Implementation of Machine Learning Algorithms for Prediction of Fluidelastic Instability in Tube Arrays

2021 ◽  
Vol 143 (2) ◽  
Author(s):  
Joaquin E. Moran ◽  
Yasser Selima
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Two Phase ◽  
Factors Affecting ◽  
Logistic Regression Models ◽  
Number Of Factors ◽  
Tube Arrays ◽  
Fluidelastic Instability

Abstract Fluidelastic instability (FEI) in tube arrays has been studied extensively experimentally and theoretically for the last 50 years, due to its potential to cause significant damage in short periods. Incidents similar to those observed at San Onofre Nuclear Generating Station indicate that the problem is not yet fully understood, probably due to the large number of factors affecting the phenomenon. In this study, a new approach for the analysis and interpretation of FEI data using machine learning (ML) algorithms is explored. FEI data for both single and two-phase flows have been collected from the literature and utilized for training a machine learning algorithm in order to either provide estimates of the reduced velocity (single and two-phase) or indicate if the bundle is stable or unstable under certain conditions (two-phase). The analysis included the use of logistic regression as a classification algorithm for two-phase flow problems to determine if specific conditions produce a stable or unstable response. The results of this study provide some insight into the capability and potential of logistic regression models to analyze FEI if appropriate quantities of experimental data are available.

scholarly journals Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Zhang ◽  
Xia Zhe ◽  
Min Tang ◽  
Jing Zhang ◽  
Jialiang Ren ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Low Grade ◽  
High Grade ◽  
Random Forest Tree

Purpose. This study aimed to investigate the value of biparametric magnetic resonance imaging (bp-MRI)-based radiomics signatures for the preoperative prediction of prostate cancer (PCa) grade compared with visual assessments by radiologists based on the Prostate Imaging Reporting and Data System Version 2.1 (PI-RADS V2.1) scores of multiparametric MRI (mp-MRI). Methods. This retrospective study included 142 consecutive patients with histologically confirmed PCa who were undergoing mp-MRI before surgery. MRI images were scored and evaluated by two independent radiologists using PI-RADS V2.1. The radiomics workflow was divided into five steps: (a) image selection and segmentation, (b) feature extraction, (c) feature selection, (d) model establishment, and (e) model evaluation. Three machine learning algorithms (random forest tree (RF), logistic regression, and support vector machine (SVM)) were constructed to differentiate high-grade from low-grade PCa. Receiver operating characteristic (ROC) analysis was used to compare the machine learning-based analysis of bp-MRI radiomics models with PI-RADS V2.1. Results. In all, 8 stable radiomics features out of 804 extracted features based on T2-weighted imaging (T2WI) and ADC sequences were selected. Radiomics signatures successfully categorized high-grade and low-grade PCa cases ( P < 0.05 ) in both the training and test datasets. The radiomics model-based RF method (area under the curve, AUC: 0.982; 0.918), logistic regression (AUC: 0.886; 0.886), and SVM (AUC: 0.943; 0.913) in both the training and test cohorts had better diagnostic performance than PI-RADS V2.1 (AUC: 0.767; 0.813) when predicting PCa grade. Conclusions. The results of this clinical study indicate that machine learning-based analysis of bp-MRI radiomic models may be helpful for distinguishing high-grade and low-grade PCa that outperformed the PI-RADS V2.1 scores based on mp-MRI. The machine learning algorithm RF model was slightly better.

scholarly journals Machine learning algorithm to predict delirium from emergency department data

2021 ◽  
Author(s):  
Sangil Lee ◽  
Brianna Mueller ◽  
W. Nick Street ◽  
Ryan M. Carnahan
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Logistic Regression ◽  
Random Forest ◽  
Barthel Index ◽  
Risk Model ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Sensitivity Threshold ◽  
Mortality And Morbidity

AbstractIntroductionDelirium is a cerebral dysfunction seen commonly in the acute care setting. Delirium is associated with increased mortality and morbidity and is frequently missed in the emergency department (ED) by clinical gestalt alone. Identifying those at risk of delirium may help prioritize screening and interventions.ObjectiveOur objective was to identify clinically valuable predictive models for prevalent delirium within the first 24 hours of hospitalization based on the available data by assessing the performance of logistic regression and a variety of machine learning models.MethodsThis was a retrospective cohort study to develop and validate a predictive risk model to detect delirium using patient data obtained around an ED encounter. Data from electronic health records for patients hospitalized from the ED between January 1, 2014, and December 31, 2019, were extracted. Eligible patients were aged 65 or older, admitted to an inpatient unit from the emergency department, and had at least one DOSS assessment or CAM-ICU recorded while hospitalized. The outcome measure of this study was delirium within one day of hospitalization determined by a positive DOSS or CAM assessment. We developed the model with and without the Barthel index for activity of daily living, since this was measured after hospital admission.ResultsThe area under the ROC curves for delirium ranged from .69 to .77 without the Barthel index. Random forest and gradient-boosted machine showed the highest AUC of .77. At the 90% sensitivity threshold, gradient-boosted machine, random forest, and logistic regression achieved a specificity of 35%. After the Barthel index was included, random forest, gradient-boosted machine, and logistic regression models demonstrated the best predictive ability with respective AUCs of .85 to .86.ConclusionThis study demonstrated the use of machine learning algorithms to identify the combination of variables that are predictive of delirium within 24 hours of hospitalization from the ED.

scholarly journals Machine Learning Algorithm’s Measurement and Analytical Visualization of User’s Reviews for Google Play Store

2020 ◽  
Author(s):  
Abdul Karim ◽  
Azhari Azhari ◽  
Samir Brahim Belhaouri ◽  
Ali Adil Qureshi
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Ve Bayes ◽  
Android Apps ◽  
Online Marketplace ◽  
Document Frequency ◽  
Logistic Regression Algorithm ◽  
Google Play

The fact is quite transparent that almost everybody around the world is using android apps. Half of the population of this planet is associated with messaging, social media, gaming, and browsers. This online marketplace provides free and paid access to users. On the Google Play store, users are encouraged to download countless of applications belonging to predefined categories. In this research paper, we have scrapped thousands of users reviews and app ratings. We have scrapped 148 apps&rsquo; reviews from 14 categories. We have collected 506259 reviews from Google play store and subsequently checked the semantics of reviews about some applications form users to determine whether reviews are positive, negative, or neutral. We have evaluated the results by using different machine learning algorithms like Na&iuml;ve Bayes, Random Forest, and Logistic Regression algorithm. we have calculated Term Frequency (TF) and Inverse Document Frequency (IDF) with different parameters like accuracy, precision, recall, and F1 and compared the statistical result of these algorithms. We have visualized these statistical results in the form of a bar chart. In this paper, the analysis of each algorithm is performed one by one, and the results have been compared. Eventually, We've discovered that Logistic Regression is the best algorithm for a review-analysis of all Google play store. We have proved that Logistic Regression gets the speed of precision, accuracy, recall, and F1 in both after preprocessing and data collection of this dataset.

A Novel Machine Learning Assisted Upscaling Workflow for Simulating the Waterflooding Process

2021 ◽  
Author(s):  
Yanji Wang ◽  
Hangyu Li ◽  
Jianchun Xu ◽  
Ling Fan ◽  
Xiaopu Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Flow Simulation ◽  
Machine Learning Algorithms ◽  
Scale Model ◽  
Two Phase ◽  
Flow Problems ◽  
Similar Accuracy

Abstract Conventional flow-based two-phase upscaling for simulating the waterflooding process requires the calculations of upscaled two-phase parameters for each coarse interface or block. The whole procedure can be greatly time-consuming especially for large-scale reservoir models. To address this problem, flow-based two-phase upscaling techniques are combined with machine learning algorithms, in which the flow-based two-phase upscaling is needed only for a small fraction of coarse interfaces (or blocks), while the upscaled two-phase parameters for the rest of the coarse interfaces (or blocks) are directly provided by the machine learning algorithms instead of performing upscaling computation on each coarse interfaces (or blocks). The new two-phase upscaling workflow was tested for generic (left to right) flow problems using a 2D large-scale model. We observed similar accuracy for results using the machine learning assisted workflow compared with the results using full flow-based upscaling. And significant speedup (nearly 70) is achieved. The workflow developed in this work is one of the pioneering work in combining machine learning algorithm with the time-consuming flow-based two-phase upscaling method. It is a valuable addition to the existing multiscale techniques for subsurface flow simulation.

scholarly journals Analisis Klasifikasi Sentimen Terhadap Sekolah Daring pada Twitter Menggunakan Supervised Machine Learning

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Ni Luh Putu Chandra Savitri ◽  
Radya Amirur Rahman ◽  
Reyhan Venyutzky ◽  
Nur Aini Rakhmawati
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Teaching Method ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Sentence Length ◽  
Supervised Machine Learning ◽  
Positive Side ◽  
Online School

Covid-19 pandemic urges countries to limit interaction of their people to reduce transmission. Indonesia requires people to do activities at home, one of which is online school. Many people share their thoughts through social media Twitter. Therefore, authors conducted sentiment analysis using supervised machine learning algorithm to determine distribution of words used in commenting on online schools, relationship between sentence, length and sentiment, and best algorithms that can be used to get most accurate results. In this study, authors used the method of crawling with RapidMiner to get data from Twitter. Then authors do data cleansing, data processing with classification methods using Random Forest Classifier , Logistic Regression , BernoulliNB and SVC algorithm. After that authors evaluate using confusion matrix, accuracy rate and classification report. In this research, authors found there are positive, negative, and neutral sentiments expressed on the online school implementation through comments. Authors ranked top three most used words used to express positive sentiments which includes bahagia, rajin and senang. On negative sentiments, top three words are capek, muak and bosen. On neutral sentiments, top three words are tidur, capek, and buka. Lengthy Tweets are usually imbued with negative remarks. On the other hand, the tweet tends to be positive and neutral tweet is usually stable. Authors conclude that the weakness of online school is the amount of workload that makes students tired alongside ineffective teaching method which makes it hard for students to understand the material given by school. However, on the positive side, some people agree with policies that are implemented and they feel like they gained some benefits from the implementation. From the four supervised machine learning algorithms that have been tested, Logistic Regression shows the highest accuracy, 0,87. The analysis shows that society tends to be neutral to the implementation of online school.

scholarly journals Credit Scoring Using Machine Learning by Combing Social Network Information: Evidence from Peer-to-Peer Lending

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 397 ◽  
Author(s):  
Beibei Niu ◽  
Jinzheng Ren ◽  
Xiaotao Li
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Social Network ◽  
Financial Institutions ◽  
Learning Algorithm ◽  
Credit Scoring ◽  
Peer To Peer ◽  
Machine Learning Algorithms ◽  
Loan Default ◽  
Network Information

Financial institutions use credit scoring to evaluate potential loan default risks. However, insufficient credit information limits the peer-to-peer (P2P) lending platform’s capacity to build effective credit scoring. In recent years, many types of data are used for credit scoring to compensate for the lack of credit history data. Whether social network information can be used to strengthen financial institutions’ predictive power has received much attention in the industry and academia. The aim of this study is to test the reliability of social network information in predicting loan default. We extract borrowers’ social network information from mobile phones and then use logistic regression to test the relationship between social network information and loan default. Three machine learning algorithms—random forest, AdaBoost, and LightGBM—were constructed to demonstrate the predictive performance of social network information. The logistic regression results show that there is a statistically significant correlation between social network information and loan default. The machine learning algorithm results show that social network information can improve loan default prediction performance significantly. The experiment results suggest that social network information is valuable for credit scoring.

scholarly journals Predicting the risk of asthma attacks in children, adolescents and adults: protocol for a machine learning algorithm derived from a primary care-based retrospective cohort

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e036099
Author(s):  
Zain Hussain ◽  
Syed Ahmar Shah ◽  
Mome Mukherjee ◽  
Aziz Sheikh
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Research Database ◽  
Data Ethics ◽  
Adolescents And Adults ◽  
And Performance

IntroductionMost asthma attacks and subsequent deaths are potentially preventable. We aim to develop a prognostic tool for identifying patients at high risk of asthma attacks in primary care by leveraging advances in machine learning.Methods and analysisCurrent prognostic tools use logistic regression to develop a risk scoring model for asthma attacks. We propose to build on this by systematically applying various well-known machine learning techniques to a large longitudinal deidentified primary care database, the Optimum Patient Care Research Database, and comparatively evaluate their performance with the existing logistic regression model and against each other. Machine learning algorithms vary in their predictive abilities based on the dataset and the approach to analysis employed. We will undertake feature selection, classification (both one-class and two-class classifiers) and performance evaluation. Patients who have had actively treated clinician-diagnosed asthma, aged 8–80 years and with 3 years of continuous data, from 2016 to 2018, will be selected. Risk factors will be obtained from the first year, while the next 2 years will form the outcome period, in which the primary endpoint will be the occurrence of an asthma attack.Ethics and disseminationWe have obtained approval from OPCRD’s Anonymous Data Ethics Protocols and Transparency (ADEPT) Committee. We will seek ethics approval from The University of Edinburgh’s Research Ethics Group (UREG). We aim to present our findings at scientific conferences and in peer-reviewed journals.

scholarly journals Clinical Classifiers to Identify Ascending Aortic Dilatation in Patients With Bicuspid Versus Tricuspid Aortic Valves

2021 ◽  
Author(s):  
Bamba Gaye ◽  
Maxime Vignac ◽  
Jesper R. Gådin ◽  
Magalie Ladouceur ◽  
Kenneth Caidahl ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Aortic Valve ◽  
Regression Models ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Multidimensional Data ◽  
Aortic Dilatation ◽  
Logistic Regression Models

Abstract Objective: We aimed to develop clinical classifiers to identify prevalent ascending aortic dilatation in patients with BAV and tricuspid aortic valve (TAV). Methods: This study included BAV (n=543) and TAV (n=491) patients with aortic valve disease and/or ascending aortic dilatation but devoid of coronary artery disease undergoing cardiothoracic surgery. We applied machine learning algorithms and classic logistic regression models, using multiple variable selection methodologies to identify predictors of high risk of ascending aortic dilatation (ascending aorta with a diameter above 40 mm). Analyses included comprehensive multidimensional data (i.e., valve morphology, clinical data, family history of cardiovascular diseases, prevalent diseases, demographic, lifestyle and medication). Results: BAV patients were younger (60.4±12.4 years) than TAV patients (70.4±9.1 years), and had a higher frequency of aortic dilatation (45.3% vs. 28.9% for BAV and TAV, respectively. P<0.001). The aneurysm prediction models showed mean AUC values above 0.8 for TAV patients, with the absence of aortic stenosis being the main predictor, followed by diabetes and high sensitivity C-Reactive Protein. Using the same clinical measures in BAV patients our prediction model resulted in AUC values between 0.5-0.55, not useful for prediction of aortic dilatation. The classification results were consistent for all machine learning algorithms and classic logistic regression models. Conclusions: Cardiovascular risk profiles appear to be more predictive of aortopathy in TAV patients than in patients with BAV. This adds evidence to the fact that BAV- and TAV-associated aortopathy involve different pathways to aneurysm formation and highlights the need for specific aneurysm preventions in these patients. Further, our results highlight that machine learning approaches do not outperform classical prediction methods in addressing complex interactions and non-linear relations between variables.

scholarly journals Using Machine Learning Algorithm to Describe the Connection between the Types and Characteristics of Music Signal

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bo Sun
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Machine Learning Algorithm ◽  
Music Classification ◽  
Music Signal

Music classification is conducive to online music retrieval, but the current music classification model finds it difficult to accurately identify various types of music, which makes the classification effect of the current music classification model poor. In order to improve the accuracy of music classification, a music classification model based on multifeature fusion and machine learning algorithm is proposed. First, we obtain the music signal, and then extract various features from the classification of the music signal, and use machine learning algorithms to describe the type of music signal and the relationship between the features. The music classifier and deep belief network machine learning models in shallow logistic regression are established, respectively. Experiments were designed for these two models to verify the applicability of the model for music classification. By comparing the experimental results, it is found that the classification accuracy of the deep confidence network model is higher than that of the logistic regression model, but the number of iterations needed for its accuracy to converge is also higher than that of the logistic regression model. Compared with other current music classification models, this model reduces the time of constructing music classifier, speeds up the speed of music classification, and can identify various types of music with high precision. The accuracy of music classification is obviously improved, which verifies the superiority of this music classification model.

scholarly journals Women Safety Prediction using Logistic Regression Model

2020 ◽  
Vol 8 (6) ◽  
pp. 5056-5060
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Coming Out ◽  
Machine Learning Algorithms ◽  
Logistic Regression Models ◽  
Proposed Model ◽  
Public Areas ◽  
Project Idea ◽  
Learning Machine ◽  
Logistic Regression Algorithm

Safety of Women has become a major issue in India. Especially at night women think a lot before coming out of their homes. We daily come up with news of how women are subjected to a lot of violence and harassment or get molested in public areas. This paper focuses on the issue of helping Women that they don’t ever never feel alone in the middle of any situations. The project idea is to predict whether the given place at any time is safe for a women to go or not. There are many preexisting applications that are useful at the time of crisis situations. At some situations when a women is in trouble, she is not able to use those applications. And there are also so many rehabilation centres which are used after the situation has happened. But our proposed model will help women to take precautions so that they never ever get that situation. For this idea we used Machine Learning. Machine learning is used to train the data and make quality predictions by recognizing the patterns in data. We applied different algorithms like Naïve Bayes, K-Nearest Neighbours, Logistic Regression models. Logistic regression is the best fit among other machine learning algorithms and it is more effective than others. In this paper, we used Logistic regression algorithm of Sklearn machine learning library to classify the dataset. Information about some set of areas in Tamilnadu are collected and was used in our project. When a women alone want to go out for any personal work or any financial work without knowing any safety details about the place she wants to go our application helps more better.

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