Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design.

Jameer Basha1, Nebojsa Bacanin2, Nikola Vukobrat2

  • 1Department of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore 641028, Tamil Nadu, India.

Sensors (Basel, Switzerland)
|October 13, 2021
PubMed
Summary

Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

219
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
219

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A novel diabetic retinopathy detection from fundus images using hybrid quantum convolutional neural network models.

Scientific reports·2026
Same author

Uncovering pathways towards sustainable transportation: Investigating factors influencing societal acceptance of end-of-life vehicle management in Indonesia.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA·2024
Same author

Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics.

PeerJ. Computer science·2024
Same author

Aczel Alsina t-norm and t-conorm-based aggregation operators under linguistic interval-valued intuitionistic fuzzy setting with application.

PeerJ. Computer science·2024
Same author

Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems.

Biomimetics (Basel, Switzerland)·2024
Same author

Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation.

PeerJ. Computer science·2024
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
This summary is machine-generated.

A novel Harris Hawks optimization algorithm enhances convolutional neural network architecture for brain tumor classification using magnetic resonance imaging. This AI approach achieves over 95% accuracy, aiding early tumor detection and diagnosis.

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computational Intelligence

Background:

  • Accurate brain tumor classification is crucial for effective treatment planning.
  • Convolutional Neural Networks (CNNs) show promise in medical image analysis but require optimized architectures.
  • Swarm intelligence algorithms offer potential for improving CNN design.

Purpose of the Study:

  • To propose an improved Harris Hawks optimization (HHO) algorithm for evolving CNN architectures.
  • To apply the optimized CNN for classifying brain tumor grades using MRI.
  • To enhance the exploration and exploitation capabilities of the HHO algorithm.

Main Methods:

  • Developed an enhanced HHO algorithm incorporating chaotic population initialization, local search, and quasi-reflection-based learning.
Keywords:
Harris Hawks optimizationchaoticclassificationconvolutional neural networksexploitation–exploration trade-offquasi-reflection-based learningswarm intelligence

Related Experiment Videos

  • Evaluated the enhanced HHO on CEC2019 benchmarks against basic HHO and state-of-the-art methods.
  • Applied the HHO-evolved CNN to classify brain tumors using two MRI datasets (IXI, Cancer Imaging Archive, and T1-weighted images) with data augmentation.

    • Main Results:

    • The enhanced HHO algorithm demonstrated superior performance on benchmark functions.
    • The HHO-evolved CNN achieved over 95% accuracy in classifying healthy brains and various tumor grades (I-IV) and types (Glioma, Meningioma, Pituitary).
    • The proposed swarm intelligence-driven CNN approach outperformed existing methods in empirical validation.

    Conclusions:

    • The novel, enhanced Harris Hawks optimization algorithm effectively evolves CNN architectures for brain tumor classification.
    • The developed approach shows significant potential for assisting clinicians in early brain tumor detection and diagnosis.
    • The method offers a robust, high-accuracy solution for automated analysis of magnetic resonance imaging data.