The prosperity of deep learning has revolutionized many machine learning tasks(such as image recognition,natural language processing,etc.).With the widespread use of autonomous sensor networks,the Internet of Things,a...The prosperity of deep learning has revolutionized many machine learning tasks(such as image recognition,natural language processing,etc.).With the widespread use of autonomous sensor networks,the Internet of Things,and crowd sourcing to monitor real-world processes,the volume,diversity,and veracity of spatial-temporal data are expanding rapidly.However,traditional methods have their limitation in coping with spatial-temporal dependencies,which either incorporate too much data from weakly connected locations or ignore the relationships between those interrelated but geographically separated regions.In this paper,a novel deep learning model(termed RF-GWN)is proposed by combining Random Forest(RF)and Graph WaveNet(GWN).In RF-GWN,a new adaptive weight matrix is formulated by combining Variable Importance Measure(VIM)of RF with the long time series feature extraction ability of GWN in order to capture potential spatial dependencies and extract long-term dependencies from the input data.Furthermore,two experiments are conducted on two real-world datasets with the purpose of predicting traffic flow and groundwater level.Baseline models are implemented by Diffusion Convolutional Recurrent Neural Network(DCRNN),Spatial-Temporal GCN(ST-GCN),and GWN to verify the effectiveness of the RF-GWN.The Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE)are selected as performance criteria.The results show that the proposed model can better capture the spatial-temporal relationships,the prediction performance on the METR-LA dataset is slightly improved,and the index of the prediction task on the PEMS-BAY dataset is significantly improved.These improvements are extended to the groundwater dataset,which can effectively improve the prediction accuracy.Thus,the applicability and effectiveness of the proposed model RF-GWN in both traffic flow and groundwater level prediction are demonstrated.展开更多
Depression is a prevalent mental health issue affecting individuals of all age groups globally.Similar to other mental health disorders,diagnosing depression presents significant challenges for medical practitioners a...Depression is a prevalent mental health issue affecting individuals of all age groups globally.Similar to other mental health disorders,diagnosing depression presents significant challenges for medical practitioners and clinical experts,primarily due to societal stigma and a lack of awareness and acceptance.Although medical interventions such as therapies,medications,and brain stimulation therapy provide hope for treatment,there is still a gap in the efficient detection of depression.Traditional methods,like in-person therapies,are both time-consuming and labor-intensive,emphasizing the necessity for technological assistance,especially through Artificial Intelligence.Alternative to this,in most cases it has been diagnosed through questionnaire-based mental status assessments.However,this method often produces inconsistent and inaccurate results.Additionally,there is currently a lack of a comprehensive diagnostic framework that could be effective achieving accurate and robust diagnostic outcomes.For a considerable time,researchers have sought methods to identify symptoms of depression through individuals’speech and responses,leveraging automation systems and computer technology.This research proposed MDD which composed of multimodal data collection,preprocessing,and feature extraction(utilizing the T5 model for text features and the WaveNet model for speech features).Canonical Correlation Analysis(CCA)is then used to create correlated projections of text and audio features,followed by feature fusion through concatenation.Finally,depression detection is performed using a neural network with a sigmoid output layer.The proposed model achieved remarkable performance,on the Distress Analysis Interview Corpus-Wizard(DAIC-WOZ)dataset,it attained an accuracy of 92.75%,precision of 92.05%,and recall of 92.22%.For the E-DAIC dataset,it achieved an accuracy of 91.74%,precision of 90.35%,and recall of 90.95%.Whereas,on CD-III dataset(Custom Dataset for Depression),the model demonstrated an accuracy of 93.05%,precision of 92.12%,and recall of 92.85%.These results underscore the model’s robust capability in accurately diagnosing depressive disorder,demonstrating the efficacy of advanced feature extraction methods and improved classification algorithm.展开更多
文摘The prosperity of deep learning has revolutionized many machine learning tasks(such as image recognition,natural language processing,etc.).With the widespread use of autonomous sensor networks,the Internet of Things,and crowd sourcing to monitor real-world processes,the volume,diversity,and veracity of spatial-temporal data are expanding rapidly.However,traditional methods have their limitation in coping with spatial-temporal dependencies,which either incorporate too much data from weakly connected locations or ignore the relationships between those interrelated but geographically separated regions.In this paper,a novel deep learning model(termed RF-GWN)is proposed by combining Random Forest(RF)and Graph WaveNet(GWN).In RF-GWN,a new adaptive weight matrix is formulated by combining Variable Importance Measure(VIM)of RF with the long time series feature extraction ability of GWN in order to capture potential spatial dependencies and extract long-term dependencies from the input data.Furthermore,two experiments are conducted on two real-world datasets with the purpose of predicting traffic flow and groundwater level.Baseline models are implemented by Diffusion Convolutional Recurrent Neural Network(DCRNN),Spatial-Temporal GCN(ST-GCN),and GWN to verify the effectiveness of the RF-GWN.The Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE)are selected as performance criteria.The results show that the proposed model can better capture the spatial-temporal relationships,the prediction performance on the METR-LA dataset is slightly improved,and the index of the prediction task on the PEMS-BAY dataset is significantly improved.These improvements are extended to the groundwater dataset,which can effectively improve the prediction accuracy.Thus,the applicability and effectiveness of the proposed model RF-GWN in both traffic flow and groundwater level prediction are demonstrated.
文摘Depression is a prevalent mental health issue affecting individuals of all age groups globally.Similar to other mental health disorders,diagnosing depression presents significant challenges for medical practitioners and clinical experts,primarily due to societal stigma and a lack of awareness and acceptance.Although medical interventions such as therapies,medications,and brain stimulation therapy provide hope for treatment,there is still a gap in the efficient detection of depression.Traditional methods,like in-person therapies,are both time-consuming and labor-intensive,emphasizing the necessity for technological assistance,especially through Artificial Intelligence.Alternative to this,in most cases it has been diagnosed through questionnaire-based mental status assessments.However,this method often produces inconsistent and inaccurate results.Additionally,there is currently a lack of a comprehensive diagnostic framework that could be effective achieving accurate and robust diagnostic outcomes.For a considerable time,researchers have sought methods to identify symptoms of depression through individuals’speech and responses,leveraging automation systems and computer technology.This research proposed MDD which composed of multimodal data collection,preprocessing,and feature extraction(utilizing the T5 model for text features and the WaveNet model for speech features).Canonical Correlation Analysis(CCA)is then used to create correlated projections of text and audio features,followed by feature fusion through concatenation.Finally,depression detection is performed using a neural network with a sigmoid output layer.The proposed model achieved remarkable performance,on the Distress Analysis Interview Corpus-Wizard(DAIC-WOZ)dataset,it attained an accuracy of 92.75%,precision of 92.05%,and recall of 92.22%.For the E-DAIC dataset,it achieved an accuracy of 91.74%,precision of 90.35%,and recall of 90.95%.Whereas,on CD-III dataset(Custom Dataset for Depression),the model demonstrated an accuracy of 93.05%,precision of 92.12%,and recall of 92.85%.These results underscore the model’s robust capability in accurately diagnosing depressive disorder,demonstrating the efficacy of advanced feature extraction methods and improved classification algorithm.
