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Öğe A machine learning approach to dental fluorosis classification(Springer Science and Business Media Deutschland GmbH, 2021) Yetis, Aysegul Demir; Yesilnacar, Mehmet Irfan; Atas, MusaFluoride in groundwater has been found to pose a severe public health threat in two villages (Karataş and Sarım) of western Sanliurfa in the southeastern Anatolia region of Turkey, where many cases of fluorosis, which detrimentally affects the teeth and bones, have been reported. Analysis of fluoride in drinking water is usually accomplished using various chemical methods, but while these techniques produce accurate and reliable results, they are expensive, labor-intensive, and cumbersome. In this study, a more cost-effective alternative, based on machine learning methods, is introduced. In this case, artificial neural network (ANN), support vector machine (SVM), and Naïve Bayes classifiers are utilized. Furthermore, a novel feature selection and ranking method known as Normalized Weighted Voting Map (NWVM) is presented. In Fisher discrimination power (FDP) scores, X-ray fluorescence (XRF) variables have higher discrimination power potential than X-Ray diffraction (XRD) attributes, the most salient feature being Zr (0.464) and CaO (219.993) from XRD and XRF, respectively. When the XRD and XRF parameters are classified separately for the effect of NWVM ranking scores on the fluoride values and dental fluoride in groundwater, CaO, SiO2, MgO, Fe2O3, P2O5, and K2O (for XRF) and Quartz and Zr (for XRD) present a stronger effect. In addition, when looking at the effects among themselves, the first order is the same XRF parameters and then the XRD parameters. Experiments revealed that XRF constituents including CaO, SiO2, MgO, P2O5, and K2O have higher class discrimination power than the XRD variables. © 2021, Saudi Society for Geosciences.Öğe A New Approach for Plotting Raster Based Image Files(IEEE, 2014) Dogan, Yahya; Atas, Musa; Ozdemir, CuneytIn this study a new approach that is used for plotting raster based image files, is proposed. Proposed method is tested on the prototype plotter which is designed as a Cartesian-robot architecture. Work list is produced by the proposed edge tracking algorithm on the canny edge detector based images. Outputs are generated from the work list as a converted atomic move commands which can be interpreted by Cartesian-robot. Edge images by using conventional vectorization methods are compared with outputs of the proposed algorithm. It is observed that proposed method is superior to other techniques, in terms of representing original images.Öğe A new approach for plotting raster based image files(IEEE Computer Society, 2014) Dogan, Yahya; Atas, Musa; Ozdemir, CuneytIn this study a new approach that is used for plotting raster based image files, is proposed. Proposed method is tested on the prototype plotter which is designed as a Cartesian-robot architecture. Work list is produced by the proposed edge tracking algorithm on the canny edge detector based images. Outputs are generated from the work list as a converted atomic move commands which can be interpreted by Cartesian-robot. Edge images by using conventional vectorization methods are compared with outputs of the proposed algorithm. It is observed that proposed method is superior to other techniques, in terms of representing original images. © 2014 IEEE.Öğe A Novel Similarity Algorithm for Fixing Erroneous Turkish Text and Detection of Roots(IEEE, 2014) Ozdemir, Cuneyt; Atas, MusaFinding roots of words is widely used in document classification and text mining. Computational methods of text similarity are intensely utilized on the English words and successful outcomes are obtained. On the other hand, applying the aforementioned methods on the Turkish words did not give the similar success. In this study, a novel similarity computation algorithm is developed. By using this algorithm it is aimed to find correct words or advice possible alternatives from the written erroneous Turkish words as a highest accuracy rate.Öğe Adaptive High Dynamic Range(IEEE, 2015) Atas, Musa; Dogan, YahyaInvestigating High Dynamic Range (HDR) approaches in the literature, a new and adaptive HDR model is developed in this study. HDR is processed based on images taken as a Low Dynamic Range (LDR) scheme that ranges between low exposure and high exposure values. Here, main focus is to present and to interpret challenging scenes or cases without having information loss by extending intensity ranges of a camera. Images, converting to HDR from LDR actually have a good satisfaction with respect to the information content, yet they are subject to effect which may deteriorate their natural quality. With respect to end user view, still it is hard to say HDR images satisfy photo-realistic characteristics. In this study, it is focused on information gain without detriment natural characteristics of the picture and thus a new HDR algorithm was developed. Proposed HDR method was crosschecked with famous methods used in the literature with regard to both photo-realistic picture quality, usability and computational cost criteria. It was observed that proposed method preferable over so called traditional algorithms and located in the bunch of first three methods by applying poll with 30 test subjects.Öğe An Efficient Rotation Invariant Feature Extraction Method Based on Ring Projection Technique(IEEE, 2013) Atas, Musa; Kaya, Yilmaz; Uyar, MuratThis study presents an efficient rotation-invariant feature extraction method based on ring projection technique. The main advantage of this method is to reduce the number of sampling frequency of standard ring projection method. The proposed method is compared with the ring projection and local binary patterns according to the computational speed of the feature extraction and classification accuracy. By incrementally rotating first image of each texture class by 30 and 45 degrees enrich the dataset and yield two texture datasets having totally 1332 and 888 samples from the original Brodatz texture image dataset, respectively. Throughout the study Weka machine learning and data mining tool is utilized. As a classifier Naive Bayes, Bagging and J48 decision tree are used due to their simplicity and speed. Classification performance is evaluated via 10 fold cross validation technique. It is observed that, the proposed method outperforms other alternatives in terms of classification accuracy and feature extraction speed.Öğe An efficient rotation invariant feature extraction method based on ring projection technique(2013) Atas, Musa; Kaya, Yilmaz; Uyar, MuratThis study presents an efficient rotation-invariant feature extraction method based on ring projection technique. The main advantage of this method is to reduce the number of sampling frequency of standard ring projection method. The proposed method is compared with the ring projection and local binary patterns according to the computational speed of the feature extraction and classification accuracy. By incrementally rotating first image of each texture class by 30 and 45 degrees enrich the dataset and yield two texture datasets having totally 1332 and 888 samples from the original Brodatz texture image dataset, respectively. Throughout the study Weka machine learning and data mining tool is utilized. As a classifier Naive Bayes, Bagging and J48 decision tree are used due to their simplicity and speed. Classification performance is evaluated via 10 fold cross validation technique. It is observed that, the proposed method outperforms other alternatives in terms of classification accuracy and feature extraction speed. © 2013 IEEE.Öğe Biometric identification using panoramic dental radiographic images with few-shot learning(Tubitak Scientific & Technological Research Council Turkey, 2022) Atas, Musa; Ozdemir, Cuneyt; Atas, Isa; Ak, Burak; Ozeroglu, EsmaDetermining identity is a crucial task especially in the cases of mass disasters such as tsunamis, earthquakes, fires, epidemics, and in forensics. Although there are various studies in the literature on biometric identification from radiographic dental images, more research is still required. In this study, a panoramic dental radiographic (PDR) image -based human identification system was developed using a customized deep convolutional neural network model in a few-shot learning scheme. The proposed model (PDR-net) was trained on 600 PDR images obtained from a total of 300 patients. As the PDR images of the patients were very different in terms of pose and intensity, they were first cropped by the domain experts according to the region of interest and adjusted to standard view with histogram equalization. A customized data augmentation approach was applied in order for the model to generalize better while it was being trained. The proposed model achieved a prediction accuracy of 84.72% and 97.91% in Rank-1 and Rank-10, respectively, by testing 144 PDR images of 72 patients that had not been previously used in training. It was concluded that well known similarity metrics such as Euclidean, Manhattan, Cosine, Pearson, Kendall's Tau and sum of absolute difference can be utilized in few-shot learning. Moreover, Cosine and Pearson similarity achieved the highest Rank 1 score of 84.72%. It was observed that as the number of rank increased, the Spearman and Kendall's Tau metrics had the same success as Cosine and Pearson. Based on the superimposed heatmap image analysis, it was determined that the maxillary, mandibular, nasal fossa, sinus and other bone forms in the mouth contributed biometric identification. It was also found that customized data augmentation parameters contributed positively to biometric identification.Öğe Chess Playing Robotic Arm(IEEE, 2014) Atas, Musa; Dogan, Yahya; Atas, IsaIn this study, a chess playing robotic arm system which has 5 degree of freedoms is developed. System comprised with various modules such as; main controller, image processing, machine learning, game engine and motion engine of robotic arm. Image processing unit is triggered only whenever opponent starts to move chessman. Meanwhile, images acquired in a specific time intervals are transmitted to the machine learning unit for classification purpose. After the classification process is taken place, opponent valid move is sent to the game engine as an input in order to generate reasonable output. Generated output is forwarded to the motion engine for positioning the robotic arm. It was observed that, developed system provides an efficient, favorable and immersive experience for player.Öğe Classification of Power Quality Disturbances Based on S-Transform and Image Processing Techniques(IEEE, 2013) Uyar, Murat; Kaya, Yilmaz; Atas, MusaThis paper presents a method that combines discrete S-transform (DST) time-frequency distribution (TFD) and local binary pattern (LBP) based image analysis technique for classifying power quality (PQ) disturbances. The purpose of this combination is to extract discriminative features by utilizing from both capability of generating the compact TFD of a non-stationary signal and the efficient image representation capability of LBP. In the proposed method, DST based TFDs of PQ disturbance signals are considered as 2-D images. LBP histograms are used to extract the features from TF images. Initially, the uniform patterns in TF images are obtained by the LBP operator. Next, the occurrence histograms of these patterns are used to produce representative feature vectors that can capture the unique and salient characteristics of each disturbance. The classification performance of the proposed method is evaluated through total 2400 disturbance signals. The experimental results have shown that the rate of correct classification is about 98 % for the different PQ disturbances.Öğe Classification of power quality disturbances based on s-transform and image processing techniques(2013) Uyar, Murat; Kaya, Yilmaz; Atas, MusaThis paper presents a method that combines discrete S-transform (DST) time-frequency distribution (TFD) and local binary pattern (LBP) based image analysis technique for classifying power quality (PQ) disturbances. The purpose of this combination is to extract discriminative features by utilizing from both capability of generating the compact TFD of a nonstationary signal and the efficient image representation capability of LBP. In the proposed method, DST based TFDs of PQ disturbance signals are considered as 2-D images. LBP histograms are used to extract the features from TF images. Initially, the uniform patterns in TF images are obtained by the LBP operator. Next, the occurrence histograms of these patterns are used to produce representative feature vectors that can capture the unique and salient characteristics of each disturbance. The classification performance of the proposed method is evaluated through total 2400 disturbance signals. The experimental results have shown that the rate of correct classification is about 98 % for the different PQ disturbances. © 2013 IEEE.Öğe Classification of Turkish spam e-mails with artificial immune system(2013) ÖzdemIr, Cüneyt; Atas, Musa; Özer, Ahmet BedriIn this study, it is aimed to detect frequently encountered spam e-mails with artificial immune algorithms. Turkish spam and non-spam e-mail dataset are generated within the scope of the work. Fisher discriminant analysis (FDA) and Euclidean Distance (ED) are utilized in order to extract features from the turkish email dataset. In order to evaluate the classification accuracies, artificial immune algorithms with Bayes as a linear and artificial neural network as a non-linear classifiers are used. Various artificial immune algorithms, including AIRS1, AIRS2, AIRS2PARALLEL, CLONALG and CSCA are investigated. Among them, CSCA reveals the best classification accuracy of 86%. Furthermore, CSCA algorithm classifies spam emails with 81% and non-spam e-mails with 90% accuracies. © 2013 IEEE.Öğe CLASSIFICATION OF TURKISH SPAM E-MAILS WITH ARTIFICIAL IMMUNE SYSTEM(IEEE, 2013) Ozdemir, Cuneyt; Atas, Musa; Ozer, Ahmet BedriIn this study, it is aimed to detect frequently encountered spam e-mails with artificial immune algorithms. Turkish spam and non-spam e-mail dataset are generated within the scope of the work. Fisher discriminant analysis (FDA) and Euclidean Distance (ED) are utilized in order to extract features from the turkish email dataset. In order to evaluate the classification accuracies, artificial immune algorithms with Bayes as a linear and artificial neural network as a non-linear classifiers are used. Various artificial immune algorithms, including AIRS1, AIRS2, AIRS2PARALLEL, CLONALG and CSCA are investigated. Among them, CSCA reveals the best classification accuracy of 86%. Furthermore, CSCA algorithm classifies spam emails with 81% and non-spam e-mails with 90% accuracies.Öğe Novel machine learning techniques based hybrid models (LR-KNN-ANN and SVM) in prediction of dental fluorosis in groundwater(Springer, 2022) Atas, Musa; Yesilnacar, Mehmet Irfan; Yetis, Aysegul DemirStudies have shown that excessive intake of fluoride into human body from drinking water may cause fluorosis adversely affects teeth and bones. Fluoride in water is mostly of geological origin and the amounts depend highly on many factors such as availability and solubility of fluoride minerals as well as hydrogeological and geochemical conditions. Chemical methods usually accomplish fluoride analysis in drinking water. The chemical methods are expensive, labor-intensive and time-consuming in general although accurate and reliable results are obtained. An alternative cost-effective approach based on machine learning (ML) technique is investigated in this study. Furthermore, most effective input parameters are selected via proposed Simulated Annealing (SA) search scheme. Selected subset (SAR, K+, NO3-, NO2-, Mn, Ba and Fe) by SA algorithm exhibited high correlation coefficient values of 0.731 and strong t test scores of 5.248. On the other hand, most frequently selected individual features were identified as NO3-, NO2-, Fe and SAR by vote map. The results of experiments revealed that selected feature subset improves the prediction performance of the learning models while feature size is reduced substantially. Thus it eventually enabled determination of fluoride in a cheap, fast and feasible way.Öğe Open Cezeri Library: A Novel JAVA Based Matrix and Computer Vision Framework(Wiley, 2016) Atas, MusaIn this paper we introduce the Open Cezeri Library (OCL) framework as a domain specific language (DSL) for researchers, scientists, and engineering students to enable them to develop basic linear algebra operations via simple matrix calculations, image processing, computer vision, and machine learning applications in JAVA programming language. OCL provides a strong intuition of coding for the developer while implementing by means of a fluent interface. The significant aspect of the OCL is to combine the methods of well-known platforms; MATLAB and JAVA, accordingly. Moreover, OCL supports a fluent interface so that users can extend a single line of codes by putting a dot between the methods because all the methods implemented actually return the host class. It was observed that the learning curve of the OCL is lower than the MATLAB and the native JAVA languages, and makes coding more readable, understandable, traceable, and enjoyable. In addition to this, the experiments revealed that the running performance of the OCL is quite comparable and can be used in a variety of diverse applications. (C) 2016 Wiley Periodicals, Inc.Öğe Prediction of Adaptive Exposure Time in Hyperspectral Bands for Industrial Cameras(IEEE, 2015) Dogan, Yahya; Atas, MusaIn this study, a new method for exposure time correction for hyperspectral imaging is introduced. Initially, hardware setup was established. Then, a look-up table holds the minimum and maximum exposure times for each band was built. By using the developed image acquisition system, images having different exposure times for each hyperspectral band were acquired. After that, various features that can represent the exposure state were identified and a dataset was established. Prediction performance of the proposed method was cross validated by artificial neural network and outcomes were interpreted. It is observed that, by using the proposed method desired exposure quality can be determined with 99% accuracy.
