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Related Experiment Video

Updated: Aug 27, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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An efficient approach for textual data classification using deep learning.

Abdullah Alqahtani1, Habib Ullah Khan2, Shtwai Alsubai1

  • 1College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.

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Summary
This summary is machine-generated.

This study demonstrates that Long Short-Term Memory (LSTM) networks achieve 92% accuracy in text classification, outperforming traditional machine learning and other deep learning models. This highlights LSTM

Keywords:
deep learningmachine learningtext categorizationtext classificationtext data

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • Text categorization is crucial for organizing and understanding large volumes of unstructured data.
  • Machine learning and deep learning models offer powerful tools for automated text classification.
  • Preprocessing textual data is essential to remove noise and improve model performance.

Purpose of the Study:

  • To compare the effectiveness of various machine learning and deep learning algorithms for text classification.
  • To identify the optimal model for accurate and efficient textual data categorization.
  • To evaluate the performance of Long Short-Term Memory (LSTM) networks against other classification methods.

Main Methods:

  • Data preprocessing techniques including cleaning, imputation, and duplicate removal were applied.
  • Machine learning algorithms such as logistic regression, random forest, and K-nearest neighbors (KNN) were utilized.
  • Deep learning models including Artificial Neural Network (ANN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) were implemented for classification.

Main Results:

  • Long Short-Term Memory (LSTM) achieved the highest accuracy at 92%.
  • LSTM significantly outperformed all other tested machine learning and deep learning models.
  • The study validated the efficacy of deep learning approaches, particularly LSTM, for text classification tasks.

Conclusions:

  • Deep learning models, especially LSTM, provide superior performance for text classification compared to traditional machine learning algorithms.
  • Effective data preprocessing is a critical step for enhancing the accuracy of text classification models.
  • LSTM networks represent a state-of-the-art approach for high-accuracy text categorization.