scholarly journals Modeling Performance of Butterfly Valves Using Machine Learning Methods

2021 ◽  
Vol 13 (24) ◽  
pp. 13545
Author(s):  
Alex Ekster ◽  
Vasiliy Alchakov ◽  
Ivan Meleshin ◽  
Alexandr Larionenko
Keyword(s):  
Machine Learning ◽  
Mathematical Model ◽  
Activated Sludge ◽  
Empirical Model ◽  
Field Testing ◽  
Gradient Boosting ◽  
Machine Model ◽  
Machine Learning Methods ◽  
Gradient Boosting Machine ◽  
Semi Empirical

Control of airflow of activated sludge systems has significant challenges due to the non-linearity of the control element (butterfly valve). To overcome this challenge, some valve manufacturers developed valves with linear characteristics. However, these valves are 10–100 times more expensive than butterfly valves. By developing models for butterfly valves installed characteristics and utilizing these models for real-time airflow control, the authors of this paper aimed to achieve the same accuracy of control using butterfly valves as achieved using valves with linear characteristics. Several approaches were tested to model the installed valve’s characteristics, such as a formal mathematical model utilizing Simscape/Matlab software, a semi-empirical model, and several machine learning methods (MLM), including regression, support vector machine, Gaussian process, decision tree, and deep learning. Several versions of the airflow-valve position models were developed using each machine learning method listed above. The one with the smallest forecast error was selected for field testing at the 55.5×103 m3/day 12 MGD City of Chico activated sludge system. Field testing of the formal mathematical model, semi-empirical model, and the regularized gradient boosting machine model (the best among MLMs) showed that the regularized gradient boosting machine model (RGBMM) provided the best accuracy. The use of the RGBMMs in airflow control loops since 2019 at the City of Chico wastewater treatment plant showed that these models are robust and accurate (2.9% median error).

scholarly journals Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension

2018 ◽  
Vol 129 (4) ◽  
pp. 675-688 ◽  
Author(s):  
Samir Kendale ◽  
Prathamesh Kulkarni ◽  
Andrew D. Rosenberg ◽  
Jing Wang
Keyword(s):  
Machine Learning ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Predictive Analytics ◽  
Characteristic Curve ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Machine Learning Methods ◽  
Gradient Boosting Machine ◽  
Operating Characteristic Curve

AbstractEditor’s PerspectiveWhat We Already Know about This TopicWhat This Article Tells Us That Is NewBackgroundHypotension is a risk factor for adverse perioperative outcomes. Machine-learning methods allow large amounts of data for development of robust predictive analytics. The authors hypothesized that machine-learning methods can provide prediction for the risk of postinduction hypotension.MethodsData was extracted from the electronic health record of a single quaternary care center from November 2015 to May 2016 for patients over age 12 that underwent general anesthesia, without procedure exclusions. Multiple supervised machine-learning classification techniques were attempted, with postinduction hypotension (mean arterial pressure less than 55 mmHg within 10 min of induction by any measurement) as primary outcome, and preoperative medications, medical comorbidities, induction medications, and intraoperative vital signs as features. Discrimination was assessed using cross-validated area under the receiver operating characteristic curve. The best performing model was tuned and final performance assessed using split-set validation.ResultsOut of 13,323 cases, 1,185 (8.9%) experienced postinduction hypotension. Area under the receiver operating characteristic curve using logistic regression was 0.71 (95% CI, 0.70 to 0.72), support vector machines was 0.63 (95% CI, 0.58 to 0.60), naive Bayes was 0.69 (95% CI, 0.67 to 0.69), k-nearest neighbor was 0.64 (95% CI, 0.63 to 0.65), linear discriminant analysis was 0.72 (95% CI, 0.71 to 0.73), random forest was 0.74 (95% CI, 0.73 to 0.75), neural nets 0.71 (95% CI, 0.69 to 0.71), and gradient boosting machine 0.76 (95% CI, 0.75 to 0.77). Test set area for the gradient boosting machine was 0.74 (95% CI, 0.72 to 0.77).ConclusionsThe success of this technique in predicting postinduction hypotension demonstrates feasibility of machine-learning models for predictive analytics in the field of anesthesiology, with performance dependent on model selection and appropriate tuning.

scholarly journals Using machine learning for sequence-level automated MRI protocol selection in neuroradiology

2017 ◽  
Vol 25 (5) ◽  
pp. 568-571 ◽  
Author(s):  
Andrew D Brown ◽  
Thomas R Marotta
Keyword(s):  
Machine Learning ◽  
Clinical Findings ◽  
Gradient Boosting ◽  
Support Vector ◽  
Machine Model ◽  
Imaging Protocol ◽  
Sequence Selection ◽  
Gradient Boosting Machine ◽  
Protocol Selection ◽  
Magnetic Resonance Imaging Mri

Abstract Incorrect imaging protocol selection can lead to important clinical findings being missed, contributing to both wasted health care resources and patient harm. We present a machine learning method for analyzing the unstructured text of clinical indications and patient demographics from magnetic resonance imaging (MRI) orders to automatically protocol MRI procedures at the sequence level. We compared 3 machine learning models – support vector machine, gradient boosting machine, and random forest – to a baseline model that predicted the most common protocol for all observations in our test set. The gradient boosting machine model significantly outperformed the baseline and demonstrated the best performance of the 3 models in terms of accuracy (95%), precision (86%), recall (80%), and Hamming loss (0.0487). This demonstrates the feasibility of automating sequence selection by applying machine learning to MRI orders. Automated sequence selection has important safety, quality, and financial implications and may facilitate improvements in the quality and safety of medical imaging service delivery.

Prediction of sports attendance: A comparative analysis

2020 ◽  
pp. 175433712098313
Author(s):  
Mehmet Şahin ◽  
Murat Uçar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Comparative Analysis ◽  
National Football League ◽  
Relevant Literature ◽  
Performance Evaluations ◽  
Influential Factor ◽  
Gradient Boosting ◽  
Home Team ◽  
Machine Learning Methods

In this study, a comparative analysis for predicting sports attendance demand is presented based on econometric, artificial intelligence, and machine learning methodologies. Data from more than 20,000 games from three major leagues, namely the National Basketball Association (NBA), National Football League (NFL), and Major League Baseball (MLB), were used for training and testing the approaches. The relevant literature was examined to determine the most useful variables as potential regressors in forecasting. To reveal the most effective approach, three scenarios containing seven cases were constructed. In the first scenario, each league was evaluated separately. In the second scenario, the three possible combinations of league pairings were evaluated, while in the third scenario, all three leagues were evaluated together. The performance evaluations of the results suggest that one of the machine learning methods, Gradient Boosting, outperformed the other methods used. However, the Artificial Neural Network, deep Convolutional Neural Network, and Decision Trees also provided productive and competitive predictions for sports games. Based on the results, the predictions for the NBA and NFL leagues are more satisfactory than the predictions of the MLB, which may be caused by the structure of the MLB. The results of the sensitivity analysis indicate that the performance of the home team is the most influential factor for all three leagues.

scholarly journals MODIS-FIRMS and ground-truthing based wildfire likelihood mapping of Sikkim Himalaya using machine learning algorithms.

2021 ◽  
Author(s):  
Polash Banerjee
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Tree Cover ◽  
Anthropogenic Factors ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Methods ◽  
Sikkim Himalaya ◽  
Environmental Features ◽  
Machine Learning Methods

Abstract Wildfires in limited extent and intensity can be a boon for the forest ecosystem. However, recent episodes of wildfires of 2019 in Australia and Brazil are sad reminders of their heavy ecological and economical costs. Understanding the role of environmental factors in the likelihood of wildfires in a spatial context would be instrumental in mitigating it. In this study, 14 environmental features encompassing meteorological, topographical, ecological, in situ and anthropogenic factors have been considered for preparing the wildfire likelihood map of Sikkim Himalaya. A comparative study on the efficiency of machine learning methods like Generalized Linear Model (GLM), Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting Model (GBM) has been performed to identify the best performing algorithm in wildfire prediction. The study indicates that all the machine learning methods are good at predicting wildfires. However, RF has outperformed, followed by GBM in the prediction. Also, environmental features like average temperature, average wind speed, proximity to roadways and tree cover percentage are the most important determinants of wildfires in Sikkim Himalaya. This study can be considered as a decision support tool for preparedness, efficient resource allocation and sensitization of people towards mitigation of wildfires in Sikkim.

scholarly journals Data Analytics for Monitoring the Satisfactory Parameters of Airline Passengers using Machine Learning Algorithms in Python

2020 ◽  
Vol 9 (3) ◽  
pp. 1231-1235
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Complex Information ◽  
Huge Data ◽  
Gradient Boosting Machine ◽  
Airline Passengers ◽  
Effective Representation

An effective representation by machine learning algorithms is to obtain the results especially in Big Data, there are numerous applications can produce outcome, whereas a Random Forest Algorithm (RF) Gradient Boosting Machine (GBM), Decision tree (DT) in Python will able to give the higher accuracy in regard with classifying various parameters of Airliner Passengers satisfactory levels. The complex information of airline passengers has provided huge data for interpretation through different parameters of satisfaction that contains large information in quantity wise. An algorithm has to support in classifying these data’s with accuracies. As a result some of the methods may provide less precision and there is an opportunity of information cancellation and furthermore information missing utilizing conventional techniques. Subsequently RF and GBM used to conquer the unpredictability and exactness about the information provided. The aim of this study is to identify an Algorithm which is suitable for classifying the satisfactory level of airline passengers with data analytics using python by knowing the output. The optimization and Implementation of independent variables by training and testing for accuracy in python platform determined the variation between the each parameters and also recognized RF and GBM as a better algorithm in comparison with other classifying algorithms.

Development of a Diabetes Melitus Detection and Prediction Model Using Light Gradient Boosting Machine and K-Nearest Neighbour

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
B. A Omodunbi
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Hybrid Model ◽  
Learning Model ◽  
Experimental Result ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Machine Learning Model ◽  
Gradient Boosting Machine ◽  
Receiver Operating

Diabetes mellitus is a health disorder that occurs when the blood sugar level becomes extremely high due to body resistance in producing the required amount of insulin. The aliment happens to be among the major causes of death in Nigeria and the world at large. This study was carried out to detect diabetes mellitus by developing a hybrid model that comprises of two machine learning model namely Light Gradient Boosting Machine (LGBM) and K-Nearest Neighbor (KNN). This research is aimed at developing a machine learning model for detecting the occurrence of diabetes in patients. The performance metrics employed in evaluating the finding for this study are Receiver Operating Characteristics (ROC) Curve, Five-fold Cross-validation, precision, and accuracy score. The proposed system had an accuracy of 91% and the area under the Receiver Operating Characteristic Curve was 93%. The experimental result shows that the prediction accuracy of the hybrid model is better than traditional machine learning

Short- and Medium-range Prediction of Relativistic Electron Flux in the Earth’s Outer Radiation Belt by Machine Learning Methods

2021 ◽  
Vol 3 ◽  
pp. 47-57
Author(s):  
I. N. Myagkova ◽  
◽  
V. R. Shirokii ◽  
Yu. S. Shugai ◽  
O. G. Barinov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Radiation Belt ◽  
Gradient Boosting ◽  
Relativistic Electrons ◽  
Learning Methods ◽  
Outer Radiation Belt ◽  
Machine Learning Methods ◽  
The Earth ◽  
Skill Scores ◽  
Medium Range

The ways are studied to improve the quality of prediction of the time series of hourly mean fluxes and daily total fluxes (fluences) of relativistic electrons in the outer radiation belt of the Earth 1 to 24 hours ahead and 1 to 4 days ahead, respectively. The prediction uses an approximation approach based on various machine learning methods, namely, artificial neural networks (ANNs), decision tree (random forest), and gradient boosting. A comparison of the skill scores of short-range forecasts with the lead time of 1 to 24 hours showed that the best results were demonstrated by ANNs. For medium-range forecasting, the accuracy of prediction of the fluences of relativistic electrons in the Earth’s outer radiation belt three to four days ahead increases significantly when the predicted values of the solar wind velocity near the Earth obtained from the UV images of the Sun of the AIA (Atmospheric Imaging Assembly) instrument of the SDO (Solar Dynamics Observatory) are included to the list of the input parameters.

scholarly journals Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model

2018 ◽  
Vol 7 (11) ◽  
pp. 428 ◽  
Author(s):  
Hyung-Chul Lee ◽  
Soo Yoon ◽  
Seong-Mi Yang ◽  
Won Kim ◽  
Ho-Geol Ryu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Transplantation ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Gradient Boosting Machine

Acute kidney injury (AKI) after liver transplantation has been reported to be associated with increased mortality. Recently, machine learning approaches were reported to have better predictive ability than the classic statistical analysis. We compared the performance of machine learning approaches with that of logistic regression analysis to predict AKI after liver transplantation. We reviewed 1211 patients and preoperative and intraoperative anesthesia and surgery-related variables were obtained. The primary outcome was postoperative AKI defined by acute kidney injury network criteria. The following machine learning techniques were used: decision tree, random forest, gradient boosting machine, support vector machine, naïve Bayes, multilayer perceptron, and deep belief networks. These techniques were compared with logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUROC). AKI developed in 365 patients (30.1%). The performance in terms of AUROC was best in gradient boosting machine among all analyses to predict AKI of all stages (0.90, 95% confidence interval [CI] 0.86–0.93) or stage 2 or 3 AKI. The AUROC of logistic regression analysis was 0.61 (95% CI 0.56–0.66). Decision tree and random forest techniques showed moderate performance (AUROC 0.86 and 0.85, respectively). The AUROC of support the vector machine, naïve Bayes, neural network, and deep belief network was smaller than that of the other models. In our comparison of seven machine learning approaches with logistic regression analysis, the gradient boosting machine showed the best performance with the highest AUROC. An internet-based risk estimator was developed based on our model of gradient boosting. However, prospective studies are required to validate our results.

Sign in / Sign up

Export Citation Format

Share Document