Background: Oral cancer is a highly lethal and disfiguring disease. Squamous cell carcinoma of the oral cavity constitutes about 90% of all oral malignancies. The aims of the study was to achieve an epidemiological description of the oral squamous cell carcinoma in Iraq in general and in Iraqi governorates except Kurdistan region retrospectively during period 2001-2013 Materials and Methods: Data were collected from department of oral and maxillofacial pathology, college of dentistry, university of Baghdad, Nuclear medicine and radiotherapy hospital, Iraqi cancer registry center, Main hospitals in Baghdad and Iraqi governorates, Private labs. for histopathological examinations. The descriptive and inferential statistical methods were used

Abstract: E2F6 is a member of the E2F family of transcription factors involved in regulation of a wide variety of genes through both activation and repression. E2F6 has been reported as overexpressed in breast cancers but whether or not this is important for tumor development is unclear. We first checked E2F6 expression in tumor cDNAs and the protein level in a range of breast cancer cell lines. RNA interference-mediated depletion was then used to assess the importance of E2F6 expression in cell lines with regard to cell cycle profile using fluorescence-activated cell sorting and a cell survival assay using (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT). The overexpression of E2F6 was confirmed in breast tumor cDNA samp

An experimental and theoretical analysis was conducted for simulation of open circuit cross flow heat
exchanger dynamics during flow reduction transient in their secondary loops. Finite difference
mathematical model was prepared to cover the heat transfer mechanism between the hot water in the
primary circuit and the cold water in the secondary circuit during transient course. This model takes under
consideration the effect of water heat up in the secondary circuit due to step reduction of its flow on the
physical and thermal properties linked to the parameters that are used for calculation of heat transfer
coefficients on both sides of their tubes. Computer program was prepared for calculation purposes which
cover a

Abstract

 The issue of inequality in distribution of income and / or consumption expenditure is related to economic welfare because there is an inverse relationship between the economic welfare on the one hand, and the degree of inequality, on the other hand. Despite the fact that inequality is considered as normal phenomenon in every society, but if it exceeded certain limits it will lead to undesirable economic, social and political consequences. Therefore, the availability of indicators about inequality is a necessary tool for planning and evaluation of economic development programs. So, current study is aiming at measuring and analyzing the degree of inequality in distribution of consumpti

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

The proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue

Estimating an individual's age from a photograph of their face is critical in many applications, including intelligence and defense, border security and human-machine interaction, as well as soft biometric recognition. There has been recent progress in this discipline that focuses on the idea of deep learning. These solutions need the creation and training of deep neural networks for the sole purpose of resolving this issue. In addition, pre-trained deep neural networks are utilized in the research process for the purpose of facial recognition and fine-tuning for accurate outcomes. The purpose of this study was to offer a method for estimating human ages from the frontal view of the face in a manner that is as accurate as possible and takes