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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Deep learning-based information retrieval with normalized dominant feature subset and weighted vector model.

Poluru Eswaraiah1, Hussain Syed1

  • 1School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.

Peerj. Computer Science
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Summary

A new Normalized Dominant Feature Subset with Weighted Vector Model (NDFS-WVM) improves text retrieval accuracy. This deep learning approach enhances feature extraction for computer vision and natural language processing applications, achieving 98.6% accuracy.

Keywords:
Big dataFeature extractionFeature selectionFeature subsetFeature vector

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Area of Science:

  • Computer Vision
  • Natural Language Processing
  • Machine Learning

Background:

  • Multimedia data, including text, is crucial for computer vision applications.
  • Increasingly complex text data on social media and news sites poses challenges for information retrieval.
  • Traditional text retrieval methods and manual feature engineering have limitations in handling large datasets.

Purpose of the Study:

  • To address the challenges in feature extraction and selection for information retrieval from big data.
  • To propose a novel deep learning-based method for enhanced text mining and retrieval.
  • To improve the accuracy and efficiency of finding meaningful text records in large archives.

Main Methods:

  • Development of a Normalized Dominant Feature Subset with Weighted Vector Model (NDFS-WVM).
  • Application of deep learning for automatic feature extraction and selection from large text volumes.
  • Utilizing natural language processing models for information retrieval within the proposed framework.

Main Results:

  • The proposed NDFS-WVM model demonstrates superior performance compared to conventional models in text retrieval.
  • Achieved a high accuracy rate of 98.6% in information retrieval tasks.
  • Successfully extracts high-quality machine learning features from extensive text data.

Conclusions:

  • The NDFS-WVM offers an effective solution for feature extraction and selection in big data information retrieval.
  • Deep learning, through the proposed model, significantly advances text mining capabilities.
  • The method enhances the ability of computer vision researchers to find relevant text information efficiently.