Abstract: This article is a comparative analysis of the concept and types of homonyms in Russian and Arabic. Homonyms are lexical units that have the same sound but different meanings. The study of homonymy in different languages can help reveal the features of the semantic structure and syntactic rules of each language. The article discusses the main aspects of homonymy in Russian and Arabic, as well as a comparative analysis of the types of homonyms that exist in both languages. The study includes an analysis of the semantic meanings of homonyms, their use in context, and possible differences in syntactic features that may affect their interpretation and perception by native speakers of these languages. The purpose of the article is to identify similarities and differences between homonyms in Russian and Arabic, as well as understanding the influence of linguistic features on the emergence and functioning of homonymous phenomena in both languages. The results of the study may be useful for linguistic researchers, teachers and students interested in the semantic and syntactic aspects of various language systems.
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In this paper, we introduce and study the notation of approximaitly quasi-primary submodules of a unitary left -module over a commutative ring with identity. This concept is a generalization of prime and primary submodules, where a proper submodule of an -module is called an approximaitly quasi-primary (for short App-qp) submodule of , if , for , , implies that either or , for some . Many basic properties, examples and characterizations of this concept are introduced.
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The current issues in spam email detection systems are directly related to spam email classification's low accuracy and feature selection's high dimensionality. However, in machine learning (ML), feature selection (FS) as a global optimization strategy reduces data redundancy and produces a collection of precise and acceptable outcomes. A black hole algorithm-based FS algorithm is suggested in this paper for reducing the dimensionality of features and improving the accuracy of spam email classification. Each star's features are represented in binary form, with the features being transformed to binary using a sigmoid function. The proposed Binary Black Hole Algorithm (BBH) searches the feature space for the best feature subsets,
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