Multiple Linear Regression Algorithm Research Articles

Evaluation and Analysis of English Predicate Constructions Incorporating Multiple Linear Regression Algorithms

Evaluation and Analysis of English Predicate Constructions Incorporating Multiple Linear Regression Algorithms

Machine Learning for the Model Prediction of Final Semester Assessment (FSA) using the Multiple Linear Regression Method

Corona virus (COVID-19) is the reason behind the collapse of the National Assembly. The first is the Final Semester Assessment (FSA) , which is a component of the student's graduation. The aforementioned evaluation process is a crucial consideration for the teacher since it uses several intricate surveys and mark components. A prediction model is employed to assist teachers in providing suitable results for student learning. The method that is used is called the multiple linear regression. This multiple linear regression algorithm yields an accuracy level of approximately 92%. The analysis results using the method are used as a guide to understanding student’s index. This index is a rating that appears based on the Minimum Credit Count (MCC). Therefore, the goal of this study is to determine students' understanding of the FSA prediction value, which will be taken into consideration through the results of the MCC weights in the form of a range in the form of "Grade." Additionally, the research aims to determine the accuracy of the results from the model obtained using multiple linear regression algorithms in predicting students' FSA.

Regression modeling and multi-objective optimization of rheological behavior of non-Newtonian hybrid antifreeze: Using different neural networks and evolutionary algorithms

The research used an artificial neural network (ANN) model to examine the rheological properties of hybrid non-Newtonian ferrofluids (HNFFs) composed of Fe-CuO, water, and ethylene glycol. The performance of neural network was optimized using seven regression methods (RMs), namely Group Method of Data Handling (GMDH), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), and Multiple Linear Regression (MLR). The findings highlighted GMDH method's superior performance when compared to neural networks. R and RMSE values attained by GMDH for the objective function (OF) μnf were 0.99436 and 2.0135, respectively. For the torque function OF, the values were 0.97652 and 4.8952. Margin of difference (MOD) calculations across various algorithms, such as MLP, SVM, RBF, D-Tree, ELM, MLR, and GMDH-Algos revealed significant disparities, indicating GMDH's efficacy. Comparison of R, RMSD, and standard deviation values between GMDH and MLR algorithms further underscored performance discrepancies. Specific parameters for which NSGA II Algo was rated highest among evaluation indices were as follows: a crossover rate of 0.7, a mutation rate of 0.02, a population size of 50, and 500 generations. Post-optimization, optimal values for μnf and torque (To) were determined as 6.595 and 3.543, respectively, with corresponding values for φ, T, and γ obtained as 0.185, 49.372, and 3.163, respectively. This comprehensive analysis sheds light on the effectiveness of various regression methods in modeling the rheological behavior of hybrid non-Newtonian ferrofluids, contributing to advancements in fluid dynamics research.

Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?

ABSTRACT Objectives To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods. Materials and Methods Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regression methods: multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed. Results In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models. Conclusions AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes.

Development of multiple explicit data-driven models for accurate prediction of CO2 minimum miscibility pressure

This study presents utilization of multiple data-driven models for predicting CO2 minimum miscibility pressure (MMP). The aim is to address the issue of existing models lacking explicit presentation. With a database of 155 data points, five models were developed using artificial neural network (ANN), multigene genetic programming (MGGP), support vector regression (SVR), multivariate adaptive regression splines (MARS), and multiple linear regression (MLR). Comparative analysis was conducted using statistical metrics (R2, MSE, MAE, RMSE), and sensitivity analysis was performed on input variables. The results showed that ANN and SVR had comparable predictive performance (ANN: R2 = 0.982, MSE = 0.00676, MAE = 0.9765, RMSE = 0.082), SVR (R2 = 0.935, MSE = 0.0041, MAE = 0.72, RMSE = 0.064) followed by MARS, MLR, and MGGP. Sensitivity analysis revealed that reservoir temperature was the most influential parameter across all models, except for the MLR algorithm where injected CO2 amount was crucial. These models can be used for a wide range of CO2 MMP ranging from 940 psi to 5830 psi, thus rendering them useful for any reservoir globally. These models offer improved accuracy and computational efficiency compared to existing ones, potentially reducing costs associated with laboratory experiments and providing rapid and precise CO2 MMP predictions.

Analysing The Development of Web-Based Multimedia Piano Playing Teaching Design Using Multiple Linear Regression Algorithm

The popularity of piano lessons has increased recently, and many individuals are taking the instrument. Using Web technology as its backbone and high-performance streaming media technology, this platform for teaching piano playing can efficiently address the issues of a limited audience, a steep learning curve, and a shortage of available materials in today's online education landscape. This kind of instruction may help bring the classroom closer together. The goals of this study are to increase the effectiveness of communicating via online text, to make it easier for students to get expert guidance from teachers, to satisfy students' interest in learning, to elevate students' piano effectiveness and creation, and to promote the widespread use of the piano. Using a Multiple Linear Regression Algorithm (MLRA), this study makes recommendations for designing an intelligent piano-playing teaching system, looks into the implementation of the piano-teaching system, and offers a technique to evaluate piano-playing using the MLRA model for the difficulties of online piano training. Teaching the piano is greatly aided by this paper's portrayal of an instructor encouraging students to keep up with their practice. The study results stated that MLRA's recognition accuracy is 95% and its mean square error rate is 12%.

Estimating the aboveground biomass of the Hulunbuir Grassland and exploring its spatial and temporal variations over the past ten years

In the past 10 years, extreme weather phenomena have increased, and global warming has markedly advanced; moreover, the intensity of human activity has gradually increased. These have an impact on the growth of global vegetation. Related studies have focused on the Tibetan Plateau and some northern provinces of China to estimate grassland AGB at a large scale but a low resolution. Hulunbuir Grassland is an important supplier of livestock products, and therefore, it is important to precisely map AGB and explore the response of Hulunbuir Grassland AGB to climate change and human activities at a high resolution to identify complex spatial details. In this study, we selected vegetation indices from Landsat 8 OLI and topographic indices and used multiple linear regression and machine learning algorithms to estimate the spatial distribution of AGB from 2013 to 2022. Then, we analyzed the correlations between AGB and cumulative precipitation and daily average temperature in summer and between population density and livestock density at the pixel level. Our results demonstrated that the RF model performed well, with an RMSE of 28.23 and R2 value of 0.74; the AGB was positively correlated with the cumulative precipitation in summer in 94.45 % of the area and negatively correlated with the daily average temperature in summer in 96.32 % of the area. We suggest that it is necessary to reduce grazing activities in future warm and drought years and to adjust sources of income to adapt to the decrease in AGB under global warming conditions. This study will provide reference for the countries or regions that depend on temperate grasslands for husbandry.

Research on monitoring model for maize entrainment loss sensor applicable to different operating parameters

The entrainment loss monitoring sensor plays a very important role in the intelligent development process of maize combine harvester. However, traditional entrainment loss sensor does not adjust its monitoring ratio according to different working conditions of the harvester, resulting in poor adaptability of the sensor to different working environments. To address the above problems and to improve the applicability of the entrainment loss monitoring sensor, this study proposed a maize entrainment loss sensor monitoring model based on the optimal sensor installation location. The distribution law of threshed material below the threshing drum was investigated, and the optimal installation location of the two symmetrical sensors was determined to be x = 1.7 m along the axial direction of the drum and r1 = 0.1 m and r2 = 0.7 m along the radial direction of the drum. Based on the above, the threshing process was simulated and analyzed using the discrete element method (DEM). A proportional model between the monitoring values and the true values of entrainment loss under different working parameters was established using multiple linear regression algorithms, and the determination coefficients (R2) of the sensor monitoring model on both sides was 0.813 and 0.876, respectively. Finally, the proposed monitoring models were validated through tests, the results show that the accuracy of the entrainment loss sensors can reach above 85 % under different working parameter conditions of the threshing system. The research findings of this paper are of great significance to improve the applicability of maize entrainment loss sensors, which can provide reference for the intelligent control system of combine harvester and provide technical support to enhance maize yield.

Advanced Machine Learning application for Permeability Prediction for (M) Formation in an Iraqi Oil Field

Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy and the performance of the algorithms. The random forest algorithm was the most accurate method leading to lowest Root Mean Square Prediction Error (RMSPE) and highest Adjusted R-Square than multiple linear regression algorithm for both training and testing subset respectively. Thus, random Forest algorithm is more trustable in permeability prediction in non-cored intervals and its distribution in the geological model.

Evaluation and Improvement of Enterprise Budget Performance Based on Multiple Linear Regression Algorithm

Evaluation and Improvement of Enterprise Budget Performance Based on Multiple Linear Regression Algorithm

Warfarin–A natural anticoagulant: A review of research trends for precision medication

BackgroundWarfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability. PurposeThe aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage. MethodsA bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature. ResultsBibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database. ConclusionFuture research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.

AI-Based Prediction of Carrot Yield and Quality on Tropical Agriculture

The adoption of artificial intelligence tools can improve production efficiency in the agroindustry. Our objective was to perform the predictive modeling of carrot yield and quality. The crop was grown in two commercial areas during the summer season in Brazil. The root samples were taken at 200 points with a 30 × 30 m sampling grid at 82 and 116 days after sowing in both areas. The total fresh biomass, aerial part, and root biometry were quantified for previous crop harvesting to measure yield. The quality of the roots was assessed by sub-sampling three carrots by the concentration of total soluble solids (°Brix) and firmness in the laboratory. Vegetation indices were extracted from satellite imagery. The most important variables for the predictive models were selected by principal component analysis and submitted to the Artificial Neural Network (ANN), Random Forest (RF), and Multiple Linear Regression (MLR) algorithms. SAVI and NDVI indices stood out as predictors of crop yield, and the results from the ANN (R2 = 0.68) were superior to the RF (R2 = 0.67) and MLR (R2 = 0.61) models. Carrot quality cannot be modeled by the predictive models in this study; however, it should be explored in future research, including other crop variables.

A Machine Learning–Based Readability Model for Gujarati Texts

This study aims to develop a machine learning–based model to predict the readability of Gujarati texts. The dataset was 50 prose passages from Gujarati literature. Fourteen lexical and syntactic readability text features were extracted from the dataset using a machine learning algorithm of the unigram parts of speech tagger and three Python programming scripts. Two samples of native Gujarati speaking secondary and higher education students rated the Gujarati texts for readability judgment on a 10-point scale of “easy” to “difficult” with the interrater agreement. After dimensionality reduction, seven text features as the independent variables and the mean readability rating as the dependent variable were used to train the readability model. As the students' level of education and gender were related to their readability rating, four readability models for school students, university students, male students, and female students were trained with a backward stepwise multiple linear regression algorithm of supervised machine learning. The trained model is comparable across the raters’ groups. The best model is the university students’ readability rating model. The model is cross-validated. It explains 91% and 88% of the variance in readability ratings at training and cross-validation, respectively, and its effect size and power are large and high.

PREDICTION OF BIODIESEL FUEL PRICES USING MULTIPLE LINEAR REGRESSION ALGORITHMS

Biodiesel is a fuel derived from palm oil and a type of fuel that is an alternative to renewable energy, can be renewed and has the potential to become a substitute for fossil sources that are used non-stop. The utilize of biodiesel can be an arrangement for Indonesia to diminish reliance on imported diesel fuel since biodiesel does not contain sulfur and is demonstrated to be ecologically inviting. The price of biodiesel-type biofuels can increase, decrease, or remain constant due to factors that influence it, including the price of biodiesel competitors, palm oil, and world crude oil. For this reason, it is necessary to have a method that can predict the price of biodiesel-type fuel so that in the future, the price of biodiesel-type biofuel does not decrease or become unable to compete with its competitors. Prediction of biodiesel fuel prices can be done by implementing a multiple linear regression algorithm, one of the data mining algorithms. RMSE results obtained in this study were 0.003 with a standard deviation of +/- 0.000 so it can be concluded that this algorithm is quite accurate in predicting the price of biodiesel-type biofuels. A comparison of the results of manual calculations with the implementation of RapidMiner in the study obtained the same results because there was a causal relationship between attributes. The use of the multiple linear regression algorithm in this research is useful in planning the right strategy and making decisions to maintain biodiesel market price stability in the future.

Development of pedestrian collision avoidance strategy based on the fusion of Markov and social force models

Abstract. In urban traffic, accurate prediction of pedestrian trajectory and advanced collision avoidance strategy can effectively reduce the collision risk between intelligent vehicles and pedestrians. In order to improve the prediction accuracy of pedestrian trajectory and the safety of collision avoidance, a longitudinal and lateral intelligent collision avoidance strategy based on pedestrian trajectory prediction is proposed. Firstly, the process of a pedestrian crossing the road is considered as a combination of free motion described by first-order Markov model and the constrained motion presented by improved social force model. The predicted pedestrian trajectory is obtained by weighted fusion of the trajectories of the two models with a multiple linear regression algorithm. Secondly, according to the predicted pedestrian trajectory and time to collision (TTC) the longitudinal and lateral collision avoidance strategy is designed. The improved artificial potential field method is used to plan the lateral collision avoidance path in real time based on the predicted pedestrian position, and a fuzzy controller is constructed to obtain the desired deceleration of the vehicle. Finally, the pedestrian motion fusion model and the longitudinal and lateral collision avoidance strategy are verified by Prescan and Simulink co-simulation. The results show that the average displacement error (ADE) and final displacement error (FDE) of pedestrian trajectory based on pedestrian motion fusion model are smaller compared with a Markov model and improved social force model, and the proposed pedestrian collision avoidance strategy can effectively achieve longitudinal and lateral collision avoidance.

Improving energy efficiency in ammonia production plants using machine learning

Energy efficiency is becoming increasingly important nowadays due to the need for energy conservation and environmental sustainability. An existing ammonia plant was simulated using Aspen Hysys software, the simulated plant was used to produce large volumes of data to train and test our machine-learning model. In this work a benchmark methodology is proposed through machine-learning (ML) techniques to identify patterns and anomalies in energy consumption. Our ML model was developed in Python programming language using a multiple linear regression algorithm. Microsoft Power BI was used to build interactive visualizations to illustrate insights to users. The ML model was able to predict energy consumption by developing equations that relate the energy consumption and the operating variables for each significant energy user in the ammonia plant. In this study, actual versus optimum energy consumption was analyzed for four ammonia production plants. The ML model identified the ammonia plant operating costs and potential savings by adjusting operating conditions. An annual saving of up to 3.9 million dollars was reached in one of the ammonia production plants operating costs. A reduction in carbon dioxide emissions of up to 4.7 ton per hour was achieved due to energy efficiency adjustment.

Quantifying soil organic matter for sustainable agricultural land management with soil color and machine learning technique

AbstractAs interest in sustainable agricultural land management continues to grow, there is a need for advanced techniques that enable easy and rapid quantification of soil characteristics. Soil organic matter (SOM) is a critical factor in determining soil health. Unfortunately, contemporary techniques for SOM content analysis are laborious, time consuming, and resource intensive. In response to this challenge, our study has developed a statistical model for forecasting the SOM content using soil color indices and machine learning algorithms. Color indices, including brightness, hue, and saturation, were derived from the soil images captured by a smartphone. The correlation between color indices and SOM reveals the negative correlations between brightness (−0.790) and hue (−0.420) with the SOM content at significance level (p) <0.01. Conversely, the saturation of soil color, which performed best with the predictive model, exhibited a positive relationship (+0.120, p<0.05). The predictive performance of our model outperformed random forest (RF) algorithm compared to both multiple linear regression (MLR) and support vector machine (SVM). The RF algorithm achieved the highest coefficient of determination (R2) value of 0.984. Furthermore, it demonstrated the lowest error metrics. Notably, the root mean squared error value for the RF algorithm was only 0.025% with the training dataset, whereas the MLR and SVM algorithms yielded relatively higher values at 0.029% and 0.110%, respectively. These findings highlight the presence of a nonlinear relationship in predicting the SOM content, which the RF algorithm effectively captures. This approach offers accurate predictions of the SOM content, supporting sustainable agricultural land management through rapid and easy quantification.

An Optimal House Price Prediction Algorithm: XGBoost

An accurate prediction of house prices is a fundamental requirement for various sectors, including real estate and mortgage lending. It is widely recognized that a property’s value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighborhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning (ML) techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare XGBoost, support vector regressor, random forest regressor, multilayer perceptron, and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction. Our findings present valuable insights and tools for stakeholders, facilitating more accurate property price estimates and, in turn, enabling more informed decision making to meet the housing needs of diverse populations while considering budget constraints.

Machine learning algorithms to study the relative roles of jet convection and natural convection in the presence of surface roughness elements on enhancement of jet impingement heat transfer

Based on data-driven approaches, eight machine-learning algorithms have been proposed to characterize the heat transfer performance of turbulent jet impingement cooling. These algorithms are designed to cool high-heat-flux surfaces modified with single protrusions, multi-protrusions, or V-grooves. To train these algorithms, an experimental dataset was used, which included 184 experiments with Reynolds numbers ranging from 10,000 to 33,000, nozzle exit-to-plate distances ranging from 2 to 10 jet diameters, and two different heat inputs of 60 W and 90 W. Multiple linear regression algorithms in Python programming were used to model jet impingement cooling, showing that the proposed machine learning models provided more accurate predictions with a 20.4 % to 66.5 % relative residual improvement over the existing conventional regression model based on performance metrics like R2 and relative residual. This improvement was leveraged to quantify natural convection's impact on the effectiveness of heat transfer enhancement due to different types and sizes of roughness elements in high-temperature applications. The research found that as the protrusion surface area increased from 0.02 % to 0.188 % compared to the flat plate, the effectiveness of heat transfer enhancement decreased from 2.69 % to 8.37 % at similar operating conditions. These results revealed that natural convection, which opposes forced jet impingement, was strengthened with increased heat input values, roughness element surface area, and the local turbulence generated. The higher natural convection weakened the enhancement afforded by jet impingement on the modified plates relative to the flat plate. Additionally, the capabilities of machine learning algorithms were explored to unlock the full potential of the jet impingement technique and to overcome the associated complexity in jet impingement applications for next-generation electronics cooling. Finally, the Decision Tree Classifier algorithm was applied to classify the performance of the surface roughness element as either effective or ineffective.

Man-Hour Estimation Model Development in BIM-Based Projects with Machine Learning-Regression Algorithms

Construction management is a service that uses project management techniques to planning, design and construction of a project. Another branch of construction management is the planning made during the bidding stages before the construction starts. At this stage, the scheduling of the work, mobilization and most importantly the project design and drawing time should be analyzed and accurate estimates should be made. For this estimation, the experience of the bidding organization is the biggest reference. Construction management techniques in a BIM-based project are challengeable. The biggest reason is that knowledge about BIM is much less than the others and still a developing system. Therefore, the margin of error in the estimated project duration to be predicted in the bids to be made for such projects increases. Considering that man-hour estimations are financially very important for the company. This study is aimed to make observations with advanced technologies which is applied ML algorithms and analysis methods for man-hour estimations in BIM projects at the bidding stage. The project information to perform a man-hour analysis of completed construction projects was requested from offices that have gained experience in BIM projects. The project information of 16 projects were tabulated. The collected information was converted into a data set and transferred into regression analysis algorithms using Python format. Data set, which was turned into a matrix, was analysed and the consistency of the parameters in data set was tested. Spent man-hour estimation was made, graphed over parameters with simple and multiple linear regression algorithms. 
 Keywords: Building Information Modelling (BIM), construction management, bidding, project management, Artificial Intelligence (AI), machine learning, regression analysis