How Partisanship Influences What Congress Says Online and How They Say It

2020 ◽  
Vol 49 (1) ◽  
pp. 76-90
Author(s):  
Richard T. Wang ◽  
Patrick D. Tucker
Keyword(s):  
Machine Learning ◽  
Strong Evidence ◽  
Gradient Boosting ◽  
Learning Approach ◽  
Press Releases ◽  
White House ◽  
Party Control ◽  
Machine Learning Approach ◽  
Modeling Software ◽  
The Way

We investigate the influence of partisanship on congressional communication by analyzing 180,000 press releases issued by members of Congress (MCs) between 2005 and 2019. Specifically, we examine whether partisan factors such as party control of the White House and/or Congress influence the tone used by MCs and whether MCs are more likely to focus on issues that their respective party owns. Our analyses include the use of multiple OLS models, the machine learning approach gradient boosting, and Grimmer’s topical modeling software “expAgenda.” We find that (1) partisanship influences the tone MCs use when communicating online; and (2) MCs are unable to prioritize discussing issues that their respective party own but devote slightly greater attention to their party’s issues than MCs from the opposite party. Our study ultimately finds strong evidence of partisan influence in the way MCs design their press releases and has important implications for online congressional communication.

Machine Learning Approach for Estimating Residual Stresses in Girth Welds of Topside Piping

2020 ◽  
Author(s):  
Sachin Bhardwaj ◽  
R. M. Chandima Ratnayake ◽  
Arvind Keprate ◽  
Xavier Ficquet
Keyword(s):  
Machine Learning ◽  
Residual Stress ◽  
Residual Stresses ◽  
Gradient Boosting ◽  
Support Vector ◽  
Welding Parameters ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Boosting Algorithm ◽  
Girth Welds

Abstract Residual stresses are internal self-equilibrating stresses that remain in the component even after the removal of external load. The aforementioned stress when superimposed by the operating stresses on the offshore piping, enhance the chances of fracture failure of the components. Thus, it is vital to accurately estimate the residual stresses in topside piping while performing their fitness for service (FFS) evaluation. In the present work, residual stress profiles of girth welded topside sections of P91 pipes piping are estimate using a machine learning approach. The training and testing data for machine learning is acquired from experimental measurements database by Veqter, UK. Twelve different machine learning algorithms, namely, multi-linear regression (MLR), Random Forest (RF), Gaussian process regression (GPR), support vector regression (SVR), Gradient boosting (GB) etc. have been trained and tested. In order to compare the accuracy of the algorithms, four metrics, namely, Root Mean Square Error (RMSE), Estimated Variance Score (EVS), Maximum Absolute Error (AAE), and Coefficient of Determination (R^2) are used. Gradient boosting algorithm gives the best prediction of the residual stress, which is then used to estimate the residual stress for the simulated input parameter space. In the future work authors shall utilize the residual stress predictions from Gradient boosting algorithm to train the Bayesian Network, which can then be used for estimating less conservative through-thickness residual stresses distribution over a wide range of pipe geometries (radius to thickness ratio) and welding parameters (based on heat input). Furthermore, besides topside piping, the proposed approach finds its potential applications in structural integrity assessment of offshore structures, and pressure equipment’s girth welds.

Comparative analysis of a deep learning approach with various classification techniques for credit score computation

2020 ◽  
Vol 13 ◽  
Author(s):  
Arvind Pandey ◽  
Shipra Shukla ◽  
Krishna Kumar Mohbey
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Gradient Boosting ◽  
Learning Approach ◽  
Probit Regression ◽  
Credit Score ◽  
Machine Learning Approach

Background: Large financial companies are perpetually creating and updating customer scoring techniques. From a risk management view, this research for the predictive accuracy of probability is of vital importance than the traditional binary result of classification, i.e., non-credible and credible customers. The customer's default payment in Taiwan is explored for the case study. Objective: The aim is to audit the comparison between the predictive accuracy of the probability of default with various techniques of statistics and machine learning. Method: In this paper, nine predictive models are compared from which the results of the six models are taken into consideration. Deep learning-based H2O, XGBoost, logistic regression, gradient boosting, naïve Bayes, logit model, and probit regression comparative analysis is performed. The software tools such as R and SAS (university edition) is employed for machine learning and statistical model evaluation. Results: Through the experimental study, we demonstrate that XGBoost performs better than other AI and ML algorithms. Conclusion: Machine learning approach such as XGBoost effectively used for credit scoring, among other data mining and statistical approaches.

A Machine Learning Approach to Cloud Masking in Sentinel-3 SLSTR Data

2020 ◽  
Author(s):  
Samuel Jackson ◽  
Jeyarajan Thiyagalingam ◽  
Caroline Cox
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Surface ◽  
Satellite Images ◽  
Binary Classification ◽  
Gradient Boosting ◽  
Learning Approach ◽  
Persistent Problem ◽  
Machine Learning Approach ◽  
Level 1

<p><span>Clouds appear ubiquitously in the Earth's atmosphere, and thus present a persistent problem for the accurate retrieval of remotely sensed information. The task of identifying which pixels are cloud, and which are not, is what we refer as the cloud masking problem. The task of cloud masking essentially boils down to assigning a binary label, representing either "cloud" or "clear", to each pixel. </span></p><p><span>Although this problem appears trivial, it is often complicated by a diverse number of issues that affect the imagery obtained from remote sensing instruments. For instance, snow, sea ice, dust, smoke, and sun glint can easily challenge the robustness and consistency of any cloud masking algorithm. The cloud masking problem is also further complicated by geographic and seasonal variation in acquired scenes. </span></p><p><span>In this work, we present a machine learning approach to handle the problem of cloud masking for the Sea and Land Surface Temperature Radiometer (SLSTR) on board the Sentinel-3 satellites. Our model uses Gradient Boosting Decision Trees (GBDTs), to perform pixel-wise segmentation of satellite images. The model is trained using a hand labelled dataset of ~12,000 individual pixels covering both the spatial and temporal domains of the SLSTR instrument and utilises the combined channels of the dual-view swaths. Pixel level annotations, while lacking spatial context, have the advantage of being cheaper to obtain compared to fully labelled images, a major problem in applying machine learning to remote sensing imagrey.</span></p><p><span>We validate the performance of our mask using cross validation and compare its performance with two baseline models provided in the SLSTR level 1 product. We show up to 10% improvement in binary classification accuracy compared with the baseline methods. Additionally, we show that our model has the ability to distinguish between different classes of cloud to reasonable accuracy.</span></p>

scholarly journals Identification of five important genes to predict GBM subtypes

2021 ◽  
Author(s):  
Yang Tang ◽  
Maleeha A Qazi ◽  
Kevin R Brown ◽  
Nicholas Mikolajewicz ◽  
Jason Moffat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gene Signature ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Tissue Cell ◽  
Learning Approach ◽  
Gene Set ◽  
Primary Brain Tumour ◽  
Extreme Gradient Boosting ◽  
Machine Learning Approach

Abstract Background Glioblastoma (GBM), the most common and aggressive primary brain tumour in adults, has been classified into three subtypes: classical, mesenchymal and proneural. While the original classification relied on an 840 gene-set, further clarification on true GBM subtypes uses a 150-gene signature to accurately classify GBM into the three subtypes. We hypothesized whether a machine learning approach could be used to identify a smaller gene-set to accurately predict GBM subtype. Methods Using a supervised machine learning approach, extreme gradient boosting (XGBoost), we developed a classifier to predict the three subtypes of glioblastoma (GBM): classical, mesenchymal and proneural. We tested the classifier on in-house GBM tissue, cell lines and xenograft samples to predict their subtype. Results We identified the five most important genes for characterizing the three subtypes based on genes that often exhibited high Importance Scores in our XGBoost analyses. On average, this approach achieved 80.12% accuracy in predicting these three subtypes of GBM. Furthermore, we applied our five-gene classifier to successfully predict the subtype of GBM samples at our centre. Conclusion Our 5-gene set classifier is the smallest classifier to date that can predict GBM subtypes with high accuracy, which could facilitate the future development of a five-gene subtype diagnostic biomarker for routine assays in GBM samples.

scholarly journals A Data-Driven Machine Learning Approach for Corrosion Risk Assessment—A Comparative Study

2019 ◽  
Vol 3 (2) ◽  
pp. 28 ◽  
Author(s):  
Chinedu I. Ossai
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Driven ◽  
Gradient Boosting ◽  
Learning Approach ◽  
Inspection Planning ◽  
Defect Depth ◽  
Corrosion Defect ◽  
Machine Learning Approach ◽  
Corrosion Risk

Understanding the corrosion risk of a pipeline is vital for maintaining health, safety and the environment. This study implemented a data-driven machine learning approach that relied on Principal Component Analysis (PCA), Particle Swarm Optimization (PSO), Feed-Forward Artificial Neural Network (FFANN), Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) to estimate the corrosion defect depth growth of aged pipelines. By modifying the hyperparameters of the FFANN algorithm with PSO and using PCA to transform the operating variables of the pipelines, different Machine Learning (ML) models were developed and tested for the X52 grade of pipeline. A comparative analysis of the computational accuracy of the corrosion defect growth was estimated for the PCA transformed and non-transformed parametric values of the training data to know the influence of the PCA transformation on the accuracy of the models. The result of the analysis showed that the ML modelling with PCA transformed data has an accuracy that is 3.52 to 5.32 times better than those carried out without PCA transformation. Again, the PCA transformed GBM model was found to have the best modeling accuracy amongst the tested algorithms; hence, it was used for computing the future corrosion defect depth growth of the pipelines. This helped to compute the corrosion risks using the failure probabilities at different lifecycle phases of the asset. The excerpts from the results of this study indicate that my technique is vital for the prognostic health monitoring of pipelines because it will provide information for maintenance and inspection planning.

scholarly journals Interpretable Machine-Learning Approach in Estimating FDI Inflow: Visualization of ML Models with LIME and H2O

2021 ◽  
Vol 11 (1) ◽  
pp. 133-152
Author(s):  
Devesh Singh
Keyword(s):  
Machine Learning ◽  
Foreign Investment ◽  
General Linear Model ◽  
Gradient Boosting ◽  
Learning Approach ◽  
Visualization Technique ◽  
Interpretable Machine Learning ◽  
Gradient Boosting Machine ◽  
Machine Learning Approach ◽  
Interpretable Model

Abstract In advancement of interpretable machine learning (IML), this research proposes local interpretable model-agnostic explanations (LIME) as a new visualization technique in a novel informative way to analyze the foreign direct investment (FDI) inflow. This article examines the determinants of FDI inflow through IML with a supervised learning method to analyze the foreign investment determinants in Hungary by using an open-source artificial intelligence H2O platform. This author used three ML algorithms—general linear model (GML), gradient boosting machine (GBM), and random forest (RF) classifier—to analyze the FDI inflow from 2001 to 2018. The result of this study shows that in all three classifiers GBM performs better to analyze FDI inflow determinants. The variable value of production in a region is the most influenced determinant to the inflow of FDI in Hungarian regions. Explanatory visualizations are presented from the analyzed dataset, which leads to their use in decision-making.

scholarly journals A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3174 ◽  
Author(s):  
Renato Torres ◽  
Orlando Ohashi ◽  
Gustavo Pessin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Traffic Accidents ◽  
Driver Distraction ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Learning Approach ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Smartphone Sensors

Driver distraction is one of the major causes of traffic accidents. In recent years, given the advance in connectivity and social networks, the use of smartphones while driving has become more frequent and a serious problem for safety. Texting, calling, and reading while driving are types of distractions caused by the use of smartphones. In this paper, we propose a non-intrusive technique that uses only data from smartphone sensors and machine learning to automatically distinguish between drivers and passengers while reading a message in a vehicle. We model and evaluate seven cutting-edge machine-learning techniques in different scenarios. The Convolutional Neural Network and Gradient Boosting were the models with the best results in our experiments. Results show accuracy, precision, recall, F1-score, and kappa metrics superior to 0.95.

scholarly journals Prediction of adolescent subjective well-being: A machine learning approach

2019 ◽  
Vol 32 (5) ◽  
pp. e100096
Author(s):  
Naixin Zhang ◽  
Chuanxin Liu ◽  
Zhixuan Chen ◽  
Lin An ◽  
Decheng Ren ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Undergraduate Students ◽  
Online Survey ◽  
Well Being ◽  
Gradient Boosting ◽  
Control Procedure ◽  
Learning Approach ◽  
Subjective Well Being ◽  
Machine Learning Approach

BackgroundSubjective well-being (SWB), also known as happiness, plays an important role in evaluating both mental and physical health. Adolescents deserve specific attention because they are under a great variety of stresses and are at risk for mental disorders during adulthood.AimThe present paper aims to predict undergraduate students’ SWB by machine learning method.MethodsGradient Boosting Classifier which was an innovative yet validated machine learning approach was used to analyse data from 10 518 Chinese adolescents. The online survey included 298 factors such as depression and personality. Quality control procedure was used to minimise biases due to online survey reports. We applied feature selection to achieve the balance between optimal prediction and result interpretation.ResultsThe top 20 happiness risks and protective factors were finally brought into the predicting model. Approximately 90% individuals’ SWB can be predicted correctly, and the sensitivity and specificity were about 92% and 90%, respectively.ConclusionsThis result identifies at-risk individuals according to new characteristics and established the foundation for adolescent prevention strategies.

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