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In today's world, the amount of data produced in every field has increased at an unexpected level. In the face of increasing data, the importance of data processing has increased remarkably. Our resource topic is on the processing of video data, which has an important place in increasing data, and the production of summary videos. Within the scope of this resource, a new method for anomaly detection with object-based unsupervised learning has been developed while creating a video summary. By using this method, the video data is processed as pixels and the result is produced as a video segment. The process flow can be briefly summarized as follows. Objects on the video are detected according to their type, and then they are tracked. Then, the tracking history data of the objects are processed, and the classifier is trained with the object type. Thanks to this classifier, anomaly behavior of objects is detected. Video segments are determined by processing video moments containing anomaly behaviors. The video summary is created by extracting the detected video segments from the original video and combining them. The model we developed has been tested and verified separately for single camera and dual camera systems.

Interpolation and internal painting are one of the basic approaches in image internal painting, which is used to eliminate undesirable parts that occur in digital images or to enhance faulty parts. This study was designed to compare the interpolation algorithms used in image in-painting in the literature. Errors and noise generated on the colour and grayscale formats of some of the commonly used standard images in the literature were corrected by using Cubic, Kriging, Radial based function and High dimensional model representation approaches and the results were compared using standard image comparison criteria, namely, PSNR (peak signal-to-noise ratio), SSIM (Structural SIMilarity), Mean Square Error (MSE). According to the results obtained from the study, the absolute superiority of the methods against each other was not observed. However, Kriging and RBF interpolation give better results both for numerical data and visual evaluation for image in-painting problems with large area losses., Comment: Image in-painting, Interpolation, Cubic interpolation, Kriging interpolation, Radial based function, High dimensional model representation

This study analyses the dose difference for a variety of phantom materials that can be used for Leksell Gamma Knife. These materials have properties that are very similar to the human tissue not including the skull bone. Geant4 was employed in the analysis the dose distributions for collimator helmet sizes of 4mm and 8mm. The phantom had a radius of 80mm. Water, brain, PMMA (Poly-methyl methacrylate) and polystyrene were used as the material types. Results showed no considerable differences for radiation dosimetries depending on the material types. In addition, the polystyrene and PMMA (Poly-methyl methacrylate) phantom are also suitable for measuring the dose profiles of the Gamma Knife unit.

With the increasing use of the internet and social media, it is obvious that cyberbullying has become a major problem. The most basic way for protection against the dangerous consequences of cyberbullying is to actively detect and control the contents containing cyberbullying. When we look at today's internet and social media statistics, it is impossible to detect cyberbullying contents only by human power. Effective cyberbullying detection methods are necessary in order to make social media a safe communication space. Current research efforts focus on using machine learning for detecting and eliminating cyberbullying. Although most of the studies have been conducted on English texts for the detection of cyberbullying, there are few studies in Turkish. Limited methods and algorithms were also used in studies conducted on the Turkish language. In addition, the scope and performance of the algorithms used to classify the texts containing cyberbullying is different, and this reveals the importance of using an appropriate algorithm. The aim of this study is to compare the performance of different machine learning algorithms in detecting Turkish messages containing cyberbullying. In this study, nineteen different classification algorithms were used to identify texts containing cyberbullying using Turkish natural language processing techniques. Precision, recall, accuracy and F1 score values were used to evaluate the performance of classifiers. It was determined that the Light Gradient Boosting Model (LGBM) algorithm showed the best performance with 90.788% accuracy and 90.949% F1 Score value.

It is well known that social media has revolutionized communication. Nowadays, citizens, companies, and public institutions actively use social media in order to express themselves better to the population they address. This active use is also carried out by the gendarmerie and police organizations to communicate with the public with the purpose of improving social relations. However, it has been seen that the posts by the gendarmerie and police organizations did not attract much attention from their target audience from time to time, and it has been discovered that there was not enough research in the literature on this issue. In this study, it was aimed to investigate the use of social media by the gendarmerie and police organizations operating in Turkey (Jandarma - Polis), Italy (Carabinieri - Polizia), France (Gendarmerie - Police) and Spain (Guardia Civil - Polic\'ia), and the extent to which they can be effective on the followers, by comparatively examining their activity on twitter. According to the obtained results, it was found that Jandarma (Turkey) has the highest power of influence in the twitter sample, and the findings were comparatively presented in the study.

Given that there are 94 clinics and more than 200,000 patients treated worldwide, proton and carbon are the most used heavily charged particles in heavy ion therapy. However, there is a recent increasing trend in using new ion beams. Each heavy ion has a different effect on the target. As each heavy ion moves through the tissue, they lose enormous energy in collisions, so their range is not long. Ionization accounts for the majority of this loss in energy. During this interaction of the heavily charged particles with the target, the particles do not only ionize, but also lose energy with the recoil. Recoil occurs by atom-to-atom collisions. With these collisions, crystalline atoms react with different combinations and form cascades in accordance with their energies. Thus, secondary particles create ionization and recoil. In this study, recoil values of boron, carbon, nitrogen and oxygen beams in the water phantom were computed in the energy range of 2.0-2.5 GeV using Monte Carlo simulation and the results were compared with carbon. It was observed that there is a regular increase in the recoil peak amplitude for carbon and boron ions, unlike oxygen and nitrogen where such a regularity could not be seen. Moreover, the gaps in the crystal structure increased as the energy increases.

This paper proposes a new machine learning based system for forest fire earlier detection in a low-cost and accurate manner. Accordingly, it is aimed to bring a new and definite perspective to visual detection in forest fires. A drone is constructed for this purpose. The microcontroller in the system has been programmed by training with deep learning methods, and the unmanned aerial vehicle has been given the ability to recognize the smoke, the earliest sign of fire detection. The common problem in the prevalent algorithms used in fire detection is the high false alarm and overlook rates. Confirming the result obtained from the visualization with an additional supervision stage will increase the reliability of the system as well as guarantee the accuracy of the result. Due to the mobile vision ability of the unmanned aerial vehicle, the data can be controlled from any point of view clearly and continuously. System performance are validated by conducting experiments in both simulation and physical environments., Comment: 14 pages, 8 Figures

Gamma Knife Radiosurgery (GKRS) is a treatment choice in newly diagnosed AVMs and residual AVMs following endovascular interventions. Aim of this study is to determine if commercially available liquid embolic agents reduce the radiation dose to the target due to the attenuation of the 60Co beam. Doses accumulated by three different embolization materials, namely: Onyx, cyanoacrylate and polyvinyl alcohol (PVA) were analysed. A collimator helmet size of 8mm was employed in a Monte-Carlo simulation implemented in Geant4 simulation toolkit. Obtained dose accumulations were evaluated and results demonstrated statistically significant differences in the dosimetries calculated., Comment: We would like to withdraw our paper due to a disagreement between authors

Heavy ions have varying effects on the target. The most important factor in comparing this effect is Linear Energy Transfer (LET). Protons and carbons are heavy ions with high LET. Since these ions lose energy through collisions as they move through the tissue, their range is not long. This loss of energy increases along the way, and the maximum energy loss is reached at the end of the range. This whole process is represented by the Bragg curve. The input dose of the Bragg curve, full width at half maximum (FWHM) value, Bragg peak amplitude and position, and Penumbra thickness are important factors in determining which particle is advantageous in tumor treatment. While heavy ions move through the tissue, small deviations occur in their direction of travel due to Coulomb collisions. These small deviations cause lateral straggle in the dose profile. Lateral straggle is important in determining the type and energy of the particle used in tumor treatments close to critical organs. In our study, when the water phantom of protons and carbon beams with different energies is taken into consideration, the input dose, FWHM value, peak amplitude and position, penumbra thickness and lateral straggle are calculated using the TRIM code and the results are compared with Monte Carlo (MC) simulation. It was found that the proton has an average of 63% more FWHM and 53% more Penumbra than the carbon ion. The carbon ion has an average of 28-45 times greater Bragg peak amplitude at the same Bragg peak location than the proton. It was observed that the proton scattered approximately 70% more in lateral straggle. The difference was found to be around 1.32 mm. In line with all these results, the most commonly used proton and carbon heavy ions in hadron therapy applications were compared.

The purpose of this study is to successfully train our vehicle detector using R-CNN, Faster R-CNN deep learning methods on a sample vehicle data sets and to optimize the success rate of the trained detector by providing efficient results for vehicle detection by testing the trained vehicle detector on the test data. The working method consists of six main stages. These are respectively; loading the data set, the design of the convolutional neural network, configuration of training options, training of the Faster R-CNN object detector and evaluation of trained detector. In addition, in the scope of the study, Faster R-CNN, R-CNN deep learning methods were mentioned and experimental analysis comparisons were made with the results obtained from vehicle detection., Comment: 7 pages, 8 Figures, 1 table

Radio frequency identification (RFID), The real-time location of objects and ability to track motion provide a wide range of useful applications in areas such as safety, security and supply chain. In recent years, radio frequency identification technology has moved from obscurity into mainstream applications that help speed the handling of manufactured goods and materials. RFID enables identification from a distance, and unlike earlier bar-code technology, it does so without requiring a line of sight. In this paper, the author introduces the principles of RFID, discusses its primary technologies and applications., Comment: 6 pages, 10 figures

This paper describes the stages faced during the development of an Android program which obtains and decodes live images from DJI Phantom 3 Professional Drone and implements certain features of the TensorFlow Android Camera Demo application. Test runs were made and outputs of the application were noted. A lake was classified as seashore, breakwater and pier with the proximities of 24.44%, 21.16% and 12.96% respectfully. The joystick of the UAV controller and laptop keyboard was classified with the proximities of 19.10% and 13.96% respectfully. The laptop monitor was classified as screen, monitor and television with the proximities of 18.77%, 14.76% and 14.00% respectfully. The computer used during the development of this study was classified as notebook and laptop with the proximities of 20.04% and 11.68% respectfully. A tractor parked at a parking lot was classified with the proximity of 12.88%. A group of cars in the same parking lot were classified as sports car, racer and convertible with the proximities of 31.75%, 18.64% and 13.45% respectfully at an inference time of 851ms., Comment: 6 pages

Light sleep is a sleeping period which occurs within each hour during the sleep. This is the period when people are closest to awakening. With this being the case people tend to move more frequently and aggressively during these periods. The characteristics of sleeping stages, detection of light sleep periods and analysis of light sleep periods were clarified. The sleeping patterns of different subjects were analyzed. In this paper the most suitable moment for waking a person up will be described. The detection of this moment and the development process of a system dedicated to this purpose will be explained, and also some experimental results that are acquired via different tests will be shared and analyzed., Comment: 5 pages, 11 figures

Matching of binary image features is an important step in many different computer vision applications. Conventionally, an arbitrary threshold is used to identify a correct match from incorrect matches using Hamming distance which may improve or degrade the matching results for different input images. This is mainly due to the image content which is affected by the scene, lighting and imaging conditions. This paper presents a fuzzy logic based approach for brute force matching of image features to overcome this situation. The method was tested using a well-known image database with known ground truth. The approach is shown to produce a higher number of correct matches when compared against constant distance thresholds. The nature of fuzzy logic which allows the vagueness of information and tolerance to errors has been successfully exploited in an image processing context. The uncertainty arising from the imaging conditions has been overcome with the use of compact fuzzy matching membership functions.

Genetic Algorithms are widely used in many different optimization problems including layout design. The layout of the shelves play an important role in the total sales metrics for superstores since this affects the customers' shopping behaviour. This paper employed a genetic algorithm based approach to design shelf layout of superstores. The layout design problem was tackled by using a novel chromosome representation which takes many different parameters to prevent dead-ends and improve shelf visibility into consideration. Results show that the approach can produce reasonably good layout designs in very short amounts of time.

The paper presents a comparative study of the performance of Back Propagation and Instance Based Learning Algorithm for classification tasks. The study is carried out by a series of experiments will all possible combinations of parameter values for the algorithms under evaluation. The algorithm's classification accuracy is compared over a range of datasets and measurements like Cross Validation, Kappa Statistics, Root Mean Squared Value and True Positive vs False Positive rate have been used to evaluate their performance. Along with performance comparison, techniques of handling missing values have also been compared that include Mean or Mode replacement and Multiple Imputation. The results showed that parameter adjustment plays vital role in improving an algorithm's accuracy and therefore, Back Propagation has shown better results as compared to Instance Based Learning. Furthermore, the problem of missing values was better handled by Multiple imputation method, however, not suitable for less amount of data., Comment: 15 pages

Crime Scene Investigation (CSI) is a carefully planned systematic process with the purpose of acquiring physical evidences to shed light upon the physical reality of the crime and eventually detect the identity of the criminal. Capturing images and videos of the crime scene is an important part of this process in order to conduct a deeper analysis on the digital evidence for possible hints. This work brings this idea further to use the acquired footage for generating a 3D model of the crime scene. Results show that realistic reconstructions can be obtained using sophisticated computer vision techniques. The paper also discusses a number of important design considerations describing key features that should be present in a powerful interactive CSI analysis tool., Comment: 9 pages, journal

Coverage of image features play an important role in many vision algorithms since their distribution affect the estimated homography. This paper presents a Genetic Algorithm (GA) in order to select the optimal set of features yielding maximum coverage of the image which is measured by a robust method based on spatial statistics. It is shown with statistical tests on two datasets that the metric yields better coverage and this is also confirmed by an accuracy test on the computed homography for the original set and the newly selected set of features. Results have demonstrated that the new set has similar performance in terms of the accuracy of the computed homography with the original one with an extra benefit of using fewer number of features ultimately reducing the time required for descriptor calculation and matching., Comment: 14 pages, journal

Finding the position of the user is an important processing step for augmented reality (AR) applications. This paper investigates the use of different motion models in order to choose the most suitable one, and eventually reduce the Kalman filter errors in sensor fusion for such applications where the accuracy of user tracking is crucial. A Deterministic Finite Automaton (DFA) was employed using the innovation parameters of the filter. Results show that the approach presented here reduces the filter error compared to a static model and prevents filter divergence. The approach was tested on a simple AR game in order to justify the accuracy and performance of the algorithm., Comment: 8 pages, journal

A tracking system that will be used for Augmented Reality (AR) applications has two main requirements: accuracy and frame rate. The first requirement is related to the performance of the pose estimation algorithm and how accurately the tracking system can find the position and orientation of the user in the environment. Accuracy problems of current tracking devices, considering that they are low-cost devices, cause static errors during this motion estimation process. The second requirement is related to dynamic errors (the end-to-end system delay; occurring because of the delay in estimating the motion of the user and displaying images based on this estimate. This paper investigates combining the vision-based estimates with measurements from other sensors, GPS and IMU, in order to improve the tracking accuracy in outdoor environments. The idea of using Fuzzy Adaptive Multiple Models (FAMM) was investigated using a novel fuzzy rule-based approach to decide on the model that results in improved accuracy and faster convergence for the fusion filter. Results show that the developed tracking system is more accurate than a conventional GPS-IMU fusion approach due to additional estimates from a camera and fuzzy motion models. The paper also presents an application in cultural heritage context., Comment: 14 pages, journal

The focus of this paper is on wearable technologies which are increasingly being employed in the medical field. From smart watches to smart glasses, from electronic textile to data gloves; several gadgets are playing important roles in diagnosis and treatment of various medical conditions. The threats posed by these technologies are another matter of concern that must be seriously taken into account. Numerous threats ranging from data privacy to big data problems are facing us as adverse effects of these technologies. The paper analyses the application areas and challenges of wearable technologies from a technical and ethical point of view and presents solutions to possible threats., Comment: 4 pages, conference, in Turkish

Brute force matching of binary image feature descriptors is conventionally performed using the Hamming distance. This paper assesses the use of alternative metrics in order to see whether they can produce feature correspondences that yield more accurate homography matrices. Two statistical tests, namely ANOVA (Analysis of Variance) and McNemar's test were employed for evaluation. Results show that Jackard-Needham and Dice metrics can display better performance for some descriptors. Yet, these performance differences were not found to be statistically significant., Comment: 2 pages, journal