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
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
Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective i...Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective intervention of college students’mental health status.Therefore,this article constructs an artificial intelligence-based psychological health and intervention system for college students.Firstly,this article obtains psychological health testing data of college students through online platforms or on-campus system design,distribution of questionnaires,feedback from close contacts of students,and internal campus resources.Then,the architecture of a mental health monitoring system is designed.Its overall architecture includes a data collection layer,a data processing layer,a decision tree algorithm layer,and an evaluation display layer.The system uses the C4.5 decision tree algorithm to calculate the information gain of the processed sample data,selects the attribute with the maximum value,and constructs a decision tree structure model to evaluate students’mental health.Finally,this article studies the evaluation of students’mental health status by combining multidimensional information such as the SCL-90 scale,self-assessment scale,and student behavior data.Experimental data shows that the system can effectively identify students’mental health problems and provide precise intervention measures based on their situation,with high accuracy and practicality.展开更多
Every second, a large volume of useful data is created in social media about the various kind of online purchases and in another forms of reviews. Particularly, purchased products review data is enormously growing in ...Every second, a large volume of useful data is created in social media about the various kind of online purchases and in another forms of reviews. Particularly, purchased products review data is enormously growing in different database repositories every day. Most of the review data are useful to new customers for theier further purchases as well as existing companies to view customers feedback about various products. Data Mining and Machine Leaning techniques are familiar to analyse such kind of data to visualise and know the potential use of the purchased items through online. The customers are making quality of products through their sentiments about the purchased items from different online companies. In this research work, it is analysed sentiments of Headphone review data, which is collected from online repositories. For the analysis of Headphone review data, some of the Machine Learning techniques like Support Vector Machines, Naive Bayes, Decision Trees and Random Forest Algorithms and a Hybrid method are applied to find the quality via the customers’ sentiments. The accuracy and performance of the taken algorithms are also analysed based on the three types of sentiments such as positive, negative and neutral.展开更多
Some techniques and methods for deriving water information from SPOT-4(XI) image were investigated and discussed in this paper. An algorithm of decision tree (DT) classification which includes several classifiers base...Some techniques and methods for deriving water information from SPOT-4(XI) image were investigated and discussed in this paper. An algorithm of decision tree (DT) classification which includes several classifiers based on the spectral responding characteristics of water bodies and other objects, was developed and put forward to delineate water bodies. Another algorithm of decision tree classification based on both spectral characteristics and auxiliary information of DEM and slope (DTDS) was also designed for water bodies extraction. In addition, supervised classification method of maximum likelyhood classification (MLC), and unsupervised method of interactive self organizing dada analysis technique (ISODATA) were used to extract waterbodies for comparison purpose. An index was designed and used to assess the accuracy of different methods adopted in the research. Results have shown that water extraction accuracy was variable with respect to the various techniques applied. It was low using ISODATA, very high using DT algorithm and much higher using both DTDS and MLC.展开更多
Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is v...Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.展开更多
Purpose As a high-intensity hadron accelerator-based user facility,minimizing the radiation dose induced by uncontrollable beam loss at the China Spallation Neutron Source(CSNS)is crucial for manual maintenance by ope...Purpose As a high-intensity hadron accelerator-based user facility,minimizing the radiation dose induced by uncontrollable beam loss at the China Spallation Neutron Source(CSNS)is crucial for manual maintenance by operators.A correlation has been observed between the beam transmission efficiency of the Rapid Cycling Synchrotron(RCS)and outdoor temperature.Given that the only RCS components located outdoors are the resonant power supply systems,further investigation into this relationship is necessary to identify opportunities for improving beam transmission efficiency during operation.Methods To address the nonlinear relationship between the resonant power supply variables(amplitude and phase)and beam transmission efficiency,this study employs machine learning techniques.Decision Tree-based algorithms,including Extra Trees,Gradient Boosting Decision Trees(GBDT),Random Forest,and LightGBM,are used for the analysis.These methods enable a statistical examination of the most influential factors affecting beam transmission efficiency in the RCS,with a focus on the amplitude and phase of the resonant power supply.Results The analysis highlights the significance of two amplitudes from higher-order harmonic components in affecting transmission efficiency.It is suggested that increasing the RCS transmission efficiency can be achieved by adjusting these critical amplitudes.Conclusion Adjusting the K3 amplitude of QPS02 and the K2 amplitude of BPS01 has the potential to significantly improve beam transmission efficiency in the RCS,contributing to more efficient operations.展开更多
Three common species of Miniopterus fuliginosus,M.magnater and M.pusillus are known to inhabit China.However,M.fuliginosus and M.magnater are so similar in external morphology as to pose great challenges for accurate ...Three common species of Miniopterus fuliginosus,M.magnater and M.pusillus are known to inhabit China.However,M.fuliginosus and M.magnater are so similar in external morphology as to pose great challenges for accurate classification.Furthermore,taxonomic statuses,distribution ranges and taxonomic keys of these three species have remained controversial.For addressing these outstanding issues,the authors integrated molecular phylogenetic analyses,ensemble species distribution models(ESDMs),multiple morphological comparisons and decision tree algorithms for reassessing their taxonomy and distribution in China.Mitochondrial cytochrome c oxidase subunit I(COI)gene phylogeny revealed three distinct monophyletic groups corresponding to M.fuliginosus,M.magnater and M.pusillus.And the observed distribution patterns indicated M.fuliginosus had a broad distribution across China while M.magnater and M.pusillus exhibited a more restricted distribution,overlapping with M.fuliginosus in South China.And cranial morphometry indicated M.magnater was slightly larger than M.fuliginosus and significantly larger than M.pusillus.Also three-dimensional(3D)skull geomorphometry uncovered distinct features for each species in rostrum,braincase,tympanic bullae and mandibular shape.Decision tree algorithms helped to identify forearm length,braincase breadth and width across the third upper molars as three major taxonomic keys for assisting species identification.This study corroborated the importance of integrative approaches for identifying Miniopterus species and validated a methodological approach applicable to other cryptic species complexes.展开更多
This work proposes a hybrid approach for solving traditional flowshop scheduling problems to reduce the makespan （total completion time）. To solve scheduling problems, a combination of Decision Tree （DT） and Scatt...This work proposes a hybrid approach for solving traditional flowshop scheduling problems to reduce the makespan （total completion time）. To solve scheduling problems, a combination of Decision Tree （DT） and Scatter Search （SS） algorithms are used. Initially, the DT is used to generate a seed solution which is then given input to the SS to obtain optimal / near optimal solutions of makespan. The DT used the entropy function to convert the given problem into a tree structured format / set of rules. The SS provides an extensive investigation of the search space through diversification. The advantages of both DT and SS are used to form a hybrid approach. The proposed algorithm is tested with various benchmark datasets available for flowshop scheduling. The statistical results prove that the proposed method is competent and efficient for solving flowshop problems.展开更多
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.
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.
文摘Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective intervention of college students’mental health status.Therefore,this article constructs an artificial intelligence-based psychological health and intervention system for college students.Firstly,this article obtains psychological health testing data of college students through online platforms or on-campus system design,distribution of questionnaires,feedback from close contacts of students,and internal campus resources.Then,the architecture of a mental health monitoring system is designed.Its overall architecture includes a data collection layer,a data processing layer,a decision tree algorithm layer,and an evaluation display layer.The system uses the C4.5 decision tree algorithm to calculate the information gain of the processed sample data,selects the attribute with the maximum value,and constructs a decision tree structure model to evaluate students’mental health.Finally,this article studies the evaluation of students’mental health status by combining multidimensional information such as the SCL-90 scale,self-assessment scale,and student behavior data.Experimental data shows that the system can effectively identify students’mental health problems and provide precise intervention measures based on their situation,with high accuracy and practicality.
文摘Every second, a large volume of useful data is created in social media about the various kind of online purchases and in another forms of reviews. Particularly, purchased products review data is enormously growing in different database repositories every day. Most of the review data are useful to new customers for theier further purchases as well as existing companies to view customers feedback about various products. Data Mining and Machine Leaning techniques are familiar to analyse such kind of data to visualise and know the potential use of the purchased items through online. The customers are making quality of products through their sentiments about the purchased items from different online companies. In this research work, it is analysed sentiments of Headphone review data, which is collected from online repositories. For the analysis of Headphone review data, some of the Machine Learning techniques like Support Vector Machines, Naive Bayes, Decision Trees and Random Forest Algorithms and a Hybrid method are applied to find the quality via the customers’ sentiments. The accuracy and performance of the taken algorithms are also analysed based on the three types of sentiments such as positive, negative and neutral.
文摘Some techniques and methods for deriving water information from SPOT-4(XI) image were investigated and discussed in this paper. An algorithm of decision tree (DT) classification which includes several classifiers based on the spectral responding characteristics of water bodies and other objects, was developed and put forward to delineate water bodies. Another algorithm of decision tree classification based on both spectral characteristics and auxiliary information of DEM and slope (DTDS) was also designed for water bodies extraction. In addition, supervised classification method of maximum likelyhood classification (MLC), and unsupervised method of interactive self organizing dada analysis technique (ISODATA) were used to extract waterbodies for comparison purpose. An index was designed and used to assess the accuracy of different methods adopted in the research. Results have shown that water extraction accuracy was variable with respect to the various techniques applied. It was low using ISODATA, very high using DT algorithm and much higher using both DTDS and MLC.
文摘Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.
文摘Purpose As a high-intensity hadron accelerator-based user facility,minimizing the radiation dose induced by uncontrollable beam loss at the China Spallation Neutron Source(CSNS)is crucial for manual maintenance by operators.A correlation has been observed between the beam transmission efficiency of the Rapid Cycling Synchrotron(RCS)and outdoor temperature.Given that the only RCS components located outdoors are the resonant power supply systems,further investigation into this relationship is necessary to identify opportunities for improving beam transmission efficiency during operation.Methods To address the nonlinear relationship between the resonant power supply variables(amplitude and phase)and beam transmission efficiency,this study employs machine learning techniques.Decision Tree-based algorithms,including Extra Trees,Gradient Boosting Decision Trees(GBDT),Random Forest,and LightGBM,are used for the analysis.These methods enable a statistical examination of the most influential factors affecting beam transmission efficiency in the RCS,with a focus on the amplitude and phase of the resonant power supply.Results The analysis highlights the significance of two amplitudes from higher-order harmonic components in affecting transmission efficiency.It is suggested that increasing the RCS transmission efficiency can be achieved by adjusting these critical amplitudes.Conclusion Adjusting the K3 amplitude of QPS02 and the K2 amplitude of BPS01 has the potential to significantly improve beam transmission efficiency in the RCS,contributing to more efficient operations.
基金the National Natural Sciences Foundation of China(32192421)the Special Grant Foundations for National Science and &Technology Basic Research Program of China(2021FY100303)the DFGP Project of Fauna of Guangdong Province(202115)。
文摘Three common species of Miniopterus fuliginosus,M.magnater and M.pusillus are known to inhabit China.However,M.fuliginosus and M.magnater are so similar in external morphology as to pose great challenges for accurate classification.Furthermore,taxonomic statuses,distribution ranges and taxonomic keys of these three species have remained controversial.For addressing these outstanding issues,the authors integrated molecular phylogenetic analyses,ensemble species distribution models(ESDMs),multiple morphological comparisons and decision tree algorithms for reassessing their taxonomy and distribution in China.Mitochondrial cytochrome c oxidase subunit I(COI)gene phylogeny revealed three distinct monophyletic groups corresponding to M.fuliginosus,M.magnater and M.pusillus.And the observed distribution patterns indicated M.fuliginosus had a broad distribution across China while M.magnater and M.pusillus exhibited a more restricted distribution,overlapping with M.fuliginosus in South China.And cranial morphometry indicated M.magnater was slightly larger than M.fuliginosus and significantly larger than M.pusillus.Also three-dimensional(3D)skull geomorphometry uncovered distinct features for each species in rostrum,braincase,tympanic bullae and mandibular shape.Decision tree algorithms helped to identify forearm length,braincase breadth and width across the third upper molars as three major taxonomic keys for assisting species identification.This study corroborated the importance of integrative approaches for identifying Miniopterus species and validated a methodological approach applicable to other cryptic species complexes.
文摘This work proposes a hybrid approach for solving traditional flowshop scheduling problems to reduce the makespan （total completion time）. To solve scheduling problems, a combination of Decision Tree （DT） and Scatter Search （SS） algorithms are used. Initially, the DT is used to generate a seed solution which is then given input to the SS to obtain optimal / near optimal solutions of makespan. The DT used the entropy function to convert the given problem into a tree structured format / set of rules. The SS provides an extensive investigation of the search space through diversification. The advantages of both DT and SS are used to form a hybrid approach. The proposed algorithm is tested with various benchmark datasets available for flowshop scheduling. The statistical results prove that the proposed method is competent and efficient for solving flowshop problems.
