Predicting depression in patients with heart failure based on a stacking model

BACKGROUND There is a lack of literature discussing the utilization of the stacking ensemble algorithm for predicting depression in patients with heart failure(HF).AIM To create a stacking model for predicting depression in patients with HF.METHODS This study analyzed data on 1084 HF patients from the National Health and Nutrition Examination Survey database spanning from 2005 to 2018.Through univariate analysis and the use of an artificial neural network algorithm,predictors significantly linked to depression were identified.These predictors were utilized to create a stacking model employing tree-based learners.The performances of both the individual models and the stacking model were assessed by using the test dataset.Furthermore,the SHapley additive exPlanations(SHAP)model was applied to interpret the stacking model.RESULTS The models included five predictors.Among these models,the stacking model demonstrated the highest performance,achieving an area under the curve of 0.77(95%CI:0.71-0.84),a sensitivity of 0.71,and a specificity of 0.68.The calibration curve supported the reliability of the models,and decision curve analysis confirmed their clinical value.The SHAP plot demonstrated that age had the most significant impact on the stacking model's output.CONCLUSION The stacking model demonstrated strong predictive performance.Clinicians can utilize this model to identify highrisk depression patients with HF,thus enabling early provision of psychological interventions.

Detecting DNS Covert Channels Using Stacking Model

A covert channel is an information channel that is used by the computer process to exfiltrate data through bypassing security policies.The DNS protocol is one of the important ways to implement a covert channel.DNS covert channels are easily used by attackers for malicious purposes.Therefore,an effective detection approach of the DNS covert channels is significant for computer systems and network securities.Aiming at the difficulty of the DNS covert channel identification,we propose a DNS covert channel detection method based on a stacking model.The stacking model is evaluated on a campus network and the experimental results show that the detection based on the stacking model can detect the DNS covert channels effectively.Besides,it can identify unknown covert channel traffic.The area under the curve(AUC)of the proposed method reaches 0.9901,which outperforms existing detection methods.

Intrusion Detection Using Ensemble Wrapper Filter Based Feature Selection with Stacking Model

The number of attacks is growing tremendously in tandem with the growth of internet technologies.As a result,protecting the private data from prying eyes has become a critical and tough undertaking.Many intrusion detection solutions have been offered by researchers in order to decrease the effect of these attacks.For attack detection,the prior system has created an SMSRPF(Stacking Model Significant Rule Power Factor)classifier.To provide creative instance detection,the SMSRPF combines the detection of trained classifiers such as DT(Decision Tree)and RF(Random Forest).Nevertheless,it does not generate any accuratefindings that are adequate.The suggested system has built an EWF(Ensemble Wrapper Filter)feature selection with SMSRPF classifier for attack detection so as to overcome this problem.The UNSW-NB15 dataset is used as an input in this proposed research project.Specifically,min–max normalization approach is used to pre-process the incoming data.The feature selection is then carried out using EWF.Based on the selected features,SMSRPF classifiers are utilized to detect the attacks.The SMSRPF is integrated with the trained classi-fiers such as DT and RF to create creative instance detection.After that,the testing data is classified using MCAR(Multi-Class Classification based on Association Rules).The SRPF judges the rules correctly even when the confidence and the lift measures fail.Regarding accuracy,precision,recall,f-measure,computation time,and error,the experimental findings suggest that the new system outperforms the prior systems.

Novel stacking models based on SMOTE for the prediction of rockburst grades at four deep gold mines

Rockburst is a frequently encountered hazard during the production of deep gold mines.Accurate prediction of rockburst is an important measure to prevent rockburst in gold mines.This study considers seven indicators to evaluate rockburst at four deep gold mines.Field research and rock tests were performed at two gold mines in China to collect these seven indicators and rockburst cases.The collected database was oversampled by the synthetic minority oversampling technique(SMOTE)to balance the categories of rockburst datasets.Stacking models combining tree-based models and logistic regression(LR)were established by the balanced database.Rockburst datasets from another two deep gold mines were implemented to verify the applicability of the predictive models.The stacking model combining extremely randomized trees and LR based on SMOTE(SMOTE-ERT-LR)was the best model,and it obtained a training accuracy of 100%and an evaluation accuracy of 100%.Moreover,model evaluation suggested that SMOTE can enhance the prediction performance for weak rockburst,thereby improving the overall performance.Finally,sensitivity analysis was performed for SMOTE-ERT-LR.The results indicated that the SMOTE-ERT-LR model can achieve satisfactory performance when only depth,maximum tangential stress index,and linear elastic energy index were available.

Prediction of uniaxial compressive strength of rock based on lithology using stacking models

Uniaxial compressive strength(UCS)of rock is an essential parameter in geotechnical engineering.Point load strength(PLS),P-wave velocity,and Schmidt hammer rebound number(SH)are more easily obtained than UCS and are extensively applied for the indirect estimation of UCS.This study collected 1080 datasets consisting of SH,P-wave velocity,PLS,and UCS.All datasets were integrated into three categories(sedimentary,igneous,and metamorphic rocks)according to lithology.Stacking models combined with tree-based models and linear regression were developed based on the datasets of three rock types.Model evaluation showed that the stacking model combined with random forest and linear regression was the optimal model for three rock types.UCS of metamorphic rocks was less predictable than that of sedimentary and igneous rocks.Nonetheless,the proposed stacking models can improve the predictive performance for UCS of metamorphic rocks.The developed predictive models can be applied to quickly predict UCS at engineering sites,which benefits the rapid and intelligent classification of rock masses.Moreover,the importance of SH,P-wave velocity,and PLS were analyzed for the estimation of UCS.SH was a reliable indicator for UCS evaluation across various rock types.P-wave velocity was a valid parameter for evaluating the UCS of igneous rocks,but it was not reliable for assessing the UCS of metamorphic rocks.

The rapid climate change-caused dichotomy on subtropical evergreen broad-leaved forest in Yunnan： Reduction in habitat diversity and increase in species diversity

Yunnan's biodiversity is under considerable pressure and subtropical evergreen broad-leaved forests in this area have become increasingly fragmented through agriculture,logging,planting of economic plants,mining activities and changing environment.The aims of the study are to investigate climate changeinduced changes of subtropical evergreen broad-leaved forests in Yunnan and identify areas of current species richness centers for conservation preparation.Stacked species distribution models were created to generate ensemble forecasting of species distributions,alpha diversity and beta diversity for Yunnan's subtropical evergreen broad-leaved forests in both current and future climate scenarios.Under stacked species distribution models in rapid climate changes scenarios,changes of water-energy dynamics may possibly reduce beta diversity and increase alpha diversity.This point provides insight for future conservation of evergreen broad-leaved forest in Yunnan,highlighting the need to fully consider the problem of vegetation homogenization caused by transformation of water-energy dynamics.

A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.While using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to train.For the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java plumleaves.Two new approaches are used to formthe hybridmodel suggested here.The guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture.As a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and guava.The proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and potatoes.These datasets are known as PLSD and PLSSD.Accuracy levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.

Data error propagation in stacked bioclimatic envelope models

Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models(BEMs).The approach can be used to investigate patterns and processes of species richness.If data limitations on individual species distributions are inevitable,but how do they affect inferences of patterns and processes of species richness?We investigate the influence of different data sources on estimated species richness gradients in China.We fitted BEMs using species distributions data for 334 bird species obtained from(1)global range maps,(2)regional checklists,(3)museum records and surveys,and(4)citizen science data using presence-only(Mahalanobis distance),presence-background(MAXENT),and presence–absence(GAM and BRT)BEMs.Individual species predictions were stacked to generate species richness gradients.Here,we show that different data sources and BEMs can generate spatially varying gradients of species richness.The environmental predictors that best explained species distributions also differed between data sources.Models using citizen-based data had the highest accuracy,whereas those using range data had the lowest accuracy.Potential richness patterns estimated by GAM and BRT models were robust to data uncertainty.When multiple data sets exist for the same region and taxa,we advise that explicit treatments of uncertainty,such as sensitivity analyses of the input data,should be conducted during the process of modeling.

A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters

An appropriate mathematical model can help researchers to simulate,evaluate,and control a proton exchange membrane fuel cell (PEMFC) stack system.Because a PEMFC is a nonlinear and strongly coupled system,many assumptions and approximations are considered during modeling.Therefore,some differences are found between model results and the real performance of PEMFCs.To increase the precision of the models so that they can describe better the actual performance,opti-mization of PEMFC model parameters is essential.In this paper,an artificial bee swarm optimization algorithm,called ABSO,is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications.For studying the usefulness of the proposed algorithm,ABSO-based results are compared with the results from a genetic algo-rithm (GA) and particle swarm optimization (PSO).The results show that the ABSO algorithm outperforms the other algorithms.

Direct tunneling gate current model for symmetric double gate junctionless transistor with SiO_2/high-k gate stacked dielectric

A junctionless transistor is emerging as a most promising device for the future technology in the decananometer regime. To explore and exploit the behavior completely, the understanding of gate tunneling current is of great importance. In this paper we have explored the gate tunneling current of a double gate junctionless transistor（DGJLT） for the first time through an analytical model, to meet the future requirement of expected high-k gate dielectric material that could replace SiO2. We therefore present the high-k gate stacked architecture of the DGJLT to minimize the gate tunneling current. This paper also demonstrates the impact of conduction band offset,workfunction difference and k-values on the tunneling current of the DGJLT.

A novel compact model for on-chip stacked transformers in RF-CMOS technology

A novel compact model for on-chip stacked transformers is presented.The proposed model topology gives a clear distinction to the eddy current,resistive and capacitive losses of the primary and secondary coils in the substrate.A method to analytically determine the non-ideal parasitics between the primary coil and substrate is provided.The model is further verified by the excellent match between the measured and simulated S-parameters on the extracted parameters for a 1：1 stacked transformer manufactured in a commercial RF-CMOS technology.

Predicting microbial extracellular electron transfer activity in paddy soils with soil physicochemical properties using machine learning

Soil extracellular electron transfer(EET)is a pivotal biological process within the realm of soil.Unfortunately,EET suffers from a lack of predictive models.Herein,an intricately crafted machine learning model has been developed for the purpose of predicting soil EET by using the physicochemical properties of soil as independent input variables and the EET capabilities in terms of current density(j_(max))and Coulombic charge(C_(out))as dependent output variables.An autoencoder ensemble stacking(AES)model was developed to address the aforementioned issue by integrating support vector machine,multilayer perceptron,extreme gradient boosting,and light gradient boosting machine algorithms as the stacking algorithms.With 10-fold crossvalidation,the AES model exhibited notable improvements in predicting j_(max)and C_(out),with average test R^(2)values of 0.83 and 0.84,respectively,surpassing those of single machine learning(ML)models and the basic ensemble model.By utilizing partial correlation plots(PDPs),Shapley Additive explanations(SHAP)values,and SHAP decision plots,we quantitatively explained the impact and contribution of the input molecules on the AES model’s predictions of j_(max)and C_(out).In the context of the SHAP method for the AES model,total carbon(TC)was identified as the most correlated descriptor for j_(max),while total organic carbon(TOC)stood out as the most relevant descriptor for C_(out).In the prediction tasks of j_(max)and C_(out)within the AES model,employing a multitask ML approach allowed the model to benefit from the shared information of input variables,thereby enhancing its overall generalizability.This study provides a feasible tool for the prediction of soil EET from soil physiochemical properties and an advanced understanding of the relationship between soil physiochemical properties and EET capability.

Fine-Grained Emotion Prediction for Movie and Television scene images

For the task of content retrieval,analysis and generation of film and television scene images in the field of intelligent editing,fine-grained emotion recognition and prediction of images is of great significance.In this paper,the fusion of traditional perceptual features,art features and multi-channel deep learning features are used to reflect the emotion expression of different levels of the image.In addition,the integrated learning model with stacking architecture based on linear regression coefficient and sentiment correlations,which is called the LS-stacking model,is proposed according to the factor association between multi-dimensional emotions.The experimental results prove that the mixed feature and LS-stacking model can predict well on the 16 emotion categories of the self-built image dataset.This study improves the fine-grained recognition ability of image emotion by computers,which helps to increase the intelligence and automation degree of visual retrieval and post-production system.

A stacked multiple kernel support vector machine for blast induced flyrock prediction

As a widely used rock excavation method in civil and mining construction works, the blasting operations and theinduced side effects are always investigated by the existing studies. The occurrence of flyrock is regarded as one ofthe most important issues induced by blasting operations, since the accurate prediction of which is crucial fordelineating safety zone. For this purpose, this study developed a flyrock prediction model based on 234 sets ofblasting data collected from Sugun Copper Mine site. A stacked multiple kernel support vector machine (stackedMK-SVM) model was proposed for flyrock prediction. The proposed stacked structure can effectively improve themodel performance by addressing the importance level of different features. For comparison purpose, 6 othermachine learning models were developed, including SVM, MK-SVM, Lagragian Twin SVM (LTSVM), ArtificialNeural Network (ANN), Random Forest (RF) and M5 Tree. This study implemented a 5-fold cross validationprocess for hyperparameters tuning purpose. According to the evaluation results, the proposed stacked MK-SVMmodel achieved the best overall performance, with RMSE of 1.73 and 1.74, MAE of 0.58 and 1.08, VAF of 98.95and 99.25 in training and testing phase, respectively.

Structures and Properties of Polyimide with Different Pre-imidization Degrees

A series of polyimide(PI)films derived from pyromellitic dianhydride(PMDA)and 4,4'-oxydianiline(ODA)were prepared with the employment of chemical pre-imidization,and the pre-imidization degree(pre-ID)was found influential on structures and properties of the films obtained.Specifically,a certain degree of chemical imidization could promote the in-plane orientation of molecular chains inside the film,which then enhanced the mechanical strength and reduced the coefficient of thermal expansion(CTE)of the films.Further,such pre-imidization process could expand the internal space gap inside the films,thereby lowering their dielectric constant and glass transition temperature.Our study provides a new approach for preparing high-performance PI films through chemical imidization.