GASVM statistics

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Citations per year

Number of citations per year for the bioinformatics software tool GASVM
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Tool usage distribution map

This map represents all the scientific publications referring to GASVM per scientific context
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GASVM specifications

Information


Unique identifier OMICS_19121
Name GASVM
Alternative name Genetic Algorithm Support Vector Machine
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Maintained Yes

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Maintainer


  • person_outline Krishna Kandaswamy

Publication for Genetic Algorithm Support Vector Machine

GASVM citations

 (10)
library_books

Prediction and Analysis of CO2 Emission in Chongqing for the Protection of Environment and Public Health

2018
PMCID: 5877075
PMID: 29547505
DOI: 10.3390/ijerph15030530

[…] lyzed by Wang et al. []. There is any little difference in the methodology of prediction on carbon emissions throughout the world. Among them, EKC curve [,], STIRPAT model [], LMDI decomposition [,], genetic Algorithm [], support vector machine stands for the mainstream research method []. Ikaga et al. estimated buildings-related CO2 emissions in Japan up to 2050 []. Panareda et al. analyzed globa […]

library_books

Genetic algorithm for the optimization of features and neural networks in ECG signals classification

2017
Sci Rep
PMCID: 5282533
PMID: 28139677
DOI: 10.1038/srep41011

[…] ficities of the two classifiers were very similar. The average positive predictive value was also raised from 96.58% to 97.81%. For making comparisons with different classifiers, we also used SVM and genetic algorithm-support vector machine (GA-SVM) to classify the same ECG features extracted by WPD-statistical method. and show the classification results of SVM and GA-SVM, individually. The comp […]

library_books

Differential Diagnosis of Erythmato Squamous Diseases Using Classification and Regression Tree

2016
PMCID: 5203752
PMID: 28077889
DOI: 10.5455/aim.2016.24.338-342

[…] ttribute discretized using equal frequency into four split, using Information Gain measure.ModellingSeveral methods presented to classify Erythmato-Squamous diseases. Such methods including, k-means, genetic algorithm, support vector machine, artificial neural network, clustering, and decision tree. Since, decision trees are the most common used and practical method for induction inference, it is […]

library_books

A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

2015
Adv Bioinformatics
PMCID: 4480804
PMID: 26170834
DOI: 10.1155/2015/198363

[…] chromosomes and the best 10% of each generation is merged with the previous ones. Part of the chromosome is the discriminant coefficient which indicates the importance of a gene for a class label []. Genetic Algorithm-Support Vector Machine (GA-SVM) [] creates a population of chromosomes as binary strings that represent the subset of features that are evaluated using SVMs. Simulated annealing work […]

library_books

mRMR ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

2015
Biomed Res Int
PMCID: 4414228
PMID: 25961028
DOI: 10.1155/2015/604910

[…] results from this study demonstrated the effectiveness of the integration of mRMR and GA, and it was concluded that the mRMR-GA method achieved better performance when compared to the mRMR filter and GASVM wrapper algorithms in all datasets. Meanwhile, with the same number of selected genes in this experimental result, the gene set obtained by the mRMR-GA selection was more representative of the s […]

library_books

A Survey of Keystroke Dynamics Biometrics

2013
Sci World J
PMCID: 3835878
PMID: 24298216
DOI: 10.1155/2013/408280

[…] n inconsistency of data is subjective and may be dissimilar among different persons []. Furthermore, manual outlier detection and removal is infeasible in an automated system. Thus, [] proposed using Genetic Algorithm-Support Vector Machine that can automatically select the relevant subset of feature and disregard noisy data without human intervention. Although evidences in the literatures show th […]


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GASVM funding source(s)
Institute for Neuro and Bioinformatics, University of Lübeck, Germany.

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