scholarly journals Identification of risk genes associated with myocardial infarction based on the recursive feature elimination algorithm and support vector machine classifier

2017 ◽  
Author(s):  
Xiaoqiang Yang
Keyword(s):  
Myocardial Infarction ◽  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Risk Genes ◽  
Elimination Algorithm

scholarly journals Serum N-Glycosylation in Parkinson’s Disease: A Novel Approach for Potential Alterations

Molecules ◽  
2019 ◽  
Vol 24 (12) ◽  
pp. 2220 ◽  
Author(s):  
Csaba Váradi ◽  
Károly Nehéz ◽  
Olivér Hornyák ◽  
Béla Viskolcz ◽  
Jonathan Bones
Keyword(s):  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Label Free ◽  
Dynamic Coating ◽  
Elimination Algorithm ◽  
Novel Approach ◽  
Label Free Quantitation

In this study, we present the application of a novel capillary electrophoresis (CE) method in combination with label-free quantitation and support vector machine-based feature selection (support vector machine-estimated recursive feature elimination or SVM-RFE) to identify potential glycan alterations in Parkinson’s disease. Specific focus was placed on the use of neutral coated capillaries, by a dynamic capillary coating strategy, to ensure stable and repeatable separations without the need of non-mass spectrometry (MS) friendly additives within the separation electrolyte. The developed online dynamic coating strategy was applied to identify serum N-glycosylation by CE-MS/MS in combination with exoglycosidase sequencing. The annotated structures were quantified in 15 controls and 15 Parkinson’s disease patients by label-free quantitation. Lower sialylation and increased fucosylation were found in Parkinson’s disease patients on tri-antennary glycans with 2 and 3 terminal sialic acids. The set of potential glycan alterations was narrowed by a recursive feature elimination algorithm resulting in the efficient classification of male patients.

Damage identification of bolt connection in steel truss structures by using sound signals

2021 ◽  
pp. 147592172110048
Author(s):  
Debing Zhuo ◽  
Hui Cao
Keyword(s):  
Support Vector Machine ◽  
Phase Transformation ◽  
Support Vector Machine Classifier ◽  
Damage Identification ◽  
Microphone Array ◽  
Truss Structures ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Steel Truss ◽  
Bolt Connection

Different from traditional health-monitoring methods based on vibrational signals recorded by contact sensors, an online diagnosis procedure for steel truss structures using sound signals was proposed. The basic idea of the procedure was to identify the features related to bolt connection damage extracted from sound signals and locate the damaged position. Before the online diagnosis was carried out, sound signals were specifically collected by a microphone array involving environmental noise and sound discharged by artificial damaged bolt connections. Then the signals were preprocessed and their time and frequency domain features were extracted, from which sensitive features were selected by support vector machine recursive feature elimination. A support vector machine classifier aiming to identify signals related to damage was trained with the selected sensitive features, and a genetic algorithm was used to optimize its parameters. An improved method called steered response power and phase transformation with offline database was put forward to compute the steered response power values of coordinates in the offline database to localize the source of identified damage signals. The pre-built database consisted of a series of coordinates indicating the positions of bolts. When the online diagnosis was implemented for a steel truss structure, sound signals were picked up by the microphone array at the same location as that used for the database construction. The signals were preprocessed and their sensitive features were extracted for damage identification by the trained support vector machine classifier. If some signals were judged to be related to bolt connection damage, steered response power and phase transformation with offline database was used to compute steered response power values, with which a fusion decision was made based on evidence theory to locate the damaged bolt connection. The experiment of a steel truss model with 24 bolt connections showed that the proposed procedure could locate the loose bolts precisely even under heavy noise effect, and had a smaller computational load compared with traditional steered response power and phase transformation.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

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