Logistic regression and neural network classification of seismic records

Vallejos, Javier; McKinnon, Stephen D.

doi:10.1016/j.ijrmms.2013.04.005

Cited by 88 publications

(38 citation statements)

References 9 publications

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“…It has tweets on Coachella arts and music festival which was held in 2015. The classification results are generated be processed connected elements (Vallejos & Mckinnon, 2013).…”

Section: Azure Sentiment Analysis Modelmentioning

confidence: 99%

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Harfoushi¹,

Hasan²,

Obiedat³

2018

MAS

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The Sentimental Analysis (SA) is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It can be used to perform social media analytics. This study sought to compare two algorithms; Logistic Regression, and Support Vector Machine (SVM) using Microsoft Azure Machine Learning. This was demonstrated by performing a series of experiments on three Twitter datasets (TD). Accordingly, data was sourced from Twitter a microblogging platform. Data were obtained in the form of individuals' opinions, image, views, and twits from Twitter. Azure cloud-based sentiment analytics models were created based on the two algorithms. This work was extended with more in-depth analysis from another Master research conducted lately. Results confirmed that Microsoft Azure ML platform can be used to build effective SA models that can be used to perform data analytics.

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“…It has tweets on Coachella arts and music festival which was held in 2015. The classification results are generated be processed connected elements (Vallejos & Mckinnon, 2013).…”

Section: Azure Sentiment Analysis Modelmentioning

confidence: 99%

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Harfoushi¹,

Hasan²,

Obiedat³

2018

MAS

View full text Add to dashboard Cite

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“…Next, the database is modified: the original rock stress factor A or is adjusted using the proportionality factor n, and the modified rock stress factor A 3D is obtained. Finally, the statistical significance of the rock stress factors A or and A 3D on the stability graph boundaries are evaluated and compared, using a contingency matrix and performance metrics analysis [24], [25]. In the following subsections, the extended Mathews database and the contingency matrix and performance metric analysis and presented.…”

Section: Influence Of the Proposed Curves On The Stability Boundarymentioning

confidence: 99%

“…The contingency matrix is a statistical analysis used to test the performance of a classifier [24]. It is based on a comparison of the outcomes predicted by the model with known values.…”

Section: Contingency Matrix and Performance Metricsmentioning

confidence: 99%

Three-dimensional effect of stresses in open stope mine design

Vallejos

Delonca

Pérez

2017

International Journal of Mining, Reclamation and Environment

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a faculty of Physical and Mathematical Sciences, department of Mining engineering, university of chile, Santiago, chile; b advanced Mining technology center (aMtc), university of chile, Santiago, chile ABSTRACTThe stability graph method is widely used for open stope mine design. During the development of the method the effect of induced stresses were included by using two-dimensional or limited three-dimensional stress analysis. In this paper, three-dimensional numerical models are used to develop new induced stress curves, and to evaluate the effects on the stability graph method and open stope mine design. Significant differences are identified for the estimated stresses at the wall of the stopes, leading to an increase of the size of the stopes.

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“…Having recorded and processed a number of seismic events within a given volume of interest Δ over time Δ , one can then quantify the changes in the strain and stress regimes and in the rheological properties of the rock mass deformation associated with the seismic radiation [1,2]. However, a variety of dynamic processes in mines which radiate seismic waves are detected by the seismic monitoring systems and in general, seismograms generated by a development or production blast and a shear fracturing or a sudden slip on a surface of weakness are the majority of records [3][4][5][6][7][8]. As recorded quarry blasts may mislead scientific interpreting and lead to erroneous results in the analysis of seismic hazards in mines, standard processing of seismic monitoring data require these events to be separated.…”

Section: Introductionmentioning

confidence: 99%

“…Vallejos and McKinnon proposed the identification of seismic records in seismically active mines by considering the logistic regression and the neural network classification techniques. An efficient methodology was presented for applying these approaches to the classification of seismic records [3]. However, the calculation of seismic source parameters requires precise signal processing, namely expertise-required and time-consuming P-and S-wave hand-picking.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Mine Seismic Discriminators Based on Features of Source Parameters to Waveform Characteristics

Zhao

Dong

et al. 2015

Shock and Vibration

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To find efficient methods for classifying mine seismic events, two features extraction approaches were proposed. Features of source parameters including the seismic moment, the seismic energy, the energy ratio of S-to P-wave, the static stress drop, time of occurrence, and the number of triggers were selected, counted, and analyzed in approach I. Waveform characteristics consisting of two slope values and the coordinates of the first peak and the maximum peak were extracted as the discriminating parameters in approach II. The discriminating performance of the two approaches was compared and discussed by applying the Bayes discriminant analysis to the characteristic parameters extracted. Classification results show that 83.5% of the original grouped cases are correctly classified by approach I, and 97.1% of original grouped cases are correctly classified by approach II. The advantages and limitations pertaining to each classifier were discussed by plotting the event magnitude versus sample number. Comparative analysis shows that the proposed method of approach II not only has a low misjudgment rate but also displays relative constancy when the testing samples fluctuate with seismic magnitude and energy.

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Logistic regression and neural network classification of seismic records

Cited by 88 publications

References 9 publications

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Three-dimensional effect of stresses in open stope mine design

A Comparison of Mine Seismic Discriminators Based on Features of Source Parameters to Waveform Characteristics

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