Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injec...Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity.To overcome these obstacles,researchers have created several network IDS models,such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques.This study provides an updated learning strategy for artificial neural network(ANN)to address data categorization problems caused by unbalanced data.Compared to traditional approaches,the augmented ANN’s 92%accuracy is a significant improvement owing to the network’s increased resilience to disturbances and computational complexity,brought about by the addition of a random weight and standard scaler.Considering the ever-evolving nature of cybersecurity threats,this study introduces a revolutionary intrusion detection method.展开更多
Core power is a key parameter of nuclear reactor.Traditionally,the proportional-integralderivative(PID)controllers are used to control the core power.Fractional-order PID(FOPID)controller represents the cutting edge i...Core power is a key parameter of nuclear reactor.Traditionally,the proportional-integralderivative(PID)controllers are used to control the core power.Fractional-order PID(FOPID)controller represents the cutting edge in core power control research.In comparing with the integer-order models,fractional-order models describe the variation of core power more accurately,thus provide a comprehensive and realistic depiction for the power and state changes of reactor core.However,current fractional-order controllers cannot adjust their parameters dynamically to response the environmental changes or demands.In this paper,we aim at the stable control and dynamic responsiveness of core power.Based on the strong selflearning ability of artificial neural network(ANN),we propose a composite controller combining the ANN and FOPID controller.The FOPID controller is firstly designed and a back propagation neural network(BPNN)is then utilized to optimize the parameters of FOPID.It is shown by simulation that the composite controller enables the real-time parameter tuning via ANN and retains the advantage of FOPID controller.展开更多
This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and...This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.展开更多
Artificial intelligence(AI)is a revolutionizing problem-solver across various domains,including scientific research.Its application to chemical processes holds remarkable potential for rapid optimization of protocols ...Artificial intelligence(AI)is a revolutionizing problem-solver across various domains,including scientific research.Its application to chemical processes holds remarkable potential for rapid optimization of protocols and methods.A notable application of AI is in the photoFenton degradation of organic compounds.Despite the high novelty and recent surge of interest in this area,a comprehensive synthesis of existing literature on AI applications in the photo-Fenton process is lacking.This review aims to bridge this gap by providing an in-depth summary of the state-of-the-art use of artificial neural networks(ANN)in the photo-Fenton process,with the goal of aiding researchers in the water treatment field to identify the most crucial and relevant variables.It examines the types and architectures of ANNs,input and output variables,and the efficiency of these networks.The findings reveal a rapidly expanding field with increasing publications highlighting AI's potential to optimize the photo-Fenton process.This review also discusses the benefits and drawbacks of using ANNs,emphasizing the need for further research to advance this promising area.展开更多
The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy...The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy composites(AACs)were predicted using ANN.The predicted alloys were then experimentally verified through X-ray diffraction(XRD)and high-resolution transmission electron microscopy(HRTEM).The prediction accuracies of the ANN for AM and IM phases are 93.12%and 85.16%,respectively,while the prediction accuracies of KNN for AM and IM phases are 93%and 84%,respectively.It is observed that when the contents of Ti,Cu,Ni,and Hf fall within the ranges of 32.7−34.5 at.%,16.4−17.3 at.%,30.9−32.7 at.%,and 17.3−18.3 at.%,respectively,it is more likely to form AACs.Based on the results of XRD and HRTEM,the Ti_(34)Cu17Ni_(31.36)Hf_(17.64)and Ti_(36)Cu_(18)Ni_(29.44)Hf_(16.56)alloys are identified as good AACs,which are in closely consistent with the predicted amorphous alloy compositions.展开更多
This research study focuses on predicting ferrofluids’viscosity using machine learning models,artificial neural networks(ANNs),and random forests(RFs)incorporating key parameters;ferrofluid type,concentration of magn...This research study focuses on predicting ferrofluids’viscosity using machine learning models,artificial neural networks(ANNs),and random forests(RFs)incorporating key parameters;ferrofluid type,concentration of magnetic nanoparticles,temperature,and magnetic field intensity as inputs.A comprehensive database of 333 datasets sourced from various literatures was utilized for training and validating models.The ANN model demonstrated high accuracy,with root mean square error(RMSE)values below 0.033 and mean absolute percentage error(MAPE)not exceeding 3.01%,while the RF model achieved similar accuracy with RMSE under 0.052 and MAPE below 4.82%.Maximum deviations observed were 9.14%for ANN and 16.48%for RF,confirming that both models accurately learned the underlying patterns without overestimating viscosity.Additionally,the ANN model successfully captured intricate physical relationships between input parameters and viscosity when it was used to predict viscosity for random input data,confirming its ability to generalize beyond the training dataset.The RF model,however,showed limitations in extrapolating beyond the range of the training data.This research study demonstrates machine learning models’effectiveness in capturing intricate relationships governing the viscosity of ferrofluid for different types,paving the way for an improved understanding of ferrofluid’s viscosity behavior.展开更多
The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system...The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system,the connection point of each network is regarded as a neuron in the nervous system,and each connection point has input and output functions.Only when the input of nodes reaches a certain threshold can the output function of nodes be stimulated.Using the networking mode of the artificial neural network model,the mobile node can output in multiple directions.If the input direction of a certain path is the same as that of other nodes,it can choose to avoid and choose another path.The weighted value of each path between nodes is different,which means that the influence of the front node on the current node varies.The control method based on the artificial neural network model can be applied to vehicle control,household sweeping robots,and other fields,and a relatively optimized scheme can be obtained from the aspect of time and energy consumption.展开更多
Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,a...Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.展开更多
Methods and systems,including computer programs encoded on a computer storage medium are described for implementing a system that predicts wireline logs used in well drilling operations at a subsurface region.The syst...Methods and systems,including computer programs encoded on a computer storage medium are described for implementing a system that predicts wireline logs used in well drilling operations at a subsurface region.The system derives inputs from a first wireline log and includes a predictive model based on a neural network trained to generate data predictions.The predictive model processes the inputs derived from the first wireline log through layers of the neural network to generate a prediction that identifies multiple second wireline logs for a reservoir in the subsurface region.Based on the multiple second wireline logs,the system controls well drilling operations that simulate hydrocarbon production at the reservoir.展开更多
OBJECTIVE:To establish and evaluate a prognostic model of immunoglobulin A nephropathy(Ig AN)based on integrated Chinese and Western Medicine.METHODS:Retrospective analysis from 1/1/2013 to 12/31/2015 was performed on...OBJECTIVE:To establish and evaluate a prognostic model of immunoglobulin A nephropathy(Ig AN)based on integrated Chinese and Western Medicine.METHODS:Retrospective analysis from 1/1/2013 to 12/31/2015 was performed on 735 patients who were diagnosed with Ig AN.In addition,105 external data sets from 1/1/2016 to 12/31/2018 were used to verify the constructed model.The end point was entry into endstage renal disease or a doubling of serum creatinine(Scr)level from baseline.Kaplan-Meier curve survival analysis and multivariable Cox regression analysis were used to find independent prognostic factors.MATLAB 2018b and artificial neural network(ANN)were used to construct prognostic risk factor prediction models each for Traditional Chinese Medicine(TCM),Western Medicine,and integrated TCM and Western Medicine.The ANN model incorporated WANG Yongjun's new five-type syndrome differentiation for Ig AN.The prediction efficiencies of the three models were compared using the confusion matrix and the area under thecurve(AUC).RESULTS:Patients from 1/1/2013 to 12/31/2015 were followed for a mean of(46±19)months.The 5-year median overall renal survival time was 58.6 months,and a total of 40 patients(5.4%)entered the endpoint.Ratio of males to females was 1.48∶1.Median age of patients undergoing renal puncture was 35 years.Median 24-hour urinary protein was 0.55 g and 37 patients(5.0%)had pronounced proteinuria(24-hour urine protein≥3.5 g).Median serum creatinine was 76μmol/L and mean estimated glomerular filtration rate was(90±33)m L/min per 1.73 m^(2).Oxford classification of renal pathology suggested a high rate of focal segmental glomerulosclerosis(80.3%).Use of immunosuppressants was the most common(71.3%)treatment after renal puncture and improved clinical outcomes of Ig AN.TCM differentiation of kidney deficiency was the most common syndrome(69.5%).Independent risk factors for the endpoint were male,anemia,high urinary protein,and an Oxford classification of segmental sclerosis(S).AUCs of the Western Medicine,TCM,and integrated Chinese and Western Medicine models were 0.89,0.87,and 0.92,respectively.In external data(1/1/2016 to 12/31/2018),the performance of the three models was 0.88,0.80,and 0.94,respectively.CONCLUSIONS:ANN can be used to successfully construct a 5-year prediction model of Ig AN after renal puncture.The efficiency of this model,which combines TCM and Western Medicine factors based on Wang's new five-type syndrome differentiation,exceeds that of Western Medicine factors or TCM factors alone in data from this single-center retrospective study.展开更多
Objective:To explore a simple method for improving the diagnostic accuracy of malignant lung nodules based on imaging features of lung nodules.Methods:A retrospective analysis was conducted on the imaging data of 114 ...Objective:To explore a simple method for improving the diagnostic accuracy of malignant lung nodules based on imaging features of lung nodules.Methods:A retrospective analysis was conducted on the imaging data of 114 patients who underwent lung nodule surgery in the Thoracic Surgery Department of the First People’s Hospital of Huzhou from June to September 2024.Imaging features of lung nodules were summarized and trained using a BP neural network.Results:Training with the BP neural network increased the diagnostic accuracy for distinguishing between benign and malignant lung nodules based on imaging features from 84.2%(manual assessment)to 94.1%.Conclusion:Training with the BP neural network significantly improves the diagnostic accuracy of lung nodule malignancy based solely on imaging features.展开更多
Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di...Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.展开更多
An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and ...An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.展开更多
This study aims to predict the undrained shear strength of remolded soil samples using non-linear regression analyses,fuzzy logic,and artificial neural network modeling.A total of 1306 undrained shear strength results...This study aims to predict the undrained shear strength of remolded soil samples using non-linear regression analyses,fuzzy logic,and artificial neural network modeling.A total of 1306 undrained shear strength results from 230 different remolded soil test settings reported in 21 publications were collected,utilizing six different measurement devices.Although water content,plastic limit,and liquid limit were used as input parameters for fuzzy logic and artificial neural network modeling,liquidity index or water content ratio was considered as an input parameter for non-linear regression analyses.In non-linear regression analyses,12 different regression equations were derived for the prediction of undrained shear strength of remolded soil.Feed-Forward backpropagation and the TANSIG transfer function were used for artificial neural network modeling,while the Mamdani inference system was preferred with trapezoidal and triangular membership functions for fuzzy logic modeling.The experimental results of 914 tests were used for training of the artificial neural network models,196 for validation and 196 for testing.It was observed that the accuracy of the artificial neural network and fuzzy logic modeling was higher than that of the non-linear regression analyses.Furthermore,a simple and reliable regression equation was proposed for assessments of undrained shear strength values with higher coefficients of determination.展开更多
For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models...For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are cr...The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.展开更多
This study investigates photonuclear reaction(γ,n)cross-sections using Bayesian neural network(BNN)analysis.After determining the optimal network architecture,which features two hidden layers,each with 50 hidden node...This study investigates photonuclear reaction(γ,n)cross-sections using Bayesian neural network(BNN)analysis.After determining the optimal network architecture,which features two hidden layers,each with 50 hidden nodes,training was conducted for 30,000 iterations to ensure comprehensive data capture.By analyzing the distribution of absolute errors positively correlated with the cross-section for the isotope 159Tb,as well as the relative errors unrelated to the cross-section,we confirmed that the network effectively captured the data features without overfitting.Comparison with the TENDL-2021 Database demonstrated the BNN's reliability in fitting photonuclear cross-sections with lower average errors.The predictions for nuclei with single and double giant dipole resonance peak cross-sections,the accurate determination of the photoneutron reaction threshold in the low-energy region,and the precise description of trends in the high-energy cross-sections further demonstrate the network's generalization ability on the validation set.This can be attributed to the consistency of the training data.By using consistent training sets from different laboratories,Bayesian neural networks can predict nearby unknown cross-sections based on existing laboratory data,thereby estimating the potential differences between other laboratories'existing data and their own measurement results.Experimental measurements of photonuclear reactions on the newly constructed SLEGS beamline will contribute to clarifying the differences in cross-sections within the existing data.展开更多
Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accu...Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.展开更多
Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments invo...Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector machine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.展开更多
文摘Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity.To overcome these obstacles,researchers have created several network IDS models,such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques.This study provides an updated learning strategy for artificial neural network(ANN)to address data categorization problems caused by unbalanced data.Compared to traditional approaches,the augmented ANN’s 92%accuracy is a significant improvement owing to the network’s increased resilience to disturbances and computational complexity,brought about by the addition of a random weight and standard scaler.Considering the ever-evolving nature of cybersecurity threats,this study introduces a revolutionary intrusion detection method.
文摘Core power is a key parameter of nuclear reactor.Traditionally,the proportional-integralderivative(PID)controllers are used to control the core power.Fractional-order PID(FOPID)controller represents the cutting edge in core power control research.In comparing with the integer-order models,fractional-order models describe the variation of core power more accurately,thus provide a comprehensive and realistic depiction for the power and state changes of reactor core.However,current fractional-order controllers cannot adjust their parameters dynamically to response the environmental changes or demands.In this paper,we aim at the stable control and dynamic responsiveness of core power.Based on the strong selflearning ability of artificial neural network(ANN),we propose a composite controller combining the ANN and FOPID controller.The FOPID controller is firstly designed and a back propagation neural network(BPNN)is then utilized to optimize the parameters of FOPID.It is shown by simulation that the composite controller enables the real-time parameter tuning via ANN and retains the advantage of FOPID controller.
基金funded by FCT/MECI through national funds and,when applicable,co-funded EU funds under UID/50008:Instituto de Telecomunicacoes.
文摘This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.
基金financial support provided by the Valencian Regional Governement(Grant No.CIPROM2023/037)Davide Palma and Alessandra Bianco Prevot acknowledge support from the Project CH4.0 under the MUR program"Dipartimenti di Eccellenza 2023-2027"(Grant No.CUP:D13C22003520001).
文摘Artificial intelligence(AI)is a revolutionizing problem-solver across various domains,including scientific research.Its application to chemical processes holds remarkable potential for rapid optimization of protocols and methods.A notable application of AI is in the photoFenton degradation of organic compounds.Despite the high novelty and recent surge of interest in this area,a comprehensive synthesis of existing literature on AI applications in the photo-Fenton process is lacking.This review aims to bridge this gap by providing an in-depth summary of the state-of-the-art use of artificial neural networks(ANN)in the photo-Fenton process,with the goal of aiding researchers in the water treatment field to identify the most crucial and relevant variables.It examines the types and architectures of ANNs,input and output variables,and the efficiency of these networks.The findings reveal a rapidly expanding field with increasing publications highlighting AI's potential to optimize the photo-Fenton process.This review also discusses the benefits and drawbacks of using ANNs,emphasizing the need for further research to advance this promising area.
基金supported by the National Natural Science Foundation of China(No.51601019)the Guangdong Basic and Applied Basic Research Foundation,China(No.2022A1515010233)+1 种基金the Key Project of Shaanxi Province of Qinchuangyuan“Scientist and Engineer”Team Construction,China(No.2023KXJ-123)the Natural Science Foundation of Shaanxi Province,China(No.2024JC-YBMS-014).
文摘The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy composites(AACs)were predicted using ANN.The predicted alloys were then experimentally verified through X-ray diffraction(XRD)and high-resolution transmission electron microscopy(HRTEM).The prediction accuracies of the ANN for AM and IM phases are 93.12%and 85.16%,respectively,while the prediction accuracies of KNN for AM and IM phases are 93%and 84%,respectively.It is observed that when the contents of Ti,Cu,Ni,and Hf fall within the ranges of 32.7−34.5 at.%,16.4−17.3 at.%,30.9−32.7 at.%,and 17.3−18.3 at.%,respectively,it is more likely to form AACs.Based on the results of XRD and HRTEM,the Ti_(34)Cu17Ni_(31.36)Hf_(17.64)and Ti_(36)Cu_(18)Ni_(29.44)Hf_(16.56)alloys are identified as good AACs,which are in closely consistent with the predicted amorphous alloy compositions.
文摘This research study focuses on predicting ferrofluids’viscosity using machine learning models,artificial neural networks(ANNs),and random forests(RFs)incorporating key parameters;ferrofluid type,concentration of magnetic nanoparticles,temperature,and magnetic field intensity as inputs.A comprehensive database of 333 datasets sourced from various literatures was utilized for training and validating models.The ANN model demonstrated high accuracy,with root mean square error(RMSE)values below 0.033 and mean absolute percentage error(MAPE)not exceeding 3.01%,while the RF model achieved similar accuracy with RMSE under 0.052 and MAPE below 4.82%.Maximum deviations observed were 9.14%for ANN and 16.48%for RF,confirming that both models accurately learned the underlying patterns without overestimating viscosity.Additionally,the ANN model successfully captured intricate physical relationships between input parameters and viscosity when it was used to predict viscosity for random input data,confirming its ability to generalize beyond the training dataset.The RF model,however,showed limitations in extrapolating beyond the range of the training data.This research study demonstrates machine learning models’effectiveness in capturing intricate relationships governing the viscosity of ferrofluid for different types,paving the way for an improved understanding of ferrofluid’s viscosity behavior.
文摘The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system,the connection point of each network is regarded as a neuron in the nervous system,and each connection point has input and output functions.Only when the input of nodes reaches a certain threshold can the output function of nodes be stimulated.Using the networking mode of the artificial neural network model,the mobile node can output in multiple directions.If the input direction of a certain path is the same as that of other nodes,it can choose to avoid and choose another path.The weighted value of each path between nodes is different,which means that the influence of the front node on the current node varies.The control method based on the artificial neural network model can be applied to vehicle control,household sweeping robots,and other fields,and a relatively optimized scheme can be obtained from the aspect of time and energy consumption.
基金supported by the National Natural Science Foundation of China(62263014)the Yunnan Provincial Basic Research Project(202301AT070443,202401AT070344).
文摘Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.
文摘Methods and systems,including computer programs encoded on a computer storage medium are described for implementing a system that predicts wireline logs used in well drilling operations at a subsurface region.The system derives inputs from a first wireline log and includes a predictive model based on a neural network trained to generate data predictions.The predictive model processes the inputs derived from the first wireline log through layers of the neural network to generate a prediction that identifies multiple second wireline logs for a reservoir in the subsurface region.Based on the multiple second wireline logs,the system controls well drilling operations that simulate hydrocarbon production at the reservoir.
基金Natural Science Foundation-funded Project:Study on the Mechanism of Compound Centella Asiatica Mediate 24-dehydrocholesterol Reductase/Liver X Receptors(DHCR24/LXR)Signaling Axis to Regulate Macrophage Activation and Alleviate Microinflammation in Diabetic Kidney Disease(No.82205008)Medical Scientific Research Foundation of Zhejiang Province:Study on the Mechanism of Asiaticoside Mediate DHCR24/LXR Signaling Axis to Regulate Macrophage Innate Immune Response in Diabetic Kidney Disease(No.2023RC242)+2 种基金Zhejiang Traditional Medicine and Technology Program:Chen Hongyu's Academic Thought and Clinical Experience in the Diagnosis and Treatment of Diabetic Nephropathy by Knowledge Map(No.2023ZF137)Health Commission of Hangzhou city:Study on Prognosis Model of Ig A Nephropathy Combined with Chinese and Western Medicine based on Artificial Neural Network(No.A20210083)Zhejiang Chinese Medical University Research Foundation:Study on Wang Yongjun's Clinical Decision Model for Diagnosis and Treatment of Diabetic Nephropathy based on Graph Convolutional Neural Network(No.2022FSYYZZ14)。
文摘OBJECTIVE:To establish and evaluate a prognostic model of immunoglobulin A nephropathy(Ig AN)based on integrated Chinese and Western Medicine.METHODS:Retrospective analysis from 1/1/2013 to 12/31/2015 was performed on 735 patients who were diagnosed with Ig AN.In addition,105 external data sets from 1/1/2016 to 12/31/2018 were used to verify the constructed model.The end point was entry into endstage renal disease or a doubling of serum creatinine(Scr)level from baseline.Kaplan-Meier curve survival analysis and multivariable Cox regression analysis were used to find independent prognostic factors.MATLAB 2018b and artificial neural network(ANN)were used to construct prognostic risk factor prediction models each for Traditional Chinese Medicine(TCM),Western Medicine,and integrated TCM and Western Medicine.The ANN model incorporated WANG Yongjun's new five-type syndrome differentiation for Ig AN.The prediction efficiencies of the three models were compared using the confusion matrix and the area under thecurve(AUC).RESULTS:Patients from 1/1/2013 to 12/31/2015 were followed for a mean of(46±19)months.The 5-year median overall renal survival time was 58.6 months,and a total of 40 patients(5.4%)entered the endpoint.Ratio of males to females was 1.48∶1.Median age of patients undergoing renal puncture was 35 years.Median 24-hour urinary protein was 0.55 g and 37 patients(5.0%)had pronounced proteinuria(24-hour urine protein≥3.5 g).Median serum creatinine was 76μmol/L and mean estimated glomerular filtration rate was(90±33)m L/min per 1.73 m^(2).Oxford classification of renal pathology suggested a high rate of focal segmental glomerulosclerosis(80.3%).Use of immunosuppressants was the most common(71.3%)treatment after renal puncture and improved clinical outcomes of Ig AN.TCM differentiation of kidney deficiency was the most common syndrome(69.5%).Independent risk factors for the endpoint were male,anemia,high urinary protein,and an Oxford classification of segmental sclerosis(S).AUCs of the Western Medicine,TCM,and integrated Chinese and Western Medicine models were 0.89,0.87,and 0.92,respectively.In external data(1/1/2016 to 12/31/2018),the performance of the three models was 0.88,0.80,and 0.94,respectively.CONCLUSIONS:ANN can be used to successfully construct a 5-year prediction model of Ig AN after renal puncture.The efficiency of this model,which combines TCM and Western Medicine factors based on Wang's new five-type syndrome differentiation,exceeds that of Western Medicine factors or TCM factors alone in data from this single-center retrospective study.
基金Zhejiang Medical and Health Technology Project(Project No.2020PY072)。
文摘Objective:To explore a simple method for improving the diagnostic accuracy of malignant lung nodules based on imaging features of lung nodules.Methods:A retrospective analysis was conducted on the imaging data of 114 patients who underwent lung nodule surgery in the Thoracic Surgery Department of the First People’s Hospital of Huzhou from June to September 2024.Imaging features of lung nodules were summarized and trained using a BP neural network.Results:Training with the BP neural network increased the diagnostic accuracy for distinguishing between benign and malignant lung nodules based on imaging features from 84.2%(manual assessment)to 94.1%.Conclusion:Training with the BP neural network significantly improves the diagnostic accuracy of lung nodule malignancy based solely on imaging features.
文摘Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.
基金the support of the National Natural Science Foundation of China(22278234,21776151)。
文摘An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.
文摘This study aims to predict the undrained shear strength of remolded soil samples using non-linear regression analyses,fuzzy logic,and artificial neural network modeling.A total of 1306 undrained shear strength results from 230 different remolded soil test settings reported in 21 publications were collected,utilizing six different measurement devices.Although water content,plastic limit,and liquid limit were used as input parameters for fuzzy logic and artificial neural network modeling,liquidity index or water content ratio was considered as an input parameter for non-linear regression analyses.In non-linear regression analyses,12 different regression equations were derived for the prediction of undrained shear strength of remolded soil.Feed-Forward backpropagation and the TANSIG transfer function were used for artificial neural network modeling,while the Mamdani inference system was preferred with trapezoidal and triangular membership functions for fuzzy logic modeling.The experimental results of 914 tests were used for training of the artificial neural network models,196 for validation and 196 for testing.It was observed that the accuracy of the artificial neural network and fuzzy logic modeling was higher than that of the non-linear regression analyses.Furthermore,a simple and reliable regression equation was proposed for assessments of undrained shear strength values with higher coefficients of determination.
基金supported by the Beijing Natural Science Foundation(Grant No.L223013)。
文摘For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
文摘The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.
基金supported by National key research and development program(No.2022YFA1602404)the National Natural Science Foundation of China(Nos.12388102,12275338,12005280)the Key Laboratory of Nuclear Data foundation(No.JCKY2022201C152)。
文摘This study investigates photonuclear reaction(γ,n)cross-sections using Bayesian neural network(BNN)analysis.After determining the optimal network architecture,which features two hidden layers,each with 50 hidden nodes,training was conducted for 30,000 iterations to ensure comprehensive data capture.By analyzing the distribution of absolute errors positively correlated with the cross-section for the isotope 159Tb,as well as the relative errors unrelated to the cross-section,we confirmed that the network effectively captured the data features without overfitting.Comparison with the TENDL-2021 Database demonstrated the BNN's reliability in fitting photonuclear cross-sections with lower average errors.The predictions for nuclei with single and double giant dipole resonance peak cross-sections,the accurate determination of the photoneutron reaction threshold in the low-energy region,and the precise description of trends in the high-energy cross-sections further demonstrate the network's generalization ability on the validation set.This can be attributed to the consistency of the training data.By using consistent training sets from different laboratories,Bayesian neural networks can predict nearby unknown cross-sections based on existing laboratory data,thereby estimating the potential differences between other laboratories'existing data and their own measurement results.Experimental measurements of photonuclear reactions on the newly constructed SLEGS beamline will contribute to clarifying the differences in cross-sections within the existing data.
基金funded by the Youth Fund of the National Natural Science Foundation of China(Grant No.42261070).
文摘Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.
基金supported by the National Key R&D Program of China(Grant No.:2022YFC3501805)the National Natural Science Foundation of China(Grant No.:82374030)+2 种基金the Science and Technology Program of Tianjin in China(Grant No.:23ZYJDSS00030)the Tianjin Outstanding Youth Fund,China(Grant No.:23JCJQJC00030)the China Postdoctoral Science Foundation-Tianjin Joint Support Program(Grant No.:2023T030TJ).
文摘Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector machine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.
