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

Heuristics01:21

Heuristics

158
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
158
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

107
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
107
Centroid of a Body: Problem Solving01:03

Centroid of a Body: Problem Solving

1.3K
The centroid of a body is a crucial concept in engineering and physics. Finding the centroid of a body can help determine its stability, its balance point, and even its design. In this context, consider a thin wire bent in the form of a quarter circular arc. Polar coordinates are used to calculate the centroid. The wire is first divided into small differential elements of a length equal to the radius multiplied by the differential angle.
The x-coordinates and y-coordinates of each element's...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Molecular-Extrusion-Driven Halogen Homogenization for Efficient Perovskite-Silicon Tandem Solar Cells.

Angewandte Chemie (International ed. in English)·2026
Same author

Icariin alleviates ovariectomy-induced osteoporosis by promoting M2 macrophage polarization and suppressing osteoclast activation.

Cytokine·2026
Same author

An Integrated Study Based on UPLC-QTOF/MS Network Pharmacology and In Vivo Validation of the Anti-Obesity Effects of the 60% Ethanol-Eluted Fraction from <i>Rheum tanguticum</i>.

Plants (Basel, Switzerland)·2026
Same author

A Comprehensive Strategy for Characterizing Metabolites and Metabolic Profile in Rat Urine and Feces Following Oral Administration of Huachansu Tablets Based on UPLC-ESI-QTOF/MS<sup>E</sup>.

Biomedical chromatography : BMC·2026
Same author

Anterior Quadratus Lumborum Block with Liposomal Bupivacaine versus Ropivacaine for Postoperative Recovery in Laparoscopic Colorectal Surgery: A Randomized Controlled Trial.

Drug design, development and therapy·2026
Same author

Sponge-like porous Pd-SnO<sub>2</sub> with atomic-level doping for ultrafast and stable CO detection: Synergistic effects of lattice distortion and oxygen vacancies.

Talanta·2026

Related Experiment Video

Updated: Sep 23, 2025

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

7.7K

Large-Scale Meta-Heuristic Feature Selection Based on BPSO Assisted Rough Hypercuboid Approach.

Chuan Luo, Sizhao Wang, Tianrui Li

    IEEE Transactions on Neural Networks and Learning Systems
    |May 13, 2022
    PubMed
    Summary

    Feature selection using the rough hypercuboid approach and binary particle swarm optimization (BPSO) enhances model efficiency. A distributed version (DiRH-BPSO) effectively handles large datasets, improving accuracy and speed.

    More Related Videos

    High-Throughput Metabolic Profiling for Model Refinements of Microalgae
    11:07

    High-Throughput Metabolic Profiling for Model Refinements of Microalgae

    Published on: December 4, 2021

    3.9K
    A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
    06:58

    A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

    Published on: November 6, 2015

    9.6K

    Related Experiment Videos

    Last Updated: Sep 23, 2025

    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

    7.7K
    High-Throughput Metabolic Profiling for Model Refinements of Microalgae
    11:07

    High-Throughput Metabolic Profiling for Model Refinements of Microalgae

    Published on: December 4, 2021

    3.9K
    A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
    06:58

    A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

    Published on: November 6, 2015

    9.6K

    Area of Science:

    • Data Mining
    • Machine Learning
    • Computational Intelligence

    Background:

    • Feature selection is crucial for building efficient data mining models.
    • The rough hypercuboid approach addresses feature redundancy and irrelevance, particularly in approximate numerical classification.
    • Existing methods may struggle with computational cost on large datasets.

    Purpose of the Study:

    • To propose a novel global search method for numerical feature selection by hybridizing the rough hypercuboid approach with the binary particle swarm optimization (BPSO) algorithm.
    • To develop a parallelized distributed version (DiRH-BPSO) for efficient large-scale dataset processing using Apache Spark.
    • To evaluate the performance of the proposed methods against existing feature selection algorithms.

    Main Methods:

    • Hybridization of the rough hypercuboid approach with the binary particle swarm optimization (BPSO) algorithm to create RH-BPSO.
    • Development of a distributed meta-heuristic optimized rough hypercuboid feature selection (DiRH-BPSO) algorithm using horizontal data partitioning and Apache Spark.
    • Parallelization strategies for feature evaluation criteria calculation.

    Main Results:

    • RH-BPSO significantly outperforms other feature selection algorithms in classification accuracy, selected feature subset size, and execution efficiency.
    • DiRH-BPSO demonstrates substantial speed improvements over sequential methods on distributed-memory multicore clusters.
    • DiRH-BPSO successfully handles large-scale feature selection tasks exceeding single-node memory capacity.
    • Analysis confirms excellent parallel scalability and extensibility of DiRH-BPSO.

    Conclusions:

    • The proposed RH-BPSO offers a promising approach for numerical feature selection.
    • DiRH-BPSO provides an efficient and scalable solution for large-scale feature selection tasks in cloud computing environments.
    • The developed distributed algorithm effectively addresses computational challenges and memory constraints associated with big data.