Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur

Finger vein recognition and user identification is a relatively recent biometric recognition technology with a broad variety of applications, and biometric authentication is extensively employed in the information age. As one of the most essential authentication technologies available today, finger vein recognition captures our attention owing to its high level of security, dependability, and track record of performance. Embedded convolutional neural networks are based on the early or intermediate fusing of input. In early fusion, pictures are categorized according to their location in the input space. In this study, we employ a highly optimized network and late fusion rather than early fusion to create a Fusion convolutional neural network

     Face detection is one of the important applications of biometric technology and image processing. Convolutional neural networks (CNN) have been successfully used with great results in the areas of image processing as well as pattern recognition. In the recent years, deep learning techniques specifically CNN techniques have achieved marvellous accuracy rates on face detection field. Therefore, this study provides a comprehensive analysis of face detection research and applications that use various CNN methods and algorithms. This paper presents ten of the most recent studies and illustrate the achieved performance of each method. 

     The seismic reflection method has a primary role in petroleum exploration. This research is a structural interpretation study of the 2D seismic reflection survey carried out in the Upper West Euphrates (Khan Al-Baghdadi area), which is located in the western part of Iraq, Al-Anbar governorate. The two objectives of this research are to interpret Base Akkas/Top Khabour reflector and to define potential hydrocarbon traps in the surveyed area. Based on the synthetic seismogram of Akk_3 well near the study area, the Akkas/Top Khabour reflector was identified on the seismic section. Also, the Silurian Akkas Hot_shale reflector was identified and followed up, which represents the source and seal rocks of the Paleozoic

Fifty five surface and subsurface soil samples were taken from the area between Tigris and Euphrates Rivers along the Main Drain course from north Baghdad to Basrah to evaluate the geochemical, physical characteristics and the probability contamination of these samples. The study area is covered by Quaternary sediments of complex alternation of sand, silt and clay. Significant variation in the textural content of the present soils is observed, where the northern and southern parts are characterized by silt predominance, while sand is prevailing in the central parts as a result of the extensive spreading of aeolian deposits represented mostly by sand dunes. Mineralogical analysis explains wide variations in the heavy minerals distribution

     This study deals with the petrology of the lower succession of the Injana Formation in the Shorr Shareen area, Wasit Governorate, Eastern Iraq. The study revealed that the sandstone is litharenite consists of 45.56% rock fragments, 22.13% quartz and 8.5% feldspars. The matrix is about 8.39%, consisting of silt and clay particles. The cement is variable (carbonates 8.42%, evaporites 1.78% and iron oxides 0.96%). The grain assemblage infers that the source of the rock fragments is nearby. The petrographic analyses indicate that the studied Injana sandstones are immature mineralogically because of their content of unstable constituents, such as lithic fragments and feldspars. In addition, the presence of such fresh feldspars indica

This study deals with the petrology of Injana Formation (Upper Miocene) at
Zawita, Amadia and Zakho areas. The sandstone of Injana Formation is of two
typesnamely, litharenite and feldspathiclitharenite. The rock fragments of Injana
Formation are mostly sedimentary and hence the sandstones are classified as
sedarenite and more specifically chertarenite owing to the predominance of chert
rock fragments. The sandstone is mineralogicallysubmature rangingfrom
mechanically and chemically stable tounstable. The petrographic studies reveal
nearness of source area with arid to semi-arid climate. The source rocks are
sedimentary, low- to medium-grade metamorphic and basic volcanic rocks. They are
mostly supplied from th