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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

488
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...
488
Classifying Matter by Composition03:35

Classifying Matter by Composition

90.3K
Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
A mixture is composed of two or...
90.3K
Statistical Significance01:50

Statistical Significance

21.3K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
21.3K
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

772
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
772
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

600
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
600
Classifying Matter by State02:49

Classifying Matter by State

103.2K
Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
103.2K

You might also read

Related Articles

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

Sort by
Same author

Combination of Yaobitong capsules and lumbar oblique pull manipulation for moderate pain in lumbar disc herniation with radiculopathy: a multicenter, randomized, three-arm, parallel-group controlled trial.

Frontiers in neurology·2026
Same author

Beyond BMI: central obesity identifies overlooked fatty liver disease risk in adults with normal body mass index undergoing routine health examinations.

Frontiers in public health·2026
Same author

A comparison of pediatric sepsis definitions based on systemic inflammatory response syndrome and Phoenix criteria: a single-center PICU retrospective study.

Italian journal of pediatrics·2026
Same author

Development of an adult whole-body PBPK model of irinotecan and its metabolites for predicting UGT1A1/CYP3A-mediated drug-drug interactions.

Frontiers in pharmacology·2026
Same author

Correction: Detection of differentially expressed genes in spatial transcriptomics data by spatial analysis of spatial transcriptomics: A novel method based on spatial statistics.

Frontiers in neuroscience·2026
Same author

Cost-effectiveness of inclisiran in patients with atherosclerotic cardiovascular disease from Chinese healthcare perspective.

PloS one·2026

Related Experiment Video

Updated: Jan 30, 2026

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

7.5K

Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network

Qingfeng He1, Himan Shahabi2, Ataollah Shirzadi3

  • 1College of Geology & Environment, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China.

The Science of the Total Environment
|February 2, 2019
PubMed
Summary

This study compared three machine learning algorithms for landslide susceptibility mapping. The Radial Basis Function (RBF) Classifier demonstrated superior performance in predicting landslide risk.

Keywords:
Landslide susceptibilityLonghai areaNaïve BayesRBF ClassifierRBF Network

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K

Related Experiment Videos

Last Updated: Jan 30, 2026

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

7.5K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K

Area of Science:

  • Geosciences
  • Environmental Science
  • Geographic Information Science

Background:

  • Landslides pose significant risks to human life and infrastructure.
  • Accurate landslide susceptibility mapping is crucial for disaster mitigation and land-use planning.

Purpose of the Study:

  • To evaluate and compare Naïve Bayes (NB), Radial Basis Function (RBF) Classifier, and RBF Network algorithms for landslide susceptibility mapping (LSM).
  • To identify the most effective machine learning algorithm for landslide prediction in the Longhai area, China.

Main Methods:

  • Utilized 14 landslide conditioning factors derived from diverse data sources.
  • Employed Frequency Ratio (FR) and Support Vector Machine (SVM) for factor correlation and selection.
  • Validated and compared three machine learning models (NB, RBF Classifier, RBF Network) using statistical metrics like AUROC and Friedman/Wilcoxon tests.

Main Results:

  • The RBF Classifier model exhibited the highest goodness-of-fit and performance on both training and validation datasets.
  • The RBF Classifier achieved a superior Area Under the Receiver Operating Characteristics (AUROC) curve (0.881) compared to NB (0.872) and RBF Network (0.854).

Conclusions:

  • The RBF Classifier model is a highly effective and promising method for spatial landslide prediction.
  • The findings provide valuable insights for developing robust landslide risk assessment strategies globally.