The current study aimed the syntheses and characterizations of Gold nanoparticles (Au NPs) using a laser ablation Q-switched Nd: YAG laser with a wave-length of 355 nm at a variety of laser pulse energies (E) and deposited on porous silicon (PS). Optical emission spectrometer was used to diagnosed medium air to study gold plasma characteristics and prepared Au nanoparticles. The laser pulse energy influence has been studied on the plasma characteristics in air. The data showed the emergence of the ionic (Au II) spectral emission lines in the gold plasma emission spectrum. XRD has been utilized to examine structural characteristics. Moreover, AFM results 37.2 nm as the mean value of the diameter that is coordinated in a shape similar to the

a laser ablation Q-switched Nd: YAG laser with a wave-length of 355 nm at a variety of laser pulse energies (E) and deposited on porous silicon (PS). Optical emission spectrometer was used to diagnosed medium air to study gold plasma characteristics and prepared Au nanoparticles. The laser pulse energy influence has been studied on the plasma characteristics in air. The data showed the emergence of the ionic (Au II) spectral emission lines in the gold plasma emission spectrum. XRD has been utilized to examine structural characteristics. Moreover, AFM results 37.2 nm as the mean value of the diameter that is coordinated in a shape similar to the rod that appears for Au NPs, in addition to that, TEM has been an indication of the fact that syn

Today's smart engineering systems are often faced with situations that are structurally uncertain, informationally incomplete, and non-probabilistically ambiguous, especially for electrical systems. ARDL models are limited in applications in complex computational environments where the uncertainty is due to vagueness, not randomness, and assume the exact parametric representation of the models and the structure of the stochastic uncertainty. This study proposes a new soft-computing paradigm using Fuzzy Autoregressive Distributed Lag (FARDL) models and compares the performance of the Linear Programming (LP) and Quadratic Programming (QP) estimation algorithms using large-scale parallel Monte Carlo simulations to overcome these drawba

In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor

The density-based spatial clustering for applications with noise (DBSCAN) is one of the most popular applications of clustering in data mining, and it is used to identify useful patterns and interesting distributions in the underlying data. Aggregation methods for classifying nonlinear aggregated data. In particular, DNA methylations, gene expression. That show the differentially skewed by distance sites and grouped nonlinearly by cancer daisies and the change Situations for gene excretion on it. Under these conditions, DBSCAN is expected to have a desirable clustering feature i that can be used to show the results of the changes. This research reviews the DBSCAN and compares its performance with other algorithms, such as the tradit

In this paper, preliminary test Shrinkage estimator have been considered for estimating the shape parameter α of pareto distribution when the scale parameter equal to the smallest loss and when a prior estimate α0 of α is available as initial value from the past experiences or from quaintance cases. The proposed estimator is shown to have a smaller mean squared error in a region around α0 when comparison with usual and existing estimators.

This research includes the study of dual data models with mixed random parameters, which contain two types of parameters, the first is random and the other is fixed. For the random parameter, it is obtained as a result of differences in the marginal tendencies of the cross sections, and for the fixed parameter, it is obtained as a result of differences in fixed limits, and random errors for each section. Accidental bearing the characteristic of heterogeneity of variance in addition to the presence of serial correlation of the first degree, and the main objective in this research is the use of efficient methods commensurate with the paired data in the case of small samples, and to achieve this goal, the feasible general least squa