The purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals

Three-dimensional cavity was investigated numerical in the current study filled with porous medium from a saturated fluid. The problem configuration consists of two insulated bottom and right wall and left vertical wall maintained at constant temperatures at variable locations, using two discretized heaters. The porous cavity fluid motion was represented by the momentum equation generalized model. The present investigation thermophysical parameters included the local thermal equilibrium condition. The isotherms and streamlines was used to examine energy transport and momentum. The meaning of changing parameters on the established average Nusselt number, temperature and velocity distribution are highlighted and discussed.

The main object of this article is to study and introduce a subclass of meromorphic univalent functions with fixed second positive defined by q-differed operator. Coefficient bounds, distortion and Growth theorems, and various are  the obtained results.

Forty lower premolars with single root canals prepared with ProtaperNext files to size 25, and obturated with GP/sealer using lateral compaction. Teeth divided randomly into four groups (group n=10). Protaper universal retreatment kit (PUR), D-Race desobturation files (DRD), R-Endo retreatment kit (RE) and Hedstrom (H) files (control) were used to remove GP/sealer in each group. Removal effectiveness assessed by measuring the GP /sealer remnants in the roots after sectioning them into two halves. Stereomicroscope with a digital camera used to capture digital images. Images processed by ImageJ software to measure the percentage of GP/sealer remnants surface area in total, coronal, middle and apical areas of the canal. In the coronal area,

In every country in the world, there are a number of amputees who have been exposed to some accidents that led to the loss of their upper limbs. The aim of this study is to suggest a system for real-time classification of five classes of shoulder girdle motions for high-level upper limb amputees using a pattern recognition system. In the suggested system, the wavelet transform was utilized for feature extraction, and the extreme learning machine was used as a classifier. The system was tested on four intact-limbed subjects and one amputee, with eight channels involving five electromyography channels and three-axis accelerometer sensor. The study shows that the suggested pattern recognition system has the ability to classify the sho

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve