Real-Time Text Document Classification Using Fully Connected Neural Network
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ID: 312795
2025
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Abstract
Automated classification of text documents stands crucial in modern times because of the rising digital information volumes. The substantial amount of textual data across different industries gets better handled through automated document classification, which helps both retrieval and analysis, and organization of massive data collections. The system enables fast classification of documents, which leads to better decisions which resulting in improved productivity and simplified organizational processes. The proposed system implements a complete automated text document classification through deep learning (DL) methodologies. The data gets saturated by first removing special characters, together with common non-alphanumeric characters. Our proposed Fully Connected Neural Network (FCNN) receives the pre-processed database that has undergone tokenization. The proposed methodology achieved maximum accuracy at 99%. The method demonstrates strong reliability in processing real-time text data classification operations.
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| Authors | Talha Ahmed Qureshi, Hamza Imran, Anees Tariq, Usama Irshad, Saqib Majeed, Muhammad Munwar Iqbal |
| Journal | Journal of Computing & Biomedical Informatics |
| Year | 2025 |
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| Keywords | Keywords not found |
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