本文采用基于支持向量机(SVM s)的方法预测了4类含有核心启动子元件的启动子和含有CCAAT-box的启动子。4类核心启动子元件分别是DPE,BRE,TATA-box和Inr。特征提取采用基于位点权重矩阵(PWM s)的程序Promoter C lassifier进行。本文预测...本文采用基于支持向量机(SVM s)的方法预测了4类含有核心启动子元件的启动子和含有CCAAT-box的启动子。4类核心启动子元件分别是DPE,BRE,TATA-box和Inr。特征提取采用基于位点权重矩阵(PWM s)的程序Promoter C lassifier进行。本文预测结果的敏感度,确定度,以及相关系数均高于三种启动子预测方法(PromoterInspec-tor(PI),Promoter 2.0 Pred iction(PP)和Neural Network Promoter Pred iction(NNPP),使敏感度和确定度同时高于0.84,其中TATA-box预测结果可使敏感度和确定度同时高于0.95。展开更多
A substitution on an amino acid sequence can be defined as“intolerant”(non-neutral)or“tolerant”(neutral)according to whether or not it detectably alters protein phenotypes(e.g.,diseases).To guide mutagenesis exper...A substitution on an amino acid sequence can be defined as“intolerant”(non-neutral)or“tolerant”(neutral)according to whether or not it detectably alters protein phenotypes(e.g.,diseases).To guide mutagenesis experiments and elucidate the underlying biological mechanisms,we applied support vector machines(SVMs)to predict protein function changes associated to amino acid substitutions using only sequence information,and cross-validated them on a large dataset extracted from the Protein Mutant Database.展开更多
The interaction between humans and machines has become an issue of concern in recent years.Besides facial expressions or gestures,speech has been evidenced as one of the foremost promising modalities for automatic emo...The interaction between humans and machines has become an issue of concern in recent years.Besides facial expressions or gestures,speech has been evidenced as one of the foremost promising modalities for automatic emotion recognition.Effective computing means to support HCI(Human-Computer Interaction)at a psychological level,allowing PCs to adjust their reactions as per human requirements.Therefore,the recognition of emotion is pivotal in High-level interactions.Each Emotion has distinctive properties that form us to recognize them.The acoustic signal produced for identical expression or sentence changes is essentially a direct result of biophysical changes,(for example,the stress instigated narrowing of the larynx)set off by emotions.This connection between acoustic cues and emotions made Speech Emotion Recognition one of the moving subjects of the emotive computing area.The most motivation behind a Speech Emotion Recognition algorithm is to observe the emotional condition of a speaker from recorded Speech signals.The results from the application of k-NN and OVA-SVM for MFCC features without and with a feature selection approach are presented in this research.The MFCC features from the audio signal were initially extracted to characterize the properties of emotional speech.Secondly,nine basic statistical measures were calculated from MFCC and 117-dimensional features were consequently obtained to train the classifiers for seven different classes(Anger,Happiness,Disgust,Fear,Sadness,Disgust,Boredom and Neutral)of emotions.Next,Classification was done in four steps.First,all the 117-features are classified using both classifiers.Second,the best classifier was found and then features were scaled to[-1,1]and classified.In the third step,the with or without feature scaling which gives better performance was derived from the results of the second step and the classification was done for each of the basic statistical measures separately.Finally,in the fourth step,the combination of statistical measures which gives better performance was derived using the forward feature selection method Experiments were carried out using k-NN with different k values and a linear OVA-based SVM classifier with different optimal values.Berlin emotional speech database for the German language was utilized for testing the planned methodology and recognition rates as high as 60%accomplished for the recognition of emotion from voice signal for the set of statistical measures(median,maximum,mean,Inter-quartile range,skewness).OVA-SVM performs better than k-NN and the use of the feature selection technique gives a high rate.展开更多
为提高柴油机装配质量和冷试性能,基于柴油机装配冷试基础数据集,选取加州大学欧文分校(University of California Irvine,UCI)机器学习资料库标准数据集中的Seeds、Wine、Wdbc三种数据集,对比支持向量机(support vector machines,SVM)...为提高柴油机装配质量和冷试性能,基于柴油机装配冷试基础数据集,选取加州大学欧文分校(University of California Irvine,UCI)机器学习资料库标准数据集中的Seeds、Wine、Wdbc三种数据集,对比支持向量机(support vector machines,SVM)模型、组合智能算法改进后SVM模型、Transformer模型应用于冷试异常数据的分析效果。结果表明:SVM、改进后SVM,Transformer模型对正常数据和异常数据分类的准确率分别为85.20%、92.54%、97.94%;相比SVM、改进SVM模型,Transformer模型的分类准确率有较大的提高,可用于分析参数异常;排气压力与转矩关系密切,排气压力较大造成转矩增大;排气门开启时间过长导致进气真空度异常,验证了Transformer模型对发动机装配异常识别方法的有效性。展开更多
In this paper, we present a new challenging task for emotion analysis, namely emotion cause extraction.In this task, we focus on the detection of emotion cause a.k.a the reason or the stimulant of an emotion, rather t...In this paper, we present a new challenging task for emotion analysis, namely emotion cause extraction.In this task, we focus on the detection of emotion cause a.k.a the reason or the stimulant of an emotion, rather than the regular emotion classification or emotion component extraction. Since there is no open dataset for this task available, we first designed and annotated an emotion cause dataset which follows the scheme of W3 C Emotion Markup Language. We then present an emotion cause detection method by using event extraction framework,where a tree structure-based representation method is used to represent the events. Since the distribution of events is imbalanced in the training data, we propose an under-sampling-based bagging algorithm to solve this problem. Even with a limited training set, the proposed approach may still extract sufficient features for analysis by a bagging of multi-kernel based SVMs method. Evaluations show that our approach achieves an F-measure 7.04%higher than the state-of-the-art methods.展开更多
文摘本文采用基于支持向量机(SVM s)的方法预测了4类含有核心启动子元件的启动子和含有CCAAT-box的启动子。4类核心启动子元件分别是DPE,BRE,TATA-box和Inr。特征提取采用基于位点权重矩阵(PWM s)的程序Promoter C lassifier进行。本文预测结果的敏感度,确定度,以及相关系数均高于三种启动子预测方法(PromoterInspec-tor(PI),Promoter 2.0 Pred iction(PP)和Neural Network Promoter Pred iction(NNPP),使敏感度和确定度同时高于0.84,其中TATA-box预测结果可使敏感度和确定度同时高于0.95。
基金supported by the National Natural Science Foundation of China(30870827)
文摘A substitution on an amino acid sequence can be defined as“intolerant”(non-neutral)or“tolerant”(neutral)according to whether or not it detectably alters protein phenotypes(e.g.,diseases).To guide mutagenesis experiments and elucidate the underlying biological mechanisms,we applied support vector machines(SVMs)to predict protein function changes associated to amino acid substitutions using only sequence information,and cross-validated them on a large dataset extracted from the Protein Mutant Database.
文摘The interaction between humans and machines has become an issue of concern in recent years.Besides facial expressions or gestures,speech has been evidenced as one of the foremost promising modalities for automatic emotion recognition.Effective computing means to support HCI(Human-Computer Interaction)at a psychological level,allowing PCs to adjust their reactions as per human requirements.Therefore,the recognition of emotion is pivotal in High-level interactions.Each Emotion has distinctive properties that form us to recognize them.The acoustic signal produced for identical expression or sentence changes is essentially a direct result of biophysical changes,(for example,the stress instigated narrowing of the larynx)set off by emotions.This connection between acoustic cues and emotions made Speech Emotion Recognition one of the moving subjects of the emotive computing area.The most motivation behind a Speech Emotion Recognition algorithm is to observe the emotional condition of a speaker from recorded Speech signals.The results from the application of k-NN and OVA-SVM for MFCC features without and with a feature selection approach are presented in this research.The MFCC features from the audio signal were initially extracted to characterize the properties of emotional speech.Secondly,nine basic statistical measures were calculated from MFCC and 117-dimensional features were consequently obtained to train the classifiers for seven different classes(Anger,Happiness,Disgust,Fear,Sadness,Disgust,Boredom and Neutral)of emotions.Next,Classification was done in four steps.First,all the 117-features are classified using both classifiers.Second,the best classifier was found and then features were scaled to[-1,1]and classified.In the third step,the with or without feature scaling which gives better performance was derived from the results of the second step and the classification was done for each of the basic statistical measures separately.Finally,in the fourth step,the combination of statistical measures which gives better performance was derived using the forward feature selection method Experiments were carried out using k-NN with different k values and a linear OVA-based SVM classifier with different optimal values.Berlin emotional speech database for the German language was utilized for testing the planned methodology and recognition rates as high as 60%accomplished for the recognition of emotion from voice signal for the set of statistical measures(median,maximum,mean,Inter-quartile range,skewness).OVA-SVM performs better than k-NN and the use of the feature selection technique gives a high rate.
文摘为提高柴油机装配质量和冷试性能,基于柴油机装配冷试基础数据集,选取加州大学欧文分校(University of California Irvine,UCI)机器学习资料库标准数据集中的Seeds、Wine、Wdbc三种数据集,对比支持向量机(support vector machines,SVM)模型、组合智能算法改进后SVM模型、Transformer模型应用于冷试异常数据的分析效果。结果表明:SVM、改进后SVM,Transformer模型对正常数据和异常数据分类的准确率分别为85.20%、92.54%、97.94%;相比SVM、改进SVM模型,Transformer模型的分类准确率有较大的提高,可用于分析参数异常;排气压力与转矩关系密切,排气压力较大造成转矩增大;排气门开启时间过长导致进气真空度异常,验证了Transformer模型对发动机装配异常识别方法的有效性。
基金supported by the National Natural Science Foundation of China(Nos.61370165,U1636103,and 61632011)Shenzhen Foundational Research Funding(Nos.JCYJ20150625142543470 and JCYJ20170307150024907)Guangdong Provincial Engineering Technology Research Center for Data Science(No.2016KF09)
文摘In this paper, we present a new challenging task for emotion analysis, namely emotion cause extraction.In this task, we focus on the detection of emotion cause a.k.a the reason or the stimulant of an emotion, rather than the regular emotion classification or emotion component extraction. Since there is no open dataset for this task available, we first designed and annotated an emotion cause dataset which follows the scheme of W3 C Emotion Markup Language. We then present an emotion cause detection method by using event extraction framework,where a tree structure-based representation method is used to represent the events. Since the distribution of events is imbalanced in the training data, we propose an under-sampling-based bagging algorithm to solve this problem. Even with a limited training set, the proposed approach may still extract sufficient features for analysis by a bagging of multi-kernel based SVMs method. Evaluations show that our approach achieves an F-measure 7.04%higher than the state-of-the-art methods.
