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 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 Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Idris Kharroubi ◽  
Thomas Lim ◽  
Xavier Warin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Differential Equations ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

scholarly journals Clustering suicides: A data-driven, exploratory machine learning approach

2019 ◽  
Vol 62 ◽  
pp. 15-19 ◽  
Author(s):  
Birgit Ludwig ◽  
Daniel König ◽  
Nestor D. Kapusta ◽  
Victor Blüml ◽  
Georg Dorffner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Analysis Data ◽  
The Other ◽  
Data Driven ◽  
Learning Approach ◽  
Suicide Methods ◽  
Machine Learning Approach ◽  
The One ◽  
Methods Of Suicide

Abstract Methods of suicide have received considerable attention in suicide research. The common approach to differentiate methods of suicide is the classification into “violent” versus “non-violent” method. Interestingly, since the proposition of this dichotomous differentiation, no further efforts have been made to question the validity of such a classification of suicides. This study aimed to challenge the traditional separation into “violent” and “non-violent” suicides by generating a cluster analysis with a data-driven, machine learning approach. In a retrospective analysis, data on all officially confirmed suicides (N = 77,894) in Austria between 1970 and 2016 were assessed. Based on a defined distance metric between distributions of suicides over age group and month of the year, a standard hierarchical clustering method was performed with the five most frequent suicide methods. In cluster analysis, poisoning emerged as distinct from all other methods – both in the entire sample as well as in the male subsample. Violent suicides could be further divided into sub-clusters: hanging, shooting, and drowning on the one hand and jumping on the other hand. In the female sample, two different clusters were revealed – hanging and drowning on the one hand and jumping, poisoning, and shooting on the other. Our data-driven results in this large epidemiological study confirmed the traditional dichotomization of suicide methods into “violent” and “non-violent” methods, but on closer inspection “violent methods” can be further divided into sub-clusters and a different cluster pattern could be identified for women, requiring further research to support these refined suicide phenotypes.

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.

Deep Optimization of Catalyst Layer Composition via Data-Driven Machine Learning Approach

2020 ◽  
Author(s):  
Bowen Wang ◽  
Biao Xie ◽  
Jin Xuan ◽  
Wen Gu ◽  
Dezong Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Catalyst Layer ◽  
Data Driven ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Layer Composition
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