scholarly journals Microarray Analysis Using Statistical Approach

2012 ◽  
pp. 168-172
Author(s):  
Smita Patnaik ◽  
Tripti Swarnkar
Keyword(s):  
Support Vector Machine ◽  
Information Technologies ◽  
Statistical Approach ◽  
Support Vector ◽  
The Past ◽  
Common Task ◽  
Two Samples ◽  
Number Of Genes ◽  
Leukemia Dataset ◽  
Microarray Classification

Over the past few decades rapid developments in genomic and other molecular research technologies and developments in information technologies have combined to produce tremendous amounts of information related to molecular biology. It is not possible to research on a large number of genes using traditional methods. DNA Microarray is one such technology which enables to monitor the expression levels for tens of thousands of genes in parallel . A common task with Microarray data is to determine which genes are differentially expressed between two samples obtained under two different conditions. To solve this problem several Statistical methods have been proposed. The Support Vector Machine is one of the most efficient & widely used statistical method for Microarray classification. In this paper we have classified leukemia dataset by using support vector machine under two conditions and also showed the performance of different type of kernels.

scholarly journals Effective Usage of Support Vector Machine in Face Detection

2020 ◽  
Vol 9 (3) ◽  
pp. 1336-1340
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Facial Recognition ◽  
Recognition System ◽  
Support Vector ◽  
Biometric Technology ◽  
Multimedia Contents ◽  
The Past ◽  
Vector Machines ◽  
Facial Recognition System

With the rapid growth in Technology in terms of multimedia contents such as Biometrics, Facial recognition etc. Facial detection got much attention over the past few years. Face recognition describes a biometric technology that attempts to establish an identity. In this paper, I would like to review about a facial recognition system using machine learning especially, using support vector machines. In any case, point of this exploration is to give extensive writing survey over face acknowledgment alongside its applications. Furthermore, after top to bottom conversation, a portion of the significant discoveries are given in end.

scholarly journals Analyzing Big Data with the Hybrid Interval Regression Methods

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Chia-Hui Huang ◽  
Keng-Chieh Yang ◽  
Han-Ying Kao
Keyword(s):  
Support Vector Machine ◽  
Big Data ◽  
Information Technologies ◽  
Large Scale ◽  
Cloud Services ◽  
Support Vector ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Smooth Support Vector Machine ◽  
Scale Data

Big data is a new trend at present, forcing the significant impacts on information technologies. In big data applications, one of the most concerned issues is dealing with large-scale data sets that often require computation resources provided by public cloud services. How to analyze big data efficiently becomes a big challenge. In this paper, we collaborate interval regression with the smooth support vector machine (SSVM) to analyze big data. Recently, the smooth support vector machine (SSVM) was proposed as an alternative of the standard SVM that has been proved more efficient than the traditional SVM in processing large-scale data. In addition the soft margin method is proposed to modify the excursion of separation margin and to be effective in the gray zone that the distribution of data becomes hard to be described and the separation margin between classes.

scholarly journals Ensemble-based support vector machine classifiers as an efficient tool for quality assessment of beef fillets from electronic nose data

2016 ◽  
Vol 8 (18) ◽  
pp. 3711-3721 ◽  
Author(s):  
Fady Mohareb ◽  
Olga Papadopoulou ◽  
Efstathios Panagou ◽  
George-John Nychas ◽  
Conrad Bessant
Keyword(s):  
Support Vector Machine ◽  
Quality Assessment ◽  
Electronic Nose ◽  
Support Vector ◽  
Efficient Tool ◽  
The Past ◽  
Food Freshness ◽  
Potential Tool

Over the past years, the application of electronic nose devices has been investigated as a potential tool for assessing food freshness.

scholarly journals Classifying Quranic Verse Topics using Word Centrality Measure

2021 ◽  
Vol 5 (3) ◽  
pp. 594-601
Author(s):  
Ferdian Yulianto ◽  
Kemas Muslim Lhaksmana ◽  
Danang Triantoro Murdiansyah
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Classification Models ◽  
Bayes Classifier ◽  
Centrality Measure ◽  
The Past ◽  
Early Islamic

Muslims believe that, as the speech of Allah, The Quran is a miracle that has specialties in itself. Some of the specialties that have studied are the regularities in the number of letters, words, vocabularies, etc. In the past, the early Islamic scholars identify these regularities manually, i.e. by counting the occurrence of each vocabulary by hand. This research tackles this problem by utilizing centrality in quranic verse topic classification. The goal of this research is to analyze the effect of The Quran word centrality measure on the topic classification of The Quran verses. To achieve this objective, the method of this research is constructing the Quran word graph, then the score of centralities included as one of the features in the verse topic classification. The effect of centrality is observed along with support vector machine (SVM) and naïve Bayes classifiers by performing two scenarios (with stopword and without stopword removal). The result shows that according to the centrality measure the word “الله” (Allah) is the most central in The Quran. The performance evaluation of the classification models shows that the use of centrality improves the hamming loss score from 0.43 to 0.21 on naïve Bayes classifier with stopword removal. Finally, both of classification method has a better performance in word graph that use stopword removal.  

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