Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security systems.The main aim of the research is to develop a system able to predicate and ...Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security systems.The main aim of the research is to develop a system able to predicate and classify gender,age,and accent.So,a newsystem calledClassifyingVoice Gender,Age,and Accent(CVGAA)is proposed.Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories.It has high precision compared to other algorithms used in this problem,as the adaptive backpropagation algorithm had an accuracy of 98%and the Bagging algorithm had an accuracy of 98.10%in the gender identification data.Bagging has the best accuracy among all algorithms,with 55.39%accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.展开更多
Screening biomolecular markers from high-dimensional biological data is one of the long-standing tasks for biomedical translational research.With its advantages in both feature shrinkage and biological interpretabilit...Screening biomolecular markers from high-dimensional biological data is one of the long-standing tasks for biomedical translational research.With its advantages in both feature shrinkage and biological interpretability,Least Absolute Shrinkage and Selection Operator(LASSO)algorithm is one of the most popular methods for the scenarios of clinical biomarker development.However,in practice,applying LASSO on omics-based data with high dimensions and low-sample size may usually result in an excess number of predictive variables,leading to the overfitting of the model.Here,we present VSOLassoBag,a wrapped LASSO approach by integrating an ensemble learning strategy to help select efficient and stable variables with high confidence from omics-based data.Using a bagging strategy in combination with a parametric method or inflection point search method,VSOLassoBag can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates.The application of VSOLassoBag on both simulation datasets and real-world datasets shows that the algorithm can effectively identify markers for either case-control binary classification or prognosis prediction.In addition,by comparing with multiple existing algorithms,VSOLassoBag shows a comparable performance under different scenarios while resulting in fewer features than others.In summary,VSOLassoBag,which is available at https://seqworld.com/VSOLassoBag/under the GPL v3 license,provides an alternative strategy for selecting reliable biomarkers from high-dimensional omics data.For user’s convenience,we implement VSOLassoBag as an R package that provides multithreading computing configurations.展开更多
文摘Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security systems.The main aim of the research is to develop a system able to predicate and classify gender,age,and accent.So,a newsystem calledClassifyingVoice Gender,Age,and Accent(CVGAA)is proposed.Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories.It has high precision compared to other algorithms used in this problem,as the adaptive backpropagation algorithm had an accuracy of 98%and the Bagging algorithm had an accuracy of 98.10%in the gender identification data.Bagging has the best accuracy among all algorithms,with 55.39%accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.
基金supported by National Key R&D Program of China(2021YFA1302100 to Q.Z)the National Natural Science Foundation of China(82172861 to Q.Z)+1 种基金Guangdong Basic and Applied Basic Research Foundation(2021A1515011743 to Q.Z)National Key Clinical Discipline(to D.Z)。
文摘Screening biomolecular markers from high-dimensional biological data is one of the long-standing tasks for biomedical translational research.With its advantages in both feature shrinkage and biological interpretability,Least Absolute Shrinkage and Selection Operator(LASSO)algorithm is one of the most popular methods for the scenarios of clinical biomarker development.However,in practice,applying LASSO on omics-based data with high dimensions and low-sample size may usually result in an excess number of predictive variables,leading to the overfitting of the model.Here,we present VSOLassoBag,a wrapped LASSO approach by integrating an ensemble learning strategy to help select efficient and stable variables with high confidence from omics-based data.Using a bagging strategy in combination with a parametric method or inflection point search method,VSOLassoBag can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates.The application of VSOLassoBag on both simulation datasets and real-world datasets shows that the algorithm can effectively identify markers for either case-control binary classification or prognosis prediction.In addition,by comparing with multiple existing algorithms,VSOLassoBag shows a comparable performance under different scenarios while resulting in fewer features than others.In summary,VSOLassoBag,which is available at https://seqworld.com/VSOLassoBag/under the GPL v3 license,provides an alternative strategy for selecting reliable biomarkers from high-dimensional omics data.For user’s convenience,we implement VSOLassoBag as an R package that provides multithreading computing configurations.
