Least Square Support Vector Machine and Minumum Redundacy Maximum Relavance for Diagnosis of Breast Cancer from Breast Microscopic Images

Korkmaz, Sevcan Aytac; Poyraz, Mustafa

doi:10.1016/j.sbspro.2015.01.1150

Cited by 16 publications

(11 citation statements)

References 8 publications

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“…Until today, there have been many studies in the field of health [20][21][22][23][24][25][26][27][28][29][30][31][32][33]. However, there are many patients who can not be diagnosed the early cancer.…”

Section: Resultsmentioning

confidence: 99%

Recognition of the İmages with Random Forest Classifier Based on the LPP and LBP Methods

Korkmaz¹

2018

Sakarya University Journal of Science

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İmage cells have taken with Light Microscope help. The local binary pattern (LBP) features have obtained for original images. High-dimensional of these LBP features is reduced to lower-dimensional with Locality Preserving Projections (LPP). These low dimensional data are classified by the Random Forest (RF), Naive Bayes (NB), and Artificial Neural Networks (ANN) classifiers. The classification results are compared with previous studies. The performance achieved with the ANN classifier is higher than the RF and NB classifiers. Moreover, feature vector size used in ANN classifier is a lower than feature vector size used in RF and NB classifiers. The success rates achieved with the ANN, RF, and NB classifiers is respectively 96.29%, 74.44%,and 70.00% according to LPP Method.

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Section: Resultsmentioning

confidence: 99%

Recognition of the İmages with Random Forest Classifier Based on the LPP and LBP Methods

Korkmaz¹

2018

Sakarya University Journal of Science

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“…Predicting the amount of electricity produced in a power plant is very important for today's economy. To date, there are many fi eld work for classifi cation or clustering [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Resultsmentioning

confidence: 99%

Classification by Artificial Neural Network according to the values affecting Electricity Generation

Korkmaz¹

2018

Trends Comput Sci Inf Technol

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Predicting the amount of electricity produced in a power plant is very important for today's economy. Oven Power (MW), Boiler Input Gas Temperature, Superheated Steam Amount, ID-Fan Speed, Feeding Water Tank data affect the electricity production. In this article, Etikrom A.Ş. The electricity production amount to be produced in Elazığ Etikrom A.Ş. was estimated by using the data of Oven Power (MW), Water Inlet Gas Temperature, Steam Vapor Volume, ID-Fan Speed, Feeding Water Tank data. Electricity generation amount is used as verifi cation data. That is, by the k-means clustering method, the electricity generation amount is divided into 3 classes (low, medium, and high). 3621 data including Oven Power (MW), Boiler Input Gas Temperature, Superheated Steam Amount, ID-Fan Speed, and Feeding Water Tank data were used after class 3 separation. With the K-means clustering method, 2742 of these data were clustered as low electricity, 296 as medium electricity and 583 as high electricity. This clustered data was given to the Artifi cal Neural Network classifi er. The success rate obtained as a result of this classifi cation is 85.81%. Classifi ed data were analyzed by ROC curve.

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“…As one of the choices to describe a future trend, time series analysis can be applied to reflect dynamic variable from one time to another [3]. From the previous studies by using Least Square [4][5][6][7][8] and Moving Average [3] [9][10][11][12][13][14] method, the data and analysis showed the future prediction. It was defined as a management process in making decision.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Poor Inhabitant Number Using Least Square and Moving Average Method

Ekawati¹

2015

ijacsa

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Abstract-The number of poor inhabitant in South Kalimantan decreased within the last three years compared with the previous years. The numbers of poor inhabitant differs from time to time. This scaled dynamical number has been a problem for the local government to take proper polices to solve this matter. It will then be necessary to predict a potential number of poor inhabitants in the next year as the basis on subsequent policy making. This research will apply both Least Square and Moving Average method as the measurement to count prediction values. From the results of the study, the prediction analysis by using those two methods is valid for predicting acquired number of poor inhabitant for the next period according to the data from the previous year. Based on the study, the validity of Least Square was 98.35% and Moving Average was 98.79% by using the data in the last seven years.

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Least Square Support Vector Machine and Minumum Redundacy Maximum Relavance for Diagnosis of Breast Cancer from Breast Microscopic Images

Cited by 16 publications

References 8 publications

Recognition of the İmages with Random Forest Classifier Based on the LPP and LBP Methods

Recognition of the İmages with Random Forest Classifier Based on the LPP and LBP Methods

Classification by Artificial Neural Network according to the values affecting Electricity Generation

Prediction of Poor Inhabitant Number Using Least Square and Moving Average Method

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