The fiscal position of governments in rentier economies depends heavily on oil revenues. The relationship between oil prices and the budget surplus or deficit is often nonlinear and characterized by complex temporal dependencies, which may limit the predictive capability of conventional econometric models. Accordingly, this study aims to forecast the Iraqi budget surplus and deficit and compare the predictive performance of the ARDL, NARDL, LSTM, 1D-CNN, and hybrid 1D-CNN-LSTM models using oil prices as the primary predictive variable. The hybrid model integrates the feature-extraction capability of One-Dimensional Convolutional Neural Networks (1D-CNN) with the ability of Long Short-Term Memory (LSTM) networks to capture long-term

The main reason for the emergence of a deepfake (deep learning and fake) term is the evolution in artificial intelligence techniques, especially deep learning. Deep learning algorithms, which auto-solve problems when giving large sets of data, are used to swap faces in digital media to create fake media with a realistic appearance. To increase the accuracy of distinguishing a real video from fake one, a new model has been developed based on deep learning and noise residuals. By using Steganalysis Rich Model (SRM) filters, we can gather a low-level noise map that is used as input to a light Convolution neural network (CNN) to classify a real face from fake one. The results of our work show that the training accuracy of the CNN model

Classifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed using the SqueezeNet convolutional neural network, pretrained on the ImageNet dataset. By discarding the final classification layer, the networ

The goal of the current study was to investigate the effects of curcumin in both formulas (supplement and standard), zinc, and then use them together to show their effect on the levels of glucose, insulin, insulin resistance (IR), and anti-mullerian hormone (AMH) in the model of female rats with induced polycystic ovary syndrome (PCOS) using 1mg/kg/day of letrozole for 21 days followed by a treatment period of 14 days including different treatments of zinc 30 mg/kg, curcumin standard 200 mg/kg, curcumin supplement 200 mg/kg, (curcumin standard plus zinc), (curcumin Supplement plus zinc) and metformin as a standard treatment. After the treatment, all female rats were sacrificed, and blood samples were collected from the inferior vena cava

The aim of this research is to identify the level of mental mindfulness among female students of the College of Education at Umm Al-Qura University, as well as to identify the statistically significant differences in the level of mental mindfulness according to academic level, specialization, and academic achievement. A mental mindfulness scale was designed to cover five dimensions. The study employed the analytic descriptive approach applied to a random sample of (217) female students from various academic specializations. The findings indicated that the level of mental mindfulness was average among female students. Statistically significant differences were attributable to the academic level, academic specializations, and academic achi

Natural frequency under initial stresses for simply supported cross-ply composite laminated plates (E glass- fiber) are obtained using Refind theory (RPT). This theory accounts for parabolic distribution of the transverse shear strain through the plate thickness and satisfies the zero traction boundary conditions on the surfaces of the plate without using shear correction factors. The governing equations for Eigen value problem under initial stress are derived using Hamilton’s principle and solved using Navier solution for simply supported cross-ply symmetric and antisymmetric laminated plates. The effect of many design factors such as modulus ratio, thickness ratio and number of laminates on the Natural frequency and buckling stresses

This work focused on principle of higher order mode excitation using in- line Double Clad Multi-Mode Mach-Zehnder Interferometer (DC-MM-MZI). The DC-MM-MZI was designed with 50 cm etched MMF. The etching length is 5cm. The tenability of this interferometer was studied using opt grating ver.4.2.2 and optiwave
ver. 7 simulator. After removing (25, 35, 45, 55) μm from MMF and immersing this segment of MMF with water bath contained distilled water and ethanol, in addition to, air. Pulsed laser source  centered at 1546.7nm ,pulse width 10ns and peak power 1.33mW was propagated via this interferometer Maximum modes were obtained in case of air surrounded media which are 9800 and 25 um removed cladding layer, with peak power 49.800 m

This study was performed on the Tigris River (Baghdad city section) during the period between December 2016 and December 2018 to assess seasonal variation in water quality using the Overall Index of Pollution (OIP). The OIP is one of the reliable tools for the assessment of surface water quality. To calculate OIP-values, eight parameters were measured ( pH, Dissolved Oxygen "DO", Biological Oxygen Demand "BOD", Total Dissolved Solid "TDS", Total Hardness "TH", calcium "Ca", Sulphate "SO4" and Alkalinity). The results showed the anthropogenic activities impact of Baghdad population that directly discharge of "inadequate treated" waste water to the river.  OIP values were acceptable (1˃OIP˃ 1.7) in 2011, 2012, 2013 and 2018. However, in

Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp