Strengthening of composite beams is highly needed to upgrade the capacities of existing beams. The strengthening methods can be classified as active or passive techniques. Therefore, the main purpose of this study is to provide detailed FE simulations for strengthened and unstrengthened steel–concrete composite beams at the sagging and hogging moment regions with and without profiled steel sheeting. The developed models were verified against experimental results from the literature. The verified models were used to present comparisons between the effect of using external post-tensioning and CFRP laminates as strengthening techniques. Applying external post-tensioning at the sagging moment regions is more effective because of the e

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

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

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col

Nowadays, the use of recycled waste construction materials instead of aggregates is becoming popular in construction owing to its environmental benefits. This paper presents an experimental and analytical campaign to study the behavior of axially loaded columns constructed from recycled aggregates. The latter was used instead of natural aggregates, and they were collected from the waste of previous concrete constructions. Different concrete mixtures made from varying amounts of recycled aggregates ranged from 0 to 50% of the total coarse aggregate were conducted to achieve 28 MPa. The effect of steel fibers is another investigated variable with volumes ranged from 0 to 2% concerning concrete’s mixture. The experimental

         The aim of this investigation is to determine how different weight percentages of alumina nanoparticles, including 0.02, 0.04, and 0.06 percent wt, affect the physical characteristics of Poly Acrylamide (PAAM). Using a hot plate magnetic stirrer, 10 g of poly acrylamide powder was dissolved in 90 g of di-ionized distillate water for 4 hours to produce PAAM with a concentration of 0.11 g/ml. Four sections of the resulting solution, each with a volume of 20 ml, were created. Each solution was added independently with alumina nanoparticles in different ratios 0.0, 0.02, 0.04, and 0.06 to create four nano fluid solutions with different alumina nanoparticle contents based on each weight percent. The hand casting process for n

One of the most important challenges facing the designers of the sewerage system is the corrosion of sewers due to the influence of sewerage contaminates which lead to failure of the main lines of sewers. In this study, a reference mix of 1: 1.5: 3 was used and the 4% Flocrete PC200 by weight of cement was added to the same mixing ratio in the second mixture. Twenty-four samples were tested for each mixture, 12 of which were used to compression strength test in ages (7, 14 and 28) day and six samples were submerged after 28 days of wet treatment at (5 and 10) % concentrations of sulfuric acid. The other six samples were painted after 28 days of wet treatment with coating Polyurethane and after 24 hours were flooded with a concentrat

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