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 Concept Videos

Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

You might also read

Related Articles

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

Sort by
Same author

Designing metaverse interaction systems for the Turkish language enhanced by fine-tuning and retrieval-augmented generation (RAG).

Scientific reports·2026
Same author

A Gated Attention-Based Multiple Instance Learning and Test-Time Augmentation Approach for Diagnosing Active Sacroiliitis in Sacroiliac Joint MRI Scans.

Journal of clinical medicine·2026
Same author

Diagnostic Value of Machine Learning Models in Inflammation of Unknown Origin.

Journal of clinical medicine·2025
Same author

Comparative Analysis of Machine Learning Methods with Chaotic AdaBoost and Logistic Mapping for Real-Time Sensor Fusion in Autonomous Vehicles: Enhancing Speed and Acceleration Prediction Under Uncertainty.

Sensors (Basel, Switzerland)·2025
Same author

Optimizing Autonomous Vehicle Performance Using Improved Proximal Policy Optimization.

Sensors (Basel, Switzerland)·2025

Related Experiment Video

Updated: May 8, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Improved chaos-enhanced FOX for clustering-based supervised medical classification.

İlker Dağlı1, Onur İnan2, Fatih Başçiftçi2

  • 1Department of Computer Engineering, Institute of Science, Selçuk University, Konya, Turkey. idagli@erbakan.edu.tr.

Scientific Reports
|May 6, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel chaos-enhanced fox optimization algorithm for medical data classification. The method improves accuracy and robustness, overcoming premature convergence in complex datasets.

Keywords:
Chaotic mapsClassificationClusteringFox optimization algorithmOptimization

Related Experiment Videos

Last Updated: May 8, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Area of Science:

  • Computational biology
  • Medical informatics
  • Machine learning

Background:

  • Optimization-based classification is vital in medical data analysis.
  • Existing methods struggle with complex, heterogeneous datasets, showing premature convergence and low robustness.

Purpose of the Study:

  • To present a chaos-enhanced, fox-inspired classification framework to improve medical data analysis.
  • To address limitations of existing methods, specifically premature convergence and robustness issues.

Main Methods:

  • A novel classification framework using the Fox Optimization Algorithm enhanced with a Gauss/Mouse chaotic map.
  • A clustering-based strategy optimizing cluster centers and assigning labels via nearest-neighbor analysis.
  • The Gauss/Mouse chaotic map regulates exploration-exploitation balance without adding parameters.

Main Results:

  • The proposed framework achieved statistically significant and consistent classification performance across six medical datasets.
  • It demonstrated superior performance compared to baseline methods, evidenced by the best average rank (1.16) in the Friedman test (p=0.0012).
  • Chaotic dynamics effectively mitigated premature convergence, enhancing search behavior and improving classification accuracy, precision, sensitivity, and specificity.

Conclusions:

  • The chaos-enhanced fox optimization framework offers stable and reproducible classification performance for benchmark medical datasets.
  • The integration of chaotic dynamics significantly improves search behavior and classification outcomes.
  • Future work may involve external clinical validation and exploring alternative methodological integrations.