scholarly journals Machine learning and linear regression models to predict catchment-level base cation weathering rates across the southern Appalachian Mountain region, USA

2014 ◽  
Vol 50 (4) ◽  
pp. 2798-2814 ◽  
Author(s):  
Nicholas A. Povak ◽  
Paul F. Hessburg ◽  
Todd C. McDonnell ◽  
Keith M. Reynolds ◽  
Timothy J. Sullivan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Models ◽  
Base Cation ◽  
Mountain Region ◽  
Linear Regression Models ◽  
Weathering Rates ◽  
Appalachian Mountain ◽  
Southern Appalachian ◽  
Southern Appalachian Mountain

scholarly journals Application of Machine Learning Techniques to Predict Mechanical Properties for Polyamide 2200 (PA12) in Additive Manufacturing

2019 ◽  
Author(s):  
Ivanna Baturynska
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Linear Regression ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Tensile Modulus ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Elongation At Break

Additive manufacturing (AM) is an attractive technology for manufacturing industry due to flexibility in design and functionality, but inconsistency in quality is one of the major limitations that does not allow utilizing this technology for production of end-use parts. Prediction of mechanical properties can be one of the possible ways to improve the repeatability of the results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress and elongation at break for polyamide 2200 (also known as PA12). EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models have prediction accuracy higher than 80% only for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about material properties, these models need to be improved in the future based on additional experimental work.

scholarly journals Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing

2019 ◽  
Vol 9 (6) ◽  
pp. 1060
Author(s):  
Ivanna Baturynska
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Linear Regression ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Tensile Modulus ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Elongation At Break

Additive manufacturing (AM) is an attractive technology for the manufacturing industry due to flexibility in its design and functionality, but inconsistency in quality is one of the major limitations preventing utilizing this technology for the production of end-use parts. The prediction of mechanical properties can be one of the possible ways to improve the repeatability of results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress, and elongation at break for polyamide 2200 (also known as PA12). An EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models only have prediction accuracy higher than 80% for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about the material properties, these models need to be improved in the future based on additional experimental work.

scholarly journals Predictive performance of linear regression models in estimation of Artemia salina abundance using field and remote sensing data

2021 ◽  
pp. 88-95
Author(s):  
D. Krivoguz ◽  
◽  
R. Borovskaya ◽  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Spatial Data ◽  
Regression Models ◽  
Remote Sensing Data ◽  
Artemia Salina ◽  
Machine Learning Algorithms ◽  
Linear Regression Models ◽  
Light Spectrum ◽  
Fourth Degree

This research has been aimed at finding the possibilities for application of the linear regression models, as a part of the machine learning methods, in visual representation of the spatial patterns of Artemia salina distribution in the Southern Sivash. Development of such models allows for estimation of A. salina biomass in water bodies with high accuracy. For investigation of maximum absorption levels in different parts of the light spectrum, spectral signatures at all the monitoring stations have been compared with the satellite data, and the analysis of the absorption spectra for astaxanthin and hemoglobin has been conducted with a spectrophotometer. As a result, Sentinel-2 satellite looks very promising as a key spatial data provider that can be of major help in increasing the frequency of A. salina monitoring in the Southern Sivash. The linear regression models, fitted by the third and the fourth degree polynomials, have shown satisfactory results, suitable for their subsequent use in fisheries. On the other hand, it should be noted that these models are slightly prone to overfitting, which to some extent can distort further forecasts feeding upon the new data. In turn, linear regression models fitted by a polynomial of the first degree show less accurate results, but their advantages include the lack of tendency to overfit. It is also worth noting that small-sized datasets within the scope of this investigation do not appear to be problematic, and simple machine learning algorithms can provide good accuracy results, which are suitable for further application in this field.

scholarly journals Development of a machine-learning model to assess terminal ileum Endoscopic healing in pediatric Crohn's disease from Magnetic Resonance Enterography data

2021 ◽  
Author(s):  
Itai Guez ◽  
Gili Focht ◽  
Mary-Louise C.Greer ◽  
Ruth Cytter-Kuint ◽  
Li-tal Pratt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Linear Regression ◽  
Regression Models ◽  
Magnetic Resonance Enterography ◽  
Linear Regression Models ◽  
Learning Models ◽  
Machine Learning Model ◽  
Relevant Variables ◽  
Machine Learning Models

Background and Aims: Endoscopic healing (EH), is a major treatment goal for Crohn's disease(CD). However, terminal ileum (TI) intubation failure is common, especially in children. We evaluated the added-value of machine-learning models in imputing a TI Simple Endoscopic Score for CD (SES-CD) from Magnetic Resonance Enterography (MRE) data of pediatric CD patients. Methods: This is a sub-study of the prospective ImageKids study. We developed machine-learning and baseline linear-regression models to predict TI SES-CD score from the Magnetic Resonance Index of Activity (MaRIA) and the Pediatric Inflammatory Crohn's MRE Index (PICMI) variables. We assessed TI SES-CD predictions' accuracy for intubated patients with a stratified 2-fold validation experimental setup, repeated 50 times. We determined clinical impact by imputing TI SES-CD in patients with ileal intubation failure during ileocolonscopy. Results: A total of 223 children were included (mean age 14.1+-2.5 years), of whom 132 had all relevant variables (107 with TI intubation and 25 with TI intubation failure). The combination of a machine-learning model with the PICMI variables achieved the lowest SES-CD prediction error compared to a baseline MaRIA-based linear regression model for the intubated patients (N=107, 11.7 (10.5-12.5) vs. 12.1 (11.4-12.9), p<0.05). The PICMI-based models suggested a higher rate of patients with TI disease among the non-intubated patients compared to a baseline MaRIA-based linear regression model (N=25, up to 25/25 (100%) vs. 23/25 (92%)). Conclusions: Machine-learning models with clinically-relevant variables as input are more accurate than linear-regression models in predicting TI SES-CD and EH when using the same MRE-based variables.

scholarly journals A machine learning-based diagnostic model associated with knee osteoarthritis severity

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Soon Bin Kwon ◽  
Yunseo Ku ◽  
Hy uk-soo Han ◽  
Myung Chul Lee ◽  
Hee Chan Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Models ◽  
Learning Algorithm ◽  
T Test ◽  
Machine Learning Algorithm ◽  
Linear Regression Models ◽  
Estimation Model ◽  
Student’S T ◽  
Student’S T Test

Abstract Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. The study included 375 volunteers with variable KOA grades. The severity of KOA was determined using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). WOMAC scores were used to classify disease severity into three groups. A total of 1087 features were extracted from the gait data. An ANOVA and student’s t-test were performed and only features that were significant were selected for inclusion in the machine learning algorithm. Three WOMAC subscales (physical function, pain and stiffness) were further divided into three classes. An ANOVA was performed to determine which selected features were significantly related to the subscales. Both linear regression models and a random forest regression was used to estimate patient the WOMAC scores. Forty-three features were selected based on ANOVA and student’s t-test results. The following number of features were selected from each joint: 12 from hip, 1 feature from pelvic, 17 features from knee, 9 features from ankle, 1 feature from foot, and 3 features from spatiotemporal parameters. A significance level of < 0.0001 and < 0.00003 was set for the ANOVA and t-test, respectively. The physical function, pain, and stiffness subscales were related to 41, 10, and 16 features, respectively. Linear regression models showed a correlation of 0.723 and the machine learning algorithm showed a correlation of 0.741. The severity of KOA was predicted by gait analysis features, which were incorporated to develop an objective estimation model for KOA severity. The identified features may serve as a tool to guide rehabilitation and progress assessments. In addition, the estimation model presented here suggests an approach for clinical application of gait analysis data for KOA evaluation.

scholarly journals Short-term electricity grid maximum demand forecasting with the ARIMAX-SVR Machine Learning Hybrid Model

2021 ◽  
Vol 28 (1) ◽  
pp. 22-30
Author(s):  
Hon Fung Chow
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Hybrid Model ◽  
Regression Models ◽  
Demand Forecasting ◽  
Learning Model ◽  
Support Vector ◽  
Linear Regression Models ◽  
Short Term ◽  
Machine Learning Model

This paper proposes and discusses the viability of a short-term grid maximum demand forecasting model combining autoregressive integrated moving average with regressors (ARIMAX) and support vector regression (SVR). Grid demand forecasting is essential to generation unit scheduling, maintenance planning and system security. Traditionally, grid demand is forecasted using multivariate linear regression models with parameters adjusted to past data. A disadvantage of the linear regression model is that the parameters require regular adjustment, otherwise the prediction accuracy will deteriorate over time. With recent advances in the field of machine learning and lower computational costs, the usage of machine learning in the power industry becomes increasingly practicable. The proposed model is a machine learning model that combines ARIMAX and SVR to exploit their respective effectiveness in predicting linear and non-linear data. In contrast to linear regression models, the machine learning model automatically updates itself when new data is included. The hybrid model is benchmarked against other forecasting models and demonstrated a marked improvement in accuracy, achieving RMSE of 67.7MW and MAPE of 1.32% in a seven-day forecast.

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