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

This paper offers a systemic review of the deep learning methods to detect violence on campus, which is a critical issue in intelligent surveillance to improve the student safety and prompt cut off of violent accidents. The review reviews studies published 2018-2025, concentrating on model structure to detect fights, bullying, vandalism, and aggressive behavior on problematic campuses due to occlusion and light variations and complicated human interactions. The research design includes a comparative study of different deep learning networks, such as CNNs, RNNs, 3D CNNs, attention-based networks, transformers, graph neural networks, neuro-fuzzy, and multimodal systems and federated learning methods. The paper also assesses benchmark

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

In this work, polyvinylpyrrolidone (PVP)/ Multi-walled carbon nanotubes (MWCNTs) nanocomposites were prepared with two concentrations of MWCNTs by casting method. Morphological, structural characteristics and electrical properties were investigated. The state of MWCNTs dispersion in a PVP matrix was indicated by Field Effect-Scanning Electron Microscopy (FESEM) which showed a uniform dispersion of MWCNTs within the PVP matrix. X-ray Diffraction (XRD) indicate strong bonding of carbonyl groups of PVP composite chains with MWCNTs. Fourier transfer infrared (FTIR) studies shows characteristics of various stretching and bending vibration bands, as well as shifts in some band locations and intensity changes in others. Hall effect was studied

The aim of this paper, study the effect of carbon nanotubes on the electrical properties of polyvinylchloride. Samples of polyvinylchloride carbon nanotubes composite prepared by using hot press technique. The weight percentages of carbon nanotubes are 0,5,10 and 20wt.%. Results showed that the D.C electrical conductivity increases with increasing of the weight percentages of carbon nanotubes. Also, the D.C electrical conductivity changed with increase temperature for different concentrations of carbon nanotubes. The activation energy of D.C electrical conductivity is decreased with increasing of carbon nanotubes concentration.

In this work, polyvinylpyrrolidone (PVP)/ Multi-walled carbon nanotubes (MWCNTs) nanocomposites were prepared with two concentrations of MWCNTs by casting method. Morphological, structural characteristics and electrical properties were investigated. The state of MWCNTs dispersion in a PVP matrix was indicated by Field Effect-Scanning Electron Microscopy (FESEM) which showed a uniform dispersion of MWCNTs within the PVP matrix. X-ray Diffraction (XRD) indicate strong bonding of carbonyl groups of PVP composite chains with MWCNTs. Fourier transfer infrared (FTIR) studies shows characteristics of various stretching and bending vibration bands, as well as shifts in some band locations and intensity changes in others. Hall effect was stu

In this work we experimentally investigated SWCNTs and MWCNTs to increase their thermal conductivity and electrically functionalization process using different reagents ((nitric acid, HNO3 followed by acid treatment with H2SO4), then washed with deionized water (DW) and then treated with H2O2 via ultrasonic technique. Then repeated the steps with MWCNTs and compare their results in an effort to improve experimental conditions that efficiently differentiate the surface of the single walled carbon nanotubes (SWCNTs) and multi walled carbon nanotubesi(MWCNTs) that less nanotubes destroy and to enhance the properties of them and also to reduce aggregation in liquid. the results were prove by XRD, and infrared spectroscopy (FTIR). The FTIR sp

Deep drawing process to produce square cup is very complex process due to a lot of process parameters which control on this process, therefore associated with it many of defects such as earing, wrinkling and fracture. Study of the effect of some process parameters to determine the values of these parameters which give the best result, the distributions for the thickness and depths of the cup were used to estimate the effect of the parameters on the cup numerically, in addition to experimental verification just to the conditions which give the best numerical predictions in order to reduce the time, efforts and costs for producing square cup with less defects experimentally is the aim of this study. The numerical analysis is used to study