This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

The collected premiums and the compensations paid are among the main variables that have a prominent role in determining the level of financial solvency of insurance companies, as the higher the financial solvency of the insurance company, the more attractive it is to the target audience to acquire the company's insurance services.

Hence the importance of the issue of the solvency of insurance companies, as it is one of the critical matters on which the effectiveness of the insurance company and its continuation in the labor market depend.

In this research, we try to clarify the role of collected premiums and compensations paid in determining the level of operational solvency of t

With the fast-growing of neural machine translation (NMT), there is still a lack of insight into the performance of these models on semantically and culturally rich texts, especially between linguistically distant languages like Arabic and English. In this paper, we investigate the performance of two state-of-the-art AI translation systems (ChatGPT, DeepSeek) when translating Arabic texts to English in three different genres: journalistic, literary, and technical. The study utilizes a mixed-method evaluation methodology based on a balanced corpus of 60 Arabic source texts from the three genres. Objective measures, including BLEU and TER, and subjective evaluations from human translators were employed to determine the semantic, contextual an

The makers of strife exist in every era and time. They differ in how these temptations are created and the methods used in doing so. In our modern era, they are more; This is due to the presence of information technology and its easy availability at their fingertips, as well as the spread of social networking sites, which are rapidly spreading among ignorant groups and groups that are deficient in their use. Which led the makers of sedition to exploit these matters and create various temptations and spread them among peoples and societies. Today, we cannot predict the future of humanity in light of this huge amount of temptation. What the Holy Qur’an mentioned about the types of temptations in its noble verses and their embodiment in seve

LandSat Satellite ETM+ image have been analyzed to detect the different depths of regions inside the Tigris river in order to detect the regions that need to remove sedimentation in Baghdad in Iraq Country. The scene consisted of six bands (without the thermal band), It was captured in March ٢٠٠١. The variance in depth is determined by applying the rationing technique on the bands ٣ and ٥. GIS ٩. ١ program is used to apply the rationing technique and determined the results.

Iranian stories have been one of the most important aspects   of Iranian society's culture and have reflected us as a mirror of all its cultural, societal and political dimensions.Among the important elements of the story can be the creation of space and the movement and the creation of characters and conflict and content and the angle of vision and landscape, language and subject. Studying the elements of the story leads to more understanding and determining the strength and skill of the author by studying the elements of his story. The story of Rustom and Suhrab is also one of the many stories in which the elements of the story are often noticed and show the skill of the great professor Al-Fardousi to benefit from these eleme

A substantial portion of today’s multimedia data exists in the form of unstructured text. However, the unstructured nature of text poses a significant task in meeting users’ information requirements. Text classification (TC) has been extensively employed in text mining to facilitate multimedia data processing. However, accurately categorizing texts becomes challenging due to the increasing presence of non-informative features within the corpus. Several reviews on TC, encompassing various feature selection (FS) approaches to eliminate non-informative features, have been previously published. However, these reviews do not adequately cover the recently explored approaches to TC problem-solving utilizing FS, such as optimization techniques.