Abstract— Arabic language is characterized by its rich morphological structure, presenting unique challenges in Nat- ural Language Processing (NLP). The categorization of Arabic plurals is the subject of this study, which uses a trans- former-based model—more precisely, the pre-trained Arabic BERT architecture—and has never been studied previously. Given the complexities of Arabic language, particularly in pluralization which includes sound masculine, sound feminine, and irregular (broken) plurals, the research aims to enhance NLP capabilities in this area. By utilizing a dataset of 7,400 instances classified into four distinct categories, the study demonstrates the effectiveness of transfer learning in achieving high classification accuracy, with results indicating an accuracy of 97% across both validation and testing sets. Addition- ally, the model achieves high precision, recall, and F1-score metrics. A confusion matrix provides insights into classifica- tion performance, highlighting areas of misclassification. The findings underscore the potential of transformer models in overcoming the linguistic challenges posed by Arabic plural forms.

Abstract— This survey explores different machine translation methods utilized in various systems and platforms for commercial and research purposes. These methods play a vital role in enabling global communication, enhancing accessibility, supporting business and trade, fostering intercultural understanding, facilitating travel and tourism, aiding education, delivering fast and efficient translations, contributing to humanitarian aid efforts, promoting research and collaboration, and preserving language and culture. The survey aims to equip software developers and researchers interested in machine translation with valuable insights into these methods. Its objective is to help them improve translation quality with great accuracy by providing them with the necessary knowledge and understanding of these approaches. The papers utilized in this survey were obtained from Open Access Journals and online databases. All these methods are essential and can differ based on the specific context, available resources, and the quality of the translation required. To achieve optimal translation results, researchers and practitioners commonly employ a combination of various methods and techniques. 

Abstract—Arabic Named Entity Recognition (NER) serves as a crucial facet within Natural Language Processing, given the intricacies of the Arabic language. This survey consolidates the current landscape of Arabic NER, covering methodologies, challenges, and advance- ments. The review encompasses an in-depth analysis of diverse approaches, from rule-based systems to modern deep learning techniques, highlighting their effectiveness and limitations. It also addresses the specific challenges inherent to Arabic NER, such as dialectal variations and limited annotated data, while exploring recent advancements and their applications in sentiment analysis, information retrieval, and other domains. This survey aims to provide a comprehensive overview, catering to researchers, practitioners, and enthusiasts in the field of Arabic NER and NLP.

Fishes are considered as good indicators of heavy metals pollution in aquatic ecosystem in addition to being a good source of protein. Thus the level of Mercury (Hg) in fish which become a health hazard, was examined. The aim of the study was to determine the concentrations of Hg in the studied tuna fish samples. Moreover, evaluation of associated health risks from fish consumption were conducted of which, the intake rates of Hg was gauged based on Hg concentrations in fish muscles and the rate of daily fish consumption by Libyan population. A total of Forty-five specimens (20 canned tuna samples, local and imported samples, five frozen tuna samples and 20 fresh tuna samples) were sampled from different markets, large commercial centers, and food distribution points in the Tripoli area. A quantity of 0.5 grams of muscle completely homogenized mixture of each canned, fresh, and frozen tuna were digested by a mixture of 5 ml of nitric acid and 5 ml of sulfuric acid. A cold vapor atomic absorption spectrometer was utilized for the tuna fish analysis. The results showed that the concentrations of Hg were at the limits set by WHO and FAO, except for three frozen tuna samples were higher than the safety limits for human consumption, (2.33, 0.64, and 1.45 mg/kg wet. weight), and one fresh tuna sample (0.51 mg/kg wet. weight). The results revealed that, the average concentration of Hg for canned, frozen, and fresh tuna samples were calculated as 0.12, 1.02, and 0.19 mg/kg wet weight, respectively. However, the target hazard quotient (THQ) for Hg was less than 1 in the analyzed canned, frozen, and fresh tuna samples indicating no potential health risks to the population in studied area. However, the risk of health effects is increased due to the presence of Hg in seafood. Therefore, groups most at risk, including young children, the elderly, and pregnant women, limit their consumption of certain types of seafood, especially tuna. further comprehensive monitoring programs should be conducted.

Abstract: The exact solution of a nonlinear fractional Volterra-Fredholm integro-differential equation is found in this paper through the successful application of the Abaoub Shkheam decomposition method. These techniques have a wider range of applications due to their dependability and decreased computational effort. 

ABSTRACT: This paper applies the Abaoub – Shkheam Decomposition Method (QDM) to obtaining solutions of linear fractional diffusion equations. The fractional derivative is described in the Caputo sense. Some illustrative examples are given, revealing the effectiveness and convenience of the method. 

Abstract: In this paper, a nonlinear fractional diffusion and wave equations are solved using the Abaoub-Shkheam Decomposition Method (QDM). The Caputo sense is used to characterise the fractional derivative. An example is provided to demonstrate the method's efficiency and practicality.