Related Experiment Video

Updated: Jul 19, 2025

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

8.2K

Retracted: Digital Industry Financial Risk Early Warning System Based on Improved K-Means Clustering Algorithm.

Computational Intelligence And Neuroscience

    Computational Intelligence and Neuroscience
    |August 18, 2023
    PubMed
    Summary

    This article has been retracted. The original study, identified by DOI 10.1155/2022/6797185, is no longer considered valid. Further research should consult updated or alternative publications.

    More Related Videos

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    7.6K
    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations12:27

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

    Published on: February 15, 2017

    7.0K

    Related Concept Videos

    Cluster Sampling Method01:20

    Cluster Sampling Method

    Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
    To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
    12.0K
    Aggregates Classification01:29

    Aggregates Classification

    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...
    345