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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Rider weed deep residual network-based incremental model for text classification using multidimensional features and

Hemn Barzan Abdalla1, Awder M Ahmed2, Subhi R M Zeebaree3

  • 1Department of Computer Science, Wenzhou-Kean University, Wenzhou, Zhejiang, China.

Peerj. Computer Science
|May 2, 2022
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Summary
This summary is machine-generated.

This study introduces a hybrid Rider invasive weed optimization (RIWO) algorithm for advanced text classification. The novel approach significantly improves accuracy in categorizing large volumes of textual data.

Keywords:
Deep Residual networkDynamic learningFuzzy theoryMapReduce ModelText classification

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • The exponential growth of big data necessitates efficient text classification methods.
  • Traditional text classification techniques struggle with the scale and complexity of modern information demands.

Purpose of the Study:

  • To develop a hybrid optimization algorithm for enhanced text classification.
  • To improve the accuracy and efficiency of extracting meaningful information from large textual datasets.

Main Methods:

  • Text pre-processing involved stemming and stop word removal.
  • Feature selection utilized Tanimoto similarity for optimal feature identification.
  • A deep residual network, trained with the Adam algorithm, was employed for classification.
  • A novel Rider invasive weed optimization (RIWO) algorithm, combining IWO and ROA, was integrated with fuzzy theory for dynamic learning.
  • The entire process was implemented within the MapReduce framework.

Main Results:

  • The proposed RIWO-based deep residual network achieved a True Positive Rate (TPR) of 85%.
  • The system demonstrated a True Negative Rate (TNR) of 94%.
  • The overall classification accuracy reached 88.7%, outperforming existing methods.

Conclusions:

  • The hybrid RIWO algorithm offers a superior approach to dynamic text classification.
  • This method effectively handles large-scale textual data, enhancing information retrieval.
  • The findings highlight the potential of combining optimization algorithms with deep learning for complex data analysis.