The prediction of mild cognitive impairment or Alzheimer’s disease(AD)has gained the attention of huge researchers as the disease occurrence is increasing,and there is a need for earlier prediction.Regrettably,due to...The prediction of mild cognitive impairment or Alzheimer’s disease(AD)has gained the attention of huge researchers as the disease occurrence is increasing,and there is a need for earlier prediction.Regrettably,due to the highdimensionality nature of neural data and the least available samples,modelling an efficient computer diagnostic system is highly solicited.Learning approaches,specifically deep learning approaches,are essential in disease prediction.Deep Learning(DL)approaches are successfully demonstrated for their higher-level performance in various fields like medical imaging.A novel 3D-Convolutional Neural Network(3D-CNN)architecture is proposed to predict AD with Magnetic resonance imaging(MRI)data.The proposed model predicts the AD occurrence while the existing approaches lack prediction accuracy and perform binary classification.The proposed prediction model is validated using the Alzheimer’s disease Neuro-Imaging Initiative(ADNI)data.The outcomes demonstrate that the anticipated model attains superior prediction accuracy and works better than the brain-image dataset’s general approaches.The predicted model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is adaptive.Keras’experimentation is carried out,and the model’s superiority is compared with various advanced approaches for multi-level classification.The proposed model gives better prediction accuracy,precision,recall,and F-measure than other systems like Long Short Term Memory-Recurrent Neural Networks(LSTM-RNN),Stacked Autoencoder with Deep Neural Networks(SAE-DNN),Deep Convolutional Neural Networks(D-CNN),Two Dimensional Convolutional Neural Networks(2D-CNN),Inception-V4,ResNet,and Two Dimensional Convolutional Neural Networks(3D-CNN).展开更多
Large high-dimensional data have posed great challenges to existing algorithms for frequent itemsets mining.To solve the problem,a hybrid method,consisting of a novel row enumeration algorithm and a column enumeration...Large high-dimensional data have posed great challenges to existing algorithms for frequent itemsets mining.To solve the problem,a hybrid method,consisting of a novel row enumeration algorithm and a column enumeration algorithm,is proposed.The intention of the hybrid method is to decompose the mining task into two subtasks and then choose appropriate algorithms to solve them respectively.The novel algorithm,i.e.,Inter-transaction is based on the characteristic that there are few common items between or among long transactions.In addition,an optimization technique is adopted to improve the performance of the intersection of bit-vectors.Experiments on synthetic data show that our method achieves high performance in large high-dimensional data.展开更多
Over the past decade,bidding in electricity mar kets has attracted widespread attention.Reinforcement learning(RL)has been widely used for electricity market bidding as a powerful artificial intelligence(AI)tool to ma...Over the past decade,bidding in electricity mar kets has attracted widespread attention.Reinforcement learning(RL)has been widely used for electricity market bidding as a powerful artificial intelligence(AI)tool to make decisions under real-world uncertainties.However,current RL-based bidding methods mostly employ low-dimensional bids(LDBs),which sig nificantly diverge from the N price-power pairs commonly used in current electricity markets.The N-pair bid format is denoted as high-dimensional bid(HDB)format,which has not been ful ly integrated into the existing RL-based bidding methods.The loss of flexibility of current RL-based bidding methods could greatly limit the bidding profits and make it difficult to address the increasing uncertainties caused by renewable energy genera tion.In this paper,we propose a framework for fully utilizing HDBs in RL-based bidding methods.First,we employ a special type of neural network called the neural network supply func tion(NNSF)to generate HDBs in the form of N price-power pairs.Second,we embed the NNSF into a Markov decision pro cess(MDP)to make it compatible with most existing RL algo rithms.Finally,the experiments on energy storage systems(ES Ss)in the Pennsylvania-New Jersey-Maryland(PJM)real-time electricity market show that the proposed bidding method with HDBs can increase the bidding flexibility,thereby increasing the profits of state-of-the-art RL-based bidding methods.展开更多
It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a ...It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Information Gain-Support Vector Machine （IG-SVM） in this study. IG was initially employed to filter irrelevant and redundant genes. Then, further removal of redundant genes was performed using SVM to eliminate the noise in the datasets more effectively. Finally, the informative genes selected by IG-SVM served as the input for the LIBSVM classifier. Compared to other related algorithms, IG-SVM showed the highest classification accuracy and superior performance as evaluated using five cancer gene expression datasets based on a few selected genes. As an example, IG-SVM achieved a classification accuracy of 90.32% for colon cancer, which is difficult to be accurately classified, only based on three genes including CSRP1, MYLg, and GUCA2B.展开更多
Quantile regression links the whole distribution of an outcome to the covariates of interest and has become an important alternative to commonly used regression models.However,the presence of censored data such as sur...Quantile regression links the whole distribution of an outcome to the covariates of interest and has become an important alternative to commonly used regression models.However,the presence of censored data such as survival time,often the main endpoint in cancer studies,has hampered the use of quantile regression techniques because of the incompleteness of data.With the advent of the precision medicine era and availability of high throughput data,quantile regression with high-dimensional predictors has attracted much attention and provided added insight compared to traditional regression approaches.This paper provides a practical guide for using quantile regression for right censored outcome data with covariates of low-or highdimensionality.We frame our discussion using a dataset from the Boston Lung Cancer Survivor Cohort,a hospital-based prospective cohort study,with the goals of broadening the scope of cancer research,maximizing the utility of collected data,and offering useful statistical alternatives.We use quantile regression to identify clinical and molecular predictors,for example CpG methylation sites,associated with high-risk lung cancer patients,for example those with short survival.展开更多
We analyze the convergence of the weighted nonlocal Laplacian(WNLL)on the high dimensional randomly distributed point cloud.Our analysis reveals the importance of the scaling weight,µ∼|P|/|S|with|P|and|S|being t...We analyze the convergence of the weighted nonlocal Laplacian(WNLL)on the high dimensional randomly distributed point cloud.Our analysis reveals the importance of the scaling weight,µ∼|P|/|S|with|P|and|S|being the number of entire and labeled data,respectively,in WNLL.The established result gives a theoretical foundation of the WNLL for high dimensional data interpolation.展开更多
文摘The prediction of mild cognitive impairment or Alzheimer’s disease(AD)has gained the attention of huge researchers as the disease occurrence is increasing,and there is a need for earlier prediction.Regrettably,due to the highdimensionality nature of neural data and the least available samples,modelling an efficient computer diagnostic system is highly solicited.Learning approaches,specifically deep learning approaches,are essential in disease prediction.Deep Learning(DL)approaches are successfully demonstrated for their higher-level performance in various fields like medical imaging.A novel 3D-Convolutional Neural Network(3D-CNN)architecture is proposed to predict AD with Magnetic resonance imaging(MRI)data.The proposed model predicts the AD occurrence while the existing approaches lack prediction accuracy and perform binary classification.The proposed prediction model is validated using the Alzheimer’s disease Neuro-Imaging Initiative(ADNI)data.The outcomes demonstrate that the anticipated model attains superior prediction accuracy and works better than the brain-image dataset’s general approaches.The predicted model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is adaptive.Keras’experimentation is carried out,and the model’s superiority is compared with various advanced approaches for multi-level classification.The proposed model gives better prediction accuracy,precision,recall,and F-measure than other systems like Long Short Term Memory-Recurrent Neural Networks(LSTM-RNN),Stacked Autoencoder with Deep Neural Networks(SAE-DNN),Deep Convolutional Neural Networks(D-CNN),Two Dimensional Convolutional Neural Networks(2D-CNN),Inception-V4,ResNet,and Two Dimensional Convolutional Neural Networks(3D-CNN).
基金The work was supported in part by Research Fund for the Doctoral Program of Higher Education of China(No.20060255006)
文摘Large high-dimensional data have posed great challenges to existing algorithms for frequent itemsets mining.To solve the problem,a hybrid method,consisting of a novel row enumeration algorithm and a column enumeration algorithm,is proposed.The intention of the hybrid method is to decompose the mining task into two subtasks and then choose appropriate algorithms to solve them respectively.The novel algorithm,i.e.,Inter-transaction is based on the characteristic that there are few common items between or among long transactions.In addition,an optimization technique is adopted to improve the performance of the intersection of bit-vectors.Experiments on synthetic data show that our method achieves high performance in large high-dimensional data.
基金supported by the Science and Technology Project of the China Southern Power Grid(No.090008KC23020006).
文摘Over the past decade,bidding in electricity mar kets has attracted widespread attention.Reinforcement learning(RL)has been widely used for electricity market bidding as a powerful artificial intelligence(AI)tool to make decisions under real-world uncertainties.However,current RL-based bidding methods mostly employ low-dimensional bids(LDBs),which sig nificantly diverge from the N price-power pairs commonly used in current electricity markets.The N-pair bid format is denoted as high-dimensional bid(HDB)format,which has not been ful ly integrated into the existing RL-based bidding methods.The loss of flexibility of current RL-based bidding methods could greatly limit the bidding profits and make it difficult to address the increasing uncertainties caused by renewable energy genera tion.In this paper,we propose a framework for fully utilizing HDBs in RL-based bidding methods.First,we employ a special type of neural network called the neural network supply func tion(NNSF)to generate HDBs in the form of N price-power pairs.Second,we embed the NNSF into a Markov decision pro cess(MDP)to make it compatible with most existing RL algo rithms.Finally,the experiments on energy storage systems(ES Ss)in the Pennsylvania-New Jersey-Maryland(PJM)real-time electricity market show that the proposed bidding method with HDBs can increase the bidding flexibility,thereby increasing the profits of state-of-the-art RL-based bidding methods.
基金supported by the National Natural Science Foundation of China(Grant No.61672386)Humanities and Social Sciences Planning Project of Ministry of Education,China(Grant No.16YJAZH071)+1 种基金Anhui Provincial Natural Science Foundation of China(Grant No.1708085MF142)the Natural Science Research Key Project of Anhui Colleges,China(Grant No.KJ2014A266)
文摘It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Information Gain-Support Vector Machine （IG-SVM） in this study. IG was initially employed to filter irrelevant and redundant genes. Then, further removal of redundant genes was performed using SVM to eliminate the noise in the datasets more effectively. Finally, the informative genes selected by IG-SVM served as the input for the LIBSVM classifier. Compared to other related algorithms, IG-SVM showed the highest classification accuracy and superior performance as evaluated using five cancer gene expression datasets based on a few selected genes. As an example, IG-SVM achieved a classification accuracy of 90.32% for colon cancer, which is difficult to be accurately classified, only based on three genes including CSRP1, MYLg, and GUCA2B.
基金This study was supported by the National Institute of Health(U01CA209414).
文摘Quantile regression links the whole distribution of an outcome to the covariates of interest and has become an important alternative to commonly used regression models.However,the presence of censored data such as survival time,often the main endpoint in cancer studies,has hampered the use of quantile regression techniques because of the incompleteness of data.With the advent of the precision medicine era and availability of high throughput data,quantile regression with high-dimensional predictors has attracted much attention and provided added insight compared to traditional regression approaches.This paper provides a practical guide for using quantile regression for right censored outcome data with covariates of low-or highdimensionality.We frame our discussion using a dataset from the Boston Lung Cancer Survivor Cohort,a hospital-based prospective cohort study,with the goals of broadening the scope of cancer research,maximizing the utility of collected data,and offering useful statistical alternatives.We use quantile regression to identify clinical and molecular predictors,for example CpG methylation sites,associated with high-risk lung cancer patients,for example those with short survival.
基金Research supported by NSFC Grant 12071244NSF DMS-1924935.
文摘We analyze the convergence of the weighted nonlocal Laplacian(WNLL)on the high dimensional randomly distributed point cloud.Our analysis reveals the importance of the scaling weight,µ∼|P|/|S|with|P|and|S|being the number of entire and labeled data,respectively,in WNLL.The established result gives a theoretical foundation of the WNLL for high dimensional data interpolation.
