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Koklu, Murat
Advanced Technology and Science (ATScience)
Computer Science & IT

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The Classification of Eye State by Using kNN and MLP Classification Models According to the EEG Signals Sabancı, Kadir; Koklu, Murat
International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 4 (2015)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.75836

Abstract

What is widely used for classification of eye state to detect human’s cognition state is electroencephalography (EEG). In this study, the usage of EEG signals for online eye state detection method was proposed. In this study, EEG eye state dataset that is obtained from UCI machine learning repository database was used. Continuous 14 EEG measurements forms the basic of the dataset. The duration of the measurement is 117 seconds (each measurement has14980 sample). Weka (Waikato Environment for Knowledge Analysis) program is used for classification of eye state. Classification success was calculated by using k-Nearest Neighbors algorithm and multilayer perceptron neural networks models. The obtained success of classification methods were compared. The classification success rates were calculated for various number of neurons in the hidden layer of a multilayer perceptron neural network model. The highest classification success rate have been obtained when the number of neurons in the hidden layer was equal to 7. And it was 56.45%. The classification success rates were calculated with k-nearest neighbors algorithm for different neighbourhood values. The highest success was achieved in the classification made with kNN algorithm.  In kNN models, the success rate for 3 nearest neighbor were calculated as 84.05%.
Classification of Siirt and Long Type Pistachios (Pistacia vera L.) by Artificial Neural Networks Sabanci, Kadir; Koklu, Murat; Unlersen, Muhammed Fahri
International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 2 (2015)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.74573

Abstract

Quality is one of the important factors in agricultural products marketing. Grading machines have great role in quality control systems. The most efficient method used in grading machines today is image processing. This study aims to do the grading of high valued agricultural product of our land called pistachio that has two different types namely Siirt and Long type of pistachios by image processing methods and artificial neural networks. Photos of Siirt and long type of pistachios are taken by a Webcam with CCD sensor. These photos were converted to gray scale in Matlab. Afterwards, these photos were converted to binary photo format using Otsu’s Method. Then this data was used to train multi-layered neural network to complete grading.  Matlab was used for both image processing and artificial neural networks. Successes of the grading with image processing and artificial neural networks for mixed type pistachios Siirt and Long were researched.
Skin Lesion Classification using Machine Learning Algorithms OZKAN, Ilker Ali; KOKLU, Murat
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 4 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Melanoma is a deadly skin cancer that breaks out in the skin’s pigment cells on the skin surface. Melanoma causes 75% of the skin cancer-related deaths. This disease can be diagnosed by a dermatology specialist through the interpretation of the dermoscopy images in accordance with ABCD rule. Even if dermatology experts use dermatological images for diagnosis, the rate of the correct diagnosis of experts is estimated to be 75-84%. The purpose of this study is to pre-classify the skin lesions in three groups as normal, abnormal and melanoma by machine learning methods and to develop a decision support system that should make the decision easier for a doctor. The objective of this study is skin lesions based on dermoscopic images PH2 datasets using 4 different machine learning methods namely; ANN, SVM, KNN and Decision Tree. Correctly classified instances were found as 92.50%, 89.50%, 82.00% and 90.00% for ANN, SVM, KNN and DT respectively. The findings show that the system developed in this study has the feature of a medical decision support system which can help dermatologists in diagnosing of the skin lesions.
Co-Authors Ozkan, Ilker Ali Sabancı, Kadir Sabanci, Kadir Unlersen, Muhammed Fahri