Sentiment analysis refers to the task of identifying polarity of positive and negative for particular text that yield an opinion. Arabic language has been expanded dramatically in the last decade especially with the emergence of social websites (e.g. Twitter, Facebook, etc.). Several studies addressed sentiment analysis for Arabic language using various techniques. The most efficient techniques according to the literature were the machine learning due to their capabilities to build a training model. Yet, there is still issues facing the Arabic sentiment analysis using machine learning techniques. Such issues are related to employing robust features that have the ability to discrimina

The purpose of this work is to concurrently estimate the UVvisible spectra of binary combinations of piroxicam and mefenamic acid using the chemometric approach. To create the model, spectral data from 73 samples (with wavelengths between 200 and 400 nm) were employed. A two-layer artificial neural network model was created, with two neurons in the output layer and fourteen neurons in the hidden layer. The model was trained to simulate the concentrations and spectra of piroxicam and mefenamic acid. For piroxicam and mefenamic acid, respectively, the Levenberg-Marquardt algorithm with feed-forward back-propagation learning produced root mean square errors of prediction of 0.1679 μg/mL and 0.1154 μg/mL, with coefficients of determination of

Electrochemical method was used to prepare carbon quantum dots (CQDs). Size of matter was nature when evaluate via X-ray diffraction (XRD). A distinct peak at 2θ equal to 31.6° and three other small peaks at 38.28°, 56.41° and 66.12° were observed. The measures of Fourier Transform Infrared Spectroscopy (FTIR) showed the bonds in the transmittance spectrum are manufactured with carbon nanostructures in view. The first peaks are the O–H stretching vibration bands at (3417 and 2922) cm−1, (C–O–H at 1400, and 1317) cm−1, (C–H), (C=C), (C–O–H), (C=O), and (C–O) bonds at 2850, 1668, 1101, and 1026 cm−1 sequentially. The transmission electron microscopy (TEM) results presented that the spherical CQDs are in shape and on a

In this study, aluminum nanoparticles (Al NPs) were prepared using explosive strips method in double-distilled deionized water (DDDW), where the effect of five different currents (25, 50, 75, 100 and 125 A) on particle size and distribution was studied. Also, the explosive strips method was used to decorate zinc oxide particles with Al particles, where Al particles were prepared in suspended from zinc oxide with DDDW. Transmission electron microscopy (TEM), UV-visible absorption spectroscopy, and x-ray diffraction are used to characterize the nanoparticles. XRD pattern were examined for three samples of aluminum particles and DDDW prepared with three current values (25, 75 and 125 A) and three samples prepared with the same currents for zin

This paper proposed a new  method to study functional non-parametric regression data analysis with conditional expectation in the case that the covariates  are functional and the Principal Component Analysis was utilized to de-correlate the multivariate response variables. It  utilized the formula of the Nadaraya Watson estimator (K-Nearest Neighbour (KNN)) for prediction with different types of the semi-metrics, (which are based on Second Derivative and Functional Principal Component Analysis (FPCA))  for measureing the closeness between curves.  Root Mean Square Errors is used for the  implementation of this model which is then compared to the independent response method. R program is used for analysing data. Then, when  the cov

In the field of data security, the critical challenge of preserving sensitive information during its transmission through public channels takes centre stage. Steganography, a method employed to conceal data within various carrier objects such as text, can be proposed to address these security challenges. Text, owing to its extensive usage and constrained bandwidth, stands out as an optimal medium for this purpose. Despite the richness of the Arabic language in its linguistic features, only a small number of studies have explored Arabic text steganography. Arabic text, characterized by its distinctive script and linguistic features, has gained notable attention as a promising domain for steganographic ventures. Arabic text steganography harn

In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri