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

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.1K
VSEPR Theory for Determination of Electron Pair Geometries
46.1K
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

429
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
429
Subatomic Particles03:37

Subatomic Particles

113.9K
Dalton was only partially correct about the particles that make up matter. All matter is composed of atoms, and atoms are composed of three smaller subatomic particles: protons, neutrons, and electrons. These three particles account for the mass and the charge of an atom.
113.9K
Prediction Intervals01:03

Prediction Intervals

3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.4K
The Nucleosome Core Particle02:10

The Nucleosome Core Particle

14.6K
Nucleosomes are the DNA-histone complex, where the DNA strand is wound around the histone core. The histone core is an octamer containing two copies of H2A, H2B, H3, and H4 histone proteins.
The paradox
Nucleosomes, paradoxically, perform two opposite functions simultaneously. On the one hand, their main responsibility is to protect the delicate DNA strands from physical damage and help achieve a higher compaction ratio. While on the other hand, they must allow polymerase enzymes to access DNA...
14.6K
Kinematic Equations for Rotation01:30

Kinematic Equations for Rotation

857
In mechanics, when one observes a rigid body in rotational motion with constant angular acceleration, it is possible to establish equations for its rotational kinematics. This process resembles how linear kinematics are dealt with in simpler motion studies.
For instance, imagine a point A on a rigid body engaged in circular motion. The translational velocity of this particular point can be calculated by taking the time derivatives of the displacement equation, which essentially measures the...
857

You might also read

Related Articles

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

Sort by
Same author

Oncological Perspective on Bone-Metastatic Colorectal Cancer: a Single-Centre Experience.

Journal of gastrointestinal cancer·2026
Same author

Systemic oxidative stress imbalance in actinic keratosis: Insights from thiol-disulfide homeostasis and ischemia-modified albumin.

Cutaneous and ocular toxicology·2025
Same author

YAU-RCC spotlight: dual VEGF/EGFR blockade shows promise for reshaping the treatment of papillary RCC in hereditary leiomyomatosis and renal cell cancer syndrome.

Minerva urology and nephrology·2025
Same author

Avelumab maintenance in patients with metastatic urothelial carcinoma in a real-life expanded-access program.

Future oncology (London, England)·2025
Same author

Racial differences in aortic perivascular adipose tissue volume and its association with arterial stiffness in middle-aged men.

Atherosclerosis·2025
Same author

Improving Predictive Efficacy for Drug Resistance in Novel HIV-1 Protease Inhibitors through Transfer Learning Mechanisms.

Journal of chemical information and modeling·2024

Related Experiment Video

Updated: Feb 12, 2026

Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence
12:34

Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence

Published on: June 24, 2016

10.6K

Prediction of Tibial Rotation Pathologies Using Particle Swarm Optimization and K-Means Algorithms.

Murat Sari1, Can Tuna2, Serkan Akogul3

  • 1Department of Mathematics, Yildiz Technical University, Istanbul 34220, Turkey. sarim@yildiz.edu.tr.

Journal of Clinical Medicine
|March 31, 2018
PubMed
Summary

A new hybrid algorithm combining particle swarm optimization (PSO) and K-means (KM) clustering effectively identifies pathological tibial rotation, aiding in treatment planning for physiotherapists and orthopedists.

Keywords:
K-means clusteringparticle swarm optimizationtibial rotation pathology

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K

Related Experiment Videos

Last Updated: Feb 12, 2026

Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence
12:34

Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence

Published on: June 24, 2016

10.6K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K

Area of Science:

  • Biomechanics
  • Medical Informatics
  • Computational Biology

Background:

  • Tibial rotation abnormalities can indicate pathological conditions.
  • Accurate classification of tibial rotation is crucial for diagnosis and treatment.
  • Existing methods may lack efficiency in analyzing complex datasets.

Purpose of the Study:

  • To develop and evaluate a hybrid algorithm for classifying pathological tibial rotation.
  • To optimize the clustering of tibial motion data using physical factors like age and weight.
  • To assess the predictability of identifying non-pathological (Type 2) subjects.

Main Methods:

  • A hybrid particle swarm optimization-K-means (PSO-KM) clustering algorithm was developed.
  • Datasets were clustered based on age and weight.
  • Tibial rotation values (RTER, RTIR, LTER, LTIR) were categorized into three types (Type 1, Type 2 - normal, Type 3).
  • The PSO-KM algorithm's performance was compared against real-world data.

Main Results:

  • The PSO-KM algorithm demonstrated high success in optimally clustering tibial rotation types based on physical criteria.
  • Type 2 (non-pathological) subjects showed particularly high predictability.
  • The algorithm proved effective for clustering and optimizing tibial motion data assessments.

Conclusions:

  • The hybrid PSO-KM algorithm is a successful tool for analyzing tibial rotation data.
  • This approach can significantly aid healthcare providers in designing patient treatment schedules.
  • The findings support the use of computational methods in clinical decision-making for orthopedic conditions.