Developing A Machine Learning Methodology That Is Both Reliable And Secure

Abstract

To swiftly and effectively obtain robust features from a representation in the bag of words, we propose a semantically improved Marginalized Stacked Denoising autoencoder. There is a critical need for robust and selective statistical representations of text learning in this vast area of research. A unique representational learning technique for addressing this issue is proposed in this research. In order to develop our own method, Semantically-Enhanced Marginalized Denoising Auto-encoder (smSDA), we take the popular deep learning model stacked Denoising auto-encoder and apply a semantic modification to it. Several academic disciplines, including topic recognition and emotional analysis, are intertwined with the research on cyberbullying detection. They paved the way for the automated detection of cyberbullying. By mining the bullying dataset's inherent feature structure, our suggested method may locate a reliable and discriminative textual representation. Our proposed methodology is extensively tested using two publicly accessible cyberbullying corpora, with findings that demonstrate its superiority over existing basic text representation learning methods. The semantic extension also includes sparsity limitations and semantic dropout noise, both of which were produced with the use of domain knowledge and the word embedding technique. Extensive testing using real-world data sets has validated the efficacy of our suggested methodology