期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到219篇文章
< 1 2 … 11 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Flood predictions from metrics to classes by multiple machine learning algorithms coupling with clustering-deduced membership degree 认领 引用
1
作者 ZHAI Xiaoyan ZHANG Yongyong +5 位作者 XIA Jun ZHANG Yongqiang TANG Qiuhong SHAO Quanxi CHEN Junxu ZHANG Fan 《Journal of Geographical Sciences》 SCIE CSCD 2026年第1期149-176,共28页
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting... Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach. 展开更多
关键词 flood regime metrics class prediction machine learning algorithms hydrological model
暂未订购 下载PDF
Machine learning algorithms for ICU mortality prediction in community-acquired pneumonia:A standardized validation and calibration study on the NACef cohort 认领 引用
2
作者 Hanane Belmouss Ahmed Aamouche 《Medical Data Mining》 CAS 2026年第3期41-49,共9页
Background:Mortality in intensive care due to community-acquired pneumonia remains high.Although machine learning models have demonstrated promising predictive performance,standardized validation and explicit calibrat... Background:Mortality in intensive care due to community-acquired pneumonia remains high.Although machine learning models have demonstrated promising predictive performance,standardized validation and explicit calibration assessment across independent clinical settings remain limited.Methods:This study systematically reviewed machine learning algorithms for mortality prediction in community-acquired pneumonia.A search across six databases identified 241 records,of which seven met the eligibility criteria.Standalone algorithms meeting predefined selection criteria were subsequently implemented within a standardized validation framework and evaluated on the NACef cohort(n=764;163 deaths,21.2%).Model performance was assessed using nested cross-validation and independent hold-out testing,with evaluation of both discrimination and calibration metrics.Results:Three standalone algorithms,XGBoost,LightGBM,and Logistic Regression,were selected for standardized implementation and evaluation.In nested cross-validation,all models achieved mean AUC values above 0.90.On the independent hold-out test set,uncalibrated AUC values were 0.941 for XGBoost,0.933 for LightGBM,and 0.915 for Logistic Regression,with small absolute differences across models.Recall ranged from 0.818(LightGBM)to 0.969(Logistic Regression),while precision ranged from 0.615(Logistic Regression)to 0.658(LightGBM).Calibration analysis indicated probability misalignment before recalibration.Platt scaling and isotonic regression improved calibration metrics,with isotonic regression achieving the lowest expected calibration error while maintaining comparable discrimination.Conclusion:Logistic Regression,XGBoost,and LightGBM demonstrated comparable discrimination,and calibration improved probability reliability,underscoring the importance of harmonized validation and explicit calibration assessment in clinical machine learning research. 展开更多
关键词 community-acquired pneumonia intensive care unit mortality machine learning algorithms model calibration nested cross-validation
暂未订购 下载PDF
Neuromorphic devices assisted by machine learning algorithms 认领 引用 被引量：6
3
作者 Ziwei Huo Qijun Sun +4 位作者 Jinran Yu Yichen Wei Yifei Wang Jeong Ho Cho Zhong Lin Wang 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2025年第4期178-215,共38页
Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks,e.g.,pattern processing,image recognition,and decisio... Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks,e.g.,pattern processing,image recognition,and decision making.It features parallel interconnected neural networks,high fault tolerance,robustness,autonomous learning capability,and ultralow energy dissipation.The algorithms of artificial neural network(ANN)have also been widely used because of their facile self-organization and self-learning capabilities,which mimic those of the human brain.To some extent,ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations.This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms.First,the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed.Second,the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures.Furthermore,the fabrication of neuromorphic devices,including stand-alone neuromorphic devices,neuromorphic device arrays,and integrated neuromorphic systems,is discussed and demonstrated with reference to some respective studies.The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated.Finally,perspectives,suggestions,and potential solutions to the current challenges of neuromorphic devices are provided. 展开更多
关键词 neuromorphic devices machine learning algorithms artificial synapses memristors field-effect transistors
暂未订购 下载PDF
Rapid pathologic grading-based diagnosis of esophageal squamous cell carcinoma via Raman spectroscopy and a deep learning algorithm 认领 引用 被引量：1
4
作者 Xin-Ying Yu Jian Chen +2 位作者 Lian-Yu Li Feng-En Chen Qiang He 《World Journal of Gastroenterology》 SCIE CAS 2025年第14期32-46,共15页
BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the e... BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the early diagnosis of tumors because it can reflect the structures of substances and their changes at the molecular level.AIM To detect alterations in Raman spectral information across different stages of esophageal neoplasia.METHODS Different grades of esophageal lesions were collected,and a total of 360 groups of Raman spectrum data were collected.A 1D-transformer network model was proposed to handle the task of classifying the spectral data of esophageal squamous cell carcinoma.In addition,a deep learning model was applied to visualize the Raman spectral data and interpret their molecular characteristics.RESULTS A comparison among Raman spectral data with different pathological grades and a visual analysis revealed that the Raman peaks with significant differences were concentrated mainly at 1095 cm-1(DNA,symmetric PO,and stretching vibration),1132 cm-1(cytochrome c),1171 cm-1(acetoacetate),1216 cm-1(amide III),and 1315 cm-1(glycerol).A comparison among the training results of different models revealed that the 1Dtransformer network performed best.A 93.30%accuracy value,a 96.65%specificity value,a 93.30%sensitivity value,and a 93.17%F1 score were achieved.CONCLUSION Raman spectroscopy revealed significantly different waveforms for the different stages of esophageal neoplasia.The combination of Raman spectroscopy and deep learning methods could significantly improve the accuracy of classification. 展开更多
关键词 Raman spectroscopy Esophageal neoplasia Early diagnosis Deep learning algorithm Rapid pathologic grading
暂未订购 下载PDF
A systematic data-driven modelling framework for nonlinear distillation processes incorporating data intervals clustering and new integrated learning algorithm 认领 引用
5
作者 Zhe Wang Renchu He Jian Long 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第5期182-199,共18页
The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficie... The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation. 展开更多
关键词 Integrated learning algorithm Data intervals clustering Feature selection Application of artificial intelligence in distillation industry Data-driven modelling
暂未订购 下载PDF
Construction and validation of a machine learning algorithm-based predictive model for difficult colonoscopy insertion 认领 引用
6
作者 Ren-Xuan Gao Xin-Lei Wang +6 位作者 Ming-Jie Tian Xiao-Ming Li Jia-Jia Zhang Jun-Jing Wang Jing Gao Chao Zhang Zhi-Ting Li 《World Journal of Gastrointestinal Endoscopy》 2025年第7期149-161,共13页
BACKGROUND Difficulty of colonoscopy insertion(DCI)significantly affects colonoscopy effectiveness and serves as a key quality indicator.Predicting and evaluating DCI risk preoperatively is crucial for optimizing intr... BACKGROUND Difficulty of colonoscopy insertion(DCI)significantly affects colonoscopy effectiveness and serves as a key quality indicator.Predicting and evaluating DCI risk preoperatively is crucial for optimizing intraoperative strategies.AIM To evaluate the predictive performance of machine learning(ML)algorithms for DCI by comparing three modeling approaches,identify factors influencing DCI,and develop a preoperative prediction model using ML algorithms to enhance colonoscopy quality and efficiency.METHODS This cross-sectional study enrolled 712 patients who underwent colonoscopy at a tertiary hospital between June 2020 and May 2021.Demographic data,past medical history,medication use,and psychological status were collected.The endoscopist assessed DCI using the visual analogue scale.After univariate screening,predictive models were developed using multivariable logistic regression,least absolute shrinkage and selection operator(LASSO)regression,and random forest(RF)algorithms.Model performance was evaluated based on discrimination,calibration,and decision curve analysis(DCA),and results were visualized using nomograms.RESULTS A total of 712 patients(53.8%male;mean age 54.5 years±12.9 years)were included.Logistic regression analysis identified constipation[odds ratio(OR)=2.254,95%confidence interval(CI):1.289-3.931],abdominal circumference(AC)(77.5–91.9 cm,OR=1.895,95%CI:1.065-3.350;AC≥92 cm,OR=1.271,95%CI:0.730-2.188),and anxiety(OR=1.071,95%CI:1.044-1.100)as predictive factors for DCI,validated by LASSO and RF methods.Model performance revealed training/validation sensitivities of 0.826/0.925,0.924/0.868,and 1.000/0.981;specificities of 0.602/0.511,0.510/0.562,and 0.977/0.526;and corresponding area under the receiver operating characteristic curves(AUCs)of 0.780(0.737-0.823)/0.726(0.654-0.799),0.754(0.710-0.798)/0.723(0.656-0.791),and 1.000(1.000-1.000)/0.754(0.688-0.820),respectively.DCA indicated optimal net benefit within probability thresholds of 0-0.9 and 0.05-0.37.The RF model demonstrated superior diagnostic accuracy,reflected by perfect training sensitivity(1.000)and highest validation AUC(0.754),outperforming other methods in clinical applicability.CONCLUSION The RF-based model exhibited superior predictive accuracy for DCI compared to multivariable logistic and LASSO regression models.This approach supports individualized preoperative optimization,enhancing colonoscopy quality through targeted risk stratification. 展开更多
关键词 Colonoscopy Difficulty of colonoscopy insertion Machine learning algorithms Predictive model Logistic regression Least absolute shrinkage and selection operator regression Random forest
暂未订购 下载PDF
A Comparison among Different Machine Learning Algorithms in Land Cover Classification Based on the Google Earth Engine Platform: The Case Study of Hung Yen Province, Vietnam 认领 引用
7
作者 Le Thi Lan Tran Quoc Vinh Phạm Quy Giang 《Journal of Environmental & Earth Sciences》 CAS 2025年第1期132-139,共8页
Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status ... Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status of land covers in Hung Yen province of Vietnam using Landsat 8 OLI satellite images,a free data source with reasonable spatial and temporal resolution.The results of the study show that all three algorithms presented good classification for five basic types of land cover including Rice land,Water bodies,Perennial vegetation,Annual vegetation,Built-up areas as their overall accuracy and Kappa coefficient were greater than 80%and 0.8,respectively.Among the three algorithms,SVM achieved the highest accuracy as its overall accuracy was 86%and the Kappa coefficient was 0.88.Land cover classification based on the SVM algorithm shows that Built-up areas cover the largest area with nearly 31,495 ha,accounting for more than 33.8%of the total natural area,followed by Rice land and Perennial vegetation which cover an area of over 30,767 ha(33%)and 15,637 ha(16.8%),respectively.Water bodies and Annual vegetation cover the smallest areas with 8,820(9.5%)ha and 6,302 ha(6.8%),respectively.The results of this study can be used for land use management and planning as well as other natural resource and environmental management purposes in the province. 展开更多
关键词 Google Earth Engine Land Cover Landsat Machine Learning Algorithm
暂未订购 下载PDF
Discrimination of Pb-Zn deposit types using sphalerite geochemistry: New insights from machine learning algorithm 认领 引用 被引量：26
8
作者 Xiao-Ming Li Yi-Xin Zhang +4 位作者 Zhan-Ke Li Xin-Fu Zhao Ren-Guang Zuo Fan Xiao Yi Zheng 《Geoscience Frontiers》 SCIE CAS CSCD 2023年第4期200-219,共20页
Due to the combined influences such as ore-forming temperature,fluid and metal sources,sphalerite tends to incorporate diverse contents of trace elements during the formation of different types of Lead-zinc(Pb-Zn)depo... Due to the combined influences such as ore-forming temperature,fluid and metal sources,sphalerite tends to incorporate diverse contents of trace elements during the formation of different types of Lead-zinc(Pb-Zn)deposits.Therefore,trace elements in sphalerite have long been utilized to distinguish Pb-Zn deposit types.However,previous discriminant diagrams usually contain two or three dimensions,which are limited to revealing the complicated interrelations between trace elements of sphalerite and the types of Pb-Zn deposits.In this study,we aim to prove that the sphalerite trace elements can be used to classify the Pb-Zn deposit types and extract key factors from sphalerite trace elements that can dis-criminate Pb-Zn deposit types using machine learning algorithms.A dataset of nearly 3600 sphalerite spot analyses from 95 Pb-Zn deposits worldwide determined by LA-ICP-MS was compiled from peer-reviewed publications,containing 12 elements(Mn,Fe,Co,Cu,Ga,Ge,Ag,Cd,In,Sn,Sb,and Pb)from 5 types,including Sedimentary Exhalative(SEDEX),Mississippi Valley Type(MVT),Volcanic Massive Sulfide(VMS),skarn,and epithermal deposits.Random Forests(RF)is applied to the data processing and the results show that trace elements of sphalerite can successfully discriminate different types of Pb-Zn deposits except for VMS deposits,most of which are falsely distinguished as skarn and epithermal types.To further discriminate VMS deposits,future studies could focus on enlarging the capacity of VMS deposits in datasets and applying other geological factors along with sphalerite trace elements when con-structing the classification model.RF’s feature importance and permutation feature importance were adopted to evaluate the element significance for classification.Besides,a visualized tool,t-distributed stochastic neighbor embedding(t-SNE),was used to verify the results of both classification and evalua-tion.The results presented here show that Mn,Co,and Ge display significant impacts on classification of Pb-Zn deposits and In,Ga,Sn,Cd,and Fe also have relatively important effects compared to the rest ele-ments,confirming that Pb-Zn deposits discrimination is mainly controlled by multi-elements in spha-lerite.Our study hence shows that machine learning algorithm can provide new insights into conventional geochemical analyses,inspiring future research on constructing classification models of mineral deposits using mineral geochemistry data. 展开更多
关键词 Discrimination Pb-Zn deposit Sphalerite trace elements Machine learning algorithms Feature analysis
Use of machine learning algorithms to assess the state of rockburst hazard in underground coal mine openings 认领 引用 被引量：17
9
作者 Lukasz Wojtecki Sebastian Iwaszenko +2 位作者 Derek B.Apel Mirosawa Bukowska Janusz Makówka 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2022年第3期703-713,共11页
The risk of rockbursts is one of the main threats in hard coal mines. Compared to other underground mines, the number of factors contributing to the rockburst at underground coal mines is much greater.Factors such as ... The risk of rockbursts is one of the main threats in hard coal mines. Compared to other underground mines, the number of factors contributing to the rockburst at underground coal mines is much greater.Factors such as the coal seam tendency to rockbursts, the thickness of the coal seam, and the stress level in the seam have to be considered, but also the entire coal seam-surrounding rock system has to be evaluated when trying to predict the rockbursts. However, in hard coal mines, there are stroke or stress-stroke rockbursts in which the fracture of a thick layer of sandstone plays an essential role in predicting rockbursts. The occurrence of rockbursts in coal mines is complex, and their prediction is even more difficult than in other mines. In recent years, the interest in machine learning algorithms for solving complex nonlinear problems has increased, which also applies to geosciences. This study attempts to use machine learning algorithms, i.e. neural network, decision tree, random forest, gradient boosting, and extreme gradient boosting(XGB), to assess the rockburst hazard of an active hard coal mine in the Upper Silesian Coal Basin. The rock mass bursting tendency index WTGthat describes the tendency of the seam-surrounding rock system to rockbursts and the anomaly of the vertical stress component were applied for this purpose. Especially, the decision tree and neural network models were proved to be effective in correctly distinguishing rockbursts from tremors, after which the excavation was not damaged. On average, these models correctly classified about 80% of the rockbursts in the testing datasets. 展开更多
关键词 Hard coal mining Rockburst hazard Machine learning algorithms
暂未订购 下载PDF
Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process 认领 引用 被引量：12
10
作者 Hamid Reza Pourghasemi Nitheshnirmal Sadhasivam +1 位作者 Narges Kariminejad Adrian L.Collins 《Geoscience Frontiers》 SCIE CAS CSCD 2020年第6期2207-2219,共13页
This investigation assessed the efficacy of 10 widely used machine learning algorithms(MLA)comprising the least absolute shrinkage and selection operator(LASSO),generalized linear model(GLM),stepwise generalized linea... This investigation assessed the efficacy of 10 widely used machine learning algorithms(MLA)comprising the least absolute shrinkage and selection operator(LASSO),generalized linear model(GLM),stepwise generalized linear model(SGLM),elastic net(ENET),partial least square(PLS),ridge regression,support vector machine(SVM),classification and regression trees(CART),bagged CART,and random forest(RF)for gully erosion susceptibility mapping(GESM)in Iran.The location of 462 previously existing gully erosion sites were mapped through widespread field investigations,of which 70%(323)and 30%(139)of observations were arbitrarily divided for algorithm calibration and validation.Twelve controlling factors for gully erosion,namely,soil texture,annual mean rainfall,digital elevation model(DEM),drainage density,slope,lithology,topographic wetness index(TWI),distance from rivers,aspect,distance from roads,plan curvature,and profile curvature were ranked in terms of their importance using each MLA.The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE(root mean square error),MAE(mean absolute error),and R-squared.Based on the comparisons among MLA,the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared,and was therefore selected as the best model.The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance.According to the GESM generated using RF,most of the study area is predicted to have a low(53.72%)or moderate(29.65%)susceptibility to gully erosion,whereas only a small area is identified to have a high(12.56%)or very high(4.07%)susceptibility.The outcome generated by RF model is validated using the ROC(Receiver Operating Characteristics)curve approach,which returned an area under the curve(AUC)of 0.985,proving the excellent forecasting ability of the model.The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion. 展开更多
关键词 Machine learning algorithm Gully erosion Random forest Controlling factors Variable importance
暂未订购 下载PDF
Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia 认领 引用 被引量：34
11
作者 Ahmed Mohamed Youssef Hamid Reza Pourghasemi 《Geoscience Frontiers》 SCIE CAS CSCD 2021年第2期639-655,共17页
The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artifici... The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their performances.Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue.This study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi Arabia.First,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources.All the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating purposes.Twelve landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and slope-aspect.The area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs performance.The results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for RF.Our findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other MLTs.The outcome of this study and the landslide susceptibility maps would be useful for environmental protection. 展开更多
关键词 Landslide susceptibility Machine learning algorithms Variables importance Saudi Arabia
暂未订购 下载PDF
Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology 认领 引用 被引量：4
12
作者 Houfa Wu Jianyun Zhang +4 位作者 Zhenxin Bao Guoqing Wang Wensheng Wang Yanqing Yang Jie Wang 《Engineering》 SCIE EI CAS CSCD 2023年第9期93-104,共12页
Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments.The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization... Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments.The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization,which is the most widely used approach.Runoff modeling was studied in 38 catchments located in the Yellow–Huai–Hai River Basin(YHHRB).The values of the Nash–Sutcliffe efficiency coefficient(NSE),coefficient of determination(R2),and percent bias(PBIAS)indicated the acceptable performance of the soil and water assessment tool(SWAT)model in the YHHRB.Nine descriptors belonging to the categories of climate,soil,vegetation,and topography were used to express the catchment characteristics related to the hydrological processes.The quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models,including linear regression(LR)equations,support vector regression(SVR),random forest(RF),k-nearest neighbor(kNN),decision tree(DT),and radial basis function(RBF).Each of the 38 catchments was assumed to be an ungauged catchment in turn.Then,the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor catchments.Furthermore,the similaritybased regionalization scheme was used for comparison with the regression-based approach.The results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged catchments.Compared with the traditional LR-based approach,the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear relationships.The performances of different approaches were similar in humid regions,while the advantages of the machine learning techniques were more evident in arid regions.When the study area contained nested catchments,the best result was calculated with the similarity-based parameter regionalization scheme because of the high catchment density and short spatial distance.The new findings could improve flood forecasting and water resources planning in regions that lack observed data. 展开更多
关键词 Parameters estimation Ungauged catchments Regionalization scheme Machine learning algorithms Soil and water assessment tool model
暂未订购 下载PDF
Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm 认领 引用 被引量：4
13
作者 Gui-xia Liu Wei Feng +2 位作者 Han Wang Lei Liu Chun-guang Zhou 《Journal of Bionic Engineering》 SCIE EI 2009年第1期86-92,共7页
In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task i... In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in bioinformatics.The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use of both simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy. 展开更多
关键词 gene regulatory networks two-stage learning algorithm Bayesian network immune evolutionary algorithm
Predicting the daily return direction of the stock market using hybrid machine learning algorithms 认领 引用 被引量：16
14
作者 Xiao Zhong David Enke 《Financial Innovation》 2019年第1期435-454,共20页
Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on f... Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks. 展开更多
关键词 Daily stock return forecasting Return direction classification Data representation Hybrid machine learning algorithms Deep neural networks(DNNs) Trading strategies
暂未订购 下载PDF
Discrimination of periodontal pathogens using Raman spectroscopy combined with machine learning algorithms 认领 引用 被引量：2
15
作者 Juan Zhang Yiping Liu +6 位作者 Hongxiao Li Shisheng Cao Xin Li Huijuan Yin Ying Li Xiaoxi Dong Xu Zhang 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2022年第3期23-35,共13页
Periodontitis is closely related to many systemic diseases linked by different periodontal pathogens.To unravel the relationship between periodontitis and systemic diseases,it is very important to correctly discrimina... Periodontitis is closely related to many systemic diseases linked by different periodontal pathogens.To unravel the relationship between periodontitis and systemic diseases,it is very important to correctly discriminate major periodontal pathogens.To realize convenient,effcient,and high-accuracy bacterial species classification,the authors use Raman spectroscopy combined with machine learning algorithms to distinguish three major periodontal pathogens Porphyromonas gingivalis(Pg),Fusobacterium nucleatum(Fn),and Aggregatibacter actinomycetemcomitans(Aa).The result shows that this novel method can successfully discriminate the three abovementioned periodontal pathogens.Moreover,the classification accuracies for the three categories of the original data were 94.7%at the sample level and 93.9%at the spectrum level by the machine learning algorithm extra trees.This study provides a fast,simple,and accurate method which is very beneficial to differentiate periodontal pathogens. 展开更多
关键词 Raman spectroscopy periodontal pathogen machine learning algorithm discrimination
暂未订购 下载PDF
Quantification of the concrete freeze-thaw environment across the Qinghai–Tibet Plateau based on machine learning algorithms 认领 引用 被引量：1
16
作者 QIN Yanhui MA Haoyuan +3 位作者 ZHANG Lele YIN Jinshuai ZHENG Xionghui LI Shuo 《Journal of Mountain Science》 SCIE CSCD 2024年第1期322-334,共13页
The reasonable quantification of the concrete freezing environment on the Qinghai-Tibet Plateau(QTP)is the primary issue in frost resistant concrete design,which is one of the challenges that the QTP engineering manag... The reasonable quantification of the concrete freezing environment on the Qinghai-Tibet Plateau(QTP)is the primary issue in frost resistant concrete design,which is one of the challenges that the QTP engineering managers should take into account.In this paper,we propose a more realistic method to calculate the number of concrete freeze-thaw cycles(NFTCs)on the QTP.The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7.Four machine learning methods,i.e.,the random forest(RF)model,generalized boosting method(GBM),generalized linear model(GLM),and generalized additive model(GAM),are used to fit the NFTCs.The root mean square error(RMSE)values of the RF,GBM,GLM,and GAM are 32.3,4.3,247.9,and 161.3,respectively.The R2values of the RF,GBM,GLM,and GAM are 0.93,0.99,0.48,and 0.66,respectively.The GBM method performs the best compared to the other three methods,which was shown by the results of RMSE and R2values.The quantitative results from the GBM method indicate that the lowest,medium,and highest NFTC values are distributed in the northern,central,and southern parts of the QTP,respectively.The annual NFTCs in the QTP region are mainly concentrated at 160 and above,and the average NFTCs is 200 across the QTP.Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP. 展开更多
关键词 Freeze-thaw cycles Quantification Machine learning algorithms Qinghai-Tibet Plateau Concrete
暂未订购 下载PDF
Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm 认领 引用 被引量：1
17
作者 Pandia Rajan Jeyaraj Aravind Chellachi Kathiresan +3 位作者 Siva Prakash Asokan Edward Rajan Samuel Nadar Hegazy Rezk Thanikanti Sudhakar Babu 《Computers, Materials & Continua》 SCIE EI 2021年第7期553-567,共15页
The power transfer capability of the smart transmission gridconnected networks needs to be reduced by inter-area oscillations.Due to the fact that inter-area modes of oscillations detain and make instability of power ... The power transfer capability of the smart transmission gridconnected networks needs to be reduced by inter-area oscillations.Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks.This fact is more noticeable in smart grid-connected systems.The smart grid infrastructure has more renewable energy resources installed for its operation.To overcome this problem,a deep learning widearea controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes.The proposed Deep Wide Area Controller(DWAC)uses the Deep Belief Network(DBN).The network weights are updated based on real-time data from Phasor measurement units.Resilience assessment based on failure probability,financial impact,and time-series data in grid failure management determine the norm H2.To demonstrate the effectiveness of the proposed framework,a time-domain simulation case study based on the IEEE-39 bus system was performed.For a one-channel attack on the test system,the resiliency index increased to 0.962,and inter-area dampingξwas reduced to 0.005.The obtained results validate the proposed deep learning algorithm’s efficiency on damping inter-area and local oscillation on the 2-channel attack as well.Results also offer robust management of power system resilience and timely control of the operating conditions. 展开更多
关键词 Neural network deep learning algorithm low-frequency oscillation resiliency assessment smart grid wide-area control
暂未订购 下载PDF
Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms 认领 引用 被引量：1
18
作者 Ashutosh Kumar Dubey Umesh Gupta Sonal Jain 《Computers, Materials & Continua》 SCIE EI 2022年第3期4523-4543,共21页
This study aims to empirically analyze teaching-learning-based optimization(TLBO)and machine learning algorithms using k-means and fuzzy c-means(FCM)algorithms for their individual performance evaluation in terms of c... This study aims to empirically analyze teaching-learning-based optimization(TLBO)and machine learning algorithms using k-means and fuzzy c-means(FCM)algorithms for their individual performance evaluation in terms of clustering and classification.In the first phase,the clustering(k-means and FCM)algorithms were employed independently and the clustering accuracy was evaluated using different computationalmeasures.During the second phase,the non-clustered data obtained from the first phase were preprocessed with TLBO.TLBO was performed using k-means(TLBO-KM)and FCM(TLBO-FCM)(TLBO-KM/FCM)algorithms.The objective function was determined by considering both minimization and maximization criteria.Non-clustered data obtained from the first phase were further utilized and fed as input for threshold optimization.Five benchmark datasets were considered from theUniversity of California,Irvine(UCI)Machine Learning Repository for comparative study and experimentation.These are breast cancer Wisconsin(BCW),Pima Indians Diabetes,Heart-Statlog,Hepatitis,and Cleveland Heart Disease datasets.The combined average accuracy obtained collectively is approximately 99.4%in case of TLBO-KM and 98.6%in case of TLBOFCM.This approach is also capable of finding the dominating attributes.The findings indicate that TLBO-KM/FCM,considering different computational measures,perform well on the non-clustered data where k-means and FCM,if employed independently,fail to provide significant results.Evaluating different feature sets,the TLBO-KM/FCM and SVM(GS)clearly outperformed all other classifiers in terms of sensitivity,specificity and accuracy.TLBOKM/FCM attained the highest average sensitivity(98.7%),highest average specificity(98.4%)and highest average accuracy(99.4%)for 10-fold cross validation with different test data. 展开更多
关键词 K-means FCM TLBO TLBO-KM TLBO-FCM TLBO-KM/FCM machine learning algorithms
暂未订购 下载PDF
Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy 认领 引用 被引量：1
19
作者 Li-Li Wei Yue-Shuai Pan +3 位作者 Yan Zhang Kai Chen Hao-Yu Wang Jing-Yuan Wang 《Frontiers of Nursing》 2021年第3期209-221,共13页
Objective:To study the application of a machine learning algorithm for predicting gestational diabetes mellitus(GDM)in early pregnancy.Methods:This study identified indicators related to GDM through a literature revie... Objective:To study the application of a machine learning algorithm for predicting gestational diabetes mellitus(GDM)in early pregnancy.Methods:This study identified indicators related to GDM through a literature review and expert discussion.Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis,and the collected indicators were retrospectively analyzed.Based on Python,the indicators were classified and modeled using a random forest regression algorithm,and the performance of the prediction model was analyzed.Results:We obtained 4806 analyzable data from 1625 pregnant women.Among these,3265 samples with all 67 indicators were used to establish data set F1;4806 samples with 38 identical indicators were used to establish data set F2.Each of F1 and F2 was used for training the random forest algorithm.The overall predictive accuracy of the F1 model was 93.10%,area under the receiver operating characteristic curve(AUC)was 0.66,and the predictive accuracy of GDM-positive cases was 37.10%.The corresponding values for the F2 model were 88.70%,0.87,and 79.44%.The results thus showed that the F2 prediction model performed better than the F1 model.To explore the impact of sacrificial indicators on GDM prediction,the F3 data set was established using 3265 samples(F1)with 38 indicators(F2).After training,the overall predictive accuracy of the F3 model was 91.60%,AUC was 0.58,and the predictive accuracy of positive cases was 15.85%.Conclusions:In this study,a model for predicting GDM with several input variables(e.g.,physical examination,past history,personal history,family history,and laboratory indicators)was established using a random forest regression algorithm.The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy.In addition,there are cer tain requirements for the propor tions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM. 展开更多
关键词 early prediction gestational diabetes mellitus machine learning algorithm random forest regression
暂未订购 下载PDF
Mapping relationship analysis of welding assembly properties for thin-walled parts with finite element and machine learning algorithm 认领 引用 被引量：1
20
作者 Pan Minghui Liao Wenhe +1 位作者 Xing Yan Tang Wencheng 《Journal of Southeast University(English Edition)》 EI CAS 2022年第2期126-136,共11页
The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The ... The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The effects of welding direction,clamping,fixture release time,fixed constraints,and welding sequences on these properties were analyzed,and the mapping relationship among welding characteristics was thoroughly examined.Different machine learning algorithms,including the generalized regression neural network(GRNN),wavelet neural network(WNN),and fuzzy neural network(FNN),are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping relationship.Compared with those from GRNN and WNN,the maximum mean relative errors for the predicted values of deformation,temperature,and residual stress with FNN were less than 4.8%,1.4%,and 4.4%,respectively.These results indicate that FNN generated the best predicted welding characteristics.Analysis under various welding conditions also shows a mapping relationship among welding deformation,temperature,and residual stress over a period of time.This finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future. 展开更多
关键词 parallel T-shaped thin-walled parts welding assembly property finite element analysis mapping relationship machine learning algorithm
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 11 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈