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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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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Updated: Oct 12, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm.

Mohamed Abd Elaziz1,2, Abdelghani Dahou3, Naser A Alsaleh4

  • 1Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt.

Entropy (Basel, Switzerland)
|November 27, 2021
PubMed
Summary

This study introduces a novel framework combining deep learning and swarm intelligence for accurate COVID-19 detection from X-ray and CT images. The Aquila Optimizer enhances classification performance by selecting key image features, aiding early diagnosis.

Keywords:
atomic orbital searchdynamic opposite-based learningfeature selectionmetaheuristic

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Last Updated: Oct 12, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

960

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • COVID-19 (coronavirus disease 2019) is a highly infectious disease with rapid global spread.
  • Shortages in diagnostic tools like X-ray machines can delay critical diagnoses, increasing mortality.
  • Deep learning and optimization algorithms offer potential for early COVID-19 detection.

Purpose of the Study:

  • To propose a hybrid framework for COVID-19 image classification using deep learning and swarm-based optimization.
  • To leverage MobileNetV3 for feature extraction and the Aquila Optimizer for feature selection.
  • To improve classification accuracy and reduce dimensionality in COVID-19 detection.

Main Methods:

  • A deep learning model (MobileNetV3) was employed as a backbone for feature extraction from medical images.
  • The Aquila Optimizer (Aqu), a swarm-based algorithm, was utilized for feature selection to reduce data dimensionality.
  • The proposed framework was validated using two distinct datasets comprising X-ray and CT images of COVID-19 patients.

Main Results:

  • The hybrid framework demonstrated strong performance in classifying COVID-19 images.
  • Significant dimensionality reduction was achieved during the feature extraction and selection processes.
  • The Aquila Optimizer outperformed other methods in terms of key performance metrics for feature selection.

Conclusions:

  • The proposed deep learning and swarm optimization framework effectively aids in the early diagnosis of COVID-19.
  • Hybridization of MobileNetV3 and Aquila Optimizer enhances classification accuracy and efficiency.
  • This approach offers a promising solution for improving diagnostic capabilities in resource-limited settings.