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By combining the undecimated wavelet transform within a Word EmbeddedSemantic Marginal Autoencoder (WESMA), this research study provides a novelstrategy for improving security measures and denoising multiple languages. Theincorporation of these strategies is intended to address the issues ofrobustness, privacy, and multilingualism in data processing applications. Theundecimated wavelet transform is used as a feature extraction tool to identifyprominent language patterns and structural qualities in the input data. Theproposed system may successfully capture significant information whilepreserving the temporal and geographical links within the data by employingthis transform. This improves security measures by increasing the system'sability to detect abnormalities, discover hidden patterns, and distinguishbetween legitimate content and dangerous threats. The Word Embedded SemanticMarginal Autoencoder also functions as an intelligent framework fordimensionality and noise reduction. The autoencoder effectively learns theunderlying semantics of the data and reduces noise components by exploitingword embeddings and semantic context. As a result, data quality and accuracyare increased in following processing stages. The suggested methodology istested using a diversified dataset that includes several languages and securityscenarios. The experimental results show that the proposed approach iseffective in attaining security enhancement and denoising capabilities acrossmultiple languages. The system is strong in dealing with linguistic variances,producing consistent outcomes regardless of the language used. Furthermore,incorporating the undecimated wavelet transform considerably improves thesystem's ability to efficiently address complex security concerns

Undecimated Wavelet Transform for Word Embedded Semantic Marginal Autoencoder in Security improvement and Denoising different Languages