Improving Quality of Code Review Datasets – Token-Based Feature Extraction Method

2021 ◽  
pp. 81-93
Author(s):  
Miroslaw Staron ◽  
Wilhelm Meding ◽  
Ola Söder ◽  
Miroslaw Ochodek
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Code Review ◽  
Feature Extraction Method

scholarly journals Gait Analysis Using Computer Vision Based on Cloud Platform and Mobile Device

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Mario Nieto-Hidalgo ◽  
Francisco Javier Ferrández-Pastor ◽  
Rafael J. Valdivieso-Sarabia ◽  
Jerónimo Mora-Pascual ◽  
Juan Manuel García-Chamizo
Keyword(s):  
Quality Of Life ◽  
Cloud Computing ◽  
Feature Extraction ◽  
Gait Analysis ◽  
Elderly People ◽  
Extraction Method ◽  
Cloud Platform ◽  
Gait Patterns ◽  
Feature Extraction Method

Frailty and senility are syndromes that affect elderly people. The ageing process involves a decay of cognitive and motor functions which often produce an impact on the quality of life of elderly people. Some studies have linked this deterioration of cognitive and motor function to gait patterns. Thus, gait analysis can be a powerful tool to assess frailty and senility syndromes. In this paper, we propose a vision-based gait analysis approach performed on a smartphone with cloud computing assistance. Gait sequences recorded by a smartphone camera are processed by the smartphone itself to obtain spatiotemporal features. These features are uploaded onto the cloud in order to analyse and compare them to a stored database to render a diagnostic. The feature extraction method presented can work with both frontal and sagittal gait sequences although the sagittal view provides a better classification since an accuracy of 95% can be obtained.

ELIC-Based Texture Advection and Information Entropy-Based Feature Extraction for the Visualization of Surface Flow Field

2013 ◽  
Vol 380-384 ◽  
pp. 2478-2481
Author(s):  
Huai Hui Wang ◽  
Si Kun Li
Keyword(s):  
Feature Extraction ◽  
Flow Field ◽  
Information Entropy ◽  
Extraction Method ◽  
Classical Method ◽  
Basic Mechanism ◽  
Surface Flow ◽  
Feature Extraction Method ◽  
Background Image

IBFVS is a classical method for visualizing surface flow field, but the quality of the final image is inadequate to get a fully understanding for the visualized flow field. In order to improve the quality of the result image of IBFVS, this paper presents an enhanced IBFVS method based on short ELIC filtering. We take IBFVS as the basic mechanism of our method, and use ELIC filtering to process the injected background image to increase the contrast of the result image. Furthermore, we use information entropy to extract the most important features with highest information in the flow field. Experiment results show that our method generates a better visualization result than IBFVS and the information entropy-based feature extraction method distinguishes the most valuable part in the flow field.

An Effective EMD-Based Feature Extraction Method for Boring Chatter Recognition

2010 ◽  
Vol 34-35 ◽  
pp. 1058-1063 ◽  
Author(s):  
Xin Li ◽  
Zhe He Yao ◽  
Zi Chen Chen
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Chatter Suppression ◽  
Mode Decomposition ◽  
Finished Surface ◽  
Precision Hole

Chatter often occurs during precision hole boring, it results in low quality of finished surface and even damages the cutting tool. In order to identify chatter rapidly and gain the precious time for chatter suppression, a chatter monitoring system was established and an effective feature extraction method for boring chatter recognition was presented. According to the characteristic of chatter signal, empirical mode decomposition (EMD) was introduced into chatter feature extraction, and its basic theories were investigated. The vibration signal was decomposed by EMD, then the intrinsic mode functions (IMF) was got. Finally, the feature of chatter symptom was extracted by analyzing the energy spectrum of each IMF. The results show that feature extracted from vibration of boring bar by EMD can indicate chatter outbreak symptom, and it can be used as feature vectors for rapidly recognizing chatter.

A Novel Prediction of Quaternary Structural Type of Proteins with Gene Ontology

2020 ◽  
Vol 27 (4) ◽  
pp. 313-320 ◽  
Author(s):  
Xuan Xiao ◽  
Wei-Jie Chen ◽  
Wang-Ren Qiu
Keyword(s):  
Gene Ontology ◽  
Feature Extraction ◽  
Extraction Method ◽  
Quaternary Structure ◽  
Structural Type ◽  
Sequence Information ◽  
Prediction System ◽  
Data Set ◽  
Feature Extraction Method ◽  
Prediction Rate

Background: The information of quaternary structure attributes of proteins is very important because it is closely related to the biological functions of proteins. With the rapid development of new generation sequencing technology, we are facing a challenge: how to automatically identify the four-level attributes of new polypeptide chains according to their sequence information (i.e., whether they are formed as just as a monomer, or as a hetero-oligomer, or a homo-oligomer). Objective: In this article, our goal is to find a new way to represent protein sequences, thereby improving the prediction rate of protein quaternary structure. Methods: In this article, we developed a prediction system for protein quaternary structural type in which a protein sequence was expressed by combining the Pfam functional-domain and gene ontology. turn protein features into digital sequences, and complete the prediction of quaternary structure through specific machine learning algorithms and verification algorithm. Results: Our data set contains 5495 protein samples. Through the method provided in this paper, we classify proteins into monomer, or as a hetero-oligomer, or a homo-oligomer, and the prediction rate is 74.38%, which is 3.24% higher than that of previous studies. Through this new feature extraction method, we can further classify the four-level structure of proteins, and the results are also correspondingly improved. Conclusion: After the applying the new prediction system, compared with the previous results, we have successfully improved the prediction rate. We have reason to believe that the feature extraction method in this paper has better practicability and can be used as a reference for other protein classification problems.

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