Decision Tree Regression Models Research Articles

A tree-based extended model to predict intention to speed for taxi drivers

The need to predict and understand drivers’ intention to violate emphasizes the importance of developing a model based on the motives behind the action. This study aims to develop a model to predict a taxi driveŕs intention to speed in an urban area using variables including psychological factors, demographic information, and exposure. Aggregate (factor-based) and disaggregate (item-based) models of input variables will also be compared. In this study, the self-reported data on Tehran taxi drivers was collected in the form of a scenario-based questionnaire which is inspired by the Theory of Planned Behaviour (TPB). The intention to commit violations was predicted and modelled using stepwise regression and decision tree regression models. The results, based on the stepwise regression model showed a higher value of fitting score for an item-based linear model (R2 = 0.695), but the final model using the tree-based regression presented a better fit for the factored model (R2 = 0.746) considering the cross-validated model with the lowest deviation criteria (RMSD = 0.26). Furthermore, in this model attitude made the greatest contribution to the prediction of intention. The results obtained from the two different modelling approaches showed that exposure and age did not have much effect on the models. A main advantage of the current study is that the variables were compared and refined in two stages of exploratory factor analysis and stepwise regression before being entered into the tree regression model. In addition, the results of the factor-based model were compared with the item-based model.

Modeling and Estimation of Reference Evapotranspiration using Machine Learning Algorithms: A Comparative Performance Analysis

Fresh, clean water is necessary for human health. Currently, the agriculture sector uses the majority of freshwater for irrigation without using planning or optimization techniques. Evapotranspiration, which may have a major impact in planning water supply management and crop yield improvement, is an element of the hydrological cycle. Accurate anticipation of reference evapotranspiration (ETO) is an intricate job due to its nonlinear behavior. Machine learning approach based model may be an intelligent tool to predict the accurate ETO. This study investigates and compares the predictive skills of three regression based supervised learning algorithms: decision tree (dtr), and random forest (rfr), and k-nearest-neighbors (knnr) along with tuning their hyper-parameters like how many neighbors there are in knnr, minimum samples in dtr at a leaf node and quantity of trees in the rfr scenario to forecast ETO. Every model's performance is quantified on four different groups of meteorological parameters. Groups are created based on close correlation of meteorological parameters with ETo. In this investigation, analysis is carried out on daily meteorological information of New Delhi, India for the periods from 2000 to 2021. The predicted results of the knnr, dtr and rfr models on four groups of meteorological inputs (twelve different models) are compared with ETO obtained from the FAO-PM56 equations. The study's conclusions show that the k-nearest-neighbors and random forest regression-based models outperform the decision tree regression models concerning performance. The finest performance noted by knnr and rfr models with r2 (coefficient of determination) of 0.99 and rmse of 0.21 and 0.22 mm/day respectively whereas dtr model noted r2 of 0.98 and rmse of 0.40 mm/day. Therefor these models may provide scientists, engineers, and farmers with more potent choices for managing water resources and scheduling irrigation.

Data Visualization and Machine Learning Trend Analysis Forecasting Based on Roblox Stock Data

The stock market, as a highly complex, unstable and dynamically changing system, is affected by a wide range of factors, such as political, economic, social and technological, in terms of price changes. Therefore, stock price prediction is a challenging problem, but also a problem of great importance. In order to provide a more accurate and reliable method for stock closing price prediction, this paper aims to reduce investment risk and improve investment returns by comparing the prediction effects of different regression models for investors to make investment strategies and decisions. In this paper, Open, High, Low, and Adj Close are used as input parameters, Close is used as the target parameter, the final closing price of the stock is determined from the various stock data, the training set, validation set, and test set are divided in accordance with 6:2:2, and the predictive effect of the model is evaluated using the parameters of MSE, RMSE, MAE, MAPE, and R2. After the comparison of multiple machine learning algorithms, XGBoost regression model and Random Forest Regression model perform the best in the prediction of stock closing price, and their MSEs are 0.061 and 0.072 respectively; followed by LightGBM regression model and decision tree regression model, the MSEs of the two models are 0.113 and 0.299 respectively, but the prediction results of BP neural network regression are poor. By comparing the prediction results of different regression models, it can provide investors with a more accurate and reliable method to predict the closing price of stocks, help investors better formulate investment strategies and decisions, reduce investment risks and improve investment returns. This is of great significance to both individual and institutional investors.

Changing Relationship between Specific Leaf Area and Leaf Matter Dry Content of Moso Bamboo Phyllostachys pubescens syn. edulis (Poales: Poaceae) under the Stress of Pantana phyllostachysae (Lepidoptera: Lymantriidae)

The objective of this study was to deeply understand the adaptation mechanism of the functional traits of Moso bamboo Phyllostachys pubescens syn. edulis (Poales: Poaceae) leaves to the environment under different Pantana phyllostachysae Chao damage levels, analyzing the changes in the relationship between specific leaf area (SLA) and leaf dry matter content (LDMC). We combined different machine learning models (decision tree, RF, XGBoost, and CatBoost regression models), and used different canopy heights and different levels of infestation, to analyze the changes in the relationship between the two under different levels of infestation based on the results of the best estimation model. The results showed the following: (1) The SLA of Ph. pubescens showed a decreasing trend with the increase om insect pest degree, and LDMC showed an inverse trend. (2) The SLA of bamboo leaves was negatively correlated with the LDMC under different insect pest degrees; the correlation of the data under the healthy class was higher than that of other insect pest levels, and at the same time better than that of the full sample, which laterally confirmed the effect of insect pest stress on the functional traits of Ph. pubescens leaves. (3) When modeling under different infestation levels, the CatBoost model was used for heavy damage and the RF model was used for the rest of the cases; the decision tree regression model was used when modeling different canopy heights. The findings contribute certain insights into the nuanced responses and adaptive mechanisms of Ph. pubescens forests to environmental fluctuations. Moreover, these results furnish a robust scientific foundation, essential for ensuring the enduring sustainability of Ph. pubescens forest ecosystems.

Machine Learning-Based Prediction of NPSH, Noise, and Vibration Levels in Radial Pumps Under Cavitation Conditions

Cavitation, a physical phenomenon that detrimentally affects pump performance and reduces pump life, can cause wear on pump elements. Various engineering methods have been developed to identify the initiation and full development of the cavitation process. One such method is the determination of the net positive suction head (NPSH) through a 3% decrease in total head (Hm) at a constant flow rate. In radial pumps, commonly used in agricultural irrigation and industry, cavitation conditions result in a sudden drop in the Hm-Q curve, making it challenging to detect the 3% Hm value drop. This study differs from others in the literature by modelling NPSH, noise, and vibration levels using three machine learning models, specifically artificial neural networks (ANN), support vector machines (SVM), and decision tree regression (DTR). The best-performing model predicts NPSH, noise, and vibration levels corresponding to a 3% decrease in Hm level. The present study determined the NPSH values of a horizontal shaft centrifugal pump at different flow rates and constant operating speed, and the vibration and noise levels were measured for these NPSH values. For each of the NPSH, noise, and vibration levels, ANN, SVM and DTR models were created. The performances of these models were evaluated using criteria such as root mean squared error (RMSE), Mean Absolute Error (MAE) and mean absolute percentage error (MAPE). In addition, Taylor and error box diagrams were created. The ANN model and DTR yielded high accuracy predictions for NPSH values (R2 = 0.86 and R2 = 0.8, respectively). The ANN model provided the best prediction performance for noise and vibration levels. By entering the level of 3% drop in the Hm value of the pump as external data input to the ANN model, NPSH3, noise, and vibration levels were determined. The ANN models can be effectively employed to determine NPSH3, noise, and vibration levels, particularly in radial flow pumps, where detecting 3% reductions in manometric height value is challenging.

Research on the Diamond Price Prediction based on Linear Regression, Decision Tree and Random Forest

Diamonds are the symbol of the pure and indestructible love and the luxury that people have always sought after. However, because people know less about diamonds, they often only rely on the introduction of salespeople and jewelers in diamond trading. Therefore, it is difficult for consumers to buy diamonds of equal value and price. To solve this problem, this paper uses Multiple Linear Regression model, Decision Tree Regression model and Random Forest Regression model to predict diamond prices based on various diamond evaluation metrics in data set, so that consumers can intuitively learn about the normal price of the evaluation metrics of selected diamonds. Through this paper, it is found that the Random Forest Regression model has the best fitting and predictive ability in diamond prediction task, which is also the most recommended model.

Prediction of surface roughness and depth of cut in abrasive waterjet milling of alumina ceramic using Machine learning algorithms

In abrasive waterjet (AWJ) milling process, the ability to accurately predict outcomes such as surface roughness (Ra) and depth of cut (DoC) holds immense significance for cost reduction, resource optimization, and minimizing material wastage. However, a major challenge lies in the limited availability of AWJ milling datasets, hindering the development of robust machine learning models. To address the predicament of outcome, the present study employs a bootstrapping data augmentation technique to expand small AWJ milling datasets. Augmented data is used to train machine learning models, and hyperparameter fine-tuning is conducted using grid search and 5-fold cross-validation.In the prediction of surface roughness (Ra) for AWJ milling of alumina ceramic, the study finds that a Multi-Layer Perceptron (MLP) Regression model with a 4–100-100–1 architecture, 'relu' activation function, 'adam' solver, alpha value of 0.0001, and 'adaptive' learning rate outperforms the decision tree and random forest regression models. In the case of prediction of AWJ milling depth of cut (DoC) in alumina ceramic, the random forest regression model surpasses MLP and decision tree regression models. It employs hyperparameters including 100 estimators, 'gini' criterion, 'log2′ for maximum features, a maximum depth of 50, a minimum sample split of 10, a minimum sample leaf of 2, bootstrap as ‘True’, and a minimum impurity decrease of 0.The methodology followed to develop the machine learning models in this study is also tested in wire electrical discharge machining (WEDM) process in the machining aspects of metal matrix composites (MMC) for the prediction of surface roughness (Ra). The experimental dataset was adopted from (Satishkumar et al., 2011). In case of prediction of surface roughness (Ra) in WEDM, the random forest regression model outperforms MLP and decision tree regression models. These developed machine learning models hold the potential to greatly assist manufacturing engineers in selecting optimal input parameters for AWJ milling and WEDM, thereby reducing the wastage of machine resources.

Assessment of machine learning methods predicting the axial vibration frequencies of microbars

AbstractMicrobars are one of the important components of microelectromechanical systems. With the recent increase in their applications, the importance of understanding their mechanical response has become an important topic. In this study, for the first time, the mechanical behavior of microbars based on the strain gradient theory is investigated using a machine learning (ML) approach. Four distinct ML models, namely artificial neural network (ANN), support vector regression (SVR), decision tree regression (DTR), and random forest regression (RFR), are developed for microbars with clamped boundary conditions. The performance of these models is individually assessed using five different metrics: coefficient of determination (R2), Mean Absolute Error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and Nash‐Sutcliffe efficiency coefficient (NSE). The best‐performing model is selected based on these comparisons. Additionally, a semi‐analytical approach is employed to determine the natural frequencies of microbars under general elastic boundary conditions using the Fourier sine series and Stokes' transform. While the R2 value for all four models indicated a good fit of 0.999, the percentage difference in MAE and RMSE values between the training and testing data for DTR and RFR models was relatively higher as compared to ANN and SVR. The results showed that ANN and SVR models exhibit the best performance in predicting the natural frequencies on both training and testing data across all three metrics. Finally, a study on the free axial vibration frequencies of microbar under various effects was conducted.

Investigating automated regression models for estimating left ventricular ejection fraction levels in heart failure patients using circadian ECG features

Heart Failure (HF) significantly impacts approximately 26 million people worldwide, causing disruptions in the normal functioning of their hearts. The estimation of left ventricular ejection fraction (LVEF) plays a crucial role in the diagnosis, risk stratification, treatment selection, and monitoring of heart failure. However, achieving a definitive assessment is challenging, necessitating the use of echocardiography. Electrocardiogram (ECG) is a relatively simple, quick to obtain, provides continuous monitoring of patient’s cardiac rhythm, and cost-effective procedure compared to echocardiography. In this study, we compare several regression models (support vector machine (SVM), extreme gradient boosting (XGBOOST), gaussian process regression (GPR) and decision tree) for the estimation of LVEF for three groups of HF patients at hourly intervals using 24-hour ECG recordings. Data from 303 HF patients with preserved, mid-range, or reduced LVEF were obtained from a multicentre cohort (American and Greek). ECG extracted features were used to train the different regression models in one-hour intervals. To enhance the best possible LVEF level estimations, hyperparameters tuning in nested loop approach was implemented (the outer loop divides the data into training and testing sets, while the inner loop further divides the training set into smaller sets for cross-validation). LVEF levels were best estimated using rational quadratic GPR and fine decision tree regression models with an average root mean square error (RMSE) of 3.83% and 3.42%, and correlation coefficients of 0.92 (p<0.01) and 0.91 (p<0.01), respectively. Furthermore, according to the experimental findings, the time periods of midnight-1 am, 8–9 am, and 10–11 pm demonstrated to be the lowest RMSE values between the actual and predicted LVEF levels. The findings could potentially lead to the development of an automated screening system for patients with coronary artery disease (CAD) by using the best measurement timings during their circadian cycles.

Surface response regression and machine learning techniques to predict the characteristics of pervious concrete using non-destructive measurement: Ultrasonic pulse velocity and electrical resistivity

It is crucial to assess the characteristics of pervious concrete even post-construction. The quality monitoring of such a procedure is tricky in pervious concrete that it is typically avoided. As a potential means of enhancing the aforementioned quality control, the current study investigates the possibility of predicting characteristics of pervious concrete through response surface methodology and machine learning techniques using non-destructive test measurement (ultrasonic velocity and electrical resistivity). A total of 225 datasets from the experimental study were taken for this study. To recognize the best reliable model for predicting characteristics of pervious concrete, response surface methodology up to sixth order polynomial and five different machine learning techniques were used as statistical assessment tools. Using both ultrasonic pulse velocity and electrical resistivity as predictors for estimating porosity and compressive strength via response surface methodology, using a quadratic model for porosity prediction and a cubic model for compressive strength prediction are recommended. The machine learning models used in the research exhibited superior performance compared to the response surface methodology. Among the many machine learning models evaluated in this study, boosted decision tree regression model better predicted porosity (R2 = 0.92) and compressive strength (R2 = 0.92) of pervious concrete. Therefore, prediction models for the characteristics of pervious concrete are created using non-destructive measurement and machine learning techniques, which may ensure that the construction sector can utilize the offered models without any theoretical expertise.

Artificial intelligence systems in dental shade-matching: A systematic review.

Uses for artificial intelligence (AI) are being explored in contemporary dentistry, but artificial intelligence in dental shade-matching has not been systematically reviewed and evaluated. The purpose of this systematic review was to evaluate the accuracy of artificial intelligence in predicting dental shades in restorative dentistry. A systematic electronic search was performed with the databases MEDLINE (PubMed), Scopus, Cochrane Library, and Google Scholar. A manual search was also conducted. All titles and abstracts were subject to the inclusion criteria of observational, interventional studies, and studies published in the English language. Narrative reviews, systematic reviews, case reports, case series, letters to the editor, commentaries, studies that were not AI-based, studies that were not related to dentistry, and studies that were related to other disciplines in dentistry, other than restorative dentistry (prosthodontics and endodontics) were excluded. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies (non-randomized experimental studies). A third investigator was consulted to resolve the lack of consensus. Fifty-three articles were initially found from all the searches combined from articles published from 2008 till March 2023. A total of 15 articles met the inclusion criteria and were included in the systematic review. AI algorithms for shade-matching include fuzzy logic, a genetic algorithm with back-propagation neural network, back-propagation neural networks, convolutional neural networks, artificial neural networks, support vector machine algorithms, K-nearest neighbor with decision tree and random forest, deep learning for detection of dental prostheses based on object-detection applications, You Only Look Once-YOLO. Moment invariant was used for feature extraction. XG (Xtreme Gradient) Boost was used in one study as a gradient-boosting machine learning algorithm. The highest accuracy in the prediction of dental shades was the decision tree regression model for leucite-based dental ceramics of 99.7% followed by the fuzzy decision of 99.62%, and support vector machine using cross-validation of 97%. Lighting conditions, shade-matching devices and color space models, and the type of AI algorithm influence the accuracy of the prediction of dental shades. Knowledge-based systems and neural networks have shown better accuracy in predicting dental shades.

Comparison of Decision Tree Regression with Linear Regression Based on Prediction of Apple Stock Price

Machine learning has been increasingly used in stock price prediction with outstanding success. Decision tree regression models and linear regression models are both important models for predicting stock prices. The paper use decision tree regression and linear regression models to predict the opening price, closing price, high price and low price of Apple's stock price data respectively. The prediction effects of the two models are evaluated by the indicators of goodness of fit, mean square error, root mean square error and mean absolute error, and the prediction effects of the two models are compared. This experimental concludes that the decision tree regression model has better and more advantageous prediction results compared to the linear regression model. This study has guiding significance for machine learning in predicting stock prices when choosing a basic model or a combination of models for prediction.

Data-driven prediction of product yields and control framework of hydrocracking unit

In this study, the relationship between the operating conditions and the product yields and a control framework of the hydrocracking process was developed. The data were collected from a hydrocracking unit in a Chinese refinery. Principal component analysis was used to decrease the number of input variables. Then support vector machine, Gaussian process regression (GPR), and decision tree regression models were developed to establish the relationship above. The best model is GPR, whose Pearson correlation coefficient between the prediction value and the actual value is greater than 0.97 for all the product yields. Shapley additive explanations were performed to interpret the results of the GPR models. A control framework of the hydrocracking unit was then proposed based on the results above. The results show that the machine learning method is a valuable tool for predicting the yield of hydrocracking products, and the control framework proposed helps optimize hydrocracking product yields.

Utilisation of Machine Learning Techniques to Model Creep Behaviour of Low-Carbon Concretes

Low-carbon concrete mixes that incorporate high volumes of fly ash and slag as cement replacements are becoming increasingly more common as part of efforts to decarbonise the construction industry. Though environmental benefits are offered, concretes containing supplementary cementitious materials exhibit different creep behaviour when compared to conventional concrete. Creep can significantly impact long-term structural behaviour and influence the overall serviceability and durability of concrete structures. This paper develops a creep compliance prediction model using supervised machine learning techniques for concretes containing fly ash and slag as cement substitutes. Gaussian process regression (GPR), artificial neural networks (ANN), random forest regression (RFR) and decision tree regression (DTR) models were all considered. The dataset for model training was developed by mining relevant data from the Infrastructure Technology Institute of Northwestern University’s comprehensive creep dataset in addition to extracting data from the literature. Holdout validation was adopted with the data partitioned into training (70%) and validation (30%) sets. Based on statistical indicators, all machine learning models can accurately model creep compliance with the RFR and GPR found to be the best-performing models. The sensitivity of the GPR model’s performance to training repetitions, input variable selection and validation methodology was assessed, with the results indicating small variability. The importance of the selected input variables was analysed using the Shapley additive explanation. It was found that time was the most significant parameter, with loading age, compressive strength, elastic modulus, volume-to-surface ratio and relative humidity also showing high importance. Fly ash and silica fume content featured the least influence on creep prediction. Furthermore, the predictions of the trained models were compared to experimental data, which showed that the GPR, RFR and ANN models can accurately reflect creep behaviour and that the DTR model does not give accurate predictions.

Prediction of high-temperature creep in concrete using supervised machine learning algorithms

In this paper, supervised machine learning techniques were employed to develop a prediction model for concrete creep at elevated temperatures. Several algorithms were considered including artificial neural networks (ANN), decision tree regression (DTR), random forest regression, linear regression and Gaussian process regression (GPR) models. A dataset of short-term, basic creep was compiled from experimental data reported in the literature for algorithm training. The selected input variables were time, temperature, 28-day compressive strength, compressive stress, fine and coarse aggregate mass, and steel and PP fiber mass. The data was split into training and verification sets, with 70% used for model training and the remaining 30% for model verification. The results demonstrate that ANN, DTR, RFR and GPR models can accurately reflect concrete creep behavior at elevated temperatures based on statistical indicators. However, further analysis showed that the GPR was the best-performing model, providing highly accurate representations of creep behaviour and demonstrating superior performance to existing empirical equations. Conversely, the DTR was found to not accurately reflect experimental data. Additionally, a Shapley additive explanation analysis was conducted that assessed the significance of each input parameter on model prediction. This research highlights the potential of machine learning techniques to accurately model high -temperature concrete creep behavior and thus represents a powerful tool for engineers and researchers.

A machine learning approach for the prediction of pore pressure using well log data of Hikurangi Tuaheni Zone of IODP Expedition 372, New Zealand

Pore pressure (PP) information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development. PP prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design, and mud weight estimation for wellbore stability. However, the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting, where sedimentation rate is relatively high and PP can be driven by various complex geo-processes. To overcome these difficulties, an advanced machine learning (ML) tool is implemented in combination with empirical methods. The empirical method for PP prediction is comprised of data pre-processing and model establishment stage. Eaton's method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition 372. Gamma-ray, sonic travel time, bulk density and sonic derived porosity are extracted from well log data for the theoretical framework construction. The normal compaction trend (NCT) curve analysis is used to check the optimum fitting of the low permeable zone data. The statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model development. The dataset is prepared and divided into two parts: Training and Testing. The PP data and well log of borehole U1517A is pre-processed to scale in between [-1, +1] to fit into the input range of the non-linear activation/transfer function of the decision tree regression model. The Decision Tree Regression (DTR) algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.

Research on Performance Control of Intercalated Melt-blown Nonwoven Materials

This paper focuses on enhancing the performance of intercalated melt-blown nonwoven materials through performance control. The study explores whether the structural variables (thickness, porosity, and compression resilience ratio) and performance indicators (filtration resistance and efficiency) of intercalated products undergo significant changes, using analysis of variance (ANOVA). The experimental results indicate that intercalation significantly alters the product’s thickness, porosity, compression resilience ratio, and filtration efficiency. The study also establishes a decision tree regression model between process parameters (acceptable distance and hot air velocity) and structural variables, and a K-nearest neighbor regression model between structural variables and product performance indicators to investigate the relationship between process parameters and product performance during intercalation. Finally, the study determines the optimal performance indicators by controlling process parameters using the simulated annealing algorithm.

Development of a machine learning regression model for accurate sugarcane crop yield prediction, Jinja – Uganda

Sugarcane is one of the key crops grown worldwide and used for sugar processing, food, alcohol, biogas, fertilizer, and other products. There is a problem with Sugarcane yield prediction, yields aren’t accurately predicted, and this creates an impact on yields. This research looks at identifying methods used for the prediction, design, development, and evaluation of the three machine learning regression models used for predicting sugarcane yields in Uganda. This research was implemented using Data Science methodology, several machine learning algorithms for prediction of yields on dataset have been analyzed. The collected and analyzed dataset in this research had one output/ dependent variable and eight independent variables. The algorithms used to develop the prediction models are the Multiple Linear Regression algorithm, Decision Tree Regression algorithm, and Random Forest Regression algorithm to predict the output. The dataset of 3 years, 2019, 2020, and 2022 was considered and merged to train and test the model at a ratio of 80% to 20%. The accuracies of the individual models were compared after training, testing the dataset, and evaluation. The multiple Linear regression model results indicate that out of 100%, the model accuracy was 76.5%, the Decision Tree Regression Model scored 89.2%, Random Forest Regression Model was 94.6%. The random forest model came out as the best model. The Random Forest model has a percentage improvement of 60.4%. In future research, researchers can work on, A web-based machine learning model, Deep learning methods used to improve the model and more data can be used to improve the accuracy

Research and analysis on the preparation of C4 olefin by ethanol coupling method based on regression algorithm with computer technology

Computer technology has been used to study the different combination of catalyst and temperature on the ethanol conversion (one-way conversion rate of 100% per unit time ethanol (alcohol intake - ethanol residue)/ethanol intake) and C4 olefin selectivity (the proportion of all products of C4 olefin), it is necessary to decompose the catalyst composition into several indicators. The effects of several indexes on ethanol conversion and C4 olefins selectivity were analyzed by computer technology. Canonical correlation analysis was used to study them, and the coefficient before each component represented the degree of importance. The results showed that the higher the catalyst combination temperature, the higher the weight of hydroxyapatite, the higher the ethanol conversion and C4 olefins selectivity, and vice versa. When the C4 olefin yield (its value is the selectivity of C4 olefin to ethanol conversion) is as high as possible, the multiple linear stepwise regression function of catalyst parameters and temperature on C4 olefin yield is obtained. MATLAB software was used to calculate the function formula. The absolute value of coefficients determines the importance of each index to C4 olefin yield. Combined with chemical reaction, when the mass ratio is 200:200 and the temperature is 400℃, the yield of C4 olefins can be maximized, and the yield of C4 olefins is 44.728%. Through the establishment of decision tree regression model by computer technology, it can be intuitively concluded that when the temperature is lower than 350℃, the best experimental condition is the C4 olefins yield of 17.264%.

Prediction of material removal rate of SS304 alloy in electrochemical micromachining using machine learning

Electrochemical micromachining (ECMM) is a non-traditional machining process used in machining microfeatures that are being used in the fabrication of MEMS, finishing surgical tools, implants, and cooling channels. Machine learning (ML) algorithms can be used to predict the output of a machining process based on the given input parameters. In this Work, ML is interpreted into ECMM where, different ML models were built based on Decision tree regression, Random Forest regression, and Support vector regression algorithms to find the relationship between the interactive effects of the type of electrolyte, voltage, feed rate, and duty cycle on Material Removal Rate (MRR). The work also aims to find the best-fit algorithm and approach toward the prediction of MRR of AISI SS304 alloy when machined in ECMM using Regression Analysis. The results showed that higher efficiency was attained in the Decision tree regression model with a training accuracy of 99.87% and testing accuracy of 99.75% and decision tree was found to be the best fit algorithm for the ECMM process for the single response parameter.