The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n...The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.展开更多
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
With the rapid development of the Artificial Intelligence of Things(AIoT),convolutional neural networks(CNNs)have demonstrated potential and remarkable performance in AIoT applications due to their excellent performan...With the rapid development of the Artificial Intelligence of Things(AIoT),convolutional neural networks(CNNs)have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks.However,the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices.Therefore,this paper proposes an efficient privacy-preserving CNN framework(i.e.,EPPA)based on the Fully Homomorphic Encryption(FHE)scheme for AIoT application scenarios.In the plaintext domain,we verify schemes with different activation structures to determine the actual activation functions applicable to the corresponding ciphertext domain.Within the encryption domain,we integrate batch normalization(BN)into the convolutional layers to simplify the computation process.For nonlinear activation functions,we use composite polynomials for approximate calculation.Regarding the noise accumulation caused by homomorphic multiplication operations,we realize the refreshment of ciphertext noise through minimal“decryption-encryption”interactions,instead of adopting bootstrapping operations.Additionally,in practical implementation,we convert three-dimensional convolution into two-dimensional convolution to reduce the amount of computation in the encryption domain.Finally,we conduct extensive experiments on four IoT datasets,different CNN architectures,and two platforms with different resource configurations to evaluate the performance of EPPA in detail.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.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
The performance of concrete can be affected by many factors,including the material composition,environmental conditions,and construction methods,and it is challenging to predict the performance evolution accurately.Th...The performance of concrete can be affected by many factors,including the material composition,environmental conditions,and construction methods,and it is challenging to predict the performance evolution accurately.The rise of artificial intelligence provides a way to meet the above challenges.This article elaborates on research overview of artificial neural network(ANN)and its prediction for concrete strength,deformation,and durability.The focus is on the comparative analysis of the prediction accuracy for different types of neural networks.Numerous studies have shown that the prediction accuracy of ANN can meet the standards of the practical engineering applications.To further improve the applicability of ANN in concrete,the model can consider the combination of multiple algorithms and the expansion of data samples.The review can provide new research ideas for development of concrete performance prediction.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
This study numerically investigates inclined magneto-hydrodynamic natural convection in a porous cavity filled with nanofluid containing gyrotactic microorganisms.The governing equations are nondimensionalized and sol...This study numerically investigates inclined magneto-hydrodynamic natural convection in a porous cavity filled with nanofluid containing gyrotactic microorganisms.The governing equations are nondimensionalized and solved using the finite volume method.The simulations examine the impact of key parameters such as heat source length and position,Peclet number,porosity,and heat generation/absorption on flow patterns,temperature distribution,concentration profiles,and microorganism rotation.Results indicate that extending the heat source length enhances convective currents and heat transfer efficiency,while optimizing the heat source position reduces entropy generation.Higher Peclet numbers amplify convective currents and microorganism distribution complexity.Variations in porosity and heat generation/absorption significantly influence flow dynamics.Additionally,the artificial neural network model reliably predicts the mean Nusselt and Sherwood numbers(Nu&Sh),demonstrating its effectiveness for such analyses.The simulation results reveal that increasing the heat source length significantly enhances heat transfer,as evidenced by a 15%increase in the mean Nusselt number.展开更多
Ferrimagnets are important for next-generation high-density ultrafast spintronic device applications.Magnetization compensation temperature(TM)is a fundamentally critical magnetic parameter for ferrimagnets besides th...Ferrimagnets are important for next-generation high-density ultrafast spintronic device applications.Magnetization compensation temperature(TM)is a fundamentally critical magnetic parameter for ferrimagnets besides their Curie temperature.Around TM,the spin-orbit switching efficiencies are extraordinarily high.Therefore,the accurate manipulation of TM from the material fabrication process is essential for the electrical steering of ferrimagnetic spins.In this work,CoTb thin films,with the 3 d and 4 f magnetic sublattices antiferromagnetically coupled to each other,are deposited at different temperatures.The magnetotransport and magnetic properties of these films are systematically investigated.It was found that the TM of this rare-earth ferrimagnet largely depends on the growth temperature and it can be tuned by over 100 K.Accordingly,the spins of an optimized ferrimagnetic CoTb thin film with its TM close to room temperature can be efficiently switched by the current-pulse-induced spin-orbit torque.Moreover,an artificial neural network utilizing the spin-orbit torque device was constructed,demonstrating an image recognition accuracy of approximately 92.5%,which is comparable to that of conventional software solutions.Thus,this work demonstrates the large tunability of TM of a rare earth ferrimagnet by chemical ordering and the great potential of such a ferrimagnet for electrically operated spintronic devices.展开更多
This study outlines a quantitative and data-driven study of the mixed convection heat transfer processes that concern Cu-water nanofluids in a I-shaped enclosure with one to five rotating cylinders.The dimensionless e...This study outlines a quantitative and data-driven study of the mixed convection heat transfer processes that concern Cu-water nanofluids in a I-shaped enclosure with one to five rotating cylinders.The dimensionless equations of mass,momentum,and energy are solved using the finite element method as implemented in the COMSOL Multiphysics 6.3 software in different rotating Reynolds numbers and cylinder geometries.An artificial Neural Network that is trained using Bayesian Regularization on data produced by the COMSOL is utilized to estimate the average Nusselt numbers.The analysis is conducted for a wide range of rotational Reynolds numbers(Re_(w)=0-100),with the fixed Prandtl number.Results are presented in terms of streamline patterns,isotherm contours,and Nusselt numbers to assess heat transfer behavior.Findings revealed that increasing the number of cylinders and optimizing their placement significantly enhances convective mixing and thermal transport.The ANN model accurately predicts the Nusselt numbers across all configurations with negligible errors.Among all configurations,the third arrangement in Scenario 5 exhibits the highest heat transfer rates,attributed to intensified vortex interaction and reduced thermal resistance.Artificial neural networks and finite element-based models will be of great value to the design of miniature and energy-efficient enclosures and electronics cooling mechanisms that make use of nanofluids.展开更多
Background:Cardiac implantable electronic devices(CIEDs)are essential for preventing sudden cardiac death in patients with cardiovascular diseases,but implantation procedures carry risks of complications such as infec...Background:Cardiac implantable electronic devices(CIEDs)are essential for preventing sudden cardiac death in patients with cardiovascular diseases,but implantation procedures carry risks of complications such as infection,hematoma,and bleeding,with incidence rates of 3–4%.Previous studies have examined individual risk factors separately,but integrated predictive models are lacking.We compared the predictive performance and interpretability of artificial neural network(ANN)and logistic regression models to evaluate their respective strengths in clinical risk assessment.Methods:This retrospective study analyzed data from 180 patients who underwent cardiac implantable electronic device(CIED)implantation in Taiwan between 2017 and 2018.To address class imbalance and enhance model training,the dataset was augmented to 540 records using the Synthetic Minority Oversampling Technique(SMOTE).A total of 13 clinical risk factors were evaluated(e.g.,age,body mass index(BMI),platelet count,left ventricular ejection fraction(LVEF),prothrombin time/international normalized ratio(PT/INR),hemoglobin(Hb),comorbidities,and antithrombotic use).Results:The most influential risk factors identified by the ANN model were platelet count,PT/INR,LVEF,Hb,and age.In the logistic regression analysis,reduced LVEF,lower hemoglobin levels,prolonged PT/INR,and lower BMI were significantly associated with an increased risk of complications.ANN model achieved a higher area under the curve(AUC=0.952)compared to the logistic regression model(AUC=0.802),indicating superior predictive performance.Additionally,the overall model quality was also higher for the ANN model(0.93)than for logistic regression(0.76).Conclusions:This study demonstrates that ANN models can effectively predict complications associated CIED procedures and identify critical preoperative risk factors.These findings support the use of ANN-based models for individualized risk stratification,enhancing procedural safety,improving patient outcomes,and potentially reducing healthcare costs associated with postoperative complications.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
The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformatio...The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.展开更多
Solar chimneys are renewable energy systems designed to enhance natural ventilation,improving thermal comfort in buildings.As passive systems,solar chimneys contribute to energy efficiency in a sustainable and environ...Solar chimneys are renewable energy systems designed to enhance natural ventilation,improving thermal comfort in buildings.As passive systems,solar chimneys contribute to energy efficiency in a sustainable and environmentally friendly way.The effectiveness of a solar chimney depends on its design and orientation relative to the cardinal directions,both of which are critical for optimal performance.This article presents a supervised learning approach using artificial neural networks to forecast the performance indicators of solar chimneys.Thedataset includes information from 2784 solar chimney configurations,which encompasses various factors such as chimney height,channel thickness,glass thickness,paint,wall material,measurement date,and orientation.The case study examines the four cardinal orientations and weather data from Mexico City,covering the period from 01 January to 31 December 2024.The main results indicate that the proposed artificial neural network models achieved higher coefficient of determination values(0.905-0.990)than the baseline method across performance indicators of the solar chimney system,demonstrating greater accuracy and improved generalization.The proposed approach highlights the potential of using artificial neural networks as a decision-making tool in the design stage of solar chimneys in sustainable architecture.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
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950).
文摘The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.
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.
基金supported by the Natural Science Foundation of China No.62362008the Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘With the rapid development of the Artificial Intelligence of Things(AIoT),convolutional neural networks(CNNs)have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks.However,the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices.Therefore,this paper proposes an efficient privacy-preserving CNN framework(i.e.,EPPA)based on the Fully Homomorphic Encryption(FHE)scheme for AIoT application scenarios.In the plaintext domain,we verify schemes with different activation structures to determine the actual activation functions applicable to the corresponding ciphertext domain.Within the encryption domain,we integrate batch normalization(BN)into the convolutional layers to simplify the computation process.For nonlinear activation functions,we use composite polynomials for approximate calculation.Regarding the noise accumulation caused by homomorphic multiplication operations,we realize the refreshment of ciphertext noise through minimal“decryption-encryption”interactions,instead of adopting bootstrapping operations.Additionally,in practical implementation,we convert three-dimensional convolution into two-dimensional convolution to reduce the amount of computation in the encryption domain.Finally,we conduct extensive experiments on four IoT datasets,different CNN architectures,and two platforms with different resource configurations to evaluate the performance of EPPA in detail.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
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funded by the Ningbo Construction Research Project(Nos.2024-23,2024-20)the National Natural Science Foundation of China(No.52478281)the Ningbo Public Welfare Science and Technology Project(No.2024S077).
文摘The performance of concrete can be affected by many factors,including the material composition,environmental conditions,and construction methods,and it is challenging to predict the performance evolution accurately.The rise of artificial intelligence provides a way to meet the above challenges.This article elaborates on research overview of artificial neural network(ANN)and its prediction for concrete strength,deformation,and durability.The focus is on the comparative analysis of the prediction accuracy for different types of neural networks.Numerous studies have shown that the prediction accuracy of ANN can meet the standards of the practical engineering applications.To further improve the applicability of ANN in concrete,the model can consider the combination of multiple algorithms and the expansion of data samples.The review can provide new research ideas for development of concrete performance prediction.
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Dean ship of Scientific Research at King Khalid University,Abha,Saudi Arabia,for funding this work through the Research Group Project(Grant No.RGP.2/610/45)funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project(Grant No.PNURSP2024R102),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘This study numerically investigates inclined magneto-hydrodynamic natural convection in a porous cavity filled with nanofluid containing gyrotactic microorganisms.The governing equations are nondimensionalized and solved using the finite volume method.The simulations examine the impact of key parameters such as heat source length and position,Peclet number,porosity,and heat generation/absorption on flow patterns,temperature distribution,concentration profiles,and microorganism rotation.Results indicate that extending the heat source length enhances convective currents and heat transfer efficiency,while optimizing the heat source position reduces entropy generation.Higher Peclet numbers amplify convective currents and microorganism distribution complexity.Variations in porosity and heat generation/absorption significantly influence flow dynamics.Additionally,the artificial neural network model reliably predicts the mean Nusselt and Sherwood numbers(Nu&Sh),demonstrating its effectiveness for such analyses.The simulation results reveal that increasing the heat source length significantly enhances heat transfer,as evidenced by a 15%increase in the mean Nusselt number.
基金financial support from the National Key R&D Program of China(Nos.2022YFB3506000 and 2022YFA1602700)financial support from Fundamental Research Funds for the Central Universities+6 种基金financial support from the National Natural Science Foundation of China(Nos.52425106,52121001,and 52271235)financial support from the Beijing Natural Science Foundation(No.JQ23005)financial support from the National Natural Science Foundation of China(No.52401300)funding from the China National Postdoctoral Program for Innovative Talents(No.BX20230451)from the China Postdoctoral Science Foundation(No.2024M754058)financial support from the National Natural Science Foundation of China(No.62401276)financial support from the National Natural Science Foundation of China(No.524B2003).
文摘Ferrimagnets are important for next-generation high-density ultrafast spintronic device applications.Magnetization compensation temperature(TM)is a fundamentally critical magnetic parameter for ferrimagnets besides their Curie temperature.Around TM,the spin-orbit switching efficiencies are extraordinarily high.Therefore,the accurate manipulation of TM from the material fabrication process is essential for the electrical steering of ferrimagnetic spins.In this work,CoTb thin films,with the 3 d and 4 f magnetic sublattices antiferromagnetically coupled to each other,are deposited at different temperatures.The magnetotransport and magnetic properties of these films are systematically investigated.It was found that the TM of this rare-earth ferrimagnet largely depends on the growth temperature and it can be tuned by over 100 K.Accordingly,the spins of an optimized ferrimagnetic CoTb thin film with its TM close to room temperature can be efficiently switched by the current-pulse-induced spin-orbit torque.Moreover,an artificial neural network utilizing the spin-orbit torque device was constructed,demonstrating an image recognition accuracy of approximately 92.5%,which is comparable to that of conventional software solutions.Thus,this work demonstrates the large tunability of TM of a rare earth ferrimagnet by chemical ordering and the great potential of such a ferrimagnet for electrically operated spintronic devices.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘This study outlines a quantitative and data-driven study of the mixed convection heat transfer processes that concern Cu-water nanofluids in a I-shaped enclosure with one to five rotating cylinders.The dimensionless equations of mass,momentum,and energy are solved using the finite element method as implemented in the COMSOL Multiphysics 6.3 software in different rotating Reynolds numbers and cylinder geometries.An artificial Neural Network that is trained using Bayesian Regularization on data produced by the COMSOL is utilized to estimate the average Nusselt numbers.The analysis is conducted for a wide range of rotational Reynolds numbers(Re_(w)=0-100),with the fixed Prandtl number.Results are presented in terms of streamline patterns,isotherm contours,and Nusselt numbers to assess heat transfer behavior.Findings revealed that increasing the number of cylinders and optimizing their placement significantly enhances convective mixing and thermal transport.The ANN model accurately predicts the Nusselt numbers across all configurations with negligible errors.Among all configurations,the third arrangement in Scenario 5 exhibits the highest heat transfer rates,attributed to intensified vortex interaction and reduced thermal resistance.Artificial neural networks and finite element-based models will be of great value to the design of miniature and energy-efficient enclosures and electronics cooling mechanisms that make use of nanofluids.
文摘Background:Cardiac implantable electronic devices(CIEDs)are essential for preventing sudden cardiac death in patients with cardiovascular diseases,but implantation procedures carry risks of complications such as infection,hematoma,and bleeding,with incidence rates of 3–4%.Previous studies have examined individual risk factors separately,but integrated predictive models are lacking.We compared the predictive performance and interpretability of artificial neural network(ANN)and logistic regression models to evaluate their respective strengths in clinical risk assessment.Methods:This retrospective study analyzed data from 180 patients who underwent cardiac implantable electronic device(CIED)implantation in Taiwan between 2017 and 2018.To address class imbalance and enhance model training,the dataset was augmented to 540 records using the Synthetic Minority Oversampling Technique(SMOTE).A total of 13 clinical risk factors were evaluated(e.g.,age,body mass index(BMI),platelet count,left ventricular ejection fraction(LVEF),prothrombin time/international normalized ratio(PT/INR),hemoglobin(Hb),comorbidities,and antithrombotic use).Results:The most influential risk factors identified by the ANN model were platelet count,PT/INR,LVEF,Hb,and age.In the logistic regression analysis,reduced LVEF,lower hemoglobin levels,prolonged PT/INR,and lower BMI were significantly associated with an increased risk of complications.ANN model achieved a higher area under the curve(AUC=0.952)compared to the logistic regression model(AUC=0.802),indicating superior predictive performance.Additionally,the overall model quality was also higher for the ANN model(0.93)than for logistic regression(0.76).Conclusions:This study demonstrates that ANN models can effectively predict complications associated CIED procedures and identify critical preoperative risk factors.These findings support the use of ANN-based models for individualized risk stratification,enhancing procedural safety,improving patient outcomes,and potentially reducing healthcare costs associated with postoperative complications.
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(NSFC)(Grant No.12322203).
文摘The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.
文摘Solar chimneys are renewable energy systems designed to enhance natural ventilation,improving thermal comfort in buildings.As passive systems,solar chimneys contribute to energy efficiency in a sustainable and environmentally friendly way.The effectiveness of a solar chimney depends on its design and orientation relative to the cardinal directions,both of which are critical for optimal performance.This article presents a supervised learning approach using artificial neural networks to forecast the performance indicators of solar chimneys.Thedataset includes information from 2784 solar chimney configurations,which encompasses various factors such as chimney height,channel thickness,glass thickness,paint,wall material,measurement date,and orientation.The case study examines the four cardinal orientations and weather data from Mexico City,covering the period from 01 January to 31 December 2024.The main results indicate that the proposed artificial neural network models achieved higher coefficient of determination values(0.905-0.990)than the baseline method across performance indicators of the solar chimney system,demonstrating greater accuracy and improved generalization.The proposed approach highlights the potential of using artificial neural networks as a decision-making tool in the design stage of solar chimneys in sustainable architecture.
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.
