Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l

      The present study discusses the semantic characteristics of the Russian newspapers journalistic headlines (The Russian newspaper "Komsomolskaya Pravda" as Example"). The study traces the characteristics of the Russian newspaper headlines, its relevance to grammar, syntax, lexical items and style. These characteristics in Russian will be investigated and compared to their Arabic correspondents in the process of translation. The study also specifies the meanings and the functions of the newspaper headlines in the light of modern linguistics stressing the grammatical, lexical and stylistic aspects of headlines translation from Russian into Arabic.

       J

The study explains how translation participates in shaping the responses of different communities to global awareness campaigns, and the extent to which language and translated terminology from English to Arabic influence the audience’s understanding and interaction with the awareness messages delivered by these campaigns. The research aims to explore the problems in translating these campaign titles, which may lead to vague meaning, as the process is considered a major challenge due to the need to navigate linguistic nuances, cultural differences, and contextual variations. In this study, some awareness phrases were randomly chosen from many types of organizations’ campaigns, like the WHO, UN, mental health, and Empowerment, a

With today's rapid and full of dangers the world banking sector is one of the most vital sectors at risk, and on the supervisory bodies responsible for monitoring the work of banks to take an active role in influencing the banks and put on the right track and is compatible with internationally approved curriculum. The lie of the research problem in the weak supervisory role of the Central Bank for banks in general and private banks in particular, limited the process of performance audit carried out by the Federal Office of Financial Supervision in auditing oversight role of the Central Bank control over the banks, according to the methods of performance audit followed by the upper bodies of financial control and accounting, And it was ba

The study aimed to examine the impact of audit committee characteristics on the practices of intellectual capital disclosure in the annual reports of Bank and Insurance companies listed on Palestine Exchange, through performing content analysis of the annual reports for the study sample which totaled thirteen companies, including six banks and seven insurance companies. To achieve the study objectives, the study employed a content analysis approach in order to analyze the content of the intellectual capital disclosure practice, in addition, the study used cross-sectional with longitudinal data for time series for a period of time between 2014-2019. The empirical results indicated that financial expertise and the number of meeting

In this paper the behavior of the quality of the gradient that implemented on an image as a function of noise error is presented. The cross correlation coefficient (ccc) between the derivative of the original image before and after introducing noise error shows dramatic decline compared with the corresponding images before taking derivatives. Mathematical equations have been constructed to control the relation between (ccc) and the noise parameter.

The transmitting and receiving of data consume the most resources in Wireless Sensor Networks (WSNs). The energy supplied by the battery is the most important resource impacting WSN's lifespan in the sensor node. Therefore, because sensor nodes run from their limited battery, energy-saving is necessary. Data aggregation can be defined as a procedure applied for the elimination of redundant transmissions, and it provides fused information to the base stations, which in turn improves the energy effectiveness and increases the lifespan of energy-constrained WSNs. In this paper, a Perceptually Important Points Based Data Aggregation (PIP-DA) method for Wireless Sensor Networks is suggested to reduce redundant data before sending them to the

Semantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. This paper proposes a multiscale Residual Dilated convolution neural network (MSRD-UNet) based on U-Net. MSRD-UNet replaced the traditional convolution block with a novel deeper block that fuses multi-layer features using dilated and residual convolution. In addition, the squeeze and execution attention mechanism (SE) and the s

In this research, the methods of Kernel estimator (nonparametric density estimator) were relied upon in estimating the two-response logistic regression, where the comparison was used between the method of Nadaraya-Watson and the method of Local Scoring algorithm, and optimal Smoothing parameter λ was estimated by the methods of Cross-validation and generalized Cross-validation, bandwidth optimal λ has a clear effect in the estimation process. It also has a key role in smoothing the curve as it approaches the real curve, and the goal of using the Kernel estimator is to modify the observations so that we can obtain estimators with characteristics close to the properties of real parameters, and based on medical data for patients with chro

A new Schiff base [I] was prepared by refluxing Amoxicillin trihydrate and 4-Hydroxy- 3,5-dimethoxybenzaldehyde in aqueous methanol solution using glacial acetic acid as a catalyst. The new 1,3-oxazepine derivative [II] was obtained by Diels- Alder reaction of Schiff base [I] with phthalic anhydride in dry benzene. The reaction of Schiff base [I] with thioglycolic acid in dry benzene led to the formation of thiazolidin-4-one derivative [III]. While the imidazolidin-4-one [IV] derivative was produced by reacting the mentioned Schiff base [I] with glycine and triethylamine in ethanol for 9 hrs. Tetrazole derivative [V] was synthesized by refluxing Schiff base [I] with sodium azide in dimethylformamid DMF. The structure of synthesized compound