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Öğe The classification of eye state by using kNN and MLP classification models according to the EEG signals(2015) Sabancı, Kadir; Koklu, MuratWhat is widely used for classification of eye state to detect humans 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%.Öğe Classification of Siirt and long type Pistachios (Pistacia vera L.) by artificial neural networks(2015) Sabancı, Kadir; Koklu, Murat; Unlersen, Muhammed FahriQuality 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 Otsus 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.Öğe A NEW ACCURATE AND EFFICIENT APPROACH TO EXTRACT CLASSIFICATION RULES(GAZI UNIV, FAC ENGINEERING ARCHITECTURE, 2014) Koklu, Murat; Kahramanli, Humar; Allahverdi, NovruzA new method for extracting rules from multi-class datasets was proposed in this study. The proposed method was applied to 4 different data set. Discrete and real attributes were decoded in different ways. Discrete attributes were coded as binary whereas real attributes were coded by using two real values These values indicate the midpoint and the expansion of intervals of the attributes that form the rules. Classification success was used as fitness function of rule extraction. CLONALG which is Artificial Immune Systems (AIS) algorithm was used to optimize the fitness function. To apply the proposed method Iris, Wine, Glass and Abalone datasets were used. The datasets were obtained from machine learning repository of University of California at Irvine (UCI). The proposed method achieved prediction accuracy ratios of 100%, 99,44%, 77,10%, and 62,59% for Iris, Wine, Glass and Abalone datasets, respectively. When it is compared with the previous studies it has been seen that the proposed method achieved more successful results and has advantage in terms of complexity.Öğe A NEW APPROACH TO CLASSIFICATION RULE EXTRACTION PROBLEM BY THE REAL VALUE CODING(ICIC INTERNATIONAL, 2012) Koklu, Murat; Kahramanli, Humar; Allahverdi, NovruzIn this study a new method that uses artificial immune system (AIS) algorithm has been presented to extract rules from medical related dataset. Four real life problems data were investigated for determining feasibility of the proposed method. The data were obtained from machine learning repository of University of California at Irvine (UCI). The datasets were obtained from Iris Dataset which is the multi-class problem, Pima Indian Diabetes Dataset and two different Wisconsin Breast Cancer datasets. The proposed method achieved prediciton accuracy ratios of 100%, 77.2%, 98.54% and 95.61% for the Iris, Pima Indians Diabetes, Wisconsin Breast Cancer (original) and Wisconsin Breast Cancer (diagnostic) datasets, respectively. It has been observed that these results are better than the results obtained from related previous studies.Öğe Prediction of Computer Type Using Benchmark Scores of Hardware Units(Selçuk Üniversitesi, 2020) Taspinar, Yavuz Selim; Cinar, Ilkay; Koklu, MuratUsers need an expert opinion to learn about their current computer or purchasing. In addition to these, computer and computer component manufacturers have to carry out innovation studies such as improving the products they produce by receiving feedback about the products they produce, and changing the marketing strategy. There is various benchmark software to meet all these needs. This benchmark software measures the software and hardware performance of the computers and enable users to gain information about the performance of their computers and components. The category of computers can also be determined as a result of the performance evaluation obtained. Various statistical and machine learning methods are used to determine these categories. In this study, it is tried to predict which category the computers fall into by using the computer features in a dataset obtained from the internet by the web scraping method by random forest and logistic regression method. The effect of computer features in the dataset on classification has been analyzed. Classification success was 89.4% with the random forest method and 84.3% with the logistic regression method.Öğe Skin Lesion Classification using Machine Learning Algorithms(2017) Ozkan, Ilker Ali; Koklu, MuratMelanoma 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
