Leveraging AutoML to provide NAFLD screening diagnosis: Proposed machine learning models

Bangash, Ali Haider

doi:10.1101/2020.10.20.20216291

Cited by 1 publication

(1 citation statement)

References 10 publications

(17 reference statements)

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“…In this study, numerous models (ML architectures) were trained and optimized using mljar , 50 an automated ML python package that has been used by the scientific community 51,52 to create models. Although it offers feature engineering, data analytics, model search, as well as feature explainability, mljar was used for training because of its hyperparameter tuning feature.…”

Section: Machine Learning: Preprocessing and Analysis Of Datamentioning

confidence: 99%

A Machine learning-based approach to determining stress in rails

Belding

Enshaeian

Rizzo

2022

Structural Health Monitoring

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Recent advancements in both software and hardware have sparked the use of machine learning (ML) in structural health monitoring (SHM) applications. This paper delves into the use of ML to determine axial stress in continuous welded rails (CWR). The overall proposed SHM strategy consists of monitoring the vibration of CWR and associating their modal characteristics to the rail longitudinal stress using a ML algorithm trained with data generated with a finite element model. In the present study, the feasibility of the proposed strategy was tested on a simple rail segment subjected to mechanical compression. Two algorithms were developed using hyperparameter search optimization techniques to infer the stress from the frequencies of vibration of a few modes of the rail. The training data were generated with a finite element model of a rail segment under varying axial stresses, rail lengths, and boundary conditions at the two ends of the segment. The algorithms were then tested with a second set of data generated numerically and the results of an experiment in which a 2.4-m-long rail was subjected to compressive load and excited with an instrumented hammer. Both tests demonstrated that ML is a viable tool to estimate axial stress in the rail segment provided a sufficient number of modes of vibrations are presented to the learning algorithm. For the future, more experiments are warranted to test the ML against data from real CWR.

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Section: Machine Learning: Preprocessing and Analysis Of Datamentioning

confidence: 99%