{"ID":2845592,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.03107","arxiv_id":"2511.03107","title":"An Efficient Classification Model for Cyber Text","abstract":"The uprising of deep learning methodology and practice in recent years has brought about a severe consequence of increasing carbon footprint due to the insatiable demand for computational resources and power. The field of text analytics also experienced a massive transformation in this trend of monopolizing methodology. In this paper, the original TF-IDF algorithm has been modified, and Clement Term Frequency-Inverse Document Frequency (CTF-IDF) has been proposed for data preprocessing. This paper primarily discusses the effectiveness of classical machine learning techniques in text analytics with CTF-IDF and a faster IRLBA algorithm for dimensionality reduction. The introduction of both of these techniques in the conventional text analytics pipeline ensures a more efficient, faster, and less computationally intensive application when compared with deep learning methodology regarding carbon footprint, with minor compromise in accuracy. The experimental results also exhibit a manifold of reduction in time complexity and improvement of model accuracy for the classical machine learning methods discussed further in this paper.","short_abstract":"The uprising of deep learning methodology and practice in recent years has brought about a severe consequence of increasing carbon footprint due to the insatiable demand for computational resources and power. The field of text analytics also experienced a massive transformation in this trend of monopolizing methodology...","url_abs":"https://arxiv.org/abs/2511.03107","url_pdf":"https://arxiv.org/pdf/2511.03107v1","authors":"[\"Md Sakhawat Hossen\",\"Md. Zashid Iqbal Borshon\",\"A. S. M. Badrudduza\"]","published":"2025-11-05T01:21:32Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.IT\"]","methods":"[]","has_code":false}