检索结果-内蒙古大学图书馆

Crop classification from full-year fully-polarimetric L-band UAVSAR time-series using the random forest algorithm

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2020年第0期87卷 102032-000页

作者： Li, Huapeng Zhang, Ce Zhang, Shuqing Atkinson, Peter M. Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130012 Peoples R China Univ Lancaster Lancaster Environm Ctr Lancaster LA1 4YQ England Ctr Ecol & Hydrol Lib Ave Lancaster LA1 4AP England Univ Lancaster Fac Sci & Technol Lancaster LA1 4YR England

Accurate and timely information on the distribution of crop types is vital to agricultural management, ecosystem services valuation and food security assessment. Synthetic Aperture Radar (SAR) systems have become increasingly popular in the field of crop monitoring and classification. However, the potential of time-series polarimetric SAR data has not been explored extensively, with several open scientific questions (e.g. the optimal combination of image dates for crop classification) that need to be answered. In this research, the usefulness of full year (both 2011 and 2014) L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data in crop classification was fully investigated over an agricultural region with a heterogeneous distribution of crop categories. In total, 11 crop classes including tree crops (almond and walnut), forage crops (grass, alfalfa, hay, and clover), a spring crop (winter wheat), and summer crops (corn, sunflower, tomato, and pepper), were discriminated using the random forest (RF) algorithm. The SAR input variables included raw linear polarization channels as well as polarimetric parameters derived from Cloude-Pottier (CP) and Freeman-Durden (FD) decompositions. Results showed clearly that the polarimetric parameters yielded much higher classification accuracies than linear polarizations. The combined use of all variables (linear polarizations and polarimetric parameters) produced the maximum overall accuracy of 90.50 % and 84.93 % for 2011 and 2014, respectively, with a significant increase of approximately 8 percentage points compared with linear polarizations alone. The variable importance provided by the RF illustrated that the polarimetric parameters had a far greater influence than linear polarizations, with the CP parameters being much more important than the FD parameters. The most important acquisitions were the images dated during the peak biomass stage (July and August) when the differences in struc

关键词： Crop classification Multitemporal SAR imagery Polarimetric SAR random forest algorithm UAVSAR

来源：评论

学校读者我要写书评

暂无评论

Evaluating total inorganic nitrogen in coastal waters through fusion of multi-temporal RADARSAT-2 and optical imagery using random forest algorithm

引用

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2014年第1期33卷 192-202页

作者： Liu, Meiling Liu, Xiangnan Li, Jin Ding, Chao Jiang, Jiale China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China Natl Marine Data & Informat Serv Tianjin 300171 Peoples R China

Satellites routinely provide frequent, large-scale, near-surface views of many oceanographic variables pertinent to plankton ecology. However, the nutrient fertility of water can be challenging to detect accurately using remote sensing technology. This research has explored an approach to estimate the nutrient fertility in coastal waters through the fusion of synthetic aperture radar (SAR) images and optical images using the random forest (RF) algorithm. The estimation of total inorganic nitrogen (TIN) in the Hong Kong Sea, China, was used as a case study. In March of 2009 and May and August of 2010, a sequence of multi-temporal in situ data and CCD images from China's HJ-1 satellite and RADARSAT-2 images were acquired. Four sensitive parameters were selected as input variables to evaluate TIN: single-band reflectance, a normalized difference spectral index (NDSI) and Hy and VH polarizations. The RF algorithm was used to merge the different input variables from the SAR and optical imagery to generate a new dataset (i.e., the TIN outputs). The results showed the temporal-spatial distribution of TIN. The TIN values decreased from coastal waters to the open water areas, and TIN values in the northeast area were higher than those found in the southwest region of the study area. The maximum TIN values occurred in May. Additionally, the estimation accuracy for estimating TIN was significantly improved when the SAR and optical data were used in combination rather than a single data type alone. This study suggests that this method of estimating nutrient fertility in coastal waters by effectively fusing data from multiple sensors is very promising. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Multisensor data fusion random forest algorithm Total inorganic nitrogen RADARSAT-2 images Optical images

来源：评论

学校读者我要写书评

暂无评论

OPTIMIZATION OF INTERNAL CONTROL FOR BUDGET OPERATIONS IN PUBLIC INSTITUTIONS BASED ON random forest algorithm

引用

SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第3期25卷 1863-1871页

作者： Jin, Yang Henan Polytech Inst Sch Econ Nanyang 473000 Henan Peoples R China

In order to promote the construction of internal control in public institutions and improve work efficiency, the author proposes an optimization of internal control in public institution budget business based on random forest algorithm. We have constructed a big data audit framework for internal control of A Maritime Bureau based on the financial cloud platform and sorted out its audit process. By using the random forest algorithm to identify suspicious points in the internal control audit of administrative institutions at the data level, an example analysis is conducted using some data from A Maritime Bureau's assets, budget, revenue and expenditure, infrastructure, and contract business. The results indicate that the design of the internal control big data audit plan for administrative institutions will promote the innovation of audit information technology and application in A Maritime Bureau, provide theoretical guidance for the internal control big data audit carried out by administrative institutions, and effectively solve the problems of high workload and low work efficiency when A Maritime Bureau conducts internal control big data audits, thereby improving audit efficiency.

关键词： random forest algorithm Public utility units Budget business internal controls

来源：评论

学校读者我要写书评

暂无评论

Severity Identification of Graves Orbitopathy via random forest algorithm

引用

HORMONE AND METABOLIC RESEARCH 2024年第10期56卷 706-711页

作者： Wang, Minghui Li, Gongfei Dong, Li Hou, Zhijia Zhang, Ju Li, Dongmei Beijing Tongren Hosp Beijing Tongren Eye Ctr Beijing Ophthalmol & Visual Sci Key Lab Beijing Peoples R China Univ Cologne Fac Med Dept Ophthalmol Cologne Germany Univ Cologne Univ Hosp Cologne Cologne Germany Univ Tubingen Dept Neurol & Stroke Tubingen Germany Capital Med Univ Beijing Tongren Hosp Beijing Tongren Eye Ctr 1 Dongjiaominxiang Beijing 100730 Peoples R China Beijing Ophthalmol Visual Sci Key Lab 1 Dongjiaominxiang Beijing 100730 Peoples R China

This study aims to establish a random forest model for detecting the severity of Graves Orbitopathy (GO) and identify significant classification factors. This is a hospital-based study of 199 patients with GO that were collected between December 2019 and February 2022. Clinical information was collected from medical records. The severity of GO can be categorized as mild, moderate-to-severe, and sight-threatening GO based on guidelines of the European Group on Graves' orbitopathy. A random forest model was constructed according to the risk factors of GO and the main ocular symptoms of patients to differentiate mild GO from severe GO and finally was compared with logistic regression analysis, Support Vector Machine (SVM), and Naive Bayes. A random forest model with 15 variables was constructed. Blurred vision, disease course, thyroid-stimulating hormone receptor antibodies, and age ranked high both in mini-decreased gini and mini decrease accuracy. The accuracy, positive predictive value, negative predictive value, and the F1 Score of the random forest model are 0.83, 0.82, 0.86, and 0.82, respectively. Compared to the three other models, our random forest model showed a more reliable performance based on AUC (0.85 vs. 0.83 vs. 0.80 vs. 0.76) and accuracy (0.83 vs. 0.78 vs. 0.77 vs. 0.70). In conclusion, this study shows the potential for applying a random forest model as a complementary tool to differentiate GO severity.

关键词： Graves orbitopathy severity random forest algorithm

来源：评论

学校读者我要写书评

暂无评论

An investigation of an athlete injury likelihood monitoring system using the random forest algorithm and DWT

引用

TECHNOLOGY AND HEALTH CARE 2024年第4期32卷 2657-2671页

作者： Li, Jiahao Beijing Sports Univ Competit Sports Coll Beijing Peoples R China

BACKGROUND: The main goal of sports science is to monitor sports injuries. Nevertheless, the existing sports injury monitoring projects have many expensive instruments and excessively extended monitoring periods, which makes it difficult to expand sports injury monitoring on a large scale. OBJECTIVE: The advancement of machine learning algorithms opens up new avenues for the tracking of sports injuries. METHODS: A training set of sports injuries was created using the Discrete Wavelet Transform (DWT) and random forest algorithms. Next, a basic analytic framework was created based on the lower-body movement of runners, and an athlete's injury likelihood monitoring system was established. First off, the wearable gyroscope device can efficiently plot the motion displacement curve and monitor the three-dimensional mechanics of the athlete's hips, thighs, and calves. Secondly, the system has a higher computational efficiency and an advantage over other classifier-based systems in terms of testing and training times RESULTS: The suggested system framework identifies athletes' injury propensity, providing preventive recommendations based on displacement curves, and offering a low total cost and high testing accuracy, making it easy to implement and cost-effective. CONCLUSION: All things considered, the sports injury monitoring device is very accurate and reasonably priced, making it appropriate for widespread use.

关键词： Discrete wavelet transform random forest algorithm three-dimensional mechanics gyroscope computational efficiency

来源：评论

学校读者我要写书评

暂无评论

A random forest algorithm based customer demand forecasting model for sports enterprises in the real economy

引用

INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED DEVELOPMENT 2023年第2-4期13卷 363-378页

作者： Feng, Yingge Xu, Xiaowei Wang, Rongna Zhejiang Shuren Univ Sports Art & Lab Educ Ctr Hangzhou 310015 Peoples R China Zhejiang Shuren Univ Sch Humanities & foreign languages Hangzhou 310015 Peoples R China

The study proposes two strategies to optimise the random forest algorithm (RF) by fine-tuning the data distribution and introducing customer life values, constructing the improved random forest algorithm (IRF), and building a customer churn prediction model based on the IRF algorithm. A churn segmentation model is constructed based on the k-means algorithm to classify customers according to their characteristics in order to predict their needs and thus develop differentiated strategies to retain them. The experimental results show that the IRF prediction model has an accuracy of 99.84% and an AUC value of 0.932. The above results show that the accuracy of the IRF algorithm can meet the actual demand, which will contribute to the long-term development of sports enterprises. In the future, it is necessary to consider the relationship and interaction between customers to further improve the prediction accuracy of customer demand.

关键词： random forest algorithm customer churn data mining K-means algorithm customer lifetime value

来源：评论

学校读者我要写书评

暂无评论

Prediction model based on the Laplacian eigenmap method combined with a random forest algorithm for rainstorm satellite images during the first annual rainy season in South China

引用

NATURAL HAZARDS 2021年第1期107卷 331-353页

作者： Huang, Xiao-yan He, Li Zhao, Hua-sheng Huang, Ying Wu, Yu-shuang Guangxi Res Inst Meteorol Sci Nanning Peoples R China

The recent emergence of satellite detection and imaging technologies has increased the demand for the application of satellite cloud images to current weather forecasting. However, approaches based on nonlinear prediction technology to forecast satellite images are lacking, and forecasting timelines are relatively short, e.g., 1-3 h in advance. In the present study, a nonlinear dimensionality reduction approach based on Laplacian eigenmaps (LEs) was combined with a random forest (RF) algorithm to construct an intelligent computing prediction model for rainstorm satellite images obtained from the first annual rainy season (April-June) in South China from 2010 to 2018. Results showed that the proposed forecasting model based on nonlinear intelligent calculation can accurately predict the key features and trends of the development and movement of strong precipitation clouds. The predicted satellite images described by the model were also consistent with the major features of the observed satellite images. This study then used a multiple linear regression (MLR) method based on the same prediction factors to establish a model for predicting satellite images for the same modeling and forecasting samples. Comparative results of the two prediction schemes showed that the LE + RF algorithm satellite image prediction scheme yields more samples exhibiting a high correlation with observed satellite images than the MLR method. Compared with that of the proposed scheme, the amount of samples of the MLR scheme in the low-correlation area was significantly larger. In general, the nonlinear intelligent computing scheme developed in this study is superior to the MLR method for predicting satellite cloud images. Thus, the LE + RF algorithm satellite image prediction scheme provides an objective and practical method for observed satellite cloud image predictions.

关键词： Satellite cloud images Heavy rainfall Laplacian eigenmaps random forest algorithm Multiple linear regression

来源：评论

学校读者我要写书评

暂无评论

Predicting the postmortem interval using human intestinal microbiome data and random forest algorithm

引用

SCIENCE & JUSTICE 2021年第5期61卷 516-527页

作者： Hu, Lai Xing, Yu Jiang, Pu Gan, Li Zhao, Fan Peng, Wenli Li, Weihan Tong, Yanqiu Deng, Shixiong Chongqing Med Univ Dept Forens Med 1 Yixueyuan Rd Chongqing 400016 Peoples R China Chongqing Jiaotong Univ Sch Humanities 66 Xuefu Rd Chongqing 400016 Peoples R China

Gradual changes in microbial communities in a human body after death can be used to determine postmortem interval (PMI). In this study, gut microflora samples were collected from the vermiform appendix and the transverse colon of human cadavers with PMIs between 5 and 192 h. The results revealed that the appendix might be an excellent intestinal sampling site and the appendix flora had an inferred succession rule during human body decomposition. Firmicutes, Bacteroidetes, and their respective subclasses showed a predictable succession rule in relative abundance over time. A random forest regression model was developed to correlate human gut microbiota with PMI. We believe that our findings have increased the knowledge of the composition and abundance of the gut microbiota in human corpses, and suggest that the use of the human appendix microbial succession may be a potential method for forensic estimation of the time of death.

关键词： Human gut microbiome High-throughput sequencing Postmortem interval random forest algorithm

来源：评论

学校读者我要写书评

暂无评论

Influencing Factors of Students' Online Learning Satisfaction During the COVID-19 Outbreak: An Empirical Study Based on random forest algorithm 19th

Influencing Factors of Students' Online Learning Satisfactio...

引用

19th International Conference on Web-Based Learning / 5th International Symposium on Emerging Technologies for Education ICWL/SETE)

作者： Zhang, Yuzhuo Zhang, Po Yang, Hongji Zhao, Kangrui Han, Chunmei Peoples Publ Secur Univ China Beijing Peoples R China Northeast Agr Univ Key Lab Dairy Sci Minist Educ Harbin Peoples R China

ISBN: (纸本)9783030669058;9783030669065

New coronavirus is wreaking havoc around the world and has a profound impact on the international community, especially on higher education. Online teaching provides an effective path for higher education to avoid the risk of cross-contagion in traditional classroom education under epidemic condition. In order to ensure the quality of online teaching during the epidemic, this study takes the students' satisfaction of online education learning as a measurement object. 1120 online learners from 126 colleges and universities in 26 provinces were investigated through 50 questions survey in 10 dimensions. First, the chi-square test is used to pre-process all the characteristics of the factors, and 30 influencing factors with the highest feature correlation are selected. random forest algorithm is used to establish a satisfaction classification model in the training set. The accuracy in the test set is 0.72. Through the ranking of feature contribution, the influential factors with higher weight are obtained. The results show that in online learning, the attractiveness of teachers' teaching methods is the most influential factor, while curriculum arrangement and learning environment rank second and third. Finally, according to the research results, this paper puts forward some suggestions and counter measures.

关键词： Online learning Online teaching Learning satisfaction Machine learning random forest algorithm COVID-19

来源：评论

学校读者我要写书评

暂无评论

random forest algorithm for the Relationship between Negative Air Ions and Environmental Factors in an Urban Park

引用

ATMOSPHERE 2018年第12期9卷 463-463页

作者： Miao, Si Zhang, Xuyi Han, Yujie Sun, Wen Liu, Chunjiang Yin, Shan Shanghai Jiao Tong Univ Sch Agr & Biol 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Res Ctr Low Carbon Agr 800 Dongchuan Rd Shanghai 200240 Peoples R China Natl Forestry & Grassland Adm Shanghai Urban Forest Ecosyst Res Stn 800 Dongchuan Rd Shanghai 200240 Peoples R China Minist Agr & Rural Affairs Key Lab Urban Agr 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Forest Stn 1053-7 Hutai Rd Shanghai 200072 Peoples R China

Negative air ions (NAIs) are a natural component of air and have a positive impact on the health of urban residents. Few studies have focused on the relationship between NAI concentration (NAIC) in the urban atmosphere and environmental factors, such as meteorological factors and air pollutants. Therefore, we established observation points in Zhongshan Park in downtown Shanghai, China, and continuously measured and recorded changes in NAIC for one year. We also monitored nine meteorological factors and six atmospheric pollutants. Through correlation analysis and multiple linear regression analysis, the key factors influencing NAIC were screened, and the effects of those factors on NAIC were explored using the random forest algorithm. The results show that NAIC is most sensitive to humidity, followed by radiation and temperature, and finally to PM2.5. Humidity is the most critical factor, primarily because it directly affects the formation of NAIs from both the environment and vegetation. Furthermore, our results reveal that the mechanisms through which NAIC is influenced by the same factor varies seasonally. We analyzed the relationship between NAIC in an urban atmosphere and environmental factors by using big data, which is a new method for studying the relationships between NAIs and environmental factors. Our results indicate potential explanations for the mechanisms underlying NAI response to various environmental factors.

关键词： negative air ion environmental influencing factor random forest algorithm effects ranking

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：