Role of Data Analytics in Infrastructure Asset Management: Overcoming Data Size and Quality Problems

Piryonesi, S. Madeh; El-Diraby, Tamer E.

doi:10.1061/jpeodx.0000175

Cited by 124 publications

(73 citation statements)

References 55 publications

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“…A query image is represented by a set of features which are assumed to be independently sampled from a class-specific feature space. Then a kernel density estimation allows the Bayesian network models to achieve higher accuracy levels [ 123 , 124 ]. The Naïve Bayes Classifier can produce very accurate classification results with a minimum training time in comparison with conventional supervised or unsupervised methods.…”

Section: Machine Learning For Mri Datamentioning

confidence: 99%

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

et al. 2021

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The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

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Section: Machine Learning For Mri Datamentioning

confidence: 99%

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

et al. 2021

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“…The minimum leaf size is the smallest number of observations in a decision tree, and the model tends to overfit when the leaf size is too large. The random forest is a classic ensemble learning method with proven applicability in many settings [19], [30], [31].…”

Section: B Random Forestmentioning

confidence: 99%

“…The prior of the data is the distribution of each class, and the likelihood is the distribution of each predictor. Because the assumption of a Gaussian distribution may not hold for features, the use of a kernel estimator in naïve Bayes has been suggested to provide higher performance than the Gaussian naïve Bayes [19].…”

Section: Naïve Bayesmentioning

confidence: 99%

“…Additionally, because resampling bootstrap data sets are used, the random forest model can robustly manage data sets with limited sample sizes [30]. Earlier studies have demonstrated its feasibility and efficacy in practical application [19], [31]. Studies have also shown the superiority of the random forest model to other single-learner-based machine learning models.…”

Section: F Superiority Of the Random Forest Modelmentioning

confidence: 99%

“…Studies have also shown the superiority of the random forest model to other single-learner-based machine learning models. A study compared the performance of different classification algorithms applied to asphalt pavement deterioration data and reported that the random forest model outperformed the naïve Bayes model [19]. Another study that assessed groundwater potential demonstrated that the random forest model was superior to kernel-mapped SVM models [31].…”

Section: F Superiority Of the Random Forest Modelmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Application With Quantitative Digital Subtraction Angiography for Detection of Hemorrhagic Brain Arteriovenous Malformations

Hong

Lin

et al. 2020

IEEE Access

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Accurate Prediction of Microstructure of Composites using Machine Learning

Sang

Chen

Fan

et al. 2022

Advcd Theory and Sims

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In this prospective work, a machine learning (ML) model based on multiple independent random forest models to predict the configuration of binary composite bars is developed. The input variables to the ML model are elastic wave signals collected at one end of the composite bar, while the targets of the ML model are binary vectors representing the configuration of the bars.This study results indicate: First, a short period of elastic wave propagated through a composite bar can collect and carry the detailed information of the entire bar; second, the patterns hidden in the collected signals can be detected, extracted, and used by the ML model; finally, the ML model can be well trained using a relatively small dataset pool (less than 0.1% of all possible samples), and make accurate predictions. For the 30 sections of bars used in this study, the average prediction accuracy for each section of this bar can reach 95% and even higher. This ML guided technique can be modified and used in different functionalities and applications such as composites characterization, structure health monitoring, limestone determination, and archaeological detection.

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Role of Data Analytics in Infrastructure Asset Management: Overcoming Data Size and Quality Problems

Cited by 124 publications

References 55 publications

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

Machine Learning Application With Quantitative Digital Subtraction Angiography for Detection of Hemorrhagic Brain Arteriovenous Malformations

Accurate Prediction of Microstructure of Composites using Machine Learning

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