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<p>The rapid growth of digital information has led to an increased demand for effective text classification methods, which play a crucial role in organizing and retrieving relevant information. In this context, the present work addresses the problem of accurately categorizing large volumes of textual data, which is often hindered by high dimensionality and noisy features. The aim of the paper is to propose a novel hybrid model that integrates Term Frequency–Inverse Document Frequency (TF-IDF) and Word2Vec for robust feature extraction, combined with Principal Component Analysis (PCA) for dimensionality reduction. The classification is then performed using the XGBoost algorithm, known for its gradient boosting capability and high accuracy, to develop a lightweight and effective text classification model that outperforms traditional techniques. The novelty of the proposed approach lies in its unique combination of classical statistical and modern semantic feature extraction techniques with an efficient machine learning classifier. Experimental results demonstrate that the proposed method achieves a 12% improvement in accuracy and a 15% reduction in processing time compared to existing TF-IDF + SVM and Word2Vec + Random Forest models. Overall, the proposed hybrid technique offers a balanced trade-off between performance and efficiency, making it suitable for real-time text classification tasks.</p>

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A novel optimized hybrid deep learning model for intrusion detection in software defined internet of things (SDIoT) environment

  • 期刊论文

  • 发表日期：

    2025年10月9日

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  • A. Daniel,
  • S. Dinesh Krishnan,
  • Dyagala Naga Sudha,
  • Balamurugan Balusamy,
  • Anand Nayyar

摘要

The rapid growth of digital information has led to an increased demand for effective text classification methods, which play a crucial role in organizing and retrieving relevant information. In this context, the present work addresses the problem of accurately categorizing large volumes of textual data, which is often hindered by high dimensionality and noisy features. The aim of the paper is to propose a novel hybrid model that integrates Term Frequency–Inverse Document Frequency (TF-IDF) and Word2Vec for robust feature extraction, combined with Principal Component Analysis (PCA) for dimensionality reduction. The classification is then performed using the XGBoost algorithm, known for its gradient boosting capability and high accuracy, to develop a lightweight and effective text classification model that outperforms traditional techniques. The novelty of the proposed approach lies in its unique combination of classical statistical and modern semantic feature extraction techniques with an efficient machine learning classifier. Experimental results demonstrate that the proposed method achieves a 12% improvement in accuracy and a 15% reduction in processing time compared to existing TF-IDF + SVM and Word2Vec + Random Forest models. Overall, the proposed hybrid technique offers a balanced trade-off between performance and efficiency, making it suitable for real-time text classification tasks.