The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Convolutional Neural Network (CNN) has been chosen as a better option for the training process because it produces a high accuracy. The final accuracy has reached 91.18% in five different classes. The results are discussed in terms of the probability of accuracy for each class in the image classification in percentage. Cats class got 99.6 %, while houses class got 100 %.Other types of classes were with an average score of 90 % and above.
It is found that hypersensitive teeth have a larger number and wider patent tubules than those of non-sensitive teeth. Objective: The aim of this study is to compare between the effects of diode laser at different power densities, with and without sodium fluoride on the sealing of exposed dentinal tubules and dentin permeability. Materials and methods: 118 teeth were used. Samples were divided into three major groups. The first consisted of 100 teeth used for permeability test. The second consisted of 16 teeth for measuring external surface temperature elevation while irradiation. The third, in turn, consisted of one pair of teeth observed under SEM for dentine surface morphology analysis. Results: For dentin permeability measurement, th
... Show MoreThis paper describes a number of new interleaving strategies based on the golden section. The new interleavers are called golden relative prime interleavers, golden interleavers, and dithered golden interleavers. The latter two approaches involve sorting a real-valued vector derived from the golden section. Random and so-called “spread” interleavers are also considered. Turbo-code performance results are presented and compared for the various interleaving strategies. Of the interleavers considered, the dithered golden interleaver typically provides the best performance, especially for low code rates and large block sizes. The golden relative prime interleaver is shown to work surprisingly well for high puncture rates. These interleav
... Show MoreThis paper proposes a new method Object Detection in Skin Cancer Image, the minimum
spanning tree Detection descriptor (MST). This ObjectDetection descriptor builds on the
structure of the minimum spanning tree constructed on the targettraining set of Skin Cancer
Images only. The Skin Cancer Image Detection of test objects relies on their distances to the
closest edge of thattree. Our experimentsshow that the Minimum Spanning Tree (MST) performs
especially well in case of Fogginessimage problems and in highNoisespaces for Skin Cancer
Image.
The proposed method of Object Detection Skin Cancer Image wasimplemented and tested on
different Skin Cancer Images. We obtained very good results . The experiment showed that
CO2 Laser (10600nm) is the recent method in the management of challenging skin scar resulting from trauma, burn and surgical wound. The aim of this study was to evaluate the efficacy & safety of fractional CO2 laser (10600nm) in treatment of skin scar. Materials and Methods:Twenty patients with different types of scars treated with fractional CO2 (10600nm) laser, (10 patients) were given additional intralesional Triamcinolone. Results: All of the twenty patients included in this study showed some sort of improvements in scar texture, height and pliability and all of the ten patients who received intralesional Triamcinolone after laser show complete satisfaction. Conclusion:Fractional CO2 (10600nm) laser can be used as alternative, ef
... Show MoreInvestigating the strength and the relationship between the Self-organized learning strategies and self-competence among talented students was the aim of this study. To do this, the researcher employed the correlation descriptive approach, whereby a sample of (120) male and female student were selected from various Iraqi cities for the academic year 2015-2016. the researcher setup two scales based on the previous studies: one to measure the Self-organized learning strategies which consist of (47) item and the other to measure the self-competence that composed of (50) item. Both of these scales were applied on the targeted sample to collect the required data
Television white spaces (TVWSs) refer to the unused part of the spectrum under the very high frequency (VHF) and ultra-high frequency (UHF) bands. TVWS are frequencies under licenced primary users (PUs) that are not being used and are available for secondary users (SUs). There are several ways of implementing TVWS in communications, one of which is the use of TVWS database (TVWSDB). The primary purpose of TVWSDB is to protect PUs from interference with SUs. There are several geolocation databases available for this purpose. However, it is unclear if those databases have the prediction feature that gives TVWSDB the capability of decreasing the number of inquiries from SUs. With this in mind, the authors present a reinforcement learning-ba
... Show MoreCompressing the speech reduces the data storage requirements, leading to reducing the time of transmitting the digitized speech over long-haul links like internet. To obtain best performance in speech compression, wavelet transforms require filters that combine a number of desirable properties, such as orthogonality and symmetry.The MCT bases functions are derived from GHM bases function using 2D linear convolution .The fast computation algorithm methods introduced here added desirable features to the current transform. We further assess the performance of the MCT in speech compression application. This paper discusses the effect of using DWT and MCT (one and two dimension) on speech compression. DWT and MCT performances in terms of comp
... Show MoreThe convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog
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