopt disaster risk reduction and management plans. In this study, the proponent developed an On-Site Earthquake Early Warning System (EEWS) with the development of a low-cost seismic monitoring prototype, a web-based earthquake event monitoring system, and a seismic arrival time prediction that can be used in the early detection of arriving seismic waves and could provide an on-site earthquake early warning notification within 3 seconds. Moreover, the overall system integration of various components of the On-Site Earthquake Early Warning System had been proven to be a usable system as a holistic approach in providing proactive earthquake risk preparedness response as agreed by experts and stakeholders. Moreover, establishing a perspective of a more resilient Metropolitan Manila in the event of an earthquake.
{"title":"Development of an on-Site Earthquake Early Warning System for one Private Higher Educational Institution (HEI) and Its Nearby Community in Manila, Philippines","authors":"Rogel M. Labanan, Bernie S. Fabito, Rodolfo C. Raga","doi":"10.1109/CCAT56798.2022.00013","DOIUrl":"https://doi.org/10.1109/CCAT56798.2022.00013","url":null,"abstract":"The Philippines, an archipelago situated along the Pacific Ring of Fire, has recorded active seismic activities over the recent years. Earthquakes ranked third in terms of occurrence and first in terms of death tolls over the past 20 years with any other types of natural hazards globally. Throughout the history of natural disaster occurrences experienced by Metropolitan Manila, earthquakes pose the greatest threat to life, property, and the economy. Thus, a significant earthquake event in the Capital of the Philippines will greatly affect the nation's economy. Due to earthquake hazards' frequency of occurrence, intensity, and variability in Metropolitan Manila, the government is compelled to adopt disaster risk reduction and management plans. In this study, the proponent developed an On-Site Earthquake Early Warning System (EEWS) with the development of a low-cost seismic monitoring prototype, a web-based earthquake event monitoring system, and a seismic arrival time prediction that can be used in the early detection of arriving seismic waves and could provide an on-site earthquake early warning notification within 3 seconds. Moreover, the overall system integration of various components of the On-Site Earthquake Early Warning System had been proven to be a usable system as a holistic approach in providing proactive earthquake risk preparedness response as agreed by experts and stakeholders. Moreover, establishing a perspective of a more resilient Metropolitan Manila in the event of an earthquake.","PeriodicalId":423535,"journal":{"name":"2022 International Conference on Computer Applications Technology (CCAT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133926839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/CCAT56798.2022.00009
Pengtao Jiang
Poverty alleviation has always been a major problem that plagues national economic development and people's livelihood. Through the research on precise poverty alleviation, it is hoped to find a feasible way, its operating mechanism and principle, so as to improve the effect of poverty alleviation. The purpose of this paper is to study the identification of poor households in precision poverty alleviation based on ensemble learning. This paper introduces the current research status of precise poverty alleviation and the application of ensemble learning algorithms in various fields, and discusses some advantages of boosting and XGBoost in classification, paving the way for the following. Combined with the actual situation of M County, the algorithm index system has been expanded to better reflect the poverty status of farmers. The ensemble learning method is applied to the poverty identification problem, and the model evaluation standard is used to measure the effectiveness and stability of multiple models. The experimental results show that the XGBoost model in this paper has the best application effect in the identification of poor households, with an accuracy rate of 98.2%.
{"title":"Identification of Poor Households in Precision Poverty Alleviation Based on Ensemble Learning","authors":"Pengtao Jiang","doi":"10.1109/CCAT56798.2022.00009","DOIUrl":"https://doi.org/10.1109/CCAT56798.2022.00009","url":null,"abstract":"Poverty alleviation has always been a major problem that plagues national economic development and people's livelihood. Through the research on precise poverty alleviation, it is hoped to find a feasible way, its operating mechanism and principle, so as to improve the effect of poverty alleviation. The purpose of this paper is to study the identification of poor households in precision poverty alleviation based on ensemble learning. This paper introduces the current research status of precise poverty alleviation and the application of ensemble learning algorithms in various fields, and discusses some advantages of boosting and XGBoost in classification, paving the way for the following. Combined with the actual situation of M County, the algorithm index system has been expanded to better reflect the poverty status of farmers. The ensemble learning method is applied to the poverty identification problem, and the model evaluation standard is used to measure the effectiveness and stability of multiple models. The experimental results show that the XGBoost model in this paper has the best application effect in the identification of poor households, with an accuracy rate of 98.2%.","PeriodicalId":423535,"journal":{"name":"2022 International Conference on Computer Applications Technology (CCAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131258330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rice is a very important agricultural product, especially in Asia country such as Japan, Thailand, etc. It is a daily essential food for many people. To better monitor the rice yield, it is necessary to pay attention to the rice seedling stage. In the past, many scholars have used image processing technologies to complete the counting of rice seedlings. However, it is common that the color of rice changing accordance with the changing weather, which may cause the counting error if using the traditional image processing method. It is also possible that there are weeds or other non-rice obstructions that confuse the image recognition and create counting errors. In the past, not many scholars used object detection technology to locate rice seedlings, however, it is important to identify the rice object for counting. Hence, this research applies the YOLO model to explore the object detection technology to complete the positioning and counting of rice seedlings. To improve the model performance, the YOLOv4 architecture was deeply studied and adjusted, to reduce the training process and training time, thereby achieving the purpose of a lightweight model, we named it as YOLO4-L1. In this study, we established a system for automatic positioning of object detection and calculation of rice seedlings. Comparisons among our proposed YOLO4-L1 model with YOLOv3-tiny, YOLOv4-tiny, YOLOv3, and YOLOv4 are conducted. Our experimental results have shown that our proposed YOLO4-L1 model can reduce 2.45hr for training time with similar counting result when comparing with YOLOv4 model.
{"title":"The Study of Light-weight YOLOv4 Model for Rice Seedling and Counting","authors":"Li-Hua Li, Kai-Lun Chung, Ling-Qi Jiang, Alok Kumar Sharma, Ye-Shan Liu","doi":"10.1109/CCAT56798.2022.00008","DOIUrl":"https://doi.org/10.1109/CCAT56798.2022.00008","url":null,"abstract":"Rice is a very important agricultural product, especially in Asia country such as Japan, Thailand, etc. It is a daily essential food for many people. To better monitor the rice yield, it is necessary to pay attention to the rice seedling stage. In the past, many scholars have used image processing technologies to complete the counting of rice seedlings. However, it is common that the color of rice changing accordance with the changing weather, which may cause the counting error if using the traditional image processing method. It is also possible that there are weeds or other non-rice obstructions that confuse the image recognition and create counting errors. In the past, not many scholars used object detection technology to locate rice seedlings, however, it is important to identify the rice object for counting. Hence, this research applies the YOLO model to explore the object detection technology to complete the positioning and counting of rice seedlings. To improve the model performance, the YOLOv4 architecture was deeply studied and adjusted, to reduce the training process and training time, thereby achieving the purpose of a lightweight model, we named it as YOLO4-L1. In this study, we established a system for automatic positioning of object detection and calculation of rice seedlings. Comparisons among our proposed YOLO4-L1 model with YOLOv3-tiny, YOLOv4-tiny, YOLOv3, and YOLOv4 are conducted. Our experimental results have shown that our proposed YOLO4-L1 model can reduce 2.45hr for training time with similar counting result when comparing with YOLOv4 model.","PeriodicalId":423535,"journal":{"name":"2022 International Conference on Computer Applications Technology (CCAT)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133606126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/CCAT56798.2022.00017
Yuwei Lu
Product quality and safety are related to the personal and property safety of the people. In recent years, the “Xi'an Aokai cable incident” and the “Fujian Zhangzhou big head doll incident” and other related product quality and safety incidents have not only caused personal harm and huge economic losses, but also have a certain negative impact on the law enforcement image of government departments. Therefore, it is particularly important to monitor the public opinion of product quality and safety accidents and explore the risk early warning mechanism. From the perspective of public opinion on product quality, this study searches, analyzes and summarizes the product quality information of 31 provinces in the country through crawler search technology and artificial data search, points out the existing problems, and puts forward corresponding countermeasures and suggestions. The survey results show that the public opinion information on product quality and safety in 2021 is mostly displayed in the second quarter, the eastern region and the categories of food, drugs and daily necessities. Quality defects are the main reason for product quality and safety incidents. Therefore, we should strictly control the production quality, strengthen market supervision, promote industry standardization and standardization, and promote market participants to implement their sense of responsibility, Provide reliable product quality assurance for economic and social development and people's life.
{"title":"Research and Analysis on Public Opinion Monitoring of Product Quality and Safety Accidents in 2021 through Crawler Retrieval Technology and Manual Data Retrieval","authors":"Yuwei Lu","doi":"10.1109/CCAT56798.2022.00017","DOIUrl":"https://doi.org/10.1109/CCAT56798.2022.00017","url":null,"abstract":"Product quality and safety are related to the personal and property safety of the people. In recent years, the “Xi'an Aokai cable incident” and the “Fujian Zhangzhou big head doll incident” and other related product quality and safety incidents have not only caused personal harm and huge economic losses, but also have a certain negative impact on the law enforcement image of government departments. Therefore, it is particularly important to monitor the public opinion of product quality and safety accidents and explore the risk early warning mechanism. From the perspective of public opinion on product quality, this study searches, analyzes and summarizes the product quality information of 31 provinces in the country through crawler search technology and artificial data search, points out the existing problems, and puts forward corresponding countermeasures and suggestions. The survey results show that the public opinion information on product quality and safety in 2021 is mostly displayed in the second quarter, the eastern region and the categories of food, drugs and daily necessities. Quality defects are the main reason for product quality and safety incidents. Therefore, we should strictly control the production quality, strengthen market supervision, promote industry standardization and standardization, and promote market participants to implement their sense of responsibility, Provide reliable product quality assurance for economic and social development and people's life.","PeriodicalId":423535,"journal":{"name":"2022 International Conference on Computer Applications Technology (CCAT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128829400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At present, the ubiquity method to diagnose the severity of diabetic feet (DF) depends on professional podiatrists. However, in most cases, professional podiatrists have a heavy workload, especially in underdeveloped and developing countries and regions, and there are often insufficient podiatrists to meet the rapidly growing treatment needs of DF patients. It is necessary to develop a medical system that assists in diagnosing DF in order to reduce part of the workload for podiatrists and to provide timely relevant information to patients with DF. In this paper, we have developed a system that can classify and locate Wagner ulcers of diabetic foot in real-time. First, we proposed a dataset of 2688 diabetic feet with annotations. Then, in order to enable the system to detect diabetic foot ulcers in real time and accurately, this paper is based on the YOLOv3 algorithm coupled with image fusion, label smoothing, and variant learning rate mode technologies to improve the robustness and predictive accuracy of the original algorithm. Finally, the refinements on YOLOv3 was used as the optimal algorithm in this paper to deploy into Android smartphone to predict the classes and localization of the diabetic foot with real-time. The experimental results validate that the improved YOLOv3 algorithm achieves a mAP of 91.95%, and meets the needs of real-time detection and analysis of diabetic foot Wagner Ulcer on mobile devices, such as smart phones. This work has the potential to lead to a paradigm shift for clinical treatment of the DF in the future, to provide an effective healthcare solution for DF tissue analysis and healing status.
{"title":"Deep Learning Methods for Real-time Detection and Analysis of Wagner Ulcer Classification System","authors":"Aifu Han, Yongze Zhang, Ajuan Li, Changjin Li, Fengying Zhao, Qiujie Dong, Yanting Liu, Ximei Shen, Sunjie Yan, Shengzong Zhou","doi":"10.1109/CCAT56798.2022.00010","DOIUrl":"https://doi.org/10.1109/CCAT56798.2022.00010","url":null,"abstract":"At present, the ubiquity method to diagnose the severity of diabetic feet (DF) depends on professional podiatrists. However, in most cases, professional podiatrists have a heavy workload, especially in underdeveloped and developing countries and regions, and there are often insufficient podiatrists to meet the rapidly growing treatment needs of DF patients. It is necessary to develop a medical system that assists in diagnosing DF in order to reduce part of the workload for podiatrists and to provide timely relevant information to patients with DF. In this paper, we have developed a system that can classify and locate Wagner ulcers of diabetic foot in real-time. First, we proposed a dataset of 2688 diabetic feet with annotations. Then, in order to enable the system to detect diabetic foot ulcers in real time and accurately, this paper is based on the YOLOv3 algorithm coupled with image fusion, label smoothing, and variant learning rate mode technologies to improve the robustness and predictive accuracy of the original algorithm. Finally, the refinements on YOLOv3 was used as the optimal algorithm in this paper to deploy into Android smartphone to predict the classes and localization of the diabetic foot with real-time. The experimental results validate that the improved YOLOv3 algorithm achieves a mAP of 91.95%, and meets the needs of real-time detection and analysis of diabetic foot Wagner Ulcer on mobile devices, such as smart phones. This work has the potential to lead to a paradigm shift for clinical treatment of the DF in the future, to provide an effective healthcare solution for DF tissue analysis and healing status.","PeriodicalId":423535,"journal":{"name":"2022 International Conference on Computer Applications Technology (CCAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125856426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：