Ensemble Modeling for the Classification of Birth Data

Machine learning(ML)and data mining are used in various fields such as data analysis,prediction,image processing and especially in healthcare.Researchers in the past decade have focused on applying ML and data mining to generate conclusions from historical data in order to improve healthcare systems by making predictions about the results.Using ML algorithms,researchers have developed applications for decision support,analyzed clinical aspects,extracted informative information from historical data,predicted the outcomes and categorized diseases which help physicians make better decisions.It is observed that there is a huge difference between women depending on the region and their social lives.Due to these differences,scholars have been encouraged to conduct studies at a local level in order to better understand those factors that affect maternal health and the expected child.In this study,the ensemble modeling technique is applied to classify birth outcomes based on either cesarean section(C-Section)or normal delivery.A voting ensemble model for the classification of a birth dataset was made by using a Random Forest(RF),Gradient Boosting Classifier,Extra Trees Classifier and Bagging Classifier as base learners.It is observed that the voting ensemble modal of proposed classifiers provides the best accuracy,i.e.,94.78%,as compared to the individual classifiers.ML algorithms are more accurate due to ensemble models,which reduce variance and classification errors.It is reported that when a suitable classification model has been developed for birth classification,decision support systems can be created to enable clinicians to gain in-depth insights into the patterns in the datasets.Developing such a system will not only allow health organizations to improve maternal health assessment processes,but also open doors for interdisciplinary research in two different fields in the region.

Ensemble Model for Spindle Thermal Displacement Prediction of Machine Tools

Numerous factors affect the increased temperature of a machine tool, including prolonged and high-intensity usage,tool-workpiece interaction, mechanical friction, and elevated ambient temperatures, among others. Consequently,spindle thermal displacement occurs, and machining precision suffers. To prevent the errors caused by thetemperature rise of the Spindle fromaffecting the accuracy during themachining process, typically, the factory willwarm up themachine before themanufacturing process.However, if there is noway to understand the tool spindle’sthermal deformation, the machining quality will be greatly affected. In order to solve the above problem, thisstudy aims to predict the thermal displacement of the machine tool by using intelligent algorithms. In the practicalapplication, only a few temperature sensors are used to input the information into the prediction model for realtimethermal displacement prediction. This approach has greatly improved the quality of tool processing.However,each algorithm has different performances in different environments. In this study, an ensemble model is used tointegrate Long Short-TermMemory (LSTM) with Support VectorMachine (SVM). The experimental results showthat the prediction performance of LSTM-SVM is higher than that of other machine learning algorithms.

An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

Breast cancer is one of the leading cancers among women.It has the second-highest mortality rate in women after lung cancer.Timely detection,especially in the early stages,can help increase survival rates.However,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience.However,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages.The research presented in this paper is focused on the multi-class classification of breast cancer.The deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound dataset.The proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble model.Deep learning models are fine-tuned along with hyperparameter tuning to achieve better results.Subsequently,entropy-based feature selection is used.Breast cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,respectively.The performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020.The consequences of this virus are quite frightening,especially when accompanied by an underlying disease.The novelty of the virus,the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process.Although the new test kits provide almost certain results,chest X-rays are extremely important to detect the progression and degree of the disease.In addition to the Covid-19 virus,pneumonia and harmless opacity of the lungs also complicate the diagnosis.Considering the negative results caused by the virus and the treatment costs,the importance of fast and accurate diagnosis is clearly seen.In this context,deep learning methods appear as an extremely popular approach.In this study,a hybrid model design with superior properties of convolutional neural networks is presented to correctly classify the Covid-19 disease.In addition,in order to contribute to the literature,a suitable dataset with balanced case numbers that can be used in all artificial intelligence classification studies is presented.With this ensemble model design,quite remarkable results are obtained for the diagnosis of three and four-class Covid-19.The proposed model can classify normal,pneumonia,and Covid-19 with 92.6%accuracy and 82.6%for normal,pneumonia,Covid-19,and lung opacity.

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance.Antenna size affects the quality factor and the radiation loss of the antenna.Metamaterial antennas can overcome the limitation of bandwidth for small antennas.Machine learning(ML)model is recently applied to predict antenna parameters.ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna.The accuracy of the prediction depends mainly on the selected model.Ensemble models combine two or more base models to produce a better-enhanced model.In this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial Antenna.Two base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the ensemble.Dynamic Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base models.The proposed model is compared with three based models and the average ensemble model.The results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

Optimized Two-Level Ensemble Model for Predicting the Parameters of Metamaterial Antenna

Employing machine learning techniques in predicting the parameters of metamaterial antennas has a significant impact on the reduction of the time needed to design an antenna with optimal parameters using simulation tools.In this paper,we propose a new approach for predicting the bandwidth of metamaterial antenna using a novel ensemble model.The proposed ensemble model is composed of two levels of regression models.The first level consists of three strong models namely,random forest,support vector regression,and light gradient boosting machine.Whereas the second level is based on the ElasticNet regression model,which receives the prediction results from the models in the first level for refinement and producing the final optimal result.To achieve the best performance of these regression models,the advanced squirrel search optimization algorithm(ASSOA)is utilized to search for the optimal set of hyper-parameters of each model.Experimental results show that the proposed two-level ensemble model could achieve a robust prediction of the bandwidth of metamaterial antenna when compared with the recently published ensemble models based on the same publicly available benchmark dataset.The findings indicate that the proposed approach results in root mean square error(RMSE)of(0.013),mean absolute error(MAE)of(0.004),and mean bias error(MBE)of(0.0017).These results are superior to the other competing ensemble models and can predict the antenna bandwidth more accurately.

Predictive analytics with ensemble modeling in laparoscopic surgery:A technical note

Predictive analytics have been widely used in the literature with respect to laparoscopic surgery and risk stratification.However,most predictive analytics in this field exploit generalized linearmodels for predictive purposes,which are limited by model assumptionsdincluding linearity between response variables and additive interactions between variables.In many instances,such assumptions may not hold true,and the complex relationship between predictors and response variables is usually unknown.To address this limitation,machine-learning algorithms can be employed to model the underlying data.The advantage of machine learning algorithms is that they usually do not require strict assumptions regarding data structure,and they are able to learn complex functional forms using a nonparametric approach.Furthermore,two or more machine learning algorithms can be synthesized to further improve predictive accuracy.Such a process is referred to as ensemble modeling,and it has been used broadly in various industries.However,this approach has not been widely reported in the laparoscopic surgical literature due to its complexity in both model training and interpretation.With this technical note,we provide a comprehensive overview of the ensemble-modeling technique and a step-by-step tutorial on how to implement ensemble modeling.

Adaptive Error Curve Learning Ensemble Model for Improving Energy Consumption Forecasting

Despite the advancement within the last decades in the field of smart grids,energy consumption forecasting utilizing the metrological features is still challenging.This paper proposes a genetic algorithm-based adaptive error curve learning ensemble(GA-ECLE)model.The proposed technique copes with the stochastic variations of improving energy consumption forecasting using a machine learning-based ensembled approach.A modified ensemble model based on a utilizing error of model as a feature is used to improve the forecast accuracy.This approach combines three models,namely CatBoost(CB),Gradient Boost(GB),and Multilayer Perceptron(MLP).The ensembled CB-GB-MLP model’s inner mechanism consists of generating a meta-data from Gradient Boosting and CatBoost models to compute the final predictions using the Multilayer Perceptron network.A genetic algorithm is used to obtain the optimal features to be used for the model.To prove the proposed model’s effectiveness,we have used a four-phase technique using Jeju island’s real energy consumption data.In the first phase,we have obtained the results by applying the CB-GB-MLP model.In the second phase,we have utilized a GA-ensembled model with optimal features.The third phase is for the comparison of the energy forecasting result with the proposed ECL-based model.The fourth stage is the final stage,where we have applied the GA-ECLE model.We obtained a mean absolute error of 3.05,and a root mean square error of 5.05.Extensive experimental results are provided,demonstrating the superiority of the proposed GA-ECLE model over traditional ensemble models.

Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills

Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.

Analysis and Forecasting COVID-19 Outbreak in Pakistan Using Decomposition and Ensemble Model

COVID-19 has caused severe health complications and produced a substantial adverse economic impact around the world.Forecasting the trend of COVID-19 infections could help in executing policies to effectively reduce the number of new cases.In this study,we apply the decomposition and ensemble model to forecast COVID-19 confirmed cases,deaths,and recoveries in Pakistan for the upcoming month until the end of July.For the decomposition of data,the Ensemble Empirical Mode Decomposition(EEMD)technique is applied.EEMD decomposes the data into small components,called Intrinsic Mode Functions(IMFs).For individual IMFs modelling,we use the Autoregressive Integrated Moving Average(ARIMA)model.The data used in this study is obtained from the official website of Pakistan that is publicly available and designated for COVID-19 outbreak with daily updates.Our analyses reveal that the number of recoveries,new cases,and deaths are increasing in Pakistan exponentially.Based on the selected EEMD-ARIMA model,the new confirmed cases are expected to rise from 213,470 to 311,454 by 31 July 2020,which is an increase of almost 1.46 times with a 95%prediction interval of 246,529 to 376,379.The 95%prediction interval for recovery is 162,414 to 224,579,with an increase of almost two times in total from 100802 to 193495 by 31 July 2020.On the other hand,the deaths are expected to increase from 4395 to 6751,which is almost 1.54 times,with a 95%prediction interval of 5617 to 7885.Thus,the COVID-19 forecasting results of Pakistan are alarming for the next month until 31 July 2020.They also confirm that the EEMD-ARIMA model is useful for the short-term forecasting of COVID-19,and that it is capable of keeping track of the real COVID-19 data in nearly all scenarios.The decomposition and ensemble strategy can be useful to help decision-makers in developing short-term strategies about the current number of disease occurrences until an appropriate vaccine is developed.

SVR-Boosting ensemble model for electricity price forecasting in electric power market

A revised support vector regression (SVR) ensemble model based on boosting algorithm (SVR-Boosting) is presented in this paper for electricity price forecasting in electric power market. In the light of characteristics of electricity price sequence, a new triangular-shaped 为oss function is constructed in the training of the forecasting model to inhibit the learning from abnormal data in electricity price sequence. The results from actual data indicate that, compared with the single support vector regression model, the proposed SVR-Boosting ensemble model is able to enhance the stability of the model output remarkably, acquire higher predicting accuracy, and possess comparatively satisfactory generalization capability.

Multi-Level Feature-Based Ensemble Model for Target-Related Stance Detection

Stance detection is the task of attitude identification toward a standpoint.Previous work of stance detection has focused on feature extraction but ignored the fact that irrelevant features exist as noise during higher-level abstracting.Moreover,because the target is not always mentioned in the text,most methods have ignored target information.In order to solve these problems,we propose a neural network ensemble method that combines the timing dependence bases on long short-term memory(LSTM)and the excellent extracting performance of convolutional neural networks(CNNs).The method can obtain multi-level features that consider both local and global features.We also introduce attention mechanisms to magnify target information-related features.Furthermore,we employ sparse coding to remove noise to obtain characteristic features.Performance was improved by using sparse coding on the basis of attention employment and feature extraction.We evaluate our approach on the SemEval-2016Task 6-A public dataset,achieving a performance that exceeds the benchmark and those of participating teams.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

Ensemble habitat suitability modeling of stomatopods with Oratosquilla oratoria as an example

Stomatopods are better known as mantis shrimp with considerable ecological importance in wide coastal waters globally. Some stomatopod species are exploited commercially, including Oratosquilla oratoria in the Northwest Pacific. Yet, few studies have published to promote accurate habitat identification of stomatopods, obstructing scientific management and conservation of these valuable organisms. This study provides an ensemble modeling framework for habitat suitability modeling of stomatopods, utilizing the O. oratoria stock in the Bohai Sea as an example. Two modeling techniques(i.e., generalized additive model(GAM) and geographical weighted regression(GWR)) were applied to select environmental predictors(especially the selection between two types of sediment metrics) that better characterize O. oratoria distribution and build separate habitat suitability models(HSM). The performance of the individual HSMs were compared on interpolation accuracy and transferability.Then, they were integrated to check whether the ensemble model outperforms either individual model, according to fishers’ knowledge and scientific survey data. As a result, grain-size metrics of sediment outperformed sediment content metrics in modeling O. oratoria habitat, possibly because grain-size metrics not only reflect the effect of substrates on burrow development, but also link to sediment heat capacity which influences individual thermoregulation. Moreover, the GWR-based HSM outperformed the GAM-based HSM in interpolation accuracy,while the latter one displayed better transferability. On balance, the ensemble HSM appeared to improve the predictive performance overall, as it could avoid dependence on a single model type and successfully identified fisher-recognized and survey-indicated suitable habitats in either sparsely sampled or well investigated areas.

Forecasting wind speed using a reinforcement learning hybrid ensemble model:a high-speed railways strong wind signal prediction study in Xinjiang,China

Considering the application of wind-forecasting technology along the railway,it becomes an effective means to reduce the risk of tain more reliable wind-speed prediction results,this study proposes an intelligent ensemble forecasting method for strong winds train derailment and overturning.Accurate prediction of crosswinds can provide scientific guidance for safe train operation.To obalong the high-speed railway.The method consists of three parts:the data preprocessing module,the hybrid prediction module and original wind speed data.Then,Broyden-Fletcher-Goldfarb-Shanno(BFGS)method,non-linear autoregressive network with exoge-the reinforcement learing ensemble module.First,fast ensemble empirical model decomposition(FEEMD)is used to process the prediction models for all the sublayers of decomposition.Finally,Q-learning is utilized to iteratively calculate the combined weights nous inputs(NARX)and deep belief network(DBN),three benchmark predictors with different characteristics are employed to build of the three models,and the prediction results of each sublayer are superimposed to obtain the model output.The real wind speed data of two railway stations in Xinjiang are used for experimental comparison.Experiments show that compared with the single benchmark model,the hybrid ensemble model has better accumacy and robustness for wind speed prediction along the railway.The 1-step forecasting results mean absolute error(MAE),mean absolute percentage error(MAPE)and root mean square error(RMSE)of Q-leaming-FEEMD-BFGS-NARX-DBN in site #1 and site #2 are 0.0894 m/s,0.6509%,0.1146 m/s,and 0.0458 m/s.0.2709%,0.0616 m/s.respectively.The proposed ensemble model is a promising method for railway wind speed prediction.

Dynamic Ensemble Multivariate Time Series Forecasting Model for PM2.5

In forecasting real time environmental factors,large data is needed to analyse the pattern behind the data values.Air pollution is a major threat towards developing countries and it is proliferating every year.Many methods in time ser-ies prediction and deep learning models to estimate the severity of air pollution.Each independent variable contributing towards pollution is necessary to analyse the trend behind the air pollution in that particular locality.This approach selects multivariate time series and coalesce a real time updatable autoregressive model to forecast Particulate matter(PM)PM2.5.To perform experimental analysis the data from the Central Pollution Control Board(CPCB)is used.Prediction is car-ried out for Chennai with seven locations and estimated PM’s using the weighted ensemble method.Proposed method for air pollution prediction unveiled effective and moored performance in long term prediction.Dynamic budge with high weighted k-models are used simultaneously and devising an ensemble helps to achieve stable forecasting.Computational time of ensemble decreases with paral-lel processing in each sub model.Weighted ensemble model shows high perfor-mance in long term prediction when compared to the traditional time series models like Vector Auto-Regression(VAR),Autoregressive Integrated with Mov-ing Average(ARIMA),Autoregressive Moving Average with Extended terms(ARMEX).Evaluation metrics like Root Mean Square Error(RMSE),Mean Absolute Error(MAE)and the time to achieve the time series are compared.

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.

Ensemble Based Temporal Weighting and Pareto Ranking (ETP) Model for Effective Root Cause Analysis

Root-cause identification plays a vital role in business decision making by providing effective future directions for the organizations.Aspect extraction and sentiment extraction plays a vital role in identifying the rootcauses.This paper proposes the Ensemble based temporal weighting and pareto ranking(ETP)model for Root-cause identification.Aspect extraction is performed based on rules and is followed by opinion identification using the proposed boosted ensemble model.The obtained aspects are validated and ranked using the proposed aspect weighing scheme.Pareto-rule based aspect selection is performed as the final selection mechanism and the results are presented for business decision making.Experiments were performed with the standard five product benchmark dataset.Performances on all five product reviews indicate the effective performance of the proposed model.Comparisons are performed using three standard state-of-the-art models and effectiveness is measured in terms of F-Measure and Detection rates.The results indicate improved performances exhibited by the proposed model with an increase in F-Measure levels at 1%–15%and detection rates at 4%–24%compared to the state-of-the-art models.

Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning

Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.

A novel ensemble model for predicting the performance of a novel vertical slot fishway

We investigate the performance of a novel vertical slot fishway by employing finite volume and surrogate models.Multiple linear regression,multiple log equation regression,gene expression programming,and combinations of these models are employed to predict the maximum turbulence,maximum velocity,resting area,and water depth of the middle pool in the fishway.The statistical parameters and error terms,including the coefficient of determination,root mean square error,normalized square error,maximum positive and negative errors,and mean absolute percentage error were employed to evaluate and compare the accuracy of the models.We also conducted a parametric study.The independent variables include the opening between bafles(OBB),the ratio of the length of the large and small baffles,the volume flow rate,and the angle of the large baffle.The results show that the key parameters of the maximum turbulence and velocity are the volume flow rate and OBB.