Sb2S3 thin films have been prepared by chemical bath deposition on a glas sub Absorbance and transmittance spectra were recorded in the wavelength range (30-900) nm. The effects of thickness on absorption coefficient, reflectance, refractive index, extinction coefficient, real and imaginary parts of dielectric constant were estimated. It was found that the reflectivity, absorption coefficient , extinction coefficient, real part of dielectric constant and refractive index, all these parameters decrease as the thickness increased, while the imaginary part of the dielectric constant increase as the thickness incre

This survey investigates the thermal evaporation of Ag2Se on glass substrates at various thermal annealing temperatures (300, 348, 398, and 448) °K. To ascertain the effect of annealing temperature on the structural, surface morphology, and optical properties of Ag2Se films, investigations and research were carried out. The crystal structure of the film was described by Xray diffraction and other methods.The physical structure and characteristics of the Ag2Se thin films were examined using X-ray and atomic force microscopy (AFM) based techniques. The Ag2Se films surface morphology was examined by AFM techniques; the investigation gave average diameter, surface roughness, and grain size mutation values with increasing annealing temperature

Acidizing is one of the most used stimulation techniques in the petroleum industry. Several reports have been issued on the difficulties encountered during the stimulation operation of the Ahdeb oil field, particularly in the development of the Mishrif reservoir, including the following: (1) high injection pressures make it difficult to inject acid into the reservoir formation, and (2) only a few acid jobs have been effective in Ahdeb oil wells, while the bulk of the others has been unsuccessful. The significant failure rate of oil well stimulation in this deposit necessitates more investigations. Thus, we carried out this experimental study to systematically investigate the influence of acid treatment on the geomechanical properties of Mi4

In this article, the influence of group nano transition metal oxides such as {(MnO₂), (Fe₂O₃) and (CuO)} thin films on the (ZnO-TiO2) electric characteristics have been analyzed. The prepared films deposited on glass substrate laser Nd-YAG with wavelength (ℷ =1064 nm) ,energy of (800mJ) and number of shots (400). The density of the film was found to be (200 nm) at room temperature (RT) and annealing temperature (573K).Using DC Conductivity and Hall Effect, we obtained the electrical properties of the films. The DC Conductivity shows that that the activation energies decrease while the σRT at annealing temperature with different elements increases the formation of mixed oxides. The Hall effect, the elec

Most companies use social media data for business. Sentiment analysis automatically gathers analyses and summarizes this type of data. Managing unstructured social media data is difficult. Noisy data is a challenge to sentiment analysis. Since over 50% of the sentiment analysis process is data pre-processing, processing big social media data is challenging too. If pre-processing is carried out correctly, data accuracy may improve. Also, sentiment analysis workflow is highly dependent. Because no pre-processing technique works well in all situations or with all data sources, choosing the most important ones is crucial. Prioritization is an excellent technique for choosing the most important ones. As one of many Multi-Criteria Decision Mak

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod