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 Systems-II01:31

Classification of Systems-II

438
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,
438
Classification of Systems-I01:26

Classification of Systems-I

511
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
511
Classification of Signals01:30

Classification of Signals

1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

452
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
452
Aggregates Classification01:29

Aggregates Classification

925
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.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
925
Binomial Probability Distribution01:15

Binomial Probability Distribution

15.0K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
15.0K

You might also read

Related Articles

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

Sort by
Same author

Fully endoscopic lumbar spinal surgery: Is it time to change?

Journal of clinical orthopaedics and trauma·2021
Same author

Fully endoscopic cervical spine surgery: What does the future hold?

Journal of clinical orthopaedics and trauma·2021
Same author

Multiplex recurrence networks from multi-lead ECG data.

Chaos (Woodbury, N.Y.)·2020
Same author

Amplitude chimera and chimera death induced by external agents in two-layer networks.

Chaos (Woodbury, N.Y.)·2020
Same author

Metabonomics and the Gut Microbiome Associated With Primary Response to Anti-TNF Therapy in Crohn's Disease.

Journal of Crohn's & colitis·2020
Same author

Role of time scales and topology on the dynamics of complex networks.

Chaos (Woodbury, N.Y.)·2019
Same journal

Gap junction architecture and synchronization clusters in the thalamic reticular nuclei.

Chaos (Woodbury, N.Y.)·2026
Same journal

Exact computation of Lyapunov exponents via system parameters in multi-triangle chaotic maps: Bifurcation analysis and circuit realization.

Chaos (Woodbury, N.Y.)·2026
Same journal

Integrating score-based generative modeling and neural ODEs for accurate representation of multiscale chaotic dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

A data-driven tuberculosis model with behavioral changes and saturated treatment: Optimal control and cost-effectiveness study.

Chaos (Woodbury, N.Y.)·2026
Same journal

Breathers, rational solutions, and their exact physical spectra in F = 1 spinor Bose-Einstein condensates.

Chaos (Woodbury, N.Y.)·2026
Same journal

Finite invariant sets with bridging points in logistic IFS.

Chaos (Woodbury, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Jan 3, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.3K

Classification of close binary stars using recurrence networks.

Sandip V George1, R Misra2, G Ambika3

  • 1Indian Institute of Science Education and Research (IISER) Pune, Pune 411008, India.

Chaos (Woodbury, N.Y.)
|November 30, 2019
PubMed
Summary
This summary is machine-generated.

Machine learning effectively classifies close binary stars using recurrence network quantifiers. This method accurately distinguishes overcontact, semidetached, and ellipsoidal binary stars from light curve data.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

457

Related Experiment Videos

Last Updated: Jan 3, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.3K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

457

Area of Science:

  • Astronomy and Astrophysics
  • Computational Astrophysics

Background:

  • Close binary stars exchange mass and energy, classified into semidetached, overcontact, and ellipsoidal types.
  • Classifying these subclasses from light curves is challenging due to subtle features and large datasets.

Purpose of the Study:

  • To develop a machine learning approach for classifying close binary stars based on light curve analysis.
  • To differentiate between semidetached, overcontact, and ellipsoidal binary star subclasses.

Main Methods:

  • Utilizing quantifiers derived from recurrence networks of light curve data.
  • Applying standard clustering algorithms to analyze the recurrence network properties.
  • Analyzing characteristic path length and average clustering coefficient.

Main Results:

  • Overcontact binary stars form a distinct region in the characteristic path length vs. average clustering coefficient plane.
  • Clustering algorithms accurately group stars into their standard classes.
  • Machine learning successfully differentiates between close binary star subclasses.

Conclusions:

  • Recurrence network quantifiers combined with machine learning offer an efficient method for close binary star classification.
  • This approach overcomes the limitations of traditional light curve analysis for large datasets.
  • The proposed method demonstrates high accuracy in subclass identification.