Effects of Data Enrichment with Image Transformations on the Performance of Deep Networks

Images cannot always be expected to come in a certain standard format and orientation. Deep networks need to be trained to take into account unexpected variations in orientation or format. For this purpose, training data should be enriched to include different conditions. In this study, the effects of data enrichment on the performance of deep networks in the super resolution problem were investigated experimentally. A total of six basic image transformations were used for the enrichment procedures. In the experiments, two deep network models were trained with variants of the ILSVRC2012 dataset enriched by these six image transformation processes. Considering a single image transformation, it has been observed that the data enriched with 180 degree rotation provides the best results. The most unsuccessful result was obtained when the models were trained on the enriched data generated by the flip upside down process. Models scored highest when trained with a mix of all transformations

Automatic and Accurate Classification of Hotel Bathrooms from Images with Deep Learning

Hotel bathrooms are one of the most important places in terms of customer satisfaction, and where the most complaints are reported. To share their experiences, guests rate hotels, comment, and share images of their positive or negative ratings. An important part of the room images shared by guests is related to bathrooms. Guests tend to prove their satisfaction or dissatisfaction with the bathrooms with images in their comments. These Positive or negative comments and visuals potentially affect the prospective guests. In this study, two different versions of a deep learning algorithm were designed to classify hotel bathrooms as satisfactory (good) or unsatisfactory (bad, when any defects such as dirtiness, deficiencies, malfunctions were present) by analyzing images. The best-performer between the two models was determined as a result of a series of extensive experimental studies. The models were trained for each of 144 combinations of 5 hyper-parameter sets with a data set containing more than 11 thousand bathroom images, specially created for this study. The "HotelBath" data set was shared also with the community with this study. Four different image sizes were taken into consideration: 128, 256, 512 and 1024 pixels in both directions. The classification performances of the models were measured with several metrics. Both algorithms showed very attractive performances even with many combinations of hyper-parameters. They can classify bathroom images with very high accuracy. Suh that the top algorithm achieved an accuracy of 92.4% and an AUC (area under the curve) score of 0.967. In addition, other metrics also proved the success..

Evaluation of 809 Cases Applicated to A Rabies Vaccination Center of Diyarbakır Government Hospital

In this study; 809 cases applicated to rabies vaccination center ofDiyarbakır Government Hospital between May 2006 and May 2007 wereevaluated. Human diploid cell vaccine was applicated as 3 doses in 708cases (%87.5) and 5 doses in 101 cases (%12.5). In 66 cases (%8.2) rabiesantiserum was also used. The sites of injury were head-neck in 45 cases(%5.6), body-arm-leg in 563 (%69.6) cases and hand in 201 (%24.8) cases.477 cases (%59) were evaluated as superficial and 332 cases (%41) wereevaluated as deep injury. 626 cases (%77.4) had dog bite, 142 cases (%17.6)had cat bite. While 689 cases (%85.2) visited the rabies vaccination center atthe first day of injury, 115 cases (%14.2) visited in 2-5 days and 5 cases(%0.6) visited after 5 days. In conclusion; the sensitivity and the rate of theearly visit of the vaccination center because of suspicious animal contactare high and when compared with developed countries there must be a greateffort in reducing the incidence of suspicious bites

An Inverse Approach to Windows' Resource-Based Permission Mechanism for Access Permission Vulnerability Detection

In organizations, employees work with information stored in files according to their duties and responsibilities. Windows uses resource-based access permissions that any permission for any user has to be set separately per resource. This approach gets complicated as the number of resources and users increase, and causes oversights in assigning permissions. Therefore, a special mechanism is required to scrutinize what permissions any employee has on any set of resources. This requirement is circumvented by reversing the Windows approach in terms of user-accessible resources. This approach is implemented by a program allowing quick and easy examination of any type of permissions granted or denied to active directory users on any folder. In this way, administrators can make sure there is no any missing or overlooked setting that could cause a security vulnerability. This approach can easily be extended to scrutinize other resources, and for other local or active directory objects

Super resolution of B-mode ultrasound images with deep learning

Ultrasound offers a safe, non-invasive, and inexpensive way of imaging. However, due to its natural intrinsic imaging characteristics, it produces poor quality images with low resolution (LR) compared to other medical imaging modalities. Various image enhancement techniques have been extensively studied to overcome these shortcomings. Super-resolution (SR) is one of these methods, which endeavor to obtain high resolution (HR) images from LR images while enlarging them. Numerous studies have already utilized different SR techniques in various stages of ultrasound imaging (USI). Unlike other studies, which aimed at obtaining SR in the pre-processing phase or early stages of the post-processing phase of USI, we achieved SR on B-mode ultrasound images, which is the last stage of USI. We constructed a deep convolutional neural network (CNN) and trained it with a very large dataset of B-mode ultrasound images for the scale factors 2, 3, 4, and 8. We evaluated the performance of our proposed model quantitatively with eight image quality measures. The quantitative results revealed that our algorithm is much more successful than other methods at each magnification factor. Furthermore, we also verified that there is a statistically significant difference between our approach and others. Besides, qualitative analysis of the reconstructed images also confirms that it produces much better quality HR images than other methods in terms of the human visual system.Consejo Nacional de Investigaciones Cienta­ficas y Taccnicas 119E015 CONICE

Antibiotic Resistance of Gram Negative Bacteria Isolated From Urine Cultures in Our Laboratory

In this study; we analyzed the antimicrobial susceptibility of Gram negative bacteria isolated from urine cultures in the Microbiology Laboratory of Dicle University Medical Faculty Hospital from January 2006 to December 2006; retrospectively. Escherichia coli and Klebsiella species were the most frequently isolated bacteria from both outpatients and hospitalized patients. The most effective antibiotics to these bacteria were carbapenems. These results were suggested to be useful for empirical treatment of urinary system infections in our hospital

Performance of copper azole treated softwoods exposed to marine borers

Wooden material has been used for shipbuilding and structural purposes in the marine environment since ancient times. Wood being used in the sea water can be damaged by marine wood boring organisms, which can turn marine wooden structures unserviceable with great economic cost. Using naturally durable species and preservative treated wood can increase the service life of wooden maritime structures and avoid or minimise the damages caused by marine borers. In this study, Scots pine (Pinus sylvestris), Black pine (Pinus nigra) and Turkish fir (Abies bornmülleriana) naturally grown and economically important wood species in Turkey were treated with copper-azole and evaluated in marine trials for 7 and 14 months in the Western Black Sea region. In this experiment, Teredo navalis was the only teredinid species identified. Copper-azole treated fir and Scots pine specimens suffered no attack, after 7 and 14 months exposure, except four panels which suffered minor damage. However, copper-azole treated Black pine panels were moderately damaged, and all of the control panels of the softwoods were strongly attacked. The average largest shell diameter was found to be 4,79 mm in Scots pine, while the longest pallets (4,71 mm) was found in Black pine. All untreated test panels scored an average of 4 (heavily attacked) after a 14 month period. The cellulose ratio of Black pine decreased from 56 % to 50 %, and the holo-cellulose ratio from 76 % to 71 %. The treated samples showed resistance against marine borers although the copper (cu) leaching was high during the 14 months exposure underwater

Sırt sırta rüzgar türbini güç çeviricilerinin bozuk ve zayıf şebekede dengeli çalıştırılabilmesi için şebeke empedansı tahminine dayalı uyarlamalı kontrolcü tasarımı.

This thesis involves grid impedance estimation based adaptive controller design for back-to-back wind turbine power converters for stable operation in distorted and weak grid. The study focuses on the low frequency harmonic elimination of injected current to the grid under distorted grid conditions and maintaining system stability under wide range of grid impedance values for Voltage Source Inverters (VSIs) connected to the grid via LCL filter. To eliminate 5th and 7th harmonics of the injected current, a current controller with harmonic compensator is developed on the grid side controller. The system stability is evaluated by means of several control methods and ensured by adjusting the parameters of Phase Locked Loop (PLL) based on real-time estimated grid impedance. The control design is analyzed by detailed computer simulations. Obtained results are verified by laboratory experiments with a 300 kW wind turbine system which consists of squirrel cage induction generator, IGBT based back-to-back converter and LCL filter. Different operating conditions are considered to provide a through performance evaluation of the designed system.Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Electrical and Electronics Engineering