This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for ar...This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.展开更多
TESL and TEFL vary in many aspects, including learning motivation, language environment, input and output of target language and target language teachers. It is significant for college English teachers to learn these ...TESL and TEFL vary in many aspects, including learning motivation, language environment, input and output of target language and target language teachers. It is significant for college English teachers to learn these differences in setting teaching objectives, finding effective ways of teaching, adapting proper teaching methods and reaching a preferable teaching result.展开更多
Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.Th...Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.展开更多
The Chinese ancient literature has a long history.More and more foreign friends give strong interest into reading and learning it.However,not all the foreign people are proficient at classical Chinese,the metre and rh...The Chinese ancient literature has a long history.More and more foreign friends give strong interest into reading and learning it.However,not all the foreign people are proficient at classical Chinese,the metre and rhyme of Chinese poems,thus,translators have to translate these words into English or other languages,which brought a discussion about whether the Chinese ancient poems can be translated or not.This paper starts from this very point,to analyze current situation of the research on Chinese ancient literatures translation,taking Chinese ancient poems as examples,to discuss this topic,and to give opinions and possible solutions.展开更多
Code-switching is an important domain in the sociolingusitics. Since the 1970 s, lots of linguists and experts has attached great importance to it. This paper is a tentative study of code-switching in the teaching Eng...Code-switching is an important domain in the sociolingusitics. Since the 1970 s, lots of linguists and experts has attached great importance to it. This paper is a tentative study of code-switching in the teaching English as a second language(TESL) from such aspects: the review of code-switching, principles adhered to the code-switching, factors which leads to the code-switching, and attitudes and functions of code-switching in the TESL.展开更多
Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine lea...Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine learning,which can lead to incorrect conclusions and outcomes.Many scientists and researchers including Nirmal et al.are unaware that feature importances in machine learning in general are model-specific and do not necessarily represent true associations between the target and features.展开更多
文摘This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.
文摘TESL and TEFL vary in many aspects, including learning motivation, language environment, input and output of target language and target language teachers. It is significant for college English teachers to learn these differences in setting teaching objectives, finding effective ways of teaching, adapting proper teaching methods and reaching a preferable teaching result.
基金supported by the Science and Technology Project of China Southern Power Grid(GZHKJXM20210043-080041KK52210002).
文摘Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.
文摘The Chinese ancient literature has a long history.More and more foreign friends give strong interest into reading and learning it.However,not all the foreign people are proficient at classical Chinese,the metre and rhyme of Chinese poems,thus,translators have to translate these words into English or other languages,which brought a discussion about whether the Chinese ancient poems can be translated or not.This paper starts from this very point,to analyze current situation of the research on Chinese ancient literatures translation,taking Chinese ancient poems as examples,to discuss this topic,and to give opinions and possible solutions.
文摘Code-switching is an important domain in the sociolingusitics. Since the 1970 s, lots of linguists and experts has attached great importance to it. This paper is a tentative study of code-switching in the teaching English as a second language(TESL) from such aspects: the review of code-switching, principles adhered to the code-switching, factors which leads to the code-switching, and attitudes and functions of code-switching in the TESL.
文摘Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine learning,which can lead to incorrect conclusions and outcomes.Many scientists and researchers including Nirmal et al.are unaware that feature importances in machine learning in general are model-specific and do not necessarily represent true associations between the target and features.
