Nowadays,wireless communication devices turn out to be transportable owing to the execution of the current technologies.The antenna is the most important component deployed for communication purposes.The antenna plays...Nowadays,wireless communication devices turn out to be transportable owing to the execution of the current technologies.The antenna is the most important component deployed for communication purposes.The antenna plays an imperative role in receiving and transmitting the signals for any sensor network.Among varied antennas,micro strip fractal antenna(MFA)significantly contributes to increasing antenna gain.This study employs a hybrid optimization method known as the elephant clan updated grey wolf algorithm to introduce an optimized MFA design.This method optimizes antenna characteristics,including directivity and gain.Here,the factors,including length,width,ground plane length,height,and feed offset-X and feed offset-Y,are taken into account to achieve the best performance of gain and directivity.Ultimately,the superiority of the suggested technique over state-of-the-art strategies is calculated for various metrics such as cost and gain.The adopted model converges to a minimal value of 0.2872.Further,the spider monkey optimization(SMO)model accomplishes the worst performance over all other existing models like elephant herding optimization(EHO),grey wolf optimization(GWO),lion algorithm(LA),support vector regressor(SVR),bacterial foraging-particle swarm optimization(BF-PSO)and shark smell optimization(SSO).Effective MFA design is obtained using the suggested strategy regarding various parameters.展开更多
The wear behavior of AZ91 alloy was investigated by considering different parameters,such as load(10−50 N),sliding speed(160−220 mm/s)and sliding distance(250−1000 m).It was found that wear volume loss increased as lo...The wear behavior of AZ91 alloy was investigated by considering different parameters,such as load(10−50 N),sliding speed(160−220 mm/s)and sliding distance(250−1000 m).It was found that wear volume loss increased as load increased for all sliding distances and some sliding speeds.For sliding speed of 220 mm/s and sliding distance of 1000 m,the wear volume losses under loads of 10,20,30,40 and 50 N were calculated to be 15.0,19.0,24.3,33.9 and 37.4 mm3,respectively.Worn surfaces show that abrasion and oxidation were present at a load of 10 N,which changes into delamination at a load of 50 N.ANOVA results show that the contributions of load,sliding distance and sliding speed were 12.99%,83.04%and 3.97%,respectively.The artificial neural networks(ANN),support vector regressor(SVR)and random forest(RF)methods were applied for the prediction of wear volume loss of AZ91 alloy.The correlation coefficient(R2)values of SVR,RF and ANN for the test were 0.9245,0.9800 and 0.9845,respectively.Thus,the ANN model has promising results for the prediction of wear performance of AZ91 alloy.展开更多
Price prediction of goods is a vital point of research due to how common e-commerce platforms are.There are several efforts conducted to forecast the price of items using classicmachine learning algorithms and statist...Price prediction of goods is a vital point of research due to how common e-commerce platforms are.There are several efforts conducted to forecast the price of items using classicmachine learning algorithms and statisticalmodels.These models can predict prices of various financial instruments,e.g.,gold,oil,cryptocurrencies,stocks,and second-hand items.Despite these efforts,the literature has no model for predicting the prices of seasonal goods(e.g.,Christmas gifts).In this context,we framed the task of seasonal goods price prediction as a regression problem.First,we utilized a real online trailer dataset of Christmas gifts and then we proposed several machine learningbased models and one statistical-based model to predict the prices of these seasonal products.Second,we utilized a real-life dataset of Christmas gifts for the prediction task.Then,we proposed support vector regressor(SVR),linear regression,random forest,and ridgemodels as machine learningmodels for price prediction.Next,we proposed an autoregressive-integrated-movingaverage(ARIMA)model for the same purpose as a statistical-based model.Finally,we evaluated the performance of the proposed models;the comparison shows that the best performing model was the random forest model,followed by the ARIMA model.展开更多
The applications of four machine learning(ML)algorithms,namely:Support Vector Regressor(SVR),Extreme Gradient Boosting(XGBoost),Least absolute shrinkage and selection operator(Lasso),and Ridge,in predicting the corros...The applications of four machine learning(ML)algorithms,namely:Support Vector Regressor(SVR),Extreme Gradient Boosting(XGBoost),Least absolute shrinkage and selection operator(Lasso),and Ridge,in predicting the corrosion inhibition efficiency(IE)of Treculia africana(TA)leaves extract on AA7075-T7351 alloy,in corrosive 1.0 M HCl environment,with a small(42)sample space,have been studied.Time and resource constraints in traditional corrosion study methods have been avoided through feature engineering to expedite prediction process.The dominant features,which affected the IE,were done through feature importance and selection processes using pair plot matrix of features and Kendall correlation etc.,to remove redundant features.The results in the form of data visualization,feature importance,and the performance of each algorithm on the test set were explicitly depicted.The evaluation metrics,including coefficients of determination(R2)and root mean square error(RMSE),validated the efficacy of the models in predicting the IE of TA on AA7075-T7351 in 1.0 M HCl environments.Ridge model demonstrated superior accuracy,with R2 score of 0.972,particularly in handling the highly correlated dataset used in this study.SVR followed closely in performance(0.969).XGBoost proved reliable at R2 score of 0.953.Lasso with R2 of 0.952 was the least of the four models,due to its random feature selection method.The RMSE scores corroborated the prediction accuracies with values;4.145,4.408,5.138 and 5.462 respectively.This study revealed the viability of using the four machine learning algorithms in potential generalization ability of IE prediction accuracy,while offering an efficient and accurate alternative to traditional methods.展开更多
Proton exchange membrane fuel cell (PEMFC) is considered essential for climate change mitigation, and a fast and accurate model is necessary for its control and operation in practical applications. In this study, vari...Proton exchange membrane fuel cell (PEMFC) is considered essential for climate change mitigation, and a fast and accurate model is necessary for its control and operation in practical applications. In this study, various machine learning methods are used to develop data-based models for PEMFC performance attributes and internal states. Techniques such as Artificial Neural Network (ANN) and Support Vector Machine Regressor (SVR) are used to predict the cell voltage, membrane resistance, and membrane hydration level for various operating conditions. Varying input features such as cell current, temperature, reactant pressures, and humidity are introduced to evaluate the accuracy of the model, especially under extreme conditions. Two different sets of data are considered in this study, which are acquired from, a physics-based semiempirical model and a 1-D reduced-dimension Computational Fluid Dynamics model, respectively. The aspect of data preprocessing and hyperparameter tuning procedures are investigated that are extensively used to calibrate the artificial neural network layers and support vector regressor to predict the fuel cell attributes. ANN clearly shows an advantage in comparison with SVR, especially on a multivariable output regression. However, the SVR is advantageous to model simple regressions as it greatly reduces the level of computation without sacrificing accuracy. Data-based models for PEMFC are successfully developed on both the data sets by adapting advanced modeling techniques and calibration procedures such as ANN incorporating the dropout technique, resulting in an R2 ≥ 0.99 for all the predicted variables, demonstrating the ability to build accurate data-based models solely on data from validated physics-based models, reducing the dependency on extensive experimentation.展开更多
文摘Nowadays,wireless communication devices turn out to be transportable owing to the execution of the current technologies.The antenna is the most important component deployed for communication purposes.The antenna plays an imperative role in receiving and transmitting the signals for any sensor network.Among varied antennas,micro strip fractal antenna(MFA)significantly contributes to increasing antenna gain.This study employs a hybrid optimization method known as the elephant clan updated grey wolf algorithm to introduce an optimized MFA design.This method optimizes antenna characteristics,including directivity and gain.Here,the factors,including length,width,ground plane length,height,and feed offset-X and feed offset-Y,are taken into account to achieve the best performance of gain and directivity.Ultimately,the superiority of the suggested technique over state-of-the-art strategies is calculated for various metrics such as cost and gain.The adopted model converges to a minimal value of 0.2872.Further,the spider monkey optimization(SMO)model accomplishes the worst performance over all other existing models like elephant herding optimization(EHO),grey wolf optimization(GWO),lion algorithm(LA),support vector regressor(SVR),bacterial foraging-particle swarm optimization(BF-PSO)and shark smell optimization(SSO).Effective MFA design is obtained using the suggested strategy regarding various parameters.
文摘The wear behavior of AZ91 alloy was investigated by considering different parameters,such as load(10−50 N),sliding speed(160−220 mm/s)and sliding distance(250−1000 m).It was found that wear volume loss increased as load increased for all sliding distances and some sliding speeds.For sliding speed of 220 mm/s and sliding distance of 1000 m,the wear volume losses under loads of 10,20,30,40 and 50 N were calculated to be 15.0,19.0,24.3,33.9 and 37.4 mm3,respectively.Worn surfaces show that abrasion and oxidation were present at a load of 10 N,which changes into delamination at a load of 50 N.ANOVA results show that the contributions of load,sliding distance and sliding speed were 12.99%,83.04%and 3.97%,respectively.The artificial neural networks(ANN),support vector regressor(SVR)and random forest(RF)methods were applied for the prediction of wear volume loss of AZ91 alloy.The correlation coefficient(R2)values of SVR,RF and ANN for the test were 0.9245,0.9800 and 0.9845,respectively.Thus,the ANN model has promising results for the prediction of wear performance of AZ91 alloy.
文摘Price prediction of goods is a vital point of research due to how common e-commerce platforms are.There are several efforts conducted to forecast the price of items using classicmachine learning algorithms and statisticalmodels.These models can predict prices of various financial instruments,e.g.,gold,oil,cryptocurrencies,stocks,and second-hand items.Despite these efforts,the literature has no model for predicting the prices of seasonal goods(e.g.,Christmas gifts).In this context,we framed the task of seasonal goods price prediction as a regression problem.First,we utilized a real online trailer dataset of Christmas gifts and then we proposed several machine learningbased models and one statistical-based model to predict the prices of these seasonal products.Second,we utilized a real-life dataset of Christmas gifts for the prediction task.Then,we proposed support vector regressor(SVR),linear regression,random forest,and ridgemodels as machine learningmodels for price prediction.Next,we proposed an autoregressive-integrated-movingaverage(ARIMA)model for the same purpose as a statistical-based model.Finally,we evaluated the performance of the proposed models;the comparison shows that the best performing model was the random forest model,followed by the ARIMA model.
文摘The applications of four machine learning(ML)algorithms,namely:Support Vector Regressor(SVR),Extreme Gradient Boosting(XGBoost),Least absolute shrinkage and selection operator(Lasso),and Ridge,in predicting the corrosion inhibition efficiency(IE)of Treculia africana(TA)leaves extract on AA7075-T7351 alloy,in corrosive 1.0 M HCl environment,with a small(42)sample space,have been studied.Time and resource constraints in traditional corrosion study methods have been avoided through feature engineering to expedite prediction process.The dominant features,which affected the IE,were done through feature importance and selection processes using pair plot matrix of features and Kendall correlation etc.,to remove redundant features.The results in the form of data visualization,feature importance,and the performance of each algorithm on the test set were explicitly depicted.The evaluation metrics,including coefficients of determination(R2)and root mean square error(RMSE),validated the efficacy of the models in predicting the IE of TA on AA7075-T7351 in 1.0 M HCl environments.Ridge model demonstrated superior accuracy,with R2 score of 0.972,particularly in handling the highly correlated dataset used in this study.SVR followed closely in performance(0.969).XGBoost proved reliable at R2 score of 0.953.Lasso with R2 of 0.952 was the least of the four models,due to its random feature selection method.The RMSE scores corroborated the prediction accuracies with values;4.145,4.408,5.138 and 5.462 respectively.This study revealed the viability of using the four machine learning algorithms in potential generalization ability of IE prediction accuracy,while offering an efficient and accurate alternative to traditional methods.
基金support from Canadian Urban Transit Research and Innovation Consortium(CUTRIC)via Project Number 160028Natural Sciences and Engineering Research Council of Canada(NSERC)via a Discovery Grant。
文摘Proton exchange membrane fuel cell (PEMFC) is considered essential for climate change mitigation, and a fast and accurate model is necessary for its control and operation in practical applications. In this study, various machine learning methods are used to develop data-based models for PEMFC performance attributes and internal states. Techniques such as Artificial Neural Network (ANN) and Support Vector Machine Regressor (SVR) are used to predict the cell voltage, membrane resistance, and membrane hydration level for various operating conditions. Varying input features such as cell current, temperature, reactant pressures, and humidity are introduced to evaluate the accuracy of the model, especially under extreme conditions. Two different sets of data are considered in this study, which are acquired from, a physics-based semiempirical model and a 1-D reduced-dimension Computational Fluid Dynamics model, respectively. The aspect of data preprocessing and hyperparameter tuning procedures are investigated that are extensively used to calibrate the artificial neural network layers and support vector regressor to predict the fuel cell attributes. ANN clearly shows an advantage in comparison with SVR, especially on a multivariable output regression. However, the SVR is advantageous to model simple regressions as it greatly reduces the level of computation without sacrificing accuracy. Data-based models for PEMFC are successfully developed on both the data sets by adapting advanced modeling techniques and calibration procedures such as ANN incorporating the dropout technique, resulting in an R2 ≥ 0.99 for all the predicted variables, demonstrating the ability to build accurate data-based models solely on data from validated physics-based models, reducing the dependency on extensive experimentation.
