لقد تسبب انتشار الإرهاب في العالم وكذلك الحروب الأهلية والصراعات في أوائل القرن الحادي والعشرين في جميع أنحاء العالم في الكثير من الأضرار وخلفت ضحايا جسيمة. أدت الهجمات الإرهابية على النساء، مثل اختطاف بوكو حرام لأكثر من 270 تلميذة في نيجيريا وتقارير عن انتشار الاغتصاب والاعتداء الجنسي في المناطق التي مزقتها الحرب، إلى إنتاج العديد من العروض المسرحية في الولايات المتحدة التي تصور خواص الجناة وكذلك الضحا

Purpose: The aim of this study was to gain insight into causes of time delays and cost overruns in a selection of thirty case projects in Iraq. Delay factors have been studied in many countries/contexts, but not much data exists from countries under the conditions characterizing Iraq during the last 10-15 years.Design/methodology/approach: A case study approach was adopted, with thirty construction projects selected from the Baghdad region, of different types and sizes. For the case of the study, the participants in the projects provided data about the projects through the data collection tool distributed through the questionnaire directed to them. Statistical data analysis was used to build statistical relationships between time and cost d

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

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

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

Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea

Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l

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

Abstract Twelve isolates of bacteria were obtained from samples of different soils and water amended with 100µg/ml of five heavy metals chlorides (i.e: Aluminum Al+2, Iron Fe+2, Lead Pb+2, Mercury Hg+2 and Zinc Zn+2). Four isolates were identified as Bacillus subtilis and B. subtilis (B2) isolate was selected for this study according to their resistance to all five heavy metals chlorides. The ability of B. subtilis (B2) isolate for growing in different concentration of heavy metals chlorides ranging from 200-1200 µg/ml was tested. The highest conc. that B. subtilis (B2) isolate tolerate was 1000 µg/ml for Al+2, Fe+2, Pb+2, and Zn+2and 300 µg/ml for Hg+2 for 24hour. The effect of heavy metals chlorides on bacterial growth for 72 hrs was