Big data is usually unstructured, and many applications require theanalysis in real-time. Decision tree (DT) algorithm is widely used to analyzebig data. Selecting the optimal depth of DT is time-consuming process as ...Big data is usually unstructured, and many applications require theanalysis in real-time. Decision tree (DT) algorithm is widely used to analyzebig data. Selecting the optimal depth of DT is time-consuming process as itrequires many iterations. In this paper, we have designed a modified versionof a (DT). The tree aims to achieve optimal depth by self-tuning runningparameters and improving the accuracy. The efficiency of the modified (DT)was verified using two datasets (airport and fire datasets). The airport datasethas 500000 instances and the fire dataset has 600000 instances. A comparisonhas been made between the modified (DT) and standard (DT) with resultsshowing that the modified performs better. This comparison was conductedon multi-node on Apache Spark tool using Amazon web services. Resultingin accuracy with an increase of 6.85% for the first dataset and 8.85% for theairport dataset. In conclusion, the modified DT showed better accuracy inhandling different-sized datasets compared to standard DT algorithm.展开更多
Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend...Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise,and the application conditions are very demanding.With the rapid development of artificial intelligence technology,big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development.Based on the data-driven artificial intelligence algorithmGradient BoostingDecision Tree(GBDT),this paper predicts the initial single-layer production by considering geological data,fluid PVT data and well data.The results show that the GBDT algorithm prediction model has great accuracy,significantly improving efficiency and strong universal applicability.The GBDTmethod trained in this paper can predict production,which is helpful for well site optimization,perforation layer optimization and engineering parameter optimization and has guiding significance for oilfield development.展开更多
Aiming at the problems of multiple types of power quality composite disturbances,strong feature correlation and high recognition error rate,a method of power quality composite disturbances identification based on mult...Aiming at the problems of multiple types of power quality composite disturbances,strong feature correlation and high recognition error rate,a method of power quality composite disturbances identification based on multiresolution S-transform and decision tree was proposed.Firstly,according to IEEE standard,the signal models of seven single power quality disturbances and 17 combined power quality disturbances are given,and the disturbance waveform samples are generated in batches.Then,in order to improve the recognition accuracy,the adjustment factor is introduced to obtain the controllable time-frequency resolution through multi-resolution S-transform time-frequency domain analysis.On this basis,five disturbance time-frequency domain features are extracted,which quantitatively reflect the characteristics of the analyzed power quality disturbance signal,which is less than the traditional method based on S-transform.Finally,three classifiers such as K-nearest neighbor,support vector machine and decision tree algorithm are used to effectively complete the identification of power quality composite disturbances.Simulation results showthat the classification accuracy of decision tree algorithmis higher than that of K-nearest neighbor and support vector machine.Finally,the proposed method is compared with other commonly used recognition algorithms.Experimental results show that the proposedmethod is effective in terms of detection accuracy,especially for combined PQ interference.展开更多
The trend toward designing an intelligent distribution system based on students’individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system,which neglects...The trend toward designing an intelligent distribution system based on students’individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system,which neglects the students’personality traits,causes dormitory disputes,and affects the students’quality of life and academic quality.This paper collects freshmen's data according to college students’personal preferences,conducts a classification comparison,uses the decision tree classification algorithm based on the information gain principle as the core algorithm of dormitory allocation,determines the description rules of students’personal preferences and decision tree classification preferences,completes the conceptual design of the database of entity relations and data dictionaries,meets students’personality classification requirements for the dormitory,and lays the foundation for the intelligent dormitory allocation system.展开更多
Maxillary sinus floor augmentation using lateral window and crestal technique is considered as predictable methods to increase the residual bone height;however,this surgery is commonly complicated by Schneiderian memb...Maxillary sinus floor augmentation using lateral window and crestal technique is considered as predictable methods to increase the residual bone height;however,this surgery is commonly complicated by Schneiderian membrane perforation,which is closely related to anatomical factors.This article aimed to assess anatomical factors on successful augmentation procedures.After review of the current evidence on sinus augmentation techniques,anatomical factors related to the stretching potential of Schneiderian membrane were assessed and a decision tree for the rational choice of surgical approaches was proposed.Schneiderian membrane perforation might occur when local tension exceeds its stretching potential,which is closely related to anatomical variations of the maxillary sinus.Choice of a surgical approach and clinical outcomes are influenced by the stretching potential of Schneiderian membrane.In addition to the residual bone height,clinicians should also consider the stretching potential affected by the membrane health condition,the contours of the maxillary sinus,and the presence of antral septa when evaluating the choice of surgical approaches and clinical outcomes.展开更多
Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study pres...Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.展开更多
Purpose:The purpose of this study was to use decision tree modeling to generate profiles of children and youth who were more and less likely to meet the Canadian 24-h movement guidelines during the coronavirus disease...Purpose:The purpose of this study was to use decision tree modeling to generate profiles of children and youth who were more and less likely to meet the Canadian 24-h movement guidelines during the coronavirus disease-2019(COVID-19)outbreak.Methods:Data for this study were from a nationally representative sample of 1472 Canadian parents(Meanage=45.12,SD=7.55)of children(511 years old)or youth(1217 years old).Data were collected in April 2020 via an online survey.Survey items assessed demographic,behavioral,social,micro-environmental,and macro-environmental characteristics.Four decision trees of adherence and non-adherence to all movement recommendations combined and each individual movement recommendation(physical activity(PA),screen time,and sleep)were generated.Results:Results revealed specific combinations of adherence and non-adherence characteristics.Characteristics associated with adherence to the recommendation(s)included high parental perceived capability to restrict screen time,annual household income ofCAD 100,000,increases in children’s and youth’s outdoor PA/sport since the COVID-19 outbreak began,being a boy,having parents younger than 43 years old,and small increases in children’s and youth’s sleep duration since the COVID-19 outbreak began.Characteristics associated with non-adherence to the recommendation(s)included low parental perceived capability to restrict screen time,youth aged 1217 years,decreases in children’s and youth’s outdoor PA/sport since the COVID-19 outbreak began,primary residences located in all provinces except Quebec,low parental perceived capability to support children’s and youth’s sleep and PA,and annual household income ofCAD 99,999.Conclusion:Our results show that specific characteristics interact to contribute to(non)adherence to the movement behavior recommendations.Results highlight the importance of targeting parents’perceived capability for the promotion of children’s and youth’s movement behaviors during challenging times of the COVID-19 pandemic,paying particular attention to enhancing parental perceived capability to restrict screen time.展开更多
BACKGROUND Diabetic retinopathy(DR)is the driving force of blindness in patients with type 2 diabetes mellitus(T2DM).DR has a high prevalence and lacks effective therapeutic strategies,underscoring the need for early ...BACKGROUND Diabetic retinopathy(DR)is the driving force of blindness in patients with type 2 diabetes mellitus(T2DM).DR has a high prevalence and lacks effective therapeutic strategies,underscoring the need for early prevention and treatment.Yunnan province,located in the southwest plateau of China,has a high prevalence of DR and an underdeveloped economy.AIM To build a clinical prediction model that will enable early prevention and treatment of DR.METHODS In this cross-sectional study,1654 Han population with T2DM were divided into groups without(n=826)and with DR(n=828)based on fundus photography.The DR group was further subdivided into non-proliferative DR(n=403)and proliferative DR(n=425)groups.A univariate analysis and logistic regression analysis were conducted and a clinical decision tree model was constructed.RESULTS Diabetes duration≥10 years,female sex,standing-or supine systolic blood pressure(SBP)≥140 mmHg,and cholesterol≥6.22 mmol/L were risk factors for DR in logistic regression analysis(odds ratio=2.118,1.520,1.417,1.881,and 1.591,respectively).A greater severity of chronic kidney disease(CKD)or hemoglobin A 1c increased the risk of DR in patients with T2DM.In the decision tree model,diabetes duration was the primary risk factor affecting the occurrence of DR in patients with T2DM,followed by CKD stage,supine SBP,standing SBP,and body mass index(BMI).DR classification outcomes were obtained by evaluating standing SBP or BMI according to the CKD stage for diabetes duration<10 years and by evaluating CKD stage according to the supine SBP for diabetes duration≥10 years.CONCLUSION Based on the simple and intuitive decision tree model constructed in this study,DR classification outcomes were easily obtained by evaluating diabetes duration,CKD stage,supine or standing SBP,and BMI.展开更多
Decision trees induction algorithms have been used for classification in a wide range of application domains. In the process of constructing a tree, the criteria of selecting test attributes will influence the classif...Decision trees induction algorithms have been used for classification in a wide range of application domains. In the process of constructing a tree, the criteria of selecting test attributes will influence the classification accuracy of the tree.In this paper,the degree of dependency of decision attribute to condition attribute,based on rough set theory,is used as a heuristic for selecting the attribute that will best separate the samples into individual classes.The result of an example shows that compared with the entropy-based approach,our approach is a better way to select nodes for constructing decision trees.展开更多
Under the modern education system of China, the annual scholarship evaluation is a vital thing for many of the collegestudents. This paper adopts the classification algorithm of decision tree C4.5 based on the betteri...Under the modern education system of China, the annual scholarship evaluation is a vital thing for many of the collegestudents. This paper adopts the classification algorithm of decision tree C4.5 based on the bettering of ID3 algorithm and constructa data set of the scholarship evaluation system through the analysis of the related attributes in scholarship evaluation information.And also having found some factors that plays a significant role in the growing up of the college students through analysis and re-search of moral education, intellectural education and culture&PE.展开更多
Background:The vital signs of trauma patients are complex and changeable,and the prediction of blood transfusion demand mainly depends on doctors'experience and trauma scoring system;therefore,it cannot be accurat...Background:The vital signs of trauma patients are complex and changeable,and the prediction of blood transfusion demand mainly depends on doctors'experience and trauma scoring system;therefore,it cannot be accurately predicted.In this study,a machine learning decision tree algorithm[classification and regression tree(CRT)and eXtreme gradient boosting(XGBoost)]was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors.Methods:A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database.The vital signs,laboratory examination parameters and blood transfusion volume were used as variables,and the non-invasive parameters and all(non-invasive+invasive)parameters were used to construct an intelligent prediction model for red blood cell(RBC)demand by logistic regression(LR),CRT and XGBoost.The prediction accuracy of the model was compared with the area under curve(AUC).Results:For non-invasive parameters,the LR method was the best,with an AUC of 0.72[95%confidence interval(CI)0.657–0.775],which was higher than the CRT(AUC 0.69,95%CI 0.633–0.751)and the XGBoost(AUC 0.71,95%CI 0.654–0.756)(P<0.05).The trauma location and shock index are important prediction parameters.For all the prediction parameters,XGBoost was the best,with an AUC of 0.94(95%CI 0.893–0.981),which was higher than the LR(AUC 0.80,95%CI 0.744–0.850)and the CRT(AUC 0.82,95%CI 0.779–0.853)(P<0.05).Haematocrit(Hct)is an important prediction parameter.Conclusions:The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method.It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment,so as to improve the success rate of patient treatment.展开更多
With the development of the service-oriented computing(SOC),web service has an important and popular solution for the design of the application system to various enterprises.Nowadays,the numerous web services are prov...With the development of the service-oriented computing(SOC),web service has an important and popular solution for the design of the application system to various enterprises.Nowadays,the numerous web services are provided by the service providers on the network,it becomes difficult for users to select the best reliable one from a large number of services with the same function.So it is necessary to design feasible selection strategies to provide users with the reliable services.Most existing methods attempt to select services according to accurate predictions for the quality of service(QoS)values.However,because the network and user needs are dynamic,it is almost impossible to accurately predict the QoS values.Furthermore,accurate prediction is generally time-consuming.This paper proposes a service decision tree based post-pruning prediction approach.This paper first defines the five reliability levels for measuring the reliability of services.By analyzing the quality data of service from the network,the proposed method can generate the training set and convert them into the service decision tree model.Using the generated model and the given predicted services,the proposed method classifies the service to the corresponding reliability level after discretizing the continuous attribute of service.Moreover,this paper applies the post-pruning strategy to optimize the generated model for avoiding the over-fitting.Experimental results show that the proposed method is effective in predicting the service reliability.展开更多
The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools.This research incorporates condition monitoring of a non-carbide tool insert using vibrat...The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools.This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach.A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe,where the condition of tool is monitored using vibration characteristics.The vibration signals for conditions such as heathy,damaged,thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system.The descriptive statistical features were extracted from the acquired vibration signal using the feature extraction techniques.The extracted statistical features were selected using a feature selection process through J48 decision tree algorithm.The selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive analysis.The decision tree model produced the classification accuracy as 94.78%with five selected features.The developed fuzzy model produced the classification accuracy as 94.02%with five membership functions.Hence,the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.展开更多
The registration rate of freshmen has been a great concern at many colleges and universities,particularly private institutions.Traditionally,there are two inquiry methods:telephone and tuition-payment-status.Unfortuna...The registration rate of freshmen has been a great concern at many colleges and universities,particularly private institutions.Traditionally,there are two inquiry methods:telephone and tuition-payment-status.Unfortunately,the former is not only time-consuming but also suffers from the fact that many students tend to keep their choices secret.On the other hand,the latter is not always feasible because only few students are willing to pay their university tuition fees in advance.It is often believed that it is impossible to predict incoming freshmen’s choice of university due to the large amount of subjectivity.However,if we look at the two major considerations a potential freshman contemplates in making a choice,such as the geographical location of the university in relation to his/her home town,and testimonies about of that college life experience by previous graduates,we believe it is possible to predict future enrollment decisions.This paper is the first to find a way to solve the problem of predicting the choice of university a freshman will attend.Our contributions include the following:1.we present a dataset on freshman registration;2.we propose a decision-tree-based approach for freshman registration prediction.Study results show that freshman registration is predictable.展开更多
Finding simple, non recursive, base noun phrase is an important step for many natural language processing applications. This paper presents a new corpus based approach using decision tree for that purpose. In contrast...Finding simple, non recursive, base noun phrase is an important step for many natural language processing applications. This paper presents a new corpus based approach using decision tree for that purpose. In contrast to previous methods for Base NP identification, we adopt a decision tree trained from Penn Treebank to identify Base NP. And a self learning mechanism is further integrated into our model. Experimental results show good performances using our method. The method can also be applied to processing of any other language.展开更多
Group communication is widely used by most of the emerging network applications like telecommunication,video conferencing,simulation applications,distributed and other interactive systems.Secured group communication p...Group communication is widely used by most of the emerging network applications like telecommunication,video conferencing,simulation applications,distributed and other interactive systems.Secured group communication plays a vital role in case of providing the integrity,authenticity,confidentiality,and availability of the message delivered among the group members with respect to communicate securely between the inter group or else within the group.In secure group communications,the time cost associated with the key updating in the proceedings of the member join and departure is an important aspect of the quality of service,particularly in the large groups with highly active membership.Hence,the paper is aimed to achieve better cost and time efficiency through an improved DC multicast routing protocol which is used to expose the path between the nodes participating in the group communication.During this process,each node constructs an adaptive Ptolemy decision tree for the purpose of generating the contributory key.Each of the node is comprised of three keys which will be exchanged between the nodes for considering the group key for the purpose of secure and cost-efficient group communication.The rekeying process is performed when a member leaves or adds into the group.The performance metrics of novel approach is measured depending on the important factors such as computational and communicational cost,rekeying process and formation of the group.It is concluded from the study that the technique has reduced the computational and communicational cost of the secure group communication when compared to the other existing methods.展开更多
In many decision making tasks,the features and decision are ordinal.Several ordinal classification learning algorithms have been developed in recent years,it is shown that these algorithms are sensitive to noisy sampl...In many decision making tasks,the features and decision are ordinal.Several ordinal classification learning algorithms have been developed in recent years,it is shown that these algorithms are sensitive to noisy samples and do not work in real-world applications.In this work,we propose a new measure of feature quality, called rank mutual information.Then,we design an ordinal decision tree(REOT) construction technique based on rank mutual information.The theoretic and experimental analysis shows that the proposed algorithm is effective.展开更多
The inherently unique qualities of the heart infer the candidacy for the domain of biometrics, which applies physiological attributes to establish the recognition of a person’s identity. The heart’s characteristics ...The inherently unique qualities of the heart infer the candidacy for the domain of biometrics, which applies physiological attributes to establish the recognition of a person’s identity. The heart’s characteristics can be ascertained by recording the electrical signal activity of the heart through the acquisition of an electrocardiogram (ECG). With the application of machine learning the subject specific ECG signal can be differentiated. However, the process of distinguishing subjects through machine learning may be considered esoteric, especially for contributing subject matter experts external to the domain of machine learning. A resolution to this dilemma is the application of the J48 decision tree available through the Waikato Environment for Knowledge Analysis (WEKA). The J48 decision tree elucidates the machine learning process through a visualized decision tree that attains classification accuracy through the application of thresholds applied to the numeric attributes of the feature set. Additionally, the numeric attributes of the feature set for the application of the J48 decision tree are derived from the temporal organization of the ECG signal maxima and minima for the respective P, Q, R, S, and T waves. The J48 decision tree achieves considerable classification accuracy for the distinction of subjects based on their ECG signal, for which the machine learning model is briskly composed.展开更多
So many potential risks can be identifed for application of trenchless technology especially using microtunneling methods.Unexpected changes in ground conditions,such as encountering boulders,tree roots,ground water a...So many potential risks can be identifed for application of trenchless technology especially using microtunneling methods.Unexpected changes in ground conditions,such as encountering boulders,tree roots,ground water and man-made structures such as old foundations are the principal geotechnical risks,which affect the selection of an appropriate microtunnel boring machine.On the other hand,the performance of each microtunneling technique will differ while encountering such conditions.Hence,predicting the potential hazards provides a better safety and risk management plan.In this study,a couple of potentially hazardous situation,which are commonly associated with ground conditions,were identifed and investigated.A decision tree aid methodology was proposed based on geotechnical risk assessment for selection of proper microtunneling technique.Based on the approach the most appropriate microtunneling technique has the minimum risk level either before or after hazards mitigation measures.In order to check the effciency of the approach in practice,selection of microtunnel boring machine for Hamadan sewerage pipeline project was evaluated.Accordingly,an earth pressure balance(EPB)MTBM was selected for the project.展开更多
文摘Big data is usually unstructured, and many applications require theanalysis in real-time. Decision tree (DT) algorithm is widely used to analyzebig data. Selecting the optimal depth of DT is time-consuming process as itrequires many iterations. In this paper, we have designed a modified versionof a (DT). The tree aims to achieve optimal depth by self-tuning runningparameters and improving the accuracy. The efficiency of the modified (DT)was verified using two datasets (airport and fire datasets). The airport datasethas 500000 instances and the fire dataset has 600000 instances. A comparisonhas been made between the modified (DT) and standard (DT) with resultsshowing that the modified performs better. This comparison was conductedon multi-node on Apache Spark tool using Amazon web services. Resultingin accuracy with an increase of 6.85% for the first dataset and 8.85% for theairport dataset. In conclusion, the modified DT showed better accuracy inhandling different-sized datasets compared to standard DT algorithm.
文摘Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise,and the application conditions are very demanding.With the rapid development of artificial intelligence technology,big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development.Based on the data-driven artificial intelligence algorithmGradient BoostingDecision Tree(GBDT),this paper predicts the initial single-layer production by considering geological data,fluid PVT data and well data.The results show that the GBDT algorithm prediction model has great accuracy,significantly improving efficiency and strong universal applicability.The GBDTmethod trained in this paper can predict production,which is helpful for well site optimization,perforation layer optimization and engineering parameter optimization and has guiding significance for oilfield development.
基金Foundation of China(No.52067013)the Key Natural Science Fund Project of Gansu Provincial Department of Science and Technology(No.21JR7RA280)+1 种基金the Tianyou Innovation Team Science Foundation of Intelligent Power Supply and State Perception for Rail Transit(No.TY202010)the Natural Science Foundation of Gansu Province(No.20JR5RA395).
文摘Aiming at the problems of multiple types of power quality composite disturbances,strong feature correlation and high recognition error rate,a method of power quality composite disturbances identification based on multiresolution S-transform and decision tree was proposed.Firstly,according to IEEE standard,the signal models of seven single power quality disturbances and 17 combined power quality disturbances are given,and the disturbance waveform samples are generated in batches.Then,in order to improve the recognition accuracy,the adjustment factor is introduced to obtain the controllable time-frequency resolution through multi-resolution S-transform time-frequency domain analysis.On this basis,five disturbance time-frequency domain features are extracted,which quantitatively reflect the characteristics of the analyzed power quality disturbance signal,which is less than the traditional method based on S-transform.Finally,three classifiers such as K-nearest neighbor,support vector machine and decision tree algorithm are used to effectively complete the identification of power quality composite disturbances.Simulation results showthat the classification accuracy of decision tree algorithmis higher than that of K-nearest neighbor and support vector machine.Finally,the proposed method is compared with other commonly used recognition algorithms.Experimental results show that the proposedmethod is effective in terms of detection accuracy,especially for combined PQ interference.
文摘The trend toward designing an intelligent distribution system based on students’individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system,which neglects the students’personality traits,causes dormitory disputes,and affects the students’quality of life and academic quality.This paper collects freshmen's data according to college students’personal preferences,conducts a classification comparison,uses the decision tree classification algorithm based on the information gain principle as the core algorithm of dormitory allocation,determines the description rules of students’personal preferences and decision tree classification preferences,completes the conceptual design of the database of entity relations and data dictionaries,meets students’personality classification requirements for the dormitory,and lays the foundation for the intelligent dormitory allocation system.
基金supported by grants from:1.Young Clinical Research Fund of the Chinese Stomatological Association[grant number CSA-SIS2022-19]Sichuan Science and Technology Program:[grant number 2023NSFSC0567].
文摘Maxillary sinus floor augmentation using lateral window and crestal technique is considered as predictable methods to increase the residual bone height;however,this surgery is commonly complicated by Schneiderian membrane perforation,which is closely related to anatomical factors.This article aimed to assess anatomical factors on successful augmentation procedures.After review of the current evidence on sinus augmentation techniques,anatomical factors related to the stretching potential of Schneiderian membrane were assessed and a decision tree for the rational choice of surgical approaches was proposed.Schneiderian membrane perforation might occur when local tension exceeds its stretching potential,which is closely related to anatomical variations of the maxillary sinus.Choice of a surgical approach and clinical outcomes are influenced by the stretching potential of Schneiderian membrane.In addition to the residual bone height,clinicians should also consider the stretching potential affected by the membrane health condition,the contours of the maxillary sinus,and the presence of antral septa when evaluating the choice of surgical approaches and clinical outcomes.
基金This research is funded by the National Natural Science Foundation of China(Grant Nos.41807285 and 51679117)Key Project of the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(SKLGP2019Z002)+3 种基金the National Science Foundation of Jiangxi Province,China(20192BAB216034)the China Postdoctoral Science Foundation(2019M652287 and 2020T130274)the Jiangxi Provincial Postdoctoral Science Foundation(2019KY08)Fundamental Research Funds for National Universities,China University of Geosciences(Wuhan)。
文摘Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.
文摘Purpose:The purpose of this study was to use decision tree modeling to generate profiles of children and youth who were more and less likely to meet the Canadian 24-h movement guidelines during the coronavirus disease-2019(COVID-19)outbreak.Methods:Data for this study were from a nationally representative sample of 1472 Canadian parents(Meanage=45.12,SD=7.55)of children(511 years old)or youth(1217 years old).Data were collected in April 2020 via an online survey.Survey items assessed demographic,behavioral,social,micro-environmental,and macro-environmental characteristics.Four decision trees of adherence and non-adherence to all movement recommendations combined and each individual movement recommendation(physical activity(PA),screen time,and sleep)were generated.Results:Results revealed specific combinations of adherence and non-adherence characteristics.Characteristics associated with adherence to the recommendation(s)included high parental perceived capability to restrict screen time,annual household income ofCAD 100,000,increases in children’s and youth’s outdoor PA/sport since the COVID-19 outbreak began,being a boy,having parents younger than 43 years old,and small increases in children’s and youth’s sleep duration since the COVID-19 outbreak began.Characteristics associated with non-adherence to the recommendation(s)included low parental perceived capability to restrict screen time,youth aged 1217 years,decreases in children’s and youth’s outdoor PA/sport since the COVID-19 outbreak began,primary residences located in all provinces except Quebec,low parental perceived capability to support children’s and youth’s sleep and PA,and annual household income ofCAD 99,999.Conclusion:Our results show that specific characteristics interact to contribute to(non)adherence to the movement behavior recommendations.Results highlight the importance of targeting parents’perceived capability for the promotion of children’s and youth’s movement behaviors during challenging times of the COVID-19 pandemic,paying particular attention to enhancing parental perceived capability to restrict screen time.
基金Supported by the Natural Science Foundation of China,No.82160159Natural Science Foundation of Yunnan Province,No.202101AY070001-199+1 种基金Scientific Research Fund of Yunnan Education Department,No.2021J0303Postgraduate Innovation Fund of Kunming Medical University,No.2020D009.
文摘BACKGROUND Diabetic retinopathy(DR)is the driving force of blindness in patients with type 2 diabetes mellitus(T2DM).DR has a high prevalence and lacks effective therapeutic strategies,underscoring the need for early prevention and treatment.Yunnan province,located in the southwest plateau of China,has a high prevalence of DR and an underdeveloped economy.AIM To build a clinical prediction model that will enable early prevention and treatment of DR.METHODS In this cross-sectional study,1654 Han population with T2DM were divided into groups without(n=826)and with DR(n=828)based on fundus photography.The DR group was further subdivided into non-proliferative DR(n=403)and proliferative DR(n=425)groups.A univariate analysis and logistic regression analysis were conducted and a clinical decision tree model was constructed.RESULTS Diabetes duration≥10 years,female sex,standing-or supine systolic blood pressure(SBP)≥140 mmHg,and cholesterol≥6.22 mmol/L were risk factors for DR in logistic regression analysis(odds ratio=2.118,1.520,1.417,1.881,and 1.591,respectively).A greater severity of chronic kidney disease(CKD)or hemoglobin A 1c increased the risk of DR in patients with T2DM.In the decision tree model,diabetes duration was the primary risk factor affecting the occurrence of DR in patients with T2DM,followed by CKD stage,supine SBP,standing SBP,and body mass index(BMI).DR classification outcomes were obtained by evaluating standing SBP or BMI according to the CKD stage for diabetes duration<10 years and by evaluating CKD stage according to the supine SBP for diabetes duration≥10 years.CONCLUSION Based on the simple and intuitive decision tree model constructed in this study,DR classification outcomes were easily obtained by evaluating diabetes duration,CKD stage,supine or standing SBP,and BMI.
文摘Decision trees induction algorithms have been used for classification in a wide range of application domains. In the process of constructing a tree, the criteria of selecting test attributes will influence the classification accuracy of the tree.In this paper,the degree of dependency of decision attribute to condition attribute,based on rough set theory,is used as a heuristic for selecting the attribute that will best separate the samples into individual classes.The result of an example shows that compared with the entropy-based approach,our approach is a better way to select nodes for constructing decision trees.
文摘Under the modern education system of China, the annual scholarship evaluation is a vital thing for many of the collegestudents. This paper adopts the classification algorithm of decision tree C4.5 based on the bettering of ID3 algorithm and constructa data set of the scholarship evaluation system through the analysis of the related attributes in scholarship evaluation information.And also having found some factors that plays a significant role in the growing up of the college students through analysis and re-search of moral education, intellectural education and culture&PE.
基金supported by the Key Project-subtopic of thea13th FiveYear PlanoMilitary Logistics Service Research of China (BWS16J006)。
文摘Background:The vital signs of trauma patients are complex and changeable,and the prediction of blood transfusion demand mainly depends on doctors'experience and trauma scoring system;therefore,it cannot be accurately predicted.In this study,a machine learning decision tree algorithm[classification and regression tree(CRT)and eXtreme gradient boosting(XGBoost)]was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors.Methods:A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database.The vital signs,laboratory examination parameters and blood transfusion volume were used as variables,and the non-invasive parameters and all(non-invasive+invasive)parameters were used to construct an intelligent prediction model for red blood cell(RBC)demand by logistic regression(LR),CRT and XGBoost.The prediction accuracy of the model was compared with the area under curve(AUC).Results:For non-invasive parameters,the LR method was the best,with an AUC of 0.72[95%confidence interval(CI)0.657–0.775],which was higher than the CRT(AUC 0.69,95%CI 0.633–0.751)and the XGBoost(AUC 0.71,95%CI 0.654–0.756)(P<0.05).The trauma location and shock index are important prediction parameters.For all the prediction parameters,XGBoost was the best,with an AUC of 0.94(95%CI 0.893–0.981),which was higher than the LR(AUC 0.80,95%CI 0.744–0.850)and the CRT(AUC 0.82,95%CI 0.779–0.853)(P<0.05).Haematocrit(Hct)is an important prediction parameter.Conclusions:The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method.It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment,so as to improve the success rate of patient treatment.
基金This paper is partially supported by the National Natural Science Foundation of China under Grant No.61972053 and No.61603054by the Scientific Research Foundation of Liaoning Education Department under Grant No.LQ2019016,No.LJ2019015by the Natural Science Foundation of Liaoning Province,China under Grant No.2019-ZD-0505.
文摘With the development of the service-oriented computing(SOC),web service has an important and popular solution for the design of the application system to various enterprises.Nowadays,the numerous web services are provided by the service providers on the network,it becomes difficult for users to select the best reliable one from a large number of services with the same function.So it is necessary to design feasible selection strategies to provide users with the reliable services.Most existing methods attempt to select services according to accurate predictions for the quality of service(QoS)values.However,because the network and user needs are dynamic,it is almost impossible to accurately predict the QoS values.Furthermore,accurate prediction is generally time-consuming.This paper proposes a service decision tree based post-pruning prediction approach.This paper first defines the five reliability levels for measuring the reliability of services.By analyzing the quality data of service from the network,the proposed method can generate the training set and convert them into the service decision tree model.Using the generated model and the given predicted services,the proposed method classifies the service to the corresponding reliability level after discretizing the continuous attribute of service.Moreover,this paper applies the post-pruning strategy to optimize the generated model for avoiding the over-fitting.Experimental results show that the proposed method is effective in predicting the service reliability.
文摘The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools.This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach.A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe,where the condition of tool is monitored using vibration characteristics.The vibration signals for conditions such as heathy,damaged,thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system.The descriptive statistical features were extracted from the acquired vibration signal using the feature extraction techniques.The extracted statistical features were selected using a feature selection process through J48 decision tree algorithm.The selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive analysis.The decision tree model produced the classification accuracy as 94.78%with five selected features.The developed fuzzy model produced the classification accuracy as 94.02%with five membership functions.Hence,the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.
基金This work is funded in part by the National Nature Science Foundation of China(File Nos.61872451 and 61872452)in part by the Science and Technology Development Fund,Macao SAR(File Nos.0098/2018/A3 and 0076/2019/A2).Li Feng is the corresponding author.
文摘The registration rate of freshmen has been a great concern at many colleges and universities,particularly private institutions.Traditionally,there are two inquiry methods:telephone and tuition-payment-status.Unfortunately,the former is not only time-consuming but also suffers from the fact that many students tend to keep their choices secret.On the other hand,the latter is not always feasible because only few students are willing to pay their university tuition fees in advance.It is often believed that it is impossible to predict incoming freshmen’s choice of university due to the large amount of subjectivity.However,if we look at the two major considerations a potential freshman contemplates in making a choice,such as the geographical location of the university in relation to his/her home town,and testimonies about of that college life experience by previous graduates,we believe it is possible to predict future enrollment decisions.This paper is the first to find a way to solve the problem of predicting the choice of university a freshman will attend.Our contributions include the following:1.we present a dataset on freshman registration;2.we propose a decision-tree-based approach for freshman registration prediction.Study results show that freshman registration is predictable.
文摘Finding simple, non recursive, base noun phrase is an important step for many natural language processing applications. This paper presents a new corpus based approach using decision tree for that purpose. In contrast to previous methods for Base NP identification, we adopt a decision tree trained from Penn Treebank to identify Base NP. And a self learning mechanism is further integrated into our model. Experimental results show good performances using our method. The method can also be applied to processing of any other language.
文摘Group communication is widely used by most of the emerging network applications like telecommunication,video conferencing,simulation applications,distributed and other interactive systems.Secured group communication plays a vital role in case of providing the integrity,authenticity,confidentiality,and availability of the message delivered among the group members with respect to communicate securely between the inter group or else within the group.In secure group communications,the time cost associated with the key updating in the proceedings of the member join and departure is an important aspect of the quality of service,particularly in the large groups with highly active membership.Hence,the paper is aimed to achieve better cost and time efficiency through an improved DC multicast routing protocol which is used to expose the path between the nodes participating in the group communication.During this process,each node constructs an adaptive Ptolemy decision tree for the purpose of generating the contributory key.Each of the node is comprised of three keys which will be exchanged between the nodes for considering the group key for the purpose of secure and cost-efficient group communication.The rekeying process is performed when a member leaves or adds into the group.The performance metrics of novel approach is measured depending on the important factors such as computational and communicational cost,rekeying process and formation of the group.It is concluded from the study that the technique has reduced the computational and communicational cost of the secure group communication when compared to the other existing methods.
基金supported by National Natural Science Foundation of China under Grant 60703013 and 10978011Key Program of National Natural Science Foundation of China under Grant 60932008+1 种基金National Science Fund for Distinguished Young Scholars under Grant 50925625China Postdoctoral Science Foundation.
文摘In many decision making tasks,the features and decision are ordinal.Several ordinal classification learning algorithms have been developed in recent years,it is shown that these algorithms are sensitive to noisy samples and do not work in real-world applications.In this work,we propose a new measure of feature quality, called rank mutual information.Then,we design an ordinal decision tree(REOT) construction technique based on rank mutual information.The theoretic and experimental analysis shows that the proposed algorithm is effective.
文摘The inherently unique qualities of the heart infer the candidacy for the domain of biometrics, which applies physiological attributes to establish the recognition of a person’s identity. The heart’s characteristics can be ascertained by recording the electrical signal activity of the heart through the acquisition of an electrocardiogram (ECG). With the application of machine learning the subject specific ECG signal can be differentiated. However, the process of distinguishing subjects through machine learning may be considered esoteric, especially for contributing subject matter experts external to the domain of machine learning. A resolution to this dilemma is the application of the J48 decision tree available through the Waikato Environment for Knowledge Analysis (WEKA). The J48 decision tree elucidates the machine learning process through a visualized decision tree that attains classification accuracy through the application of thresholds applied to the numeric attributes of the feature set. Additionally, the numeric attributes of the feature set for the application of the J48 decision tree are derived from the temporal organization of the ECG signal maxima and minima for the respective P, Q, R, S, and T waves. The J48 decision tree achieves considerable classification accuracy for the distinction of subjects based on their ECG signal, for which the machine learning model is briskly composed.
文摘So many potential risks can be identifed for application of trenchless technology especially using microtunneling methods.Unexpected changes in ground conditions,such as encountering boulders,tree roots,ground water and man-made structures such as old foundations are the principal geotechnical risks,which affect the selection of an appropriate microtunnel boring machine.On the other hand,the performance of each microtunneling technique will differ while encountering such conditions.Hence,predicting the potential hazards provides a better safety and risk management plan.In this study,a couple of potentially hazardous situation,which are commonly associated with ground conditions,were identifed and investigated.A decision tree aid methodology was proposed based on geotechnical risk assessment for selection of proper microtunneling technique.Based on the approach the most appropriate microtunneling technique has the minimum risk level either before or after hazards mitigation measures.In order to check the effciency of the approach in practice,selection of microtunnel boring machine for Hamadan sewerage pipeline project was evaluated.Accordingly,an earth pressure balance(EPB)MTBM was selected for the project.
