  By combining the undecimated wavelet transform within a Word Embedded
Semantic Marginal Autoencoder (WESMA), this research study provides a novel
strategy for improving security measures and denoising multiple languages. The
incorporation of these strategies is intended to address the issues of
robustness, privacy, and multilingualism in data processing applications. The
undecimated wavelet transform is used as a feature extraction tool to identify
prominent language patterns and structural qualities in the input data. The
proposed system may successfully capture significant information while
preserving the temporal and geographical links within the data by employing
this transform. This improves security measures by increasing the system's
ability to detect abnormalities, discover hidden patterns, and distinguish
between legitimate content and dangerous threats. The Word Embedded Semantic
Marginal Autoencoder also functions as an intelligent framework for
dimensionality and noise reduction. The autoencoder effectively learns the
underlying semantics of the data and reduces noise components by exploiting
word embeddings and semantic context. As a result, data quality and accuracy
are increased in following processing stages. The suggested methodology is
tested using a diversified dataset that includes several languages and security
scenarios. The experimental results show that the proposed approach is
effective in attaining security enhancement and denoising capabilities across
multiple 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 the
system's ability to efficiently address complex security concerns
