2) Makale
Bu koleksiyon için kalıcı URI
Güncel Gönderiler
Öğe A Novel Quantitative Volumetric Spreading Index Definition and Assessment of Astrocyte Spreading In Vitro(International Society for Advancement of Cytometry, 2017) TİRYAKİ, VOLKAN MÜJDAT; AYRES, VIRGINIA M; AHMED, IJAZ; SHREIBER, DAVID IA novel quantitative volumetric spreading index (VSI) is defined that depends on the total distance between object voxels and the contact surface plane in three-dimensional (3D) space. The VSI, which ranges from 0 to 1, is rotationally invariant around the zaxis. VSI can be used to quantify the degree of individual cell spreading, which is important for analysis of cell interactions with their environment. The VSIs of astrocytes cultured on a nanofibrillar surface and three different comparative planar surfaces have been calculated from confocal laser scanning microscope z-series images, and the effects of both culture surface and immunoreactivity on the degree of cell spreading were investigated. VSI calculations indicated a statistical correlation between increased reactivity, based on immunolabeling for glial fibrillary acidic protein, and decreased cell spreading. Further results provided a quantitative measure for the increased spreading of quiescent-like and reactive-like astrocytes on planar substrates functionalized with poly-L-lysine.Öğe Mitigating Cyber Security Attacks by being Aware of Vulnerabilities and Bugs(IEEE, 2017) Aslan, ömer; samet, RefikBecause the Internet makes human lives easier, many devices are connected to the Internet daily. The private data of individuals and large companies, including health-related data, user bank accounts, and military and manufacturing data, are increasingly accessible via the Internet. Because almost all data is now accessible through the Internet, protecting these valuable assets has become a major concern. The goal of cyber security is to protect such assets from unauthorized use. Attackers use automated tools and manual techniques to penetrate systems by exploiting existing vulnerabilities and software bugs. To provide good enough security; attack methodologies, vulnerability concepts and defence strategies should be thoroughly investigated. The main purpose of this study is to show that the patches released for existing vulnerabilities at the operating system (OS) level and in software programs does not completely prevent cyber-attack. Instead, producing specific patches for each company and fixing software bugs by being aware of the software running on each specific system can provide a better result. This study also demonstrates that firewalls, antivirus software, Windows Defender and other prevention techniques are not sufficient to prevent attacks. Instead, this study examines different aspects of penetration testing to determine vulnerable applications and hosts using the Nmap and Metasploit frameworks. For a test case, a virtualized system is used that includes different versions of Windows and Linux OS.Öğe Investigation of Possibilities to Detect Malware Using Existing Tools(IEEE, 2017-10) Aslan, ömer; samet, RefikMalware stands for malicious software, which is installed on a computer system without the knowledge of the system owner. It performs malicious actions such as stealing confidential information and allowing remote code execution, and it can cause denial of service. Recently, malware creators started to publish new malware, which can bypass anti-malware software, intrusion detection systems (IDS) and sandbox execution. Due to this evasion, the protection of computer networks and computerized systems against these programs has become one of the biggest challenges in the information security realm. This paper proposes a methodology to learn the well-known malware analysis and detection tools, to implement these tools on well-known malware and benign programs and to compare the obtained results. Further, this research will suggest to users how to analyze and detect existing and unknown malware. In a test case, 100 malware and 100 benign program samples were collected from different sources and analyzed under different versions of Windows machines. The test results indicated that it is almost impossible to detect malware by only using one tool. Using static and dynamic analysis tools together increased accuracy and the detection rate. The test results also showed that dynamicÖğe Hand Tremor Based Biometric Recognition Using Leap Motion Device(IEEE ACCESS, 2017-10-20) Ataş, MusaIn this paper, the applicability of hand tremor-based biometric recognition via leap motion device is investigated. The hypothesis is that the hand tremor is unique for humans and can be utilized as a biometric identification. In order to verify our hypothesis, spatiotemporal hand tremor signals are acquired from subjects. The objective is to establish a live and secure identification system to avoid mimic and cloning of password by attackers. Various feature extraction methods, including statistical, fast Fourier transform, discrete wavelet transform, and 1-D local binary pattern are used. For evaluating recognition performance, Naïve Bayes and Multi-Layer Perceptron are utilized as linear-simple and nonlinear-complex classifiers, respectively. Since the conducted experiments produced promising results (above 95% of classification accuracy rate), it is considered that the proposed approach has the potential to be used as a new biometric identification manner in the field of security.Öğe A Method for Determination of Object-Camera Distance by Using Single Camera(International Journal Of Natural and Engineering Sciences, 2016-12-12) Kuncan, Fatma; Yıldırım, MehmetIn this study, widely used in many areas of the detection object using image processing methods, depending on the technological development in recent years and has been studied determining the distance from the camera of the specified object. The method can be used in robotic applications more cost-effective to measure the distance of the object to the camera using a single webcam proposed. An algorithm has been developed out of the results obtained are optimized algorithm can be used to determine the object distance. The system has been tested and the results are monitored in real time. Comparison with other method (Euclidean distance and histogram thresholding) has been developed algorithm used. The algorithm developed in MATLAB.Öğe A new approach to aflatoxin detection in chili pepper by machine vision(2012) Ataş, Musa; Çetin, Yasemin Yardımcı; Temizel, AlptekinAflatoxins are the toxic metabolites of Aspergillus molds, especially by Aspergillus flavus and Aspergillus parasiticus. They have been studied extensively because of being associated with various chronic and acute diseases especially immunosuppression and cancer. Aflatoxin occurrence is influenced by certain environmental conditions such as drought seasons and agronomic practices. Chili pepper may also be contaminated by aflatoxins during harvesting, production and storage. Aflatoxin detection based on chemical methods is fairly accurate. However, they are time consuming, expensive and destructive. We use hyperspectral imaging as an alternative for detection of such contaminants in a rapid and nondestructive manner. In order to classify aflatoxin contaminated chili peppers from uncontaminated ones, a compact machine vision system based on hyperspectral imaging and machine learning is proposed. In this study, both UV and Halogen excitations are used. Energy values of individual spectral bands and also difference images of consecutive spectral bands were utilized as feature vectors. Another set of features were extracted from those features by applying quantization on the histogram of the images. Significant features were selected based on proposed method of hierarchical bottleneck backward elimination (HBBE), Guyon’s SVM-RFE, classical Fisher discrimination power and Principal Component Analysis (PCA). Multi layer perceptrons (MLPs) and linear discriminant analysis (LDA) were used as the classifiers. It was observed that with the proposed features and selection methods, robust and higher classification performance was achieved with fewer numbers of spectral bands enabling the design of simpler machine vision systems.Öğe Prediction of resonance frequency of aperture coupled microstrip antennas By Artificial Neural network(2016) Ataş, Musa; Ataş, İsaIn this study, the simulation model of Aperture-Coupled Micro-Strip Antenna (ACMA) by using Artificial Neural Network (ANN) is proposed. The developed model tries to predict the output resonance frequency of the ACMA according to the input physical parameters of the antenna. ACMA models were designed in High Frequency Structure Simulator (HFSS) software tool that could conduct three dimensional full-wave electromagnetic structure analysis based on Finite Element Method. Main objective is to simulate HFSS model via proposed learning model. Levenberg-Marquardt (LM) is utilized as a learning algorithm. 500 different ACMA models was designed in HFSS tool. Physical dimensions and output operating frequencies of the ACMA models were recorded in order to establish the dataset. Prediction performance of the proposed ANN simulation model was evaluated by 5-fold cross-validation scheme. Overall generalization error was calculated as 3.58 %. Experiments revealed that proposed simulation model operates at least ten thousand times faster than HFSS software. Due to its overwhelming running speed, it was concluded that proposed LM-ANN simulation model can be utilized as a preliminary search tool for optimizing the industrial ACMA models.Öğe Use of interactive multisensor snow and ice mapping system snow cover maps (IMS) and artificial neural networks for simulating river discharges in Eastern Turkey(2016) Ataş, Musa; Tekeli, Ahmet Emre; Dönmez, Senayi; Fouli, HeshamBasins located in Eastern Turkey are largely fed by snowmelt runoff during spring and early summer seasons. This study investigates the efficiency of artificial neural networks (ANNs) in snowmelt runoff generation. Although ANNs have been used for streamflow simulating/forecasting in the last two decades, using satellite-based snow-covered area (SCA) maps and meteorological observations as inputs to ANN provides a novel basis for estimating streamflow. The proposed methodology is implemented over Upper Euphrates River Basin in Eastern Turkey. SCA data was acquired from Interactive Multisensor Snow and Ice Mapping System (IMS) for an 8-year period from February 2004 to September 2011. Meteorological observations including daily cumulative precipitation and daily average air temperatures were obtained from Turkish State Meteorological Services. The simulation results are promising with coefficient of correlation varying from 0.67 to 0.98 among proposed models. Past days discharge was found to substantially improve the forecast accuracy. The paper presents the expected basin discharge for 2011 water year based on meteorological observations and SCA input.Öğe Open Cezeri Library: A novel java based matrix and computer vision framework: Open Cezeri Library(2016) Ataş, MusaIn this paper we introduce the Open Cezeri Library (OCL) framework as a domain speci?c language(DSL) for researchers, scientists, and engineering students to enable them to develop basic linear algebraoperations via simple matrix calculations, image processing, computer vision, and machine learning applicationsin JAVA programming language. OCL provides a strong intuition of coding for the developer while implementing bymeans of a ?uent interface. The signi?cant aspect of the OCL is to combine the methods of well-known platforms;MATLAB and JAVA, accordingly. Moreover, OCL supports a ?uent interface so that users can extend a single line ofcodes by putting a dot between the methods because all the methods implemented actually return the host class. Itwas observed that the learning curve of the OCL is lower than the MATLAB and the native JAVA languages, andmakes coding more readable, understandable, traceable, and enjoyable. In addition to this, the experimentsrevealed that the running performance of the OCL is quite comparable and can be used in a variety of diverseapplications. ß 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 24:736–743, 2016; View this article online atwileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21745Öğe Fast weighing of pistachio nuts by vibration sensor array(2016-01-01) Ataş, Musa; Doğan, Yahya; Ataş, İsaImpact acoustic sound signal is previously used to discriminate open-shell pistachios from closed ones and for crack detection purposes. Weight of the pistachio samples can be utilized as a feature vector for sorting and grading processes. Nevertheless, traditional weighing procedure is time consuming. Moreover, efficient fast weighing system based on impact acoustic signals for pistachio nuts has not been studied yet. This study aims to discuss the design and evaluation of a real time fast weighing system for pistachio nuts. Proposed system can be extended to other agricultural or industrial products where weight information is critical as well. In order to eliminate the sensor noise and improve the signal quality, piezoelectric sensor arrays containing 15 piezoelectric vibration sensors are employed. Final impact acoustic signal energy is determined by averaging the sensor array signals.10 pistachio samples with incremented weights ranging from 0.56 to 1.64 gr are utilized for calibration process of the sensor array. Extra two heavy objects (4.05 and 5.65 gr) are participated to the calibration set also. In order to improve accuracy and achieve consistent measurements repetitive trials approach is adopted. Excessive repetition of experiments theoretically yields more accurate and consistent measurements with minimum standard deviation. Consequently it is observed that 10 times repetition scheme produces satisfactory results with 3% coefficient of variation and 5ms of computational cost indicates that proposed system can be applicable for fast weighing of pistachio nuts.Öğe Classification of Siirt and Antep Pistachio nuts based on Computer Vision(2015-01-01) Ataş, Musa; Doğan, Yahyain this study machine vision based pistachio nut classifier system is presented. Proposed system is evaluated on the Siirt pistachio species. Siirt pistachio nuts differ from other pistachio species such as Antep pistachio according to their shape, size and taste properties. Traditionally, pistachio nuts are inspected/classified via visual inspection of workers, manually. As a result, classification process is subjected to poor efficiency in terms of time and cost. Moreover, visual inspection and classification by hand is a tedious process and may contain various health risks. Our developed machine vision system aims to classify pistachio nuts to closed and open shell classes in a fully automated manner.For the sake of simplicity and rules extraction ability from training dataset, J48 decision tree was utilized as a main classifier. Classification performance of J48 was also compared to other well-known classifiers including Naïve Bayes and Multi-Layer Perceptron (MLP). Experiments revealed that proposed system using J48 decision-tree yields simple and interpretable classifier along with satisfactory classification accuracy performance of 94.5%.Öğe A mathematical representation for the concept of knowledge and truth based on TBKMM model(2016-09-05) Alhaj Ahmad, BasharAbstract: This paper aimed to propose new model called “Truth Based Knowledge Management Model” and abbreviated, “TBKMM”. This model is different from other models where it contains many different stages and it presents a new pyramid of knowledge which also contains many different levels. In the other side, the paper presents a mathematical representation for the proposed KM model (TBKMM) to make the process of understanding it easier to comprehend . In his proposed representation, the researcher depends on the research which was proposed by Alkhaldi, F. M. (2005). Furthermore, the paper presents a new mathematical formula to represent the tacit knowledge which is composed inside the heart of human being which is considered to be the main storage area for knowledge from the researcher point of view. In addition, this paper found that the received information should pass through the following stages: Information reception, Information purifying, Knowledge composition, Wisdom composition, Certainty composition, and finally access to the Truth. Lastly, the paper presents the Truth formula which clarify the way by which we can access the top of the knowledge pyramid based on the TBKMM model.Öğe Gender classification from facial images using gray relational analysis with novel local binary pattern descriptors, Signal Image and Video Processing , DOI: 10.1007/s11760-016-1021-3(2017) Kaya, Yılmaz; Ertuğrul, ÖmerGender classification (GC) is one of the major tasks in human identification that increase its accuracy. Local binary pattern (LBP) is a texture method that employed successfully. But LBP suffers a major problem; it cannot capture spatial relationships among local textures. Therefore, in order to increase the accuracy of GC, two LBP descriptors, which are based on (1) spatial relations between neighbors with a distance parameter, and (2) spatial relations between a reference pixel and its neighbor on the same orientation, were employed to extract features from facial images. Additionally, gray relational analysis (GRA) was carried out to identify gender through extracted features. Experiments on the FEI database illustrated the effectiveness of the proposed approaches. Achieved accuracies are 97.14, 93.33, and 92.50% by applying GRA with the nLBP dd , dLBP ?? , and traditional LBP features, respectively. Experimental results indicated that the proposed approaches were very competitive feature extraction methods in GC. Present work also showed that the nLBP dd , dLBP ?? methods were obtained more acceptable results than traditional LBP.Öğe A Novel Feature Extraction Approach in SMS Spam Filtering for Mobile Communication: One-Dimensional Ternary Patterns,Security and Communication Networks ,9(17), 4680-4690,2016(2016) Kaya, Yılmaz; Ertuğrul, ÖmerThe importance and utilization of mobile communication are increasing day by day, and the short message service (SMS) is one of them. Although SMS is a widely used communication way, it brings together a major problem, which is SMS spam messages. SMS spams do not only use vain in the mobile communication traffic but also disturb users. Based on this fact, blacklisting methods, statistical methods which are built on the frequency of occurrence of words or characters, and machine learning methods have been employed. Because punishments and legal laws are not enough to solve this problem and the Group Special Mobile number of SMS spam can easily be changed, a content-based approach must be proposed. Content-based methods showed high success in spam e-mail filtering, but it is hard in the SMS spam filtering because SMS messages are extremely short and generally contains many abbreviations. In this study, an image processing method, local ternary pattern was improved to extract features from SMS messages in the feature extraction stage. In the proposed one-dimensional ternary patterns, firstly, text message was converted to their UTF-8 values. Later, each character (its UTF-8 value) in the message was compared with its neighbors. Two different feature sets were extracted from the results of these comparisons. Finally, some machine learning methods were employed to classify these features. In order to validate the proposed approach, three different SMS corpora were used. The achieved accuracies and other employee performance measures showed that the proposed approach, one-dimensional ternary patterns, can be effectively employed in SMS spam filtering.Öğe Doküman dili tanıma için yeni bir öznitelik çıkarım yaklaşımı: İkili Desenler,Journal of the Faculty of Engineering and Architecture ofGazi University, 31(4): 1085-1094, 2016(2016) Kaya, Yılmaz; Ertuğrul, ÖmerDoğal dil işlemenin önemli alt konularından biri olan dil tanıma (DT), bir dokümanın içeriğine göre yazıldığı dili belirleme işlemidir. Bu çalışmada, karakterlerin UTF-8 değerlerini birbirleri ile karşılaştırmalar sonucu elde edilen ikili desenler kullanarak yeni bir dil tanıma yaklaşımı, bir boyutlu yerel ikili örüntüler (1B-YİÖ) önerilmiştir. Önerilen yöntem farklı sayıda dillerden oluşan metinler içeren dört veri kümesi ile test edilmiştir. 1B-YİÖ ile dokümanlardan elde edilen öznitelikler kullanılarak farklı makine öğrenmesi yöntemleri ile sınıflandırma işlemi gerçekleştirilmiştir. Dört veri kümesi için sınıflandırma başarıları sırası ile %86.20, %92.75, %100 ve %89.77 olarak gözlenmiştir. Elde edilen sonuçlara göre önerilen öznitelik çıkarım yönteminin dil tanıma için önemli örüntüler sağladığı görülmüştür.Öğe Detection of parkinson's disease by shifted one dimensional local binary patterns from gait, expert systems with applications(2016) Ertuğrul, Ömer; Kaya, Yılmaz; Tekin, Ramazan; Almalı, NuriThe Parkinson's disease (PD) is one of the most common diseases, especially in elderly people. Although the previous studies showed that the PD can be diagnosed by expert systems through its cardinal symptoms such as the tremor, muscular rigidity, disorders of movements and voice, it was reported that the presented approaches, which utilize simple motor tasks, were limited and lack of standardization. To achieve a standard approach in PD detection, an approach, which is built on shifted one-dimensional local binary patterns (Shifted 1D-LBP) and machine learning methods, was proposed. Shifted 1D-LBP is built on 1D-LBP, which is sensitive to local changes in a signal. In 1D-LBP the positions of neighbors around center data are constant and therefore, the number of patterns that can be exacted by it is limited. This drawback was solved by Shifted 1D-LBP by changeable positions of neighbors. In evaluation and validation stages, the Gait in Parkinson's Disease (gaitpdb) dataset, which consists of three gait datasets that were recorded in different tasks or experiment protocols, were employed. Statistical features were exacted from formed histograms of gait signals transformed by Shifted 1D-LBP. Whole features and selected features were classified by machine learning methods. Obtained results were compared with statistical features exacted from signals in both time and frequency domains and results reported in the literature. Achieved results showed that the proposed approach can be successfully employed in PD detection from gait. This work is not only an attempt to develop a PD detection method, but also a general-purpose approach that is based on detecting local changes in time ordered signals.Öğe Determining the Optimal Number of Body-Worn Sensors for Human Activity Recognition, Soft Computing, DOI: 10.1007 / s00500-016-2100-7(2016) Ertuğrul, Ömer; Kaya, YılmazRecent developments in sensors increased the importance of action recognition. Generally, the previous studies were based on the assumption that the complex actions can be recognized by more features. Therefore, generally more than required body-worn sensor types and sensor nodes were used by the researchers. On the other hand, this assumption leads many drawbacks, such as computational complexity, storage and communication requirements. The main aim of this paper is to investigate the applicability of recognizing the actions without degrading the accuracy with less number of sensors by using a more sophisticated feature extraction and classification method. Since, human activities are complex and include variable temporal information in nature, in this study one-dimensional local binary pattern, which is sensitive to local changes, and the grey relational analysis, which can successfully classify incomplete or insufficient datasets, were employed for feature extraction and classification purposes, respectively. Achieved mean classification accuracies by the proposed approach are 95.69, 98.88, and 99.08 % while utilizing all data, data obtained from a sensor node attached to left calf and data obtained from only 3D gyro sensors, respectively. Furthermore, the results of this study showed that the accuracy obtained by using only a 3D acceleration sensor attached in the left calf, 98.8 %, is higher than accuracy obtained by using all sensor nodes, 95.69 %, and reported accuracies in the previous studies that made use of the same dataset. This result highlighted that the position and type of sensors are much more important than the number of utilized sensors.Öğe A Novel Approach for SEMG Signal Classification with Adaptive Local Binary Patterns, Medical & Biological Engineering & Computing,DOI: 10.1007/s11517-015-1443-z(2016) Ertuğrul, Ömer; Kaya, Yılmaz; Tekin, RamazanFeature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.Öğe A Novel Approach for Spam Email Detection Based On Shifted Binary Patterns, Security and Communication Networks, DOI: 10.1002/sec.1412(2016) Kaya, Yılmaz; Ertuğrul, ÖmerAdvances in communication allow people flexibility to communicate in various ways. Electronic mail (email) is one of the most used communication methods for personal or business purposes. However, it brings one of the most tackling issues, called spam email, which also raises concerns about data safety. Thus, the requirement of detecting spams is crucial for keeping the users safe and saving them from the waste of time while tackling those issues. In this study, an effective approach based on the probability of the usage of the characters that has similar orders with respect to their UTF-8 value by employing shifted one-dimensional local binary pattern (shifted-1D-LBP) was used to extract quantitative features from emails for spam email detection. Shifted-1D-LBP, which can be described as an ordered set of binary comparisons of the center value with its neighboring values, is a content-based approach to spam detection with low-level information. To validate the performance of the proposed approach, three benchmark corpora, Spamassasian, Ling-Spam, and TREC email corpuses, were used. The average classification accuracies of the proposed approach were 92.34%, 92.57%, and 95.15%, respectively. Analysis and promising experimental results indicated that the proposed approach was a very competitive feature extraction method in spam email filtering.Öğe Hidden Pattern Discovery on Epileptic EEG with 1-D Local Binary Patterns and Epileptic Seizures Detection by Grey Relational Analysis, Australasian Physical and Engineering Sciences in Medicine (APES), 2015,DOI: 10.1007/s13246-015-0362-5(2015) Kaya, YılmazThis paper proposes a novel approach to detect epilepsy seizures by using Electroencephalography (EEG), which is one of the most common methods for the diagnosis of epilepsy, based on 1-Dimension Local Binary Pattern (1D-LBP) and grey relational analysis (GRA) methods. The main aim of this paper is to evaluate and validate a novel approach, which is a computer-based quantitative EEG analyzing method and based on grey systems, aimed to help decision-maker. In this study, 1D-LBP, which utilizes all data points, was employed for extracting features in raw EEG signals, Fisher score (FS) was employed to select the representative features, which can also be determined as hidden patterns. Additionally, GRA is performed to classify EEG signals through these Fisher scored features. The experimental results of the proposed approach, which was employed in a public dataset for validation, showed that it has a high accuracy in identifying epileptic EEG signals. For various combinations of epileptic EEG, such as A–E, B–E, C–E, D–E, and A–D clusters, 100, 96, 100, 99.00 and 100 % were achieved, respectively. Also, this work presents an attempt to develop a new general-purpose hidden pattern determination scheme, which can be utilized for different categories of time-varying signals.
