Modeling Track Geometry Degradation Using Support Vector Machine Technique

2016 Joint Rail Conference ◽

10.1115/jrc2016-5739 ◽

2016 ◽

Cited By ~ 10

Author(s):

Can Hu ◽

Xiang Liu

Keyword(s):

Support Vector Machine ◽

Data Model ◽

Data Cleaning ◽

Analytical Data ◽

Support Vector ◽

Crucial Importance ◽

Track Geometry ◽

Maintenance Schedule ◽

Volume Defect ◽

Potential Factors

Analyzing track geometry defects is of crucial importance for railway safety. Understanding when a defect will need to be repaired can help in both planning a preventive maintenance schedule and reducing the probability of track failures. This paper discusses the data cleaning and analysis processes for modeling track geometry degradation. An analytical data model named the Support Vector Machine (SVM) was developed to model the deterioration of track geometry defects. This paper mainly focuses on the following three defect types — surface, cross level and dip. The model accounts for traffic volume, defect amplitude, track class, speed and other potential factors. Results demonstrate that the proposed analytical data model can have a prediction accuracy above 70%.

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Sentiment Analysis in Portuguese Texts from Online Health Community Forums: Data, Model and Evaluation

10.5753/stil.2021.17785 ◽

2021 ◽

Author(s):

Yohan Bonescki Gumiel ◽

Isabela Lee ◽

Tayane Arantes Soares ◽

Thiago Castro Ferreira ◽

Adriana Pagano

Keyword(s):

Diabetes Mellitus ◽

Machine Learning ◽

Support Vector Machine ◽

Sentiment Analysis ◽

Data Model ◽

Negative Evaluation ◽

Support Vector ◽

Machine Learning Classifiers ◽

Online Health Community ◽

Health Community

This study introduces novel data and models for the task of Sentiment Analysis in Portuguese texts about Diabetes Mellitus. The corpus contains 1290 posts retrieved from online health community forums in Portuguese and annotated by two annotators according to 3 sentiment categories (e.g. Positive, Neutral and Negative). Evaluation of traditional (Support Vector Machine, Decision Tree, Random Forest and Logistic Regression classifiers) and state-ofthe-art (BERT-based models) machine learning classifiers for the task showed the advantage in performance of the latter models as expected. Data and models are available to the community upon request.

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Monitoring of lateral and cross level track geometry irregularities through onboard vehicle dynamics measurements using machine learning classification algorithms

Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit ◽

10.1177/0954409720906649 ◽

2020 ◽

Vol 235 (1) ◽

pp. 107-120

Author(s):

A De Rosa ◽

R Kulkarni ◽

A Qazizadeh ◽

M Berg ◽

E Di Gialleonardo ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Vehicle Dynamics ◽

High Speed ◽

Support Vector ◽

Lateral Direction ◽

Track Geometry ◽

Lateral Dynamics ◽

Machine Learning Classification ◽

Track Irregularities

In recent years, significant studies have focused on monitoring the track geometry irregularities through measurements of vehicle dynamics acquired onboard. Most of these studies analyse the vertical irregularity and the vertical vehicle dynamics since the lateral direction is much more challenging due to the non-linearities caused by the contact between the wheels and the rails. In the present work, a machine learning-based fault classifier for the condition monitoring of track irregularities in the lateral direction is proposed. The classifiers are trained with a dataset composed of numerical simulation results and validated with a dataset of measurements acquired by a diagnostic vehicle on the straight track sections of a high-speed line (300 km/h). Classifiers based on decision tree, linear and Gaussian support vector machine algorithms are developed and compared in terms of performance: good results are achieved with the three algorithms, especially with the Gaussian support vector machine. Even though classifiers are data driven, they retain the essence of lateral dynamics.

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Data Cleaning Using Complementary Fuzzy Support Vector Machine Technique

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-319-46672-9_19 ◽

2016 ◽

pp. 160-167 ◽

Cited By ~ 7

Author(s):

Ratchakoon Pruengkarn ◽

Kok Wai Wong ◽

Chun Che Fung

Keyword(s):

Support Vector Machine ◽

Data Cleaning ◽

Support Vector ◽

Fuzzy Support Vector Machine

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Support vector machine? – not only for MRI-images

10.1055/s-0039-3403050 ◽

2020 ◽

Author(s):

V Vasilevska ◽

K Schlaaf ◽

H Dobrowolny ◽

G Meyer-Lotz ◽

HG Bernstein ◽

...

Keyword(s):

Support Vector Machine ◽

Support Vector

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KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.693 ◽

2019 ◽

Vol 15 (2) ◽

pp. 275-280

Author(s):

Agus Setiyono ◽

Hilman F Pardede

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Spam Detection ◽

Support Vector Machine Algorithm ◽

Data Mining Techniques ◽

To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam. One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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