. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a

Background:Oriental sore occurs mostly in the
mediteranian region , North Africa ,and the Middle East .
Rodents are the main reservoir for the parasite . The wet
type caused by L. major is rural and the dry type caused by
L. tropica is urban and humans are presumably the only
reservoir. Sand fly vectors are involved in all forms.
Objectives: This study aimed to show the most
important bacterial infections concomitant with cutaneous
leishmaniasis .
Methods; The study was performed on 75 patients (ages
1-50 years ) from both sexes were attending Skin Diseases
Department of Ramadi General Hospital during the period
extended from January to June 2000. These patients were
clinically diagnosed as patients

Low conversion copolymerization of acrylamide AM (monomer-1) have been conducted with acrylic acid AA in dry benzene at 70°C , using Benzoyl peroxide BPO as initiator . The copolymer composition has been determined by elemental analysis. The monomer reactivity ratios have been calculated by the Kelen-Tudos and Finman-Ross graphical procedures. The derived reactivity ratios (r1, r2) are: (0.620, 0.996) for (AM / AA) systems , and found that the reactivity of the monomer AA is more than the monomer AM in the copolymerization of (AA/AM) system. The reactivity ratios values were used for microstructures calculation.

Background: Pervasive Developmental Disorder (PDD) is a term refers to the overarching group of conditions to which autism spectrum disorder (ASD) belongs .

Objective: This study was designed to determine the existing behavior of children with autism in dental sitting, the behavior improvements in recall dental visits and evaluate the improvement in oral hygiene with using specific visual pedagogy chart.

Type of the study: Cross-sectional study.

Methods: Forty children of both genders, ages ranged from 4 – 6 years having primary teeth only were selected whose medical history included a diagnosis

Transactions on Engineering and Sciences

In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc

In this paper we reported the microfabrication of three-dimensional structures using two-photon polymerization (2PP) in a mixture of MEH-PPV and an acrylic resin. Femtosecond laser operating at 800nm was employed for the two-photon polymerization processes. As a first step in this project we obtained the better composition in order to fabricate microstructers of MEH-PPV in the resin via two-photon polymerzation. Acknowledgement:This research is support by Mazur Group, Harvrad Universirt.

In Automatic Speech Recognition (ASR) the non-linear data projection provided by a one hidden layer Multilayer Perceptron (MLP), trained to recognize phonemes, and has previous experiments to provide feature enhancement substantially increased ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification increases with the number of speakers used to train the MLP and that improved identification is obtained as this number increases beyond sixty.

Spelling correction is considered a challenging task for resource-scarce languages. The Arabic language is one of these resource-scarce languages, which suffers from the absence of a large spelling correction dataset, thus datasets injected with artificial errors are used to overcome this problem. In this paper, we trained the Text-to-Text Transfer Transformer (T5) model using artificial errors to correct Arabic soft spelling mistakes. Our T5 model can correct 97.8% of the artificial errors that were injected into the test set. Additionally, our T5 model achieves a character error rate (CER) of 0.77% on a set that contains real soft spelling mistakes. We achieved these results using a 4-layer T5 model trained with a 90% error inject