Lumpy Investment and Filtering Techniques

2012 ◽  
Author(s):  
Hyun Joong Im
Keyword(s):  
Lumpy Investment ◽  
Filtering Techniques

A Comparative Study of Digital Filtering Techniques in Ultrasound and Carotid Bifurcation Atherosclerosis

2018 ◽  
Vol 1 (1) ◽  
pp. 5-12
Author(s):  
Adriana Milășan ◽  
◽  
Cristian Molder ◽  
Silviu Dumitrescu ◽  
◽  
...  
Keyword(s):  
Comparative Study ◽  
Digital Filtering ◽  
Carotid Bifurcation ◽  
Filtering Techniques

Survey on Spatial Filtering Techniques

2012 ◽  
Vol 3 (4) ◽  
pp. 153-156
Author(s):  
A.Rajamani A.Rajamani ◽  
◽  
Dr.V.Krishnaveni Dr.V.Krishnaveni
Keyword(s):  
Spatial Filtering ◽  
Filtering Techniques

FETAL ECG EXTRACTION USING WAVELET AND ADAPTIVE FILTERING TECHNIQUES

2016 ◽  
Vol 4 (2) ◽  
pp. 1
Author(s):  
PRASAD K. PURUSHOTHAM ◽  
ANURADHA B. ◽  
◽  
Keyword(s):  
Adaptive Filtering ◽  
Fetal Ecg ◽  
Filtering Techniques

Data Extraction Techniques for Spreadsheet Records

2007 ◽  
Vol 2 (1) ◽  
pp. 119-129 ◽  
Author(s):  
Mark G. Simkin
Keyword(s):  
Data Extraction ◽  
Stratified Sampling ◽  
Specific Information ◽  
Extraction Techniques ◽  
Filtering Techniques

Abstract Many accounting applications use spreadsheets as repositories of accounting records, and a common requirement is the need to extract specific information from them. This paper describes a number of techniques that accountants can use to perform such tasks directly using common spreadsheet tools. These techniques include (1) simple and advanced filtering techniques, (2) database functions, (3) methods for both simple and stratified sampling, and, (4) tools for finding duplicate or unmatched records.

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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