This research suggests a robust and systematic way for Arabic Sentiment Analysis using a vast dataset of 66,666 text reviews. One of the main advantages of this study is that the dataset was perfectly balanced (33,333 positive samples and 33,333 negative samples). In machine learning, this 50/50 split is important because it eliminates class bias and enables the predictive model to treat both sentiment classes equally. As shown in the values of the metrics — overall accuracy, weighted precision, weighted recall, and F1 score — there is great similarity among them, indicating a stable and reliable assessment of the model's real potential throughout the Arabic dataset. Based on data profile, the average word count per review is 42.3

Witnessing human societies with the turn of the century atheist twenty huge revolution in information , the result of scientific and technological developments rapidly in space science and communications , and that made the whole world is like a small village not linked by road as it was in ancient times, through the rapid transportation as was the case a few years ago , thanks to the remote sensing devices that roam in space observant everything on the ground , that the information networks that overflowed the world a tremendous amount of information provided for each inhabitants of the earth , which made this information requirement for human life and human survival and well-being , as it has allowed that information to humans opportun

Many undergraduate learners at English departments who study English as a foreign language are unable to speak and use language correctly in their post -graduate careers. This problem can be attributed to certain difficulties, which they faced throughout their education years that hinder their endeavors to learn. Therefore, this study aims to discover the main difficulties faced by EFL students in language learning and test the difficulty variable according to gender and college variables then find suitable solutions for enhancing learning. A questionnaire with 15 items and 5 scales were used to help in discovering the difficulties. The questionnaire was distributed to the selected sample of study which consists of 90 (male and female) stud

Diabetic retinopathy is an eye disease in diabetic patients due to damage to the small blood vessels in the retina due to high and low blood sugar levels. Accurate detection and classification of Diabetic Retinopathy is an important task in computer-aided diagnosis, especially when planning for diabetic retinopathy surgery. Therefore, this study aims to design an automated model based on deep learning, which helps ophthalmologists detect and classify diabetic retinopathy severity through fundus images. In this work, a deep convolutional neural network (CNN) with transfer learning and fine tunes has been proposed by using pre-trained networks known as Residual Network-50 (ResNet-50). The overall framework of the proposed

A field experiment was conducted to grow the wheat crop during the fall season 2020 in Karbala province, north of Ain Al-Tamr District in two locations of different textures and parent materials. The first site (calcareous soil) with a sandy loam texture, is located at (44° 40′ 37′) east longitude and (32° 41′ 34′) north latitude, at an altitude of 32 m above sea level, and an area of 20 hectares. As for the second location (gypsum soil) with a loam texture, it is located at a longitude (45° 41′ 39′) east and a latitude (33° 43′ 34′ north) and at an altitude of 33 m above sea level and an area of 20 hectares. To find out the effect of different tillage systems on water productivity and wheat yield under center pivot irri

This study proposes a hybrid predictive maintenance framework that integrates the Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) for intelligent fault diagnosis in industrial rotating machinery. The method is designed to address challenges posed by non-linear and non-stationary vibration signals under varying operational conditions. Experimental validation using the FALEX multispecimen test bench demonstrated a high classification accuracy of 97.5%, outperforming traditional models such as SVM, Random Forest, and XGBoost. The approach maintained robust performance across dynamic load scenarios and noisy environments, with precision and recall exceeding 95%. Key contributions include a hardware-accelerated K

Vascular patterns were seen to be a probable identification characteristic of the biometric system. Since then, many studies have investigated and proposed different techniques which exploited this feature and used it for the identification and verification purposes. The conventional biometric features like the iris, fingerprints and face recognition have been thoroughly investigated, however, during the past few years, finger vein patterns have been recognized as a reliable biometric feature. This study discusses the application of the vein biometric system. Though the vein pattern can be a very appealing topic of research, there are many challenges in this field and some improvements need to be carried out. Here, the researchers reviewed

Astronomers have known since the invention of the telescope that atmospheric turbulence affects celestial images. So, in order to compensate for the atmospheric aberrations of the observed wavefront, an Adaptive Optics (AO) system has been introduced. The AO can be arranged into two systems: closedloop and open-loop systems. The aim of this paper is to model and compare the performance of both AO loop systems by using one of the most recent Adaptive Optics simulation tools, the Objected-Oriented Matlab Adaptive Optics (OOMAO). Then assess the performance of closed and open loop systems by their capabilities to compensate for wavefront aberrations and improve image quality, also their effect by the observed optical bands (near-infrared band