Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed model.Through optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction accuracy.The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost methods.The established ML models exhibited better predic-tion performance and generalization ability via property transformation descriptors.In addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion rate.The results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models.

Prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines.To ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines.Aiming at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is proposed.The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil pipelines.The two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,respectively.Additionally,it shows a higher prediction accuracy compared to the ENN prediction model.In fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.

Failure evolution and disaster prediction of rock under uniaxial compression based on non-extensive statistical analysis of electric potential

Rock failure can cause serious geological disasters,and the non-extensive statistical features of electric potential(EP)are expected to provide valuable information for disaster prediction.In this paper,the uniaxial compression experiments with EP monitoring were carried out on fine sandstone,marble and granite samples under four displacement rates.The Tsallis entropy q value of EPs is used to analyze the selforganization evolution of rock failure.Then the influence of displacement rate and rock type on q value are explored by mineral structure and fracture modes.A self-organized critical prediction method with q value is proposed.The results show that the probability density function(PDF)of EPs follows the q-Gaussian distribution.The displacement rate is positively correlated with q value.With the displacement rate increasing,the fracture mode changes,the damage degree intensifies,and the microcrack network becomes denser.The influence of rock type on q value is related to the burst intensity of energy release and the crack fracture mode.The q value of EPs can be used as an effective prediction index for rock failure like b value of acoustic emission(AE).The results provide useful reference and method for the monitoring and early warning of geological disasters.

Design of a Multi-Stage Ensemble Model for Thyroid Prediction Using Learning Approaches

This research concentrates to model an efficient thyroid prediction approach,which is considered a baseline for significant problems faced by the women community.The major research problem is the lack of automated model to attain earlier prediction.Some existing model fails to give better prediction accuracy.Here,a novel clinical decision support system is framed to make the proper decision during a time of complexity.Multiple stages are followed in the proposed framework,which plays a substantial role in thyroid prediction.These steps include i)data acquisition,ii)outlier prediction,and iii)multi-stage weight-based ensemble learning process(MS-WEL).The weighted analysis of the base classifier and other classifier models helps bridge the gap encountered in one single classifier model.Various classifiers aremerged to handle the issues identified in others and intend to enhance the prediction rate.The proposed model provides superior outcomes and gives good quality prediction rate.The simulation is done in the MATLAB 2020a environment and establishes a better trade-off than various existing approaches.The model gives a prediction accuracy of 97.28%accuracy compared to other models and shows a better trade than others.

Machine Learning Prediction of Fetal Health Status from Cardiotocography Examination in Developing Healthcare Contexts

Reducing neonatal mortality is a critical global health objective,especially in resource-constrained developing countries.This study employs machine learning(ML)techniques to predict fetal health status based on cardiotocography(CTG)examination findings,utilizing a dataset from the Kaggle repository due to the limited comprehensive healthcare data available in developing nations.Features such as baseline fetal heart rate,uterine contractions,and waveform characteristics were extracted using the RFE wrapper feature engineering technique and scaled with a standard scaler.Six ML models—Logistic Regression(LR),Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),Categorical Boosting(CB),and Extended Gradient Boosting(XGB)—are trained via cross-validation and evaluated using performance metrics.The developed models were trained via cross-validation and evaluated using ML performance metrics.Eight out of the 21 features selected by GB returned their maximum Matthews Correlation Coefficient(MCC)score of 0.6255,while CB,with 20 of the 21 features,returned the maximum and highest MCC score of 0.6321.The study demonstrated the ability of ML models to predict fetal health conditions from CTG exam results,facilitating early identification of high-risk pregnancies and enabling prompt treatment to prevent severe neonatal outcomes.

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial products.The stockholders focused on the prediction of return rate and risk rate of financial products.Therefore,an automatic return rate bitcoin prediction model becomes essential for BC financial products.The newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive method.This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial products.The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products.Besides,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results.The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates.The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects.The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

Accurate Machine Learning Predictions of Sci-Fi Film Performance

A groundbreaking method is introduced to leverage machine learn-ing algorithms to revolutionize the prediction of success rates for science fiction films.In the captivating world of the film industry,extensive research and accurate forecasting are vital to anticipating a movie’s triumph prior to its debut.Our study aims to harness the power of available data to estimate a film’s early success rate.With the vast resources offered by the internet,we can access a plethora of movie-related information,including actors,directors,critic reviews,user reviews,ratings,writers,budgets,genres,Facebook likes,YouTube views for movie trailers,and Twitter followers.The first few weeks of a film’s release are crucial in determining its fate,and online reviews and film evaluations profoundly impact its opening-week earnings.Hence,our research employs advanced supervised machine learning techniques to predict a film’s triumph.The Internet Movie Database(IMDb)is a comprehensive data repository for nearly all movies.A robust predictive classification approach is developed by employing various machine learning algorithms,such as fine,medium,coarse,cosine,cubic,and weighted KNN.To determine the best model,the performance of each feature was evaluated based on composite metrics.Moreover,the significant influences of social media platforms were recognized including Twitter,Instagram,and Facebook on shaping individuals’opinions.A hybrid success rating prediction model is obtained by integrating the proposed prediction models with sentiment analysis from available platforms.The findings of this study demonstrate that the chosen algorithms offer more precise estimations,faster execution times,and higher accuracy rates when compared to previous research.By integrating the features of existing prediction models and social media sentiment analysis models,our proposed approach provides a remarkably accurate prediction of a movie’s success.This breakthrough can help movie producers and marketers anticipate a film’s triumph before its release,allowing them to tailor their promotional activities accordingly.Furthermore,the adopted research lays the foundation for developing even more accurate prediction models,considering the ever-increasing significance of social media platforms in shaping individ-uals’opinions.In conclusion,this study showcases the immense potential of machine learning algorithms in predicting the success rate of science fiction films,opening new avenues for the film industry.

Element yield rate prediction in ladle furnace based on improved GA-ANFIS

The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and the sample data that are used to establish data-driven models are always insufficient.Aiming at this problem,a combined method of genetic algorithm(GA) and adaptive neuro-fuzzy inference system(ANFIS) is proposed and applied to element yield rate prediction in ladle furnace(LF).In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples,smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS method.For facilitating the combination of fuzzy rules,feature construction method based on GA is used to reduce input dimension,and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local optima.The experimental and practical testing results show that the proposed method is more accurate than other prediction methods.

Prediction of leaching rate in heap leaching process by grey dynamic model GDM(1,1)

The method of developing GM（1,1） model is extended on the basis of grey system theory. Conditions for the transfer function that improve smoothness of original data sequence and decrease the revert error are given. The grey dynamic model is first combined with the transfer function to predict the leaching rate in heap leaching process. The results show that high prediction accuracy can be expected by using the proposed method. This provides a new approach to realize prediction and control of the future behavior of leaching kinetics.

Settlement prediction model of slurry suspension based on sedimentation rate attenuation

This paper introduces a slurry suspension settlement prediction model for cohesive sediment in a still water environment. With no sediment input and a still water environment condition, control forces between settling particles are significantly different in the process of sedimentation rate attenuation, and the settlement process includes the free sedimentation stage, the log-linear attenuation stage, and the stable consolidation stage according to sedimentation rate attenuation. Settlement equations for sedimentation height and time were established based on sedimentation rate attenuation properties of different sedimentation stages. Finally, a slurry suspension settlement prediction model based on slurry parameters was set up with a foundation being that the model parameters were determined by the basic parameters of slurry. The results of the settlement prediction model show good agreement with those of the settlement column experiment and reflect the main characteristics of cohesive sediment. The model can be applied to the prediction of cohesive soil settlement in still water environments.

Prediction of film ratings based on domain adaptive transfer learning

This paper examines the prediction of film ratings.Firstly,in the data feature engineering,feature construction is performed based on the original features of the film dataset.Secondly,the clustering algorithm is utilized to remove singular film samples,and feature selections are carried out.When solving the problem that film samples of the target domain are unlabelled,it is impossible to train a model and address the inconsistency in the feature dimension for film samples from the source domain.Therefore,the domain adaptive transfer learning model combined with dimensionality reduction algorithms is adopted in this paper.At the same time,in order to reduce the prediction error of models,the stacking ensemble learning model for regression is also used.Finally,through comparative experiments,the effectiveness of the proposed method is verified,which proves to be better predicting film ratings in the target domain.

Lung Cancer Prediction from Elvira Biomedical Dataset Using Ensemble Classifier with Principal Component Analysis

Machine learning algorithms (MLs) can potentially improve disease diagnostics, leading to early detection and treatment of these diseases. As a malignant tumor whose primary focus is located in the bronchial mucosal epithelium, lung cancer has the highest mortality and morbidity among cancer types, threatening health and life of patients suffering from the disease. Machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) have been used for lung cancer prediction. However they still face challenges such as high dimensionality of the feature space, over-fitting, high computational complexity, noise and missing data, low accuracies, low precision and high error rates. Ensemble learning, which combines classifiers, may be helpful to boost prediction on new data. However, current ensemble ML techniques rarely consider comprehensive evaluation metrics to evaluate the performance of individual classifiers. The main purpose of this study was to develop an ensemble classifier that improves lung cancer prediction. An ensemble machine learning algorithm is developed based on RF, SVM, NB, and KNN. Feature selection is done based on Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). This algorithm is then executed on lung cancer data and evaluated using execution time, true positives (TP), true negatives (TN), false positives (FP), false negatives (FN), false positive rate (FPR), recall (R), precision (P) and F-measure (FM). Experimental results show that the proposed ensemble classifier has the best classification of 0.9825% with the lowest error rate of 0.0193. This is followed by SVM in which the probability of having the best classification is 0.9652% at an error rate of 0.0206. On the other hand, NB had the worst performance of 0.8475% classification at 0.0738 error rate.

Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions

Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.

Annual variation rate of global sea－level rise and the prediction for the 21st century

An analytics method of predicting the annual variation rate (AVR) of global sea-level (GSL) is developed.Through the calculation by using the mean sea-level data collected from the tidal gauge stations over the world, a GSL rise of 0. 15～0. 16 cm/a is obtained. The predicted values of AVR of GSL for the 21st century are presented. The authors' results have been compared to those reported by other scientists at home and abroad. The method proposedhere is more convenient and precise.

Country-based modelling of COVID-19 case fatality rate:A multiple regression analysis

BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale cannot be fully understood due to lack of information.AIM To identify key factors that may explain the variability in case lethality across countries.METHODS We identified 21 Potential risk factors for coronavirus disease 2019(COVID-19)case fatality rate for all the countries with available data.We examined univariate relationships of each variable with case fatality rate(CFR),and all independent variables to identify candidate variables for our final multiple model.Multiple regression analysis technique was used to assess the strength of relationship.RESULTS The mean of COVID-19 mortality was 1.52±1.72%.There was a statistically significant inverse correlation between health expenditure,and number of computed tomography scanners per 1 million with CFR,and significant direct correlation was found between literacy,and air pollution with CFR.This final model can predict approximately 97%of the changes in CFR.CONCLUSION The current study recommends some new predictors explaining affect mortality rate.Thus,it could help decision-makers develop health policies to fight COVID-19.

Prediction of Photosynthetic Carbon Assimilation Rate of Individual Rice Leaves under Changes in Light Environment Using BLSTM-Augmented LSTM

A model to predict photosynthetic carbon assimilation rate(A)with high accuracy is important for forecasting crop yield and productivity.Long short-term memory(LSTM),a neural network suitable for time-series data,enables prediction with high accuracy but requires mesophyll variables.In addition,for practical use,it is desirable to have a technique that can predict A from easily available information.In this study,we propose a BLSTM augmented LSTM(BALSTM)model,which utilizes bi-directional LSTM(BLSTM)to indirectly reproduce the mesophyll variables required for LSTM.The most significant feature of the proposed model is that its hybrid architecture uses only three relatively easy-to-collect external environmental variables—photosynthetic photon flux density(Q_(in)),ambient CO_(2) concentration(C_(a)),and temperature(T_(air))—to generate mesophyll CO_(2) concentration(C_(i))and stomatal conductance to water vapor(g_(sw))as intermediate outputs.Then,A is predicted by applying the obtained intermediate outputs to the learning model.Accordingly,in this study,1)BALSTM(Q_(in),C_(a),T_(air))had a significantly higher A prediction accuracy than LSTM(Q_(in),C_(a),T_(air))in case of using only Q_(in),C_(a),and T_(air);2)BALSTMC_(i),g_(sw),which had C_(i) and g_(sw) as intermediate products,had the highest A prediction accuracy compared with other candidates;and 3)for samples where LSTM(Q_(in),C_(a),T_(air))had poor prediction accuracy,BALSTMC_(i),g_(sw)(Q_(in),C_(a),T_(air))clearly improved the results.However,it was found that incorrect predictions may be formed when certain factors are not reflected in the data(e.g.,timing,cultivar,and growth stage)or when the training data distribution that accounts for these factors differs from the predicted data distribution.Therefore,a robust model should be constructed in the future to improve the prediction accuracy of A by conducting gasexchange measurements(including a wide range of external environmental values)and by increasing the number of training data samples.

Prediction of maximal heart rate percent during constant intensity efforts on trained subjects

The purpose of this study is to evaluate the relationship between %HRmax and %vVO2max at constant efforts made at different intensities. In randomized order, males healthy subjects (Age: 25 ± 7 years, Weight: 70 ± 11 kg, VO2max: 55 ± 8 ml·kg–1·min–1) were divided into two groups, a trained one with more than 3 training sessions per week (n = 10) a moderately trained one with 3 drives or less per week (n = 15). The difference between the two groups corresponds to a time to exhaustion above and below 40 min at 80% vVO2max. All subjects performed 5 tests with a gradual increase in speed of 1 km·h–1 every 2 min and 4 constant speed tests at 60%, 70%, 80% and 90% VO2max. All test were performed at the same time of day (i.e., 18:00 h). The results of this study showed that eighteen collective regressions including different independent variables were developed to predict %HRmax. The individual equations developed, have r values between 0.974 and 0.993 and Syx, between 1.2 and 1.9 ml·kg–1·min–1, they are more accurate than the collective equations (one equation for all subjects) with r values between 0.81 to 0.89 and Syx, between 4.1 and 5.3 ml·kg–1·min–1. In conclusion, this study has demonstrated that the model of predictions of %HRmax from %vVO2max in triangular tests were not appropriate for rectangular efforts. From the equations developed, we find that the time to exhaustion at 90% vVO2max is the best predictor of level of endurance then the time limit to 80% vVO2max.

Click-Through Rate Prediction Network Based on User Behavior Sequences and Feature Interactions

In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,the design and implementation of recommendation models using information related to user behavior sequences is an important direction of current research in recommendation systems,and models calculate the likelihood of users clicking on target items based on their behavior sequence information.In order to explore the relationship between features,this paper improves and optimizes on the basis of deep interest network(DIN)proposed by Ali’s team.Based on the user behavioral sequences information,the attentional factorization machine(AFM)is integrated to obtain richer and more accurate behavioral sequence information.In addition,this paper designs a new way of calculating attention weights,which uses the relationship between the cosine similarity of any two vectors and the absolute value of their modal length difference to measure their relevance degree.Thus,a novel deep learning CTR prediction mode is proposed,that is,the CTR prediction network based on user behavior sequence and feature interactions deep interest and machines network(DIMN).We conduct extensive comparison experiments on three public datasets and one private music dataset,which are more recognized in the industry,and the results show that the DIMN obtains a better performance compared with the classical CTR prediction model.

Prediction and validation of diffusive uptake rates for indoor volatile organic compounds in axial passive samplers

The diffusive uptake rate is essential for using passive samplers to measure indoor volatile organic compounds(VOCs).The traditional theoretical model of passive samplers requires available regression formulas of uptake rates and physicochemical properties of adsorbents to predict the uptake rate.However,it is difficult to obtain the uptake rates of different VOCs under different sampling periods,and it is also difficult to obtain the physical parameters of adsorbents accurately and effectively.This study provides a reliable numerical prediction method of diffusive uptake rates of VOCs.The modeling was based on the standard automated thermal desorption(ATD)tubes packed with Tenax TA and the mass transfer process during adsorption.The experimental determinations of toluene uptake rate are carried out to verify the prediction model.Diffusive uptake rates of typical indoor VOCs are obtained from the literature to calibrate the key apparent parameters in the model by statistical regression fitting.The predicted model can provide the VOC diffusive uptake rates under different sampling duration with an average deviation of less than 5%.This study can provide the basis for fast and accurate prediction of diffusive uptake rates for various VOC pollutants in built environments.

A Lambda Layer-Based Convolutional Sequence Embedding Model for Click-Through Rate Prediction

In the era of intelligent economy, the click-through rate(CTR) prediction system can evaluate massive service information based on user historical information, and screen out the products that are most likely to be favored by users, thus realizing customized push of information and achieve the ultimate goal of improving economic benefits. Sequence modeling is one of the main research directions of CTR prediction models based on deep learning. The user's general interest hidden in the entire click history and the short-term interest hidden in the recent click behaviors have different influences on the CTR prediction results, which are highly important. In terms of capturing the user's general interest, existing models paid more attention to the relationships between item embedding vectors(point-level), while ignoring the relationships between elements in item embedding vectors(union-level). The Lambda layer-based Convolutional Sequence Embedding(LCSE) model proposed in this paper uses the Lambda layer to capture features from click history through weight distribution, and uses horizontal and vertical filters on this basis to learn the user's general preferences from union-level and point-level. In addition, we also incorporate the user's short-term preferences captured by the embedding-based convolutional model to further improve the prediction results. The AUC(Area Under Curve) values of the LCSE model on the datasets Electronic, Movie & TV and MovieLens are 0.870 7, 0.903 6 and 0.946 7, improving 0.45%, 0.36% and 0.07% over the Caser model, proving the effectiveness of our proposed model.