Background: The treatment of dental tissues proceeding to adhesive procedures is a crucial step in the bonding protocol and decides the clinical success ofrestorations. This study was conducted in vitro, with the aim of evaluating thenanoleakage on the interface between the adhesive system and the dentine treated by five surface modalities using scanning electron microscopy and energydispersiveX-ray spectrometry. Materials and methods: Twenty five extracted premolars teeth were selected in the study. Standardized class V cavities were prepared on the buccal and lingual surfaces then the teeth divided into five main groups of (5 teeth in each group n=10) according to the type of dentine surface treatment that was used: Group (A): dentine was

Abstract:          

                Interest in the topic of prediction has increased in recent years and appeared modern methods such as Artificial Neural Networks models, if these methods are able to learn and adapt self with any model, and does not require assumptions on the nature of the time series. On the other hand, the methods currently used to predict the classic method such as Box-Jenkins may be difficult to diagnose chain and modeling because they assume strict conditions.

Abstract

In this research we study the wavelet characteristics for the important time series known as Sunspot, on the aim of verifying the periodogram that other researchers had reached by the spectral transform, and noticing the variation in the period length on one side and the shifting on another.

A continuous wavelet analysis is done for this series and the periodogram in it is marked primarily. for more accuracy, the series is partitioned to its the approximate and the details components to five levels, filtering these components by using fixed threshold on one time and independent threshold on another, finding the noise series which represents the difference between

The aim of the thesis is to estimate the partial and inaccessible population groups, which is a field study to estimate the number of drug’s users in the Baghdad governorate for males who are (15-60) years old.

Because of the absence of data approved by government institutions, as well as the difficulty of estimating the numbers of these people from the traditional survey, in which the respondent expresses himself or his family members in some cases. In these challenges, the NSUM Network Scale-Up Method Is mainly based on asking respondents about the number of people they know in their network of drug addicts.

Based on this principle, a statistical questionnaire was designed to

The research aims at identify the role of the electronic trading system used in the Iraq stock exchange to promote trading activity for the stocks of listed companies in this market.

To prove the hypothesis of research, it was selected the main trading indicators for the market to be a main field in test the research hypothesis.it was selected as the period of time for (9)years span between the years (2005-2013) because they represent the first two articles of equal time periods represent aperiod that preceded the introduction of electronic trading system  while the second represents the period of time that followed 

The research found a number of conclusions but the mo

Diyala river is the most important tributaries in Iraq, this river suffering from pollution, therefore, this research aimed to predict organic pollutants that represented by biological oxygen demand BOD, and inorganic pollutants that represented by total dissolved solids TDS for Diyala river in Iraq, the data used in this research were collected for the period from 2011-2016 for the last station in the river known as D17, before the river meeting Tigris river in Baghdad city. Analysis Neural Network ANN was used in order to find the mathematical models, the parameters used to predict BOD were seven parameters EC, Alk, Cl, K, TH, NO3, DO, after removing the less importance parameters. While the parameters that used to predict TDS were fourte

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d