Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants(WWTPs).However,some water quality metrics are not measurable in...Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants(WWTPs).However,some water quality metrics are not measurable in real time,thus influencing the judgment of the operators and may increase energy consumption and carbon emission.One of the solutions is using a soft-sensor prediction technique.This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit(BiGRU)combined with Gaussian Progress Regression(GPR)optimized by Tree-structured Parzen Estimator(TPE).TPE automatically optimizes the hyperparameters of BiGRU,and BiGRU is trained to obtain the point prediction with GPR for the interval prediction.Then,a case study applying this prediction method for an actual anaerobic process(2500 m^(3)/d)is carried out.Results show that TPE effectively optimizes the hyperparameters of BiGRU.For point prediction of CODeff and biogas yield,R^(2)values of BiGRU,which are 0.973 and 0.939,respectively,are increased by 1.03%–7.61%and 1.28%–10.33%,compared with those of other models,and the valid prediction interval can be obtained.Besides,the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation.It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.展开更多
Background:Coronary heart disease(CHD)remains a prominent cause of mortality globally,necessitating early and accurate detection methods.Traditional diagnostic approaches can be invasive,costly,and time-consuming,nece...Background:Coronary heart disease(CHD)remains a prominent cause of mortality globally,necessitating early and accurate detection methods.Traditional diagnostic approaches can be invasive,costly,and time-consuming,necessitating the need for more efficient alternatives.This aimed to optimize the Light Gradient-Boosting Machine(LightGBM)algorithm to enhance its performance and accuracy in the early detection of CHD,providing a reliable,cost-effective,and non-invasive diagnostic tool.Methods:The Framingham Heart Study(FHS)dataset publicly available on Kaggle was used in this study.Multiple Imputations by Chained Equations(MICE)were applied separately to the training and testing sets to handle missing data.Borderline-SMOTE(Synthetic Minority Over-sampling Technique)was used on the training set to balance the dataset.The LightGBM algorithm was selected for its efficiency in classification tasks,and Bayesian Optimization with Tree-structured Parzen Estimator(TPE)was employed to fine-tune its hyperparameters.The optimized LightGBM model was trained and evaluated using metrics such as accuracy,precision,and AUC-ROC on the test set,with cross-validation to ensure robustness and generalizability.Findings:The optimized LightGBM model showed significant improvement in early CHD detection.The baseline LightGBM model with dropped missing values had an accuracy of 0.8333,sensitivity of 0.1081,precision of 0.3429,F1 score of 0.1644,and AUC of 0.6875.With MICE imputation,performance improved to an accuracy of 0.9399,sensitivity of 0.6693,precision of 0.9043,F1 score of 0.7692,and AUC of 0.9457.The combined approach of Borderline-SMOTE,MICE imputation,and TPE for LightGBM achieved an accuracy of 0.9882,sensitivity of 0.9370,precision of 0.9835,F1 score of 0.9597,and AUC of 0.9963,indicating a highly effective and robust model.Interpretation:The optimized model demonstrated outstanding performance in early CHD detection.The study’s strengths include its comprehensive approach to addressing missing data and class imbalance and the fine-tuning of hyperparameters through Bayesian Optimization.However,there is a need to test with other datasets for its generalizability to be well-established.This study provides a strong framework for early CHD detection,improving clinical practice by allowing for more precise and dependable diagnostics and effective interventions.展开更多
With the recent advancements of deep learning-based methods in image classification,the requirement of a huge amount of training data is inevitable to avoid overfitting problems.Moreover,supervised deep learning model...With the recent advancements of deep learning-based methods in image classification,the requirement of a huge amount of training data is inevitable to avoid overfitting problems.Moreover,supervised deep learning models require labelled datasets for train-ing.Preparing such a huge amount of labelled data requires considerable human effort and time.In this scenario,self-supervised models are becoming popular because of their ability to learn even from unlabelled datasets.However,the efficient transfer of knowledge learned by self-supervised models into a target task,is an unsolved problem.This paper proposes a method for the efficient transfer of know-ledge learned by a self-supervised model,into a target task.Hyperparameters such as the number of layers,the number of units in each layer,learning rate,and dropout are automatically tuned in these fully connected(FC)layers using a Bayesian optimization technique called the tree-structured parzen estimator(TPE)approach algorithm.To evaluate the performance of the proposed method,state-of-the-art self-supervised models such as SimClr and SWAV are used to extract the learned features.Experiments are carried out on the CIFAR-10,CIFAR-100,and Tiny ImageNet datasets.The proposed method outperforms the baseline approach with margins of 2.97%,2.45%,and 0.91%for the CIFAR-100,Tiny ImageNet,and CIFAR-10 datasets,respectively.展开更多
Proton-exchange membrane fuel cells (PEMFCs) are critical components of zero-emission electro-hydrogen generators. Accurate performance prediction is vital to the optimal operation management and preventive maintenanc...Proton-exchange membrane fuel cells (PEMFCs) are critical components of zero-emission electro-hydrogen generators. Accurate performance prediction is vital to the optimal operation management and preventive maintenance of these generators. Polarization curve remains one of the most important features representing the performance of PEMFCs in terms of efficiency and durability. However, predicting the polarization curve is not trivial as PEMFCs involve complex electrochemical reactions that feature multiple nonlinear relationships between the operating variables as inputs and the voltage as outputs. Herein, we present an artificial-intelligence-based approach for predicting the PEMFCs’ performance. In that way, we propose first an explainable solution for selecting the relevant features based on kernel principal component analysis and mutual information. Then, we develop a machine learning approach based on XGBRegressor and Bayesian optimization to explore the complex features and predict the PEMFCs’ performance. The performance and the robustness of the proposed machine learning based prediction approach is tested and validated through a real industrial dataset including 10 PEMFCs. Furthermore, several comparison studies with XGBRegressor and the two popular machine learning-based methods in predicting PEMFC performance, such as artificial neural network (ANN) and support vector machine regressor (SVR) are also conducted. The obtained results show that the proposed approach is more robust and outperforms the two conventional methods and the XGBRegressor for all the considered PEMFCs. Indeed, according to the coefficient of determination criterion, the proposed model gains an improvement of 6.35%, 6.8%, and 4.8% compared with ANN, SVR, and XGBRegressor respectively.展开更多
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
基金This research was supported by the National Natural Science Foundation of China(Nos.41977300 and 41907297)the Science and Technology Program of Guangzhou(China)(No.202002020055)the Fujian Provincial Natural Science Foundation(China)(No.2020I1001).
文摘Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants(WWTPs).However,some water quality metrics are not measurable in real time,thus influencing the judgment of the operators and may increase energy consumption and carbon emission.One of the solutions is using a soft-sensor prediction technique.This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit(BiGRU)combined with Gaussian Progress Regression(GPR)optimized by Tree-structured Parzen Estimator(TPE).TPE automatically optimizes the hyperparameters of BiGRU,and BiGRU is trained to obtain the point prediction with GPR for the interval prediction.Then,a case study applying this prediction method for an actual anaerobic process(2500 m^(3)/d)is carried out.Results show that TPE effectively optimizes the hyperparameters of BiGRU.For point prediction of CODeff and biogas yield,R^(2)values of BiGRU,which are 0.973 and 0.939,respectively,are increased by 1.03%–7.61%and 1.28%–10.33%,compared with those of other models,and the valid prediction interval can be obtained.Besides,the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation.It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.
文摘Background:Coronary heart disease(CHD)remains a prominent cause of mortality globally,necessitating early and accurate detection methods.Traditional diagnostic approaches can be invasive,costly,and time-consuming,necessitating the need for more efficient alternatives.This aimed to optimize the Light Gradient-Boosting Machine(LightGBM)algorithm to enhance its performance and accuracy in the early detection of CHD,providing a reliable,cost-effective,and non-invasive diagnostic tool.Methods:The Framingham Heart Study(FHS)dataset publicly available on Kaggle was used in this study.Multiple Imputations by Chained Equations(MICE)were applied separately to the training and testing sets to handle missing data.Borderline-SMOTE(Synthetic Minority Over-sampling Technique)was used on the training set to balance the dataset.The LightGBM algorithm was selected for its efficiency in classification tasks,and Bayesian Optimization with Tree-structured Parzen Estimator(TPE)was employed to fine-tune its hyperparameters.The optimized LightGBM model was trained and evaluated using metrics such as accuracy,precision,and AUC-ROC on the test set,with cross-validation to ensure robustness and generalizability.Findings:The optimized LightGBM model showed significant improvement in early CHD detection.The baseline LightGBM model with dropped missing values had an accuracy of 0.8333,sensitivity of 0.1081,precision of 0.3429,F1 score of 0.1644,and AUC of 0.6875.With MICE imputation,performance improved to an accuracy of 0.9399,sensitivity of 0.6693,precision of 0.9043,F1 score of 0.7692,and AUC of 0.9457.The combined approach of Borderline-SMOTE,MICE imputation,and TPE for LightGBM achieved an accuracy of 0.9882,sensitivity of 0.9370,precision of 0.9835,F1 score of 0.9597,and AUC of 0.9963,indicating a highly effective and robust model.Interpretation:The optimized model demonstrated outstanding performance in early CHD detection.The study’s strengths include its comprehensive approach to addressing missing data and class imbalance and the fine-tuning of hyperparameters through Bayesian Optimization.However,there is a need to test with other datasets for its generalizability to be well-established.This study provides a strong framework for early CHD detection,improving clinical practice by allowing for more precise and dependable diagnostics and effective interventions.
文摘With the recent advancements of deep learning-based methods in image classification,the requirement of a huge amount of training data is inevitable to avoid overfitting problems.Moreover,supervised deep learning models require labelled datasets for train-ing.Preparing such a huge amount of labelled data requires considerable human effort and time.In this scenario,self-supervised models are becoming popular because of their ability to learn even from unlabelled datasets.However,the efficient transfer of knowledge learned by self-supervised models into a target task,is an unsolved problem.This paper proposes a method for the efficient transfer of know-ledge learned by a self-supervised model,into a target task.Hyperparameters such as the number of layers,the number of units in each layer,learning rate,and dropout are automatically tuned in these fully connected(FC)layers using a Bayesian optimization technique called the tree-structured parzen estimator(TPE)approach algorithm.To evaluate the performance of the proposed method,state-of-the-art self-supervised models such as SimClr and SWAV are used to extract the learned features.Experiments are carried out on the CIFAR-10,CIFAR-100,and Tiny ImageNet datasets.The proposed method outperforms the baseline approach with margins of 2.97%,2.45%,and 0.91%for the CIFAR-100,Tiny ImageNet,and CIFAR-10 datasets,respectively.
基金financed in part by the ANRT(Association nationale de la recherche et de la technologie)with a CIFRE n°2023/0482.
文摘Proton-exchange membrane fuel cells (PEMFCs) are critical components of zero-emission electro-hydrogen generators. Accurate performance prediction is vital to the optimal operation management and preventive maintenance of these generators. Polarization curve remains one of the most important features representing the performance of PEMFCs in terms of efficiency and durability. However, predicting the polarization curve is not trivial as PEMFCs involve complex electrochemical reactions that feature multiple nonlinear relationships between the operating variables as inputs and the voltage as outputs. Herein, we present an artificial-intelligence-based approach for predicting the PEMFCs’ performance. In that way, we propose first an explainable solution for selecting the relevant features based on kernel principal component analysis and mutual information. Then, we develop a machine learning approach based on XGBRegressor and Bayesian optimization to explore the complex features and predict the PEMFCs’ performance. The performance and the robustness of the proposed machine learning based prediction approach is tested and validated through a real industrial dataset including 10 PEMFCs. Furthermore, several comparison studies with XGBRegressor and the two popular machine learning-based methods in predicting PEMFC performance, such as artificial neural network (ANN) and support vector machine regressor (SVR) are also conducted. The obtained results show that the proposed approach is more robust and outperforms the two conventional methods and the XGBRegressor for all the considered PEMFCs. Indeed, according to the coefficient of determination criterion, the proposed model gains an improvement of 6.35%, 6.8%, and 4.8% compared with ANN, SVR, and XGBRegressor respectively.
