scholarly journals Turnout Monitoring with Vehicle Based Inertial Measurements of Operational Trains: A Machine Learning Approach

2019 ◽  
Vol 21 (1) ◽  
pp. 42-48 ◽  
Author(s):  
Mykola Sysyn ◽  
Dimitri Gruen ◽  
Ulf Gerber ◽  
Olga Nabochenko ◽  
Vitalii Kovalchuk
Keyword(s):  
Machine Learning ◽  
Classification Model ◽  
Learning Approach ◽  
Classification Methods ◽  
Machine Learning Approach ◽  
Sequential Feature Selection ◽  
Filter Approach ◽  
Inertial Measurements ◽  
Signal Processing Methods ◽  
Simple Filter

A machine learning approach for the recent detection of crossing faults is presented in the paper. The basis for the research are the data of the axle box inertial measurements on operational trains with the system ESAH-F. Within the machine learning approach the signal processing methods, as well as data reduction classification methods, are used. The wavelet analysis is applied to detect the spectral features at measured signals. The simple filter approach and sequential feature selection is used to find the most significant features and train the classification model. The validation and error estimates are presented and its relation to the number of selected features is analysed, as well.

scholarly journals Machine Learning Approach to Dysphonia Detection

2018 ◽  
Vol 8 (10) ◽  
pp. 1927 ◽  
Author(s):  
Zuzana Dankovičová ◽  
Dávid Sovák ◽  
Peter Drotár ◽  
Liberios Vokorokos
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Nearest Neighbors ◽  
Classification Model ◽  
Support Vector ◽  
Learning Approach ◽  
K Nearest Neighbors ◽  
Machine Learning Methods ◽  
Machine Learning Approach ◽  
Speech Features

This paper addresses the processing of speech data and their utilization in a decision support system. The main aim of this work is to utilize machine learning methods to recognize pathological speech, particularly dysphonia. We extracted 1560 speech features and used these to train the classification model. As classifiers, three state-of-the-art methods were used: K-nearest neighbors, random forests, and support vector machine. We analyzed the performance of classifiers with and without gender taken into account. The experimental results showed that it is possible to recognize pathological speech with as high as a 91.3% classification accuracy.

scholarly journals Handwritten digits recognition with decision tree classification: a machine learning approach

2019 ◽  
Vol 9 (5) ◽  
pp. 4446
Author(s):  
Tsehay Admassu Assegie ◽  
Pramod Sekharan Nair
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Classification Model ◽  
Learning Approach ◽  
Data Sets ◽  
The Past ◽  
Decision Tree Classification ◽  
Machine Learning Approach ◽  
Future Data ◽  
Class Labels

Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9.

scholarly journals Machine Learning approach to discriminate Saccharomyces cerevisiae yeast cells using sophisticated image features

2015 ◽  
Vol 12 (3) ◽  
pp. 44-64
Author(s):  
Mohamed Tleis ◽  
Fons J. Verbeek
Keyword(s):  
Machine Learning ◽  
Saccharomyces Cerevisiae ◽  
Image Features ◽  
Classification Systems ◽  
Attribute Selection ◽  
Classification Model ◽  
Yeast Cells ◽  
Biological Research ◽  
Learning Approach ◽  
Machine Learning Approach

Summary In biological research, Saccharomyces cerevisiae yeast cells are used to study the behaviour of proteins. This is a time consuming and not completely objective process. Hence, Image analysis platforms are developed to address these problems and to offer analysis per cell as well. The robust segmentation algorithms implemented in such platforms enables us to apply a machine learning approach on the measured cells. Such approach is based on a set of relevant individual cell features extracted from the microscope images of the yeast cells. In this paper, we composed a set of features to represent the intensity and morphology characteristics in a more sophisticated way. These features are based on first and second order histograms and wavelet-based texture measurement. To show the discrimination power of these features, we built a classification model to discriminate between different groups. The building process involved evaluation of a set of classification systems, data sampling techniques, data normalization schemes and attribute selection algorithms. The results show a significant ability to discriminate different cell strains and conditions; subsequently it reveals the benefits of the classification model based on the introduced features. This model is promising in revealing subtle patterns in future high-throughput yeast studies.

scholarly journals Machine Learning Approach for Classification of Cancer Stages

2019 ◽  
pp. 1-4
Author(s):  
Shubham Hingmire
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Health Care ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Classification Methods ◽  
Medical Diagnoses ◽  
Machine Learning Approach ◽  
Computer Aided

The simplest form of health care is diagnosis and prevention. of disease. Machine learning (ML) methods help achieve this goal. This project aims to compare method of computer aided medical diagnoses. The ?rst of these methods is a classify disease diagnosis according to their data. This involves the training of an Arti?cial Neural Network to respond to several patient parameters. And also comparing various classification methods the purpose research classifier classi?es the patients in two class ?rst is malignant and second is benign.

scholarly journals Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4017
Author(s):  
Ghasem Akbari ◽  
Mohammad Nikkhoo ◽  
Lizhen Wang ◽  
Carl P. C. Chen ◽  
Der-Sheng Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
The Elderly ◽  
Microsoft Kinect ◽  
Classification Model ◽  
Support Vector ◽  
Learning Approach ◽  
Kinect Sensor ◽  
Increased Risk ◽  
Machine Learning Approach

Frailty is one of the most important geriatric syndromes, which can be associated with increased risk for incident disability and hospitalization. Developing a real-time classification model of elderly frailty level could be beneficial for designing a clinical predictive assessment tool. Hence, the objective of this study was to predict the elderly frailty level utilizing the machine learning approach on skeleton data acquired from a Kinect sensor. Seven hundred and eighty-seven community elderly were recruited in this study. The Kinect data were acquired from the elderly performing different functional assessment exercises including: (1) 30-s arm curl; (2) 30-s chair sit-to-stand; (3) 2-min step; and (4) gait analysis tests. The proposed methodology was successfully validated by gender classification with accuracies up to 84 percent. Regarding frailty level evaluation and prediction, the results indicated that support vector classifier (SVC) and multi-layer perceptron (MLP) are the most successful estimators in prediction of the Fried’s frailty level with median accuracies up to 97.5 percent. The high level of accuracy achieved with the proposed methodology indicates that ML modeling can identify the risk of frailty in elderly individuals based on evaluating the real-time skeletal movements using the Kinect sensor.

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