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Related Experiment Video

Updated: Sep 26, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

661

Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application.

Nebojsa Bacanin1, Miodrag Zivkovic2, Fadi Al-Turjman3

  • 1Singidunum University, Danijelova 32, 11000, Belgrade, Serbia. nbacanin@singidunum.ac.rs.

Scientific Reports
|April 20, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning framework using a hybridized sine cosine algorithm to optimize the dropout regularization parameter, effectively combating overfitting in neural networks for improved accuracy.

Related Experiment Videos

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661

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Deep learning models, particularly convolutional neural networks, excel in various applications but face overfitting challenges.
  • Overfitting occurs when models perform well on training data but poorly on new, unseen data.
  • Regularization methods are crucial for mitigating overfitting in deep neural networks.

Purpose of the Study:

  • To address the overfitting issue in deep neural networks by optimizing the dropout regularization parameter.
  • To propose an automated framework for selecting the optimal dropout value, overcoming manual selection challenges.
  • To investigate the potential of swarm intelligence, specifically a hybridized sine cosine algorithm, for this optimization task.

Main Methods:

  • Utilized a hybridized sine cosine algorithm, a swarm intelligence approach, to automatically determine the optimal dropout regularization parameter.
  • Implemented and tested the proposed framework on benchmark datasets (MNIST, CIFAR10, Semeion, UPS) and a brain tumor MRI classification task.
  • Compared the performance of the proposed method against several state-of-the-art approaches.

Main Results:

  • The proposed automated framework successfully tackled the overfitting challenge in deep learning models.
  • Experimental results demonstrated superior performance compared to existing methods on benchmark and medical imaging datasets.
  • The method achieved significant improvements in classification accuracy and reductions in classification error.

Conclusions:

  • The hybridized sine cosine algorithm offers an effective and automated solution for optimizing dropout regularization in deep learning.
  • The proposed framework enhances model generalization and predictive accuracy, outperforming current state-of-the-art techniques.
  • This research contributes a valuable tool for improving the reliability and performance of deep neural networks.