检索结果-内蒙古大学图书馆

Spatio-temporal variation of reference evapotranspiration in northwest China based on CORDEX-EA

ATMOSPHERIC RESEARCH 2020年 238卷 104868-104868页

作者： Yang, Linshan Feng, Qi Adamowski, Jan F. Yin, Zhenliang Wen, Xiaohu Wu, Min Jia, Bing Hao, Qiang Chinese Acad Sci Resources Lanzhou 730000 Gansu Peoples R China McGill Univ Fac Agr & Environm Sci Dept Bioresource Engn Quebec City PQ H9X 3V9 Canada Wuwei Hydrol & Water Resources Survey Bur Gansu P Wuwei 733000 Gansu Peoples R China

One of the major components of the hydrological cycle, reference evapotranspiration (ET0) represents the maximum amount of water transferred from the land surface to the atmosphere. Vital to quantifying crop water needs, accurate predictions of ET0 are particularly critical in arid regions, where they allow for informed water resources management adjustments through changes to agricultural irrigation rates and scheduling. Drawing upon 84 meteorological stations in northwest China, spatiotemporal variations in present-day ET0 were investigated. Support vector regression (SVR), Extreme learning machine (ELM), and Multivariate adaptive regression spline (MARS) - three machine learning (ML) techniques - served to establish relationships between historical ET0 and the Coordinated Regional Climate Downscaling Experiment - East Asia (CORDEX-EA), drawn from the output data sets of each of three regional climate models (RCM): Weather Research and Forecasting (WRF), Regional Climate Model version 4.0 (RegCM4) and the Mesoscale Model version 5 (MM5). The ML-RCM combinations were calibrated and validated with separate batches (66:34, respectively) of historical ET0 data, and their respective performance and level of uncertainty were assessed statistically. In the historical period (1960-2017) ET0 declined by -0.15, -0.75, and - 0.42 mm y(-1) in north Xinjiang, south Xinjiang, and Qinghai region, respectively, and increased in the Hexi Corridor by 0.5 mm y(-1). For all four regions, the MARS-WRF and MARS-MM5 combinations performed well and showed greater predictive accuracy than either ELM-WRF or ELM-MM5 combinations. Performances in predicting future (2035-2050) ET0 from CORDEX-EA outputs based on regional climate predictions RCP 4.5 and RCP 8.5 scenarios, depended to a greater extent on the RCM outputs that were selected, rather than the modeling methods. Future ET0 predicted from RCMs generally exhibit increasing trends, and more significantly under the RCP 8.5 scenario. The

关键词： CORDEX-EA Reference evapotranspiration machine learning algorithm Northwest China

来源：评论

学校读者我要写书评

暂无评论

A hybrid approach for portfolio selection with higher-order moments: Empirical evidence from Shanghai Stock Exchange

引用

EXPERT SYSTEMS WITH APPLICATIONS 2020年 145卷 113104-113104页

作者： Chen, Bilian Zhong, Jingdong Chen, Yuanyuan Xiamen Univ Dept Automat Xiamen 361005 Peoples R China Amoy Key Lab Big Data Intelligent Anal & Decis Xiamen 361005 Peoples R China Peking Univ Sch Econ Beijing 100871 Peoples R China Nanjing Univ Dept Finance & Insurance Nanjing 210093 Jiangsu Peoples R China

Skewness and kurtosis, the third and fourth order moments, are statistics to summarize the shape of a distribution function. Recent studies show that investors would take these higher-order moments into consideration to make a profitable investment decision. Unfortunately, due to the difficulties in solving the multi-objective problem with higher-order moments, the literature on portfolio selection problem with higher-order moments is few. This paper proposes a new hybrid approach to solve the portfolio selection problem with skewness and kurtosis, which includes not only the multi-objective optimization but also the data-driven asset selection and return prediction, where the techniques of two-stage clustering, radial basis function neural network and genetic algorithm are employed. With the historical data from Shanghai stock exchange, we find that the out-of-sample performance of our model with higher-order moments is significantly better than that of traditional mean-variance model and verify the robustness of our hybrid algorithm. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Portfolio optimization Higher-order moments Genetic algorithm machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Prediction model of the response to neoadjuvant chemotherapy in breast cancers by a Naive Bayes algorithm

引用

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020年 192卷 105458-105458页

作者： Yang, Libo Fu, Bo Li, Yan Liu, Yueping Huang, Wenting Feng, Sha Xiao, Lin Sun, Linyong Deng, Ling Zheng, Xinyi Ye, Feng Bu, Hong Sichuan Univ West China Hosp Lab Pathol Guo Xue Xiang 37 Hao Chengdu 610041 Sichuan Peoples R China Sichuan Univ West China Hosp Key Lab Transplant Engn & Immunol Minist Hlth Chengdu Peoples R China Sichuan Univ West China Hosp Dept Pathol Chengdu Peoples R China Univ Elect Sci & Technol China Big Data Res Ctr Sch Comp Sci & Engn Chengdu Peoples R China Hebei Med Univ Dept Pathol Hosp 4 Shijiazhuang Hebei Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Natl Clin Res Ctr Canc Natl Canc Ctr Canc Hosp Beijing 100021 Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Natl Clin Res Ctr Canc Natl Canc Ctr Canc Hosp Shenzhen 518116 Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Shenzhen Hosp Shenzhen 518116 Peoples R China

Background and Objective: Chemotherapy is useful to many breast cancer patients, however, it is not therapeutic for some patients. Pathologic complete response (pCR) is an indicator to good response in Neoadjuvant chemotherapy (NAC). In this study, we aimed to develop a way to predict pCR before ***: We retrospectively collected 287 stage II-III breast cancer cases either to a training set (N = 197) or to a test set (N = 90). Fourteen candidate genes were selected from four public microarray data sets. A prediction model was built, by using these fourteen candidate genes and three reference genes expression which were tested by TaqMan probe-based quantitative polymerase chain reaction, after selecting a better ***: The Naive Bayes algorithm had a relatively higher predictive value, compared with random forest, support vector machine (SVM), and k-nearest neighbor (knn) algorithms (P < 0.05). This 17-gene prediction model showed a high positive correlation with pCR (odds ratio, 8.914, 95% confidence interval, 4.430-17.934, P < 0.001). By using this model, the enrolled patients were classified into sensitive (SE) and insensitive (INS) groups. The pCR rates between the SE and INS groups were highly different (42.3% vs.7.6%, P < 0.001). The sensitivity and specificity of this prediction model were 84.5% and 62.0%.Conclusions: Instead of whole transcriptome-based technologies, panel gene expression with tens of essential genes implemented in a machine learning model has predictive potential for chemosensitivity in breast cancers.

关键词： Neoadjuvant chemotherapy Breast cancer Pathologic complete response machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Comprehensive mutation profiling and mRNA expression analysis in atypical chronic myeloid leukemia in comparison with chronic myelomonocytic leukemia

引用

CANCER MEDICINE 2019年第2期8卷 742-750页

作者： Faisal, Muhammad Stark, Helge Buesche, Guntram Schlue, Jerome Teiken, Kristin Kreipe, Hans H. Lehmann, Ulrich Bartels, Stephan Hannover Med Sch Inst Pathol Hannover Germany

Atypical chronic myeloid leukemia (aCML) and chronic myelomonocytic leukemia (CMML) represent two histologically and clinically overlapping myelodysplastic/myeloproliferative neoplasms. Also the mutational landscapes of both entities show congruencies. We analyzed and compared an aCML cohort (n = 26) and a CMML cohort (n = 59) by next-generation sequencing of 25 genes and by an nCounter approach for differential expression in 107 genes. Significant differences were found with regard to the mutation frequency of TET2, SETBP1, and CSF3R. Blast content of the bone marrow revealed an inverse correlation with the mutation status of SETBP1 in aCML and TET2 in CMML, respectively. By linear discriminant analysis, a mutation-based machine learning algorithm was generated which placed 19/26 aCML cases (73%) and 54/59 (92%) CMML cases into the correct category. After multiple correction, differential mRNA expression could be detected between both cohorts in a subset of genes (FLT3, CSF3R, and SETBP1 showed the strongest correlation). However, due to high variances in the mRNA expression, the potential utility for the clinic is limited. We conclude that a medium-sized NGS panel provides a valuable assistance for the correct classification of aCML and CMML.

关键词： aCML CMML machine learning algorithm nCounter NGS

来源：评论

学校读者我要写书评

暂无评论

Human Motion Recognition by Textile Sensors Based on machine learning algorithms

引用

SENSORS 2018年第9期18卷 3109-3109页

作者： Chi Cuong Vu Kim, Jooyong Soongsil Univ Dept Organ Mat & Fiber Engn Seoul 156743 South Korea

Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research involved simple trials without any significant analytical algorithms. This study provides a way of recognizing human motion by combining textile stretch sensors based on single-walled carbon nanotubes (SWCNTs) and spandex fabric (PET/SP) and machine learning algorithms in a realistic application. In the study, the performance of the system will be evaluated by identification rate and accuracy of the motion standardized. This research aims to provide a realistic motion sensing wearable product without unnecessary heavy and uncomfortable electronic devices.

关键词： wearables human motion monitoring SWCNT textiles machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Remote sensing-based framework to predict and assess the interannual variability of maize yields in Pakistan using Landsat imagery

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2020年 178卷 105732-105732页

作者： Ahmad, Ishfaq Singh, Aditya Fahad, Muhammad Waqas, Muhammad Mohsin COMSATS Univ Islamabad Ctr Climate Res & Dev Islamabad Pakistan Univ Florida Dept Agr & Biol Engn Gainesville FL USA PMAS Arid Agr Univ Dept Agron Rawalpindi Pakistan Khawaja Fareed Univ Engn & Informat Technol Dept Agr Engn Rahim Yar Khan Pakistan

Predicting crop yields and their spatio-temporal variability under a changing climate is a challenging but essential undertaking for crop management and policymaking purposes. The availability of information on risks associated with effects of climatic variability on agricultural activity outcomes is critical for stakeholders ranging from individual landowners to national economists alike. This research was conducted as a pilot study to (1) develop satellite remote sensing based estimates of maize acreage in a typical Maize growing region in Pakistan, (2) to develop a statistical-empirical model for prediction of maize yields, and finally, (3) to assess the influence of temperature on inter-annual variability in maize yields across a decade. A total of eight machine learning algorithms were tested for identifying maize growing operations in the Faisalabad district of Pakistan using Landsat 8 imagery. Classification models were evaluated via 200 randomly selected ground-verified points across the study region. Results of the maize mapping exercise were used to estimate interannual maize yields using Landsat-derived multi-temporal normalized difference vegetation index (NDVI) and land surface temperature (LST) data as predictors. Predictors for the yield forecasting model were selected via principal component screening and were fed into a least absolute shrinkage and selection (LASSO) regression model. The yield model thus developed was applied to 10 years of past data (2006-2017) and validated against data recorded by government sources. Finally, predictions spanning the ten years were tested for effects of temperature variability to find evidence of influence of ambient temperature on maize yields. Results indicate that support vector machine classifiers work the best in this landscape (accuracies 90%) and reveal that maize cropping area may be underestimated in government sources by as much as 14%. The LASSO regression models also showed very good fits (validation

关键词： machine learning algorithm Classification Yield forecasting Land surface temperature Climate variability

来源：评论

学校读者我要写书评

暂无评论

Comparison of machine learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018年第3期15卷 861-868页

作者： Hashem, Somaya Esmat, Gamal Elakel, Wafaa Habashy, Shahira Raouf, Safaa Abdel Elhefnawi, Mohamed Eladawy, Mohamed I. ElHefnawi, Mahmoud Natl Res Ctr Informat & Syst Dept Engn Res Div Giza Egypt Cairo Univ Dept Endem Med & Hepatol Fac Med Cairo Egypt Helwan Univ Commun Elect & Comp Dept Fac Engn Cairo Egypt Natl Res Ctr Informat & Syst Dept Engn Res Div Giza Egypt Modern Univ Commun & Comp Dept Fac Engn Cairo Egypt Natl Res Ctr Informat & Syst Dept Giza Egypt Natl Res Ctr Biomed Informat & Chemo Informat Grp Engn Res Div Giza Egypt Natl Res Ctr Biomed Informat & Chemo Informat Grp Ctr Excellence Adv Sci Giza Egypt Nile Univ Ctr Informat Giza Egypt

Background/Aim: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to evaluate different machine learning techniques in prediction of advanced fibrosis by combining the serum bio-markers and clinical information to develop the classification models. Methods: A prospective cohort of 39,567 patients with chronic hepatitis C was divided into two sets-one categorized as mild to moderate fibrosis (F0-F2), and the other categorized as advanced fibrosis (F3-F4) according to METAVIR score. Decision tree, genetic algorithm, particle swarm optimization, and multi-linear regression models for advanced fibrosis risk prediction were developed. Receiver operating characteristic curve analysis was performed to evaluate the performance of the proposed models. Results: Age, platelet count, AST, and albumin were found to be statistically significant to advanced fibrosis. The machine learning algorithms under study were able to predict advanced fibrosis in patients with HCC with AUROC ranging between 0.73 and 0.76 and accuracy between 66.3 and 84.4 percent. Conclusions: machine-learning approaches could be used as alternative methods in prediction of the risk of advanced liver fibrosis due to chronic hepatitis C.

关键词： Liver fibrosis prediction machine learning algorithm particle swarm optimization decision learning tree serum marker Hepatitis C virus

来源：评论

学校读者我要写书评

暂无评论

From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning

引用

ASTRONOMY AND COMPUTING 2018年 25卷 213-220页

作者： Cabral, J. B. Sanchez, B. Ramos, F. Gurovich, S. Granitto, P. M. Vanderplas, J. IATE OAC UNC CONICET Laprida 854X5000BGR Cordoba Argentina UNR Fac Ciencias Exactas Ingn & Agrimensura Pellegrini 250 S2000BTP Rosario Santa Fe Argentina CIFASIS CONICET UNR S2000EZP Rosario Santa Fe Argentina Univ Washington eSci Inst Campus Box 351570 Seattle WA 98195 USA Univ Nacl Cordoba FaMAF UNC Fac Matemat Astron & Fis Bvd Medina Allende S-NCiudad UnivX5000BGR Cordoba Argentina

machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale public survey data releases. These methods can facilitate the discovery of new unknown events in most astrophysical areas, as well as improving the analysis of samples of known phenomena. machine learning algorithms use features extracted from collected data as input predictive variables. A public tool called Feature Analysis for Time Series (FATS) has proved an excellent workhorse for feature extraction, particularly light curve classification for variable objects. In this study, we present a major improvement to FATS, which corrects inconvenient design choices, minor details, and documentation for the re-engineering process. This improvement comprises a new Python package called feets, which is important for future code-refactoring for astronomical software tools. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Astroinformatics machine learning algorithm Feature selection Software and its engineering Software post-development issue

来源：评论

学校读者我要写书评

暂无评论

Quantitative assessment of the contribution of environmental factors to divergent population trends in two lady beetles

引用

BIOLOGICAL CONTROL 2020年 145卷 104259-000页

作者： Cheng, Jiao Li, Peiling Zhang, Yunhui Zhan, Yidi Liu, Yong Shandong Agr Univ Coll Plant Protect 61 Daizong Rd Tai An 271018 Shandong Peoples R China Shandong Plant Protect Stn 200 Gongyebei Rd Jinan 250013 Shandong Peoples R China Chinese Acad Agr Sci Inst Plant Protect West Yuan Ming Yuan Rd Beijing 100193 Peoples R China

Coccinellid beetles are important functional components of many terrestrial ecosystems. Coccinella septempunctata L. and Harmonia axyridis (Pallas) (Coleoptera: Coccinellidae) are dominant, predacious beneficial beetles that both contribute to the suppression of many different pests, especially in temperate regions. Here, we analyse temporal trends in the populations of these two species over a 28-year period in wheat agroecosystem of northern China. To this, we used three machine learning algorithms (support vector machine [SVM], random forest [RF] and multilayer perceptron [MLP]) to relate occurrences of C. septempunctata and H. axyridis to selected environmental variables. SVM was the algorithm that most accurately forecast species abundance of these two lady beetles, followed by RF and MLP, using the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). Average temperature, average wind velocity, and average relative humidity were the most explanatory factors associated with the absolute abundance of C. septempunctata and H. axyridis in agroecosystems.

关键词： Coccinella septempunctata Harmonia axyridis machine learning algorithm Agroecosystem Correlation analysis Forecasting

来源：评论

学校读者我要写书评

暂无评论

A Multiple Linear Regressions Model for Crop Prediction with Adam Optimizer and Neural Network Mlraonn

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第4期11卷 253-257页

作者： Lavanya, M. Parameswari, R. VISTAS Sch Comp Sci Dept Comp Sci Chennai Tamil Nadu India

Due to the increase in population, demand for the food is increasing day by day. Crop prediction is necessary or need of the hour to fill the gap between the demand and the supply. Instead of following a traditional system for crop selection method, a successful crop selection for the given soil properties will help the farmers to get the expected crop yield. The objective of the proposed work is to develop one such system. The proposed system is developed using real data with various soil parameters acquired from soil laboratory located in Chennai. This system uses 16 parameters of soil which includes all the micro, macro nutrients along with that pH, EC, OM values and the recommended crop for the soil parameter. The proposed Mlraonn (Multiple Linear Regression with Adam Optimization in Neural Network) model is developed using Keras software mainly used for Deep learning. A neural network approach is used to construct a regression model. The model is evaluated with Loss Metrics such as RMSE, MSE, and MAE. The proposed algorithm is compared with the existing standardized machine learning algorithms. It is found that the proposed algorithm gave very minimal error as output in all the above three categories of loss metrics than the standardized algorithm such as Random Forest Regression and Multiple Linear Regression.

关键词： Multiple Linear Regression Adam Optimization Neural Network Keras machine learning algorithm Root Mean Square Error (RMSE) Mean Square Error (MSE) Mean Absolute Error (MAE) presence of Hydrogen (pH) Electrical Conductivity (EC) Organic Matter (OM)

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：