There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them.However,the volume of internet data is beyond the...There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them.However,the volume of internet data is beyond the processing capabilities of the current internet infrastructure.Therefore,engineering works using technology to organize and analyze information and extract useful information are interesting in both industry and academia.The goal of this paper is to explore the entity relationship based on deep learning,introduce semantic knowledge by using the prepared language model,develop an advanced entity relationship information extraction method by combining Robustly Optimized BERT Approach(RoBERTa)and multi-task learning,and combine the intelligent characters in the field of linguistic,called Robustly Optimized BERT Approach+Multi-Task Learning(RoBERTa+MTL).To improve the effectiveness of model interaction,multi-task teaching is used to implement the observation information of auxiliary tasks.Experimental results show that our method has achieved an accuracy of 88.95 entity relationship extraction,and a further it has achieved 86.35%of accuracy after being combined with multi-task learning.展开更多
Objective: The purposes of this study were to analyze the influencing factors of self-directed learning readiness(SDLR) of nursing undergraduates and explore the impacts of learning attitude and self-efficacy on nursi...Objective: The purposes of this study were to analyze the influencing factors of self-directed learning readiness(SDLR) of nursing undergraduates and explore the impacts of learning attitude and self-efficacy on nursing undergraduates.Methods: A total of 500 nursing undergraduates were investigated in Tianjin, with the Chinese version of SDLR scale, learning attitude questionnaire of nursing college students, academic self-efficacy scale, and the general information questionnaire.Result: The score of SDLR was 149.99±15.73. Multiple stepwise regressions indicated that academic self-efficacy, learning attitude, attitudes to major of nursing, and level of learning difficulties were major influential factors and explained 48.1% of the variance in SDLR of nursing interns.Conclusions: The score of SDLR of nursing undergraduates is not promising. It is imperative to correct students' learning attitude, improve self-efficacy, and adopt appropriate teaching model to improve SDLR.展开更多
This paper sets out to review the relationship of learner autonomy and motivation in English learning based on previous theoretical and empirical studies. This study can be of great help for learners to realize the gr...This paper sets out to review the relationship of learner autonomy and motivation in English learning based on previous theoretical and empirical studies. This study can be of great help for learners to realize the great importance of learner autonomy and learning motivation, making them more autonomous, motivated and successful in English learning.展开更多
Language and culture are closely related with each other: language is the carrier and a fundamental part of culture, while culture has an intrinsic and indispensable impact on language and is also reflected in languag...Language and culture are closely related with each other: language is the carrier and a fundamental part of culture, while culture has an intrinsic and indispensable impact on language and is also reflected in language. So if in the language learning and teaching, attention is only paid to the linguistic forms but the relationship between language and culture is ignored, then the learner's linguistic ability will not be equivalent to his or her social and cultural communication competence. Especially in the times of globalization with increasing transna tional and cross-cultural communication, the knowledge of cultural background is the must prerequisite for not only the avoidance of cul tural "conflict" but the success of communication. Based on the studies of several cross-cultural communication cases and the analysis of the relationship between language and culture, including the famous Sapir-Whorf hypothesis, this paper is aimed to analyze the intrinsic and in dispensable relationship between language learning and culture learning.展开更多
Suicide has become a critical concern,necessitating the development of effective preventative strategies.Social media platforms offer a valuable resource for identifying signs of suicidal ideation.Despite progress in ...Suicide has become a critical concern,necessitating the development of effective preventative strategies.Social media platforms offer a valuable resource for identifying signs of suicidal ideation.Despite progress in detecting suicidal ideation on social media,accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge.To tackle this,we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships(TCNN-SN).This model enhances predictive performance by leveraging social network relationship features and applying correction factors within a weighted linear fusion framework.It is specifically designed to identify key individuals who can help uncover hidden suicidal users and clusters.Our model,assessed using the bespoke dataset and benchmarked against alternative classification approaches,demonstrates superior accuracy,F1-score and AUC metrics,achieving 88.57%,88.75%and 94.25%,respectively,outperforming traditional TextCNN models by 12.18%,10.84%and 10.85%.We assert that our methodology offers a significant advancement in the predictive identification of individuals at risk,thereby contributing to the prevention and reduction of suicide incidences.展开更多
Global learning professional competencies (GLPCs) are essential for college students to be able to address the impact of globalization in the 21st century. Organizations and society-at-large look to higher education t...Global learning professional competencies (GLPCs) are essential for college students to be able to address the impact of globalization in the 21st century. Organizations and society-at-large look to higher education to prepare college students with GLPCs. In addition, there is a body of literature that suggest personal tacit knowledge enhance GLPCs. However, researchers have done little from an empirical perspective to determine the relationship between the use of P-T K and enhancement of GLPCs, hence the purpose of this study. The statistical results revealed significant correlations, p st century knowledge society through use of P-T K.展开更多
In this paper,we propose a hybrid model aiming to map the input noise vector to the label of the generated image by the generative adversarial network(GAN).This model mainly consists of a pre-trained deep convolution ...In this paper,we propose a hybrid model aiming to map the input noise vector to the label of the generated image by the generative adversarial network(GAN).This model mainly consists of a pre-trained deep convolution generative adversarial network(DCGAN)and a classifier.By using the model,we visualize the distribution of two-dimensional input noise,leading to a specific type of the generated image after each training epoch of GAN.The visualization reveals the distribution feature of the input noise vector and the performance of the generator.With this feature,we try to build a guided generator(GG)with the ability to produce a fake image we need.Two methods are proposed to build GG.One is the most significant noise(MSN)method,and the other utilizes labeled noise.The MSN method can generate images precisely but with less variations.In contrast,the labeled noise method has more variations but is slightly less stable.Finally,we propose a criterion to measure the performance of the generator,which can be used as a loss function to effectively train the network.展开更多
Objective:The study is to analyze the influence of parent-child relationship on pupils’learning motivation,and to explore the mediating mechanism of teacher-student relationship in parent-child relationship and learn...Objective:The study is to analyze the influence of parent-child relationship on pupils’learning motivation,and to explore the mediating mechanism of teacher-student relationship in parent-child relationship and learning motivation.Method:This study conducted a questionnaire survey on 213 pupils in Grades 5 and 6 in two schools in Beijing using Pianta’s teacher-student relationship scale revised by Qu,Dornbush’s parent-child intimacy scale revised by Zhang and the learning motivation scale adapted by Hu.Results:Gender,grade,whether they are the only child and to be a class cadre or not show significant differences in some dimensions of parent-child relationship,teacher-student relationship and learning motivation.The total scores of parent-child relationship,teacher-student relationship and learning motivation are positively correlated,and some sub dimensions are also significantly correlated.Parent-child relationship and teacher-student relationship have a significant positive predictive effect on learning motivation,and parent-child relationship has a significant positive predictive effect on teacher-student relationship.Teacher-student relationship plays a mediating role in the influence of parent-child relationship on learning motivation.Conclusions:Parent-child relationship can promote the relationship between teachers and students,and then enhance pupils’learning motivation.展开更多
In nature,the properties of matter are ultimately governed by the electronic structures.Quantum chemistry(QC)at electronic level matches well with a few simple physical assumptions in solving simple problems.To date,m...In nature,the properties of matter are ultimately governed by the electronic structures.Quantum chemistry(QC)at electronic level matches well with a few simple physical assumptions in solving simple problems.To date,machine learning(ML)algorithm has been migrated to this field to simplify calculations and improve fidelity.This review introduces the basic information on universal electron structures of emerging energy materials and ML algorithms involved in the prediction of material properties.Then,the structure-property relationships based on ML algorithm and QC theory are reviewed.Especially,the summary of recently reported applications on classifying crystal structure,modeling electronic structure,optimizing experimental method,and predicting performance is provided.Last,an outlook on ML assisted QC calculation towards identifying emerging energy materials is also presented.展开更多
Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials a...Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.展开更多
The research of human facial age estimation(AE)has attracted increasing attention for its wide applications.Up to date,a number of models have been constructed or employed to perform AE.Although the goal of AE can be ...The research of human facial age estimation(AE)has attracted increasing attention for its wide applications.Up to date,a number of models have been constructed or employed to perform AE.Although the goal of AE can be achieved by either classification or regression,the latter based methods generally yield more promising results because the continuity and gradualness of human aging can naturally be preserved in age regression.However,the neighbor-similarity and ordinality of age labels are not taken into account yet.To overcome this issue,the cumulative attribute(CA)coding was introduced.Although such age label relationships can be parameterized via CA coding,the potential relationships behind age features are not incorporated to estimate age.To this end,in this paper we propose to perform AE by encoding the potential age feature relationships with CA coding via an implicit modeling strategy.Besides that,we further extend our model to gender-aware AE by taking into account gender variance in aging process.Finally,we experimentally validate the superiority of the proposed methodology.展开更多
Supportive learning plays a substantial role in providing a quality edu-cation system.The evaluation of students’performance impacts their deeper insight into the subject knowledge.Specifically,it is essential to mai...Supportive learning plays a substantial role in providing a quality edu-cation system.The evaluation of students’performance impacts their deeper insight into the subject knowledge.Specifically,it is essential to maintain the baseline foundation for building a broader understanding of their careers.This research concentrates on establishing the students’knowledge relationship even in reduced samples.Here,Synthetic Minority Oversampling TEchnique(SMOTE)technique is used for pre-processing the missing value in the provided input dataset to enhance the prediction accuracy.When the initial processing is not done substantially,it leads to misleading prediction accuracy.This research concentrates on modelling an efficient classifier model to predict students’perfor-mance.Generally,the online available student dataset comprises a lesser amount of sample,and k-fold cross-validation is performed to balance the dataset.Then,the relationship among the students’performance(features)is measured using the auto-encoder.The stacked Long Short Term Memory(s-LSTM)is used to learn the previous feedback connection.The stacked model handles the provided data and the data sequence for understanding the long-term dependencies.The simula-tion is done in the MATLAB 2020a environment,and the proposed model shows a better trade-off than the existing approaches.Some evaluation metrics like pre-diction accuracy,sensitivity,specificity,AUROC,F1-score and recall are evalu-ated using the proposed model.The performance of the s?LSTM model is compared with existing approaches.The proposed model gives 89% accuracy,83% precision,86%recall,and 87%F-score.The proposed model outperforms the existing systems in terms of the earlier metrics.展开更多
In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the d...In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the distribution network.To effectively improve the lean management of LVSA,the paper proposes an identification method for the UTR based on Local Selective Combination in ParallelOutlier Ensembles algorithm(LSCP).Firstly,the voltage data is reconstructed based on the information entropy to highlight the differences in between.Then,the LSCP algorithmcombines four base outlier detection algorithms,namely Isolation Forest(I-Forest),One-Class Support VectorMachine(OC-SVM),Copula-Based Outlier Detection(COPOD)and Local Outlier Factor(LOF),to construct the identification model of UTR.This model can accurately detect users’differences in voltage data,and identify users with wrong UTR.Meanwhile,the key input parameter of the LSCP algorithm is determined automatically through the line loss rate,and the influence of artificial settings on recognition accuracy can be reduced.Finally,thismethod is verified in the actual LVSA where the recall and precision rates are 100%compared with othermethods.Furthermore,the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed.The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually.And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity,which improves the stability and accuracy of UTR identification in LVSA.展开更多
Most methods for classifying hyperspectral data only consider the local spatial relation-ship among samples,ignoring the important non-local topological relationship.However,the non-local topological relationship is b...Most methods for classifying hyperspectral data only consider the local spatial relation-ship among samples,ignoring the important non-local topological relationship.However,the non-local topological relationship is better at representing the structure of hyperspectral data.This paper proposes a deep learning model called Topology and semantic information fusion classification network(TSFnet)that incorporates a topology structure and semantic information transmis-sion network to accurately classify traditional Chinese medicine in hyperspectral images.TSFnet uses a convolutional neural network(CNN)to extract features and a graph convolution network(GCN)to capture potential topological relationships among different types of Chinese herbal medicines.The results show that TSFnet outperforms other state-of-the-art deep learning classification algorithms in two different scenarios of herbal medicine datasets.Additionally,the proposed TSFnet model is lightweight and can be easily deployed for mobile herbal medicine classification.展开更多
This paper creates a LM (Levenberg-Marquardt) algorithm model which is appropriate to solve the problem about weights value of feedforward neural network. On the base of this model, we provide two applications in the ...This paper creates a LM (Levenberg-Marquardt) algorithm model which is appropriate to solve the problem about weights value of feedforward neural network. On the base of this model, we provide two applications in the oilfield production. Firstly, we simulated the functional relationships between the petrophysical and electrical properties of the rock by neural networks model, and studied oil saturation. Under the precision of data is confirmed, this method can reduce the number of experiments. Secondly, we simulated the relationships between investment and income by the neural networks model, and studied invest saturation point and income growth rate. It is very significant to guide the investment decision. The research result shows that the model is suitable for the modeling and identification of nonlinear systems due to the great fit characteristic of neural network and very fast convergence speed of LM algorithm.展开更多
文摘There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them.However,the volume of internet data is beyond the processing capabilities of the current internet infrastructure.Therefore,engineering works using technology to organize and analyze information and extract useful information are interesting in both industry and academia.The goal of this paper is to explore the entity relationship based on deep learning,introduce semantic knowledge by using the prepared language model,develop an advanced entity relationship information extraction method by combining Robustly Optimized BERT Approach(RoBERTa)and multi-task learning,and combine the intelligent characters in the field of linguistic,called Robustly Optimized BERT Approach+Multi-Task Learning(RoBERTa+MTL).To improve the effectiveness of model interaction,multi-task teaching is used to implement the observation information of auxiliary tasks.Experimental results show that our method has achieved an accuracy of 88.95 entity relationship extraction,and a further it has achieved 86.35%of accuracy after being combined with multi-task learning.
文摘Objective: The purposes of this study were to analyze the influencing factors of self-directed learning readiness(SDLR) of nursing undergraduates and explore the impacts of learning attitude and self-efficacy on nursing undergraduates.Methods: A total of 500 nursing undergraduates were investigated in Tianjin, with the Chinese version of SDLR scale, learning attitude questionnaire of nursing college students, academic self-efficacy scale, and the general information questionnaire.Result: The score of SDLR was 149.99±15.73. Multiple stepwise regressions indicated that academic self-efficacy, learning attitude, attitudes to major of nursing, and level of learning difficulties were major influential factors and explained 48.1% of the variance in SDLR of nursing interns.Conclusions: The score of SDLR of nursing undergraduates is not promising. It is imperative to correct students' learning attitude, improve self-efficacy, and adopt appropriate teaching model to improve SDLR.
文摘This paper sets out to review the relationship of learner autonomy and motivation in English learning based on previous theoretical and empirical studies. This study can be of great help for learners to realize the great importance of learner autonomy and learning motivation, making them more autonomous, motivated and successful in English learning.
文摘Language and culture are closely related with each other: language is the carrier and a fundamental part of culture, while culture has an intrinsic and indispensable impact on language and is also reflected in language. So if in the language learning and teaching, attention is only paid to the linguistic forms but the relationship between language and culture is ignored, then the learner's linguistic ability will not be equivalent to his or her social and cultural communication competence. Especially in the times of globalization with increasing transna tional and cross-cultural communication, the knowledge of cultural background is the must prerequisite for not only the avoidance of cul tural "conflict" but the success of communication. Based on the studies of several cross-cultural communication cases and the analysis of the relationship between language and culture, including the famous Sapir-Whorf hypothesis, this paper is aimed to analyze the intrinsic and in dispensable relationship between language learning and culture learning.
基金funded by Outstanding Youth Team Project of Central Universities(QNTD202308).
文摘Suicide has become a critical concern,necessitating the development of effective preventative strategies.Social media platforms offer a valuable resource for identifying signs of suicidal ideation.Despite progress in detecting suicidal ideation on social media,accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge.To tackle this,we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships(TCNN-SN).This model enhances predictive performance by leveraging social network relationship features and applying correction factors within a weighted linear fusion framework.It is specifically designed to identify key individuals who can help uncover hidden suicidal users and clusters.Our model,assessed using the bespoke dataset and benchmarked against alternative classification approaches,demonstrates superior accuracy,F1-score and AUC metrics,achieving 88.57%,88.75%and 94.25%,respectively,outperforming traditional TextCNN models by 12.18%,10.84%and 10.85%.We assert that our methodology offers a significant advancement in the predictive identification of individuals at risk,thereby contributing to the prevention and reduction of suicide incidences.
文摘Global learning professional competencies (GLPCs) are essential for college students to be able to address the impact of globalization in the 21st century. Organizations and society-at-large look to higher education to prepare college students with GLPCs. In addition, there is a body of literature that suggest personal tacit knowledge enhance GLPCs. However, researchers have done little from an empirical perspective to determine the relationship between the use of P-T K and enhancement of GLPCs, hence the purpose of this study. The statistical results revealed significant correlations, p st century knowledge society through use of P-T K.
基金supported by Shenzhen Science and Technology Innovation Committee under Grants No. JCYJ20170306170559215 and No. JCYJ20180302153918689。
文摘In this paper,we propose a hybrid model aiming to map the input noise vector to the label of the generated image by the generative adversarial network(GAN).This model mainly consists of a pre-trained deep convolution generative adversarial network(DCGAN)and a classifier.By using the model,we visualize the distribution of two-dimensional input noise,leading to a specific type of the generated image after each training epoch of GAN.The visualization reveals the distribution feature of the input noise vector and the performance of the generator.With this feature,we try to build a guided generator(GG)with the ability to produce a fake image we need.Two methods are proposed to build GG.One is the most significant noise(MSN)method,and the other utilizes labeled noise.The MSN method can generate images precisely but with less variations.In contrast,the labeled noise method has more variations but is slightly less stable.Finally,we propose a criterion to measure the performance of the generator,which can be used as a loss function to effectively train the network.
基金Collaborative education project of industry university cooperation of the Ministry of Education of China:Research on practice teaching of the competency of future mental health teachers based on virtual reality(No.202102080005).
文摘Objective:The study is to analyze the influence of parent-child relationship on pupils’learning motivation,and to explore the mediating mechanism of teacher-student relationship in parent-child relationship and learning motivation.Method:This study conducted a questionnaire survey on 213 pupils in Grades 5 and 6 in two schools in Beijing using Pianta’s teacher-student relationship scale revised by Qu,Dornbush’s parent-child intimacy scale revised by Zhang and the learning motivation scale adapted by Hu.Results:Gender,grade,whether they are the only child and to be a class cadre or not show significant differences in some dimensions of parent-child relationship,teacher-student relationship and learning motivation.The total scores of parent-child relationship,teacher-student relationship and learning motivation are positively correlated,and some sub dimensions are also significantly correlated.Parent-child relationship and teacher-student relationship have a significant positive predictive effect on learning motivation,and parent-child relationship has a significant positive predictive effect on teacher-student relationship.Teacher-student relationship plays a mediating role in the influence of parent-child relationship on learning motivation.Conclusions:Parent-child relationship can promote the relationship between teachers and students,and then enhance pupils’learning motivation.
基金supported by the National Natural Science Foundation of China(grant number 51872157)Shenzhen Technical Plan Project(grant number KQJSCX20160226191136 and JCYJ20170412170911187)Research Grants Council of the Hong Kong Special Administrative Region,China[grant number PF17-10186]。
文摘In nature,the properties of matter are ultimately governed by the electronic structures.Quantum chemistry(QC)at electronic level matches well with a few simple physical assumptions in solving simple problems.To date,machine learning(ML)algorithm has been migrated to this field to simplify calculations and improve fidelity.This review introduces the basic information on universal electron structures of emerging energy materials and ML algorithms involved in the prediction of material properties.Then,the structure-property relationships based on ML algorithm and QC theory are reviewed.Especially,the summary of recently reported applications on classifying crystal structure,modeling electronic structure,optimizing experimental method,and predicting performance is provided.Last,an outlook on ML assisted QC calculation towards identifying emerging energy materials is also presented.
基金Project support by the National Natural Science Foundation of China(Grant Nos.11674237 and 51602211)the National Key Research and Development Program of China(Grant No.2016YFB0700700)+1 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),ChinaChina Post-doctoral Foundation(Grant No.7131705619).
文摘Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.
基金This work was partially supported by the National Natural Science Foundation of China(61702273 and 61472186)the Natural Science Foundation of Jiangsu Province(BK20170956)+1 种基金the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(17KJB520022)the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions,and the Startup Foundation for Talents of Nanjing University of Information Science and Technology.
文摘The research of human facial age estimation(AE)has attracted increasing attention for its wide applications.Up to date,a number of models have been constructed or employed to perform AE.Although the goal of AE can be achieved by either classification or regression,the latter based methods generally yield more promising results because the continuity and gradualness of human aging can naturally be preserved in age regression.However,the neighbor-similarity and ordinality of age labels are not taken into account yet.To overcome this issue,the cumulative attribute(CA)coding was introduced.Although such age label relationships can be parameterized via CA coding,the potential relationships behind age features are not incorporated to estimate age.To this end,in this paper we propose to perform AE by encoding the potential age feature relationships with CA coding via an implicit modeling strategy.Besides that,we further extend our model to gender-aware AE by taking into account gender variance in aging process.Finally,we experimentally validate the superiority of the proposed methodology.
文摘Supportive learning plays a substantial role in providing a quality edu-cation system.The evaluation of students’performance impacts their deeper insight into the subject knowledge.Specifically,it is essential to maintain the baseline foundation for building a broader understanding of their careers.This research concentrates on establishing the students’knowledge relationship even in reduced samples.Here,Synthetic Minority Oversampling TEchnique(SMOTE)technique is used for pre-processing the missing value in the provided input dataset to enhance the prediction accuracy.When the initial processing is not done substantially,it leads to misleading prediction accuracy.This research concentrates on modelling an efficient classifier model to predict students’perfor-mance.Generally,the online available student dataset comprises a lesser amount of sample,and k-fold cross-validation is performed to balance the dataset.Then,the relationship among the students’performance(features)is measured using the auto-encoder.The stacked Long Short Term Memory(s-LSTM)is used to learn the previous feedback connection.The stacked model handles the provided data and the data sequence for understanding the long-term dependencies.The simula-tion is done in the MATLAB 2020a environment,and the proposed model shows a better trade-off than the existing approaches.Some evaluation metrics like pre-diction accuracy,sensitivity,specificity,AUROC,F1-score and recall are evalu-ated using the proposed model.The performance of the s?LSTM model is compared with existing approaches.The proposed model gives 89% accuracy,83% precision,86%recall,and 87%F-score.The proposed model outperforms the existing systems in terms of the earlier metrics.
文摘In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the distribution network.To effectively improve the lean management of LVSA,the paper proposes an identification method for the UTR based on Local Selective Combination in ParallelOutlier Ensembles algorithm(LSCP).Firstly,the voltage data is reconstructed based on the information entropy to highlight the differences in between.Then,the LSCP algorithmcombines four base outlier detection algorithms,namely Isolation Forest(I-Forest),One-Class Support VectorMachine(OC-SVM),Copula-Based Outlier Detection(COPOD)and Local Outlier Factor(LOF),to construct the identification model of UTR.This model can accurately detect users’differences in voltage data,and identify users with wrong UTR.Meanwhile,the key input parameter of the LSCP algorithm is determined automatically through the line loss rate,and the influence of artificial settings on recognition accuracy can be reduced.Finally,thismethod is verified in the actual LVSA where the recall and precision rates are 100%compared with othermethods.Furthermore,the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed.The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually.And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity,which improves the stability and accuracy of UTR identification in LVSA.
基金supported by the National Natural Science Foundation of China(No.62001023)Beijing Natural Science Foundation(No.JQ20021)。
文摘Most methods for classifying hyperspectral data only consider the local spatial relation-ship among samples,ignoring the important non-local topological relationship.However,the non-local topological relationship is better at representing the structure of hyperspectral data.This paper proposes a deep learning model called Topology and semantic information fusion classification network(TSFnet)that incorporates a topology structure and semantic information transmis-sion network to accurately classify traditional Chinese medicine in hyperspectral images.TSFnet uses a convolutional neural network(CNN)to extract features and a graph convolution network(GCN)to capture potential topological relationships among different types of Chinese herbal medicines.The results show that TSFnet outperforms other state-of-the-art deep learning classification algorithms in two different scenarios of herbal medicine datasets.Additionally,the proposed TSFnet model is lightweight and can be easily deployed for mobile herbal medicine classification.
基金This work is supported by the National Natural Science Foundation of China (61070109) and Opening Project of Beijing Key Laboratory of Mobile Computing and Pervasive Device.
文摘This paper creates a LM (Levenberg-Marquardt) algorithm model which is appropriate to solve the problem about weights value of feedforward neural network. On the base of this model, we provide two applications in the oilfield production. Firstly, we simulated the functional relationships between the petrophysical and electrical properties of the rock by neural networks model, and studied oil saturation. Under the precision of data is confirmed, this method can reduce the number of experiments. Secondly, we simulated the relationships between investment and income by the neural networks model, and studied invest saturation point and income growth rate. It is very significant to guide the investment decision. The research result shows that the model is suitable for the modeling and identification of nonlinear systems due to the great fit characteristic of neural network and very fast convergence speed of LM algorithm.