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Öğe Büyük veri üzerine perspektif bir bakiş(Institute of Electrical and Electronics Engineers Inc., 2019) Demirol D.; Das R.; Hanbay D.Data, which is the new trend of 21st century, has made difficult to extract meaningful information from itself because of its increasing volume and variety Big data platforms and tools to analyze the size and type of data that cannot be processed by traditional methods have brought new perspectives to data analytics. Big Data are gathered from non-Traditional sources such as wireless sensors, blogs, social media, emails etc. In addition, companies can make better insights of large data sets by using large data analysis mechanisms. In this study, the definition of big data, its relationship with other technologies, technologies used in big data area and general information about analysis techniques are given. © 2019 IEEE.Öğe Classification of breast masses in mammogram images using KNN(Institute of Electrical and Electronics Engineers Inc., 2015) Alpaslan N.; Kara A.; Zencir B.; Hanbay D.Breast cancer is one of the most deadly diseases for women. Mammogram is very important imaging technique used diagnosis in early stages of breast cancer. In this study, a decision support system which helps experts to examine mammogram images in the fight against breast cancer is developed. In this study, firstly several preprocesses are applied to mammogram to make image clear and segmentation of mass is provided with an appropriate threshold value. After the segmentation processes, features of the tumor mass are obtained. The obtained features are classified as normal, benign or malignant using kNN (k-nearest neighbours) classifiers. In this study, its have been were shown that, effect of kurtosis, skewness and wavelet energy features on classification performance is shown. As a result, it has been seen that, these features improve the classification performance. © 2015 IEEE.Öğe Classification of hyperspectral images using 3D CNN based ResNet50(Institute of Electrical and Electronics Engineers Inc., 2021) Firat H.; Hanbay D.Hyperspectral images are images containing rich spectral and spatial information widely used in remote sensing applications. The development of deep learning techniques has had a significant impact on the classification of hyperspectral images. Different Convolutional Neural Network architectures have been used in many hyperspectral image analysis studies. However, the high dimensions of the hyperspectral images increased the computational complexity. For this reason, dimensionality reduction has been used in the preprocessing stage in many studies. Another difficulty encountered in hyperspectral image classification studies is the need to consider both spectral and spatial features. When deep spatial and spectral features are to be extracted, problems such as loss of gradient properties and degradation due to increased depth arise. In this study, the 3D convolutional neural network (CNN) based ResNet50 method is proposed to solve these problems encountered in hyperspectral studies and to extract sufficient spatial spectral properties from the network. Principal Component Analysis (PCA) was used to reduce spectral band excess. The proposed method has been applied to Pavia University and Salinas data sets. Overall accuracy, average accuracy and kappa values were used to measure the performance of the method. Calculated overall accuracy, average accuracy, and kappa values are 99.99% for the Pavia University data set, and while the overall accuracy and kappa values were 99.99% for the Salinas data set, the average accuracy value was 99.98%. © 2021 IEEE.Öğe Computer-aided tumor detection system using brain MR images(Institute of Electrical and Electronics Engineers Inc., 2016) Ari A.; Alpaslan N.; Hanbay D.In today's technology, computer assisted detection applications have managed to make great contributions to the field of medicine. Computer assisted detection systems aim to help radiologist about mass detection by using image processing systems. In this study, it's aimed to carry out mass detection process on the images of 3D brain MRI (Magnetic Resonance Imaging). The steps followed in this study are the stage of pre-processing the stage of segmentation, identification of the areas of interests and identification of tumor. As a result of processing's in the stages of preprocessing and segmentation, obtained areas of interests are labelled and attributes of these areas of interests are extracted during the stage of attributes extraction and in the last stage, the areas of interests are identified as whether they are mass or not according to these attributes. With this method applied on 845 number of magnetic resonance image sections belonging to 13 patients, it has been achieved classification success with 86.39%. © 2015 IEEE.Öğe An expert system for the prediction of stroke disease by different least squares support vector machines models(Scientific Publishers of India, 2017) Sarihan M.E.; Hanbay D.Objective: One of the important life-threatening ailment is stroke across the world. The current paper was performed to classify the outcome of stroke by using Least-Squares Support Vector Machines (LSSVMs) models. Materials and methods: The medical dataset related to stroke disease was achieved from the clinical database of the emergency medicine department. 28 predictors were recorded in raw dataset. For dimension reduction, correlations between input and target (stroke) variables were evaluated. Different LS-SVMs models were performed with radial basis function (RBF), linear and polynomial kernels. 5- fold cross-validation was used in composing stages to achieve the best model using all of the data. The accuracy and the Area under Receiver Operating Curve (AUC ROC) values were used for performance assessment. Results: At first, feature selection stage was performed. 14 input variables were determined after this stage. Whole dataset was partitioned into 5 sub-datasets (D1,D2, D3, D4, D5) to use all data both training and testing. LS-SVMs models performance were evaluated by using 5-fold cross validation method. Accuracy and AUC values of the models were used as performance criteria. The best model performance was evaluated with LS-SVMs model using linear kernel. That model average accuracy was 86.6%. The best accuracy was evaluated with LS-SVM model using linear kernel on dataset D5 was 94%. As a consequence, the LS-SVMs model can be used for predicting the outcome of stroke. Conclusion: The results point out that LS-SVMs with linear kernel have much more accuracy and AUC values for predicting stroke disease. The suggested LS-SVMs with linear kernel may produce beneficial prediction results related to stroke disease. In future studies, several data mining techniques may be tested and assembled for better classification performance of stroke disease. © 2017, Scientific Publishers of India, All rights reserved.Öğe Flame detection using HSI color space(Institute of Electrical and Electronics Engineers Inc., 2017) Toptaş B.; Hanbay D.Image processing based systems take important place in systems used to detect fires in open spaces. Vision-based systems can detect fires in open spaces from distances and detect the fire at an early stage. In this study, a fire/flame detection method based on the color analysis of the fire image is presented. The proposed method consists of three steps. First, the image in RGB space is converted to the HSI color space. Then a color filter is applied to determine the fire/flame candidate zone. In the second stage, fake fire zones within the candidate zone identified as fire are eliminated. In this phase, image difference and gauss mixture model is used to recover the fake fire areas. In the third step, the result of the two methods is subjected to AND processing. The AND operation ensures to detect the exact flame zone. As a result, the proposed algorithm has been tested using fire video images. The highest calculated accuracy is 96%. © 2017 IEEE.Öğe A novel texture classification method based on Hessian matrix and principal curvatures(IEEE Computer Society, 2014) Alpaslan N.; Hanbay K.; Hanbay D.; Talu M.F.In this study, in order to obtain similar effect with conventional gradient operation and extract more robust feature for texture, we use the principal curvature informations instead of the gradient calculation. Through this methods, sharp and important informations about the texture images were obtained by analyzing images of the second order. Considering the classification results obtained, it is shown that the proposed method improve the performance of original CoHOG and HOG feature extraction methods. As a result of experiments on datasets with different characteristics, it is seen that, the proposed method has higher classification performance. © 2014 IEEE.Öğe Particle Swarm Optimization-Enhanced Virtual Multicast Trees Embedding in SDNs(Institute of Electrical and Electronics Engineers Inc., 2023) Guler E.; Ayaz F.; Karakus M.; Hanbay D.The truly innovative network virtualization technology allows for multi-tenancy, enabling different Virtual Network (VN) requests to share the same physical network. This is achievable because network services are no longer bound to the architecture of the lessor hardware. Virtual Network Embedding (VNE) is a multi-dimensional NP-Hard problem that maps VN entities such as virtual nodes and virtual links onto a shared Substrate Network (SN) while assuring the requested network resources (e.g., bandwidth, computing power, etc.). This research explores how to efficiently map VNs with one-to-many (multicast) interactions, in the form of Virtual Multicast Trees (VMTs), onto an SN in contrast to the VNE problem where one-to-one (unicast) communication is at focus. Thus, we propose a Virtual Multicast Tree Embedding (VMTE) enhanced by Particle Swarm Optimization framework, VMTE-PSO, to put VMTs on a shared SN. The VMTE-PSO aims to minimize the amount of network resource consumption (i.e., bandwidth) in the SN while simultaneously satisfying the computing demand of virtual nodes and minimizing the redundant substrate link usage. Extensive simulations reveal our algorithm outperforms the dynamic node ranking and traditional greedy-based VMTE approaches with respect to bandwidth consumption and redundant multicast transmission on NSFNET and USNET network topologies. © 2023 IEEE.Öğe Robot Arm Control with for SSVEP-based brain signals in brain computer interface(Institute of Electrical and Electronics Engineers Inc., 2017) Çi? H.; Hanbay D.; Tüysüz F.Hilbert Transform (HT) and Multi Wavelet Transform (MWT) has been used to recognize the same frequency harmonics that occur in the brain with the Steady State Visual Evoked Potentials(SSVEP). In this study, harmonics of certain frequencies in brain are used which are detected by SSVEP and visual stimulus potentials to be used in Robot Arm Control. This stimulus has been made using shapes of box that oscillated at certain frequencies. The signal components were clustered according to the same direction and stimulus frequency on the data set for the desired work, task or movement. These signals were processed by the band pass filters at 5-30 Hz then HD process were applied. The filtered signals classified by Neural Network and Cubic-Support Vector Machine after MWT analysis were applied to these. Evaluated average success rate is over 90 %. Finally, the test brain signals recorded for 3 tasks over the trained network have been successfully used for Robot Arm Control. The use of the proposed HD-MWT method is promising for the development of a real-time robot control with SSVEP-based BCI. © 2017 IEEE.
