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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Updated: Aug 25, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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A Multimodel-Based Deep Learning Framework for Short Text Multiclass Classification with the Imbalanced and Extremely

Jiajun Tong1, Zhixiao Wang1, Xiaobin Rui1

  • 1China University of Mining and Technology, School of Computer Science and Technology, Xuzhou, China.

Computational Intelligence and Neuroscience
|October 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a compressed deep learning framework for text classification on small, imbalanced datasets. The model achieves state-of-the-art performance while being lightweight for mobile deployment.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Text classification is crucial for applications, but small, imbalanced datasets pose challenges.
  • Existing pretrained models are often too large for mobile devices and may not capture deep semantic nuances.
  • Efficient and effective text classification methods are needed for real-world, resource-constrained scenarios.

Purpose of the Study:

  • To propose a novel multimodel-based deep learning framework for short-text multiclass classification.
  • To address the limitations of existing methods concerning dataset size, model deployment, and semantic information extraction.
  • To develop a compressed and efficient framework suitable for mobile devices.

Main Methods:

  • A five-layer framework incorporating DistilBERT for context-sensitive word vectors.
  • Hierarchical feature extraction using word-level and sentence-level bidirectional LSTM networks.
  • Max-pooling and a SoftMax layer for dimensionality reduction and classification.

Main Results:

  • The proposed framework demonstrates effectiveness on extremely small, imbalanced datasets.
  • It achieves state-of-the-art performance in precision, recall, accuracy, and F1 score.
  • The model exhibits reduced size, faster training times, and quicker convergence compared to baselines.

Conclusions:

  • The developed framework is highly effective for short-text multiclass classification with limited data.
  • Its compressed nature and efficient performance make it suitable for deployment on mobile devices.
  • This approach offers a practical solution for real-world text classification challenges with scarce data.