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

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

127
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
127
Reducing Line Loss01:18

Reducing Line Loss

188
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
188
Graphical and Analytic Representation of Sinusoids01:20

Graphical and Analytic Representation of Sinusoids

458
Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
The first step is measuring the peak-to-peak value, which is twice the amplitude of the sinusoid. This provides information about the maximum voltage swing of the waveform.
Secondly, the period and angular frequency are determined. The period is the time taken for one complete cycle of the waveform, while...
458
Sinusoidal Sources01:18

Sinusoidal Sources

607
Direct current (DC) refers to an electric current that flows in a single direction, maintaining a constant polarity. This is in contrast to alternating current (AC), which periodically changes its direction and magnitude. AC forms the backbone of modern electricity transmission and distribution systems due to its efficient long-distance transmission capabilities.
In homes, the power supplies use sinusoidal sources to provide electricity. These sources generate a voltage that varies sinusoidally...
607
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

119
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
119
Exponential and Sinusoidal Signals01:18

Exponential and Sinusoidal Signals

357
The exponential function is crucial for characterizing waveforms that rise and decay rapidly. This continuous-time exponential function is defined using exponential terms with constants α and A. When both constants are real, the function is represented as,
357

You might also read

Related Articles

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

Sort by
Same author

Dual drug-loaded nanofibrous scaffolds for targeting dormant breast cancer cells: design, mechanisms, and translational perspectives.

International journal of pharmaceutics·2026
Same author

Hybrid deep learning and feature selection approach for autism detection from rs-fMRI data.

PloS one·2026
Same author

A comprehensive review of advances in analytical chromatography for the characterization of therapeutic biomacromolecule and bioparticle.

Journal of chromatography. A·2026
Same author

Aerial image segmentation using multilevel thresholding based on multi strategy Osprey optimization algorithm.

Scientific reports·2026
Same author

A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application.

Drug metabolism and disposition: the biological fate of chemicals·2026
Same author

Enhancing particle swarm optimization based on optical computing mechanism: application to dyslexia detection.

Frontiers in artificial intelligence·2026

Related Experiment Video

Updated: Aug 25, 2025

Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice
09:28

Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice

Published on: June 23, 2023

3.0K

Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature

Bilal H Abed-Alguni1, Noor Aldeen Alawad1, Mohammed Azmi Al-Betar2

  • 1Department of Computer Sciences, Yarmouk University, Irbid, Jordan.

Applied Intelligence (Dordrecht, Netherlands)
|October 17, 2022
PubMed
Summary

This study introduces improved binary Sine Cosine Algorithms (BSCA) for effective feature selection (FS). The enhanced IBSCA3 model significantly boosts classification accuracy and fitness values on real-world datasets.

Keywords:
Feature selectionLaplace distributionMutation methodsOpposition-based learningRefraction learningSine cosine algorithm

More Related Videos

Subjective Refraction Test Using a Smartphone for Vision Screening
05:36

Subjective Refraction Test Using a Smartphone for Vision Screening

Published on: October 18, 2024

956
Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability
07:23

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability

Published on: August 6, 2021

2.8K

Related Experiment Videos

Last Updated: Aug 25, 2025

Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice
09:28

Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice

Published on: June 23, 2023

3.0K
Subjective Refraction Test Using a Smartphone for Vision Screening
05:36

Subjective Refraction Test Using a Smartphone for Vision Screening

Published on: October 18, 2024

956
Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability
07:23

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability

Published on: August 6, 2021

2.8K

Area of Science:

  • Machine Learning
  • Data Mining
  • Optimization Algorithms

Background:

  • Feature selection (FS) is crucial for handling high-dimensional data in machine learning.
  • The Sine Cosine Algorithm (SCA) is a metaheuristic effective for continuous optimization but requires adaptation for binary problems like FS.
  • Existing binary versions of SCA may lack optimal performance in complex feature selection scenarios.

Purpose of the Study:

  • To develop and evaluate improved binary versions of the Sine Cosine Algorithm (SCA) specifically for the feature selection (FS) problem.
  • To enhance the performance of the Binary SCA (BSCA) through cumulative improvements incorporating advanced learning and search strategies.
  • To rigorously assess the efficacy of the proposed algorithms on diverse real-world datasets, including a COVID-19 dataset.

Main Methods:

  • Proposed three cumulative improved binary Sine Cosine Algorithms: IBSCA1 (with Opposition Based Learning), IBSCA2 (adding Variable Neighborhood Search and Laplace distribution), and IBSCA3 (incorporating Refraction Learning).
  • Evaluated algorithm performance on 19 real-world datasets using classification accuracy, number of features selected, and fitness values.
  • Compared the best proposed algorithm (IBSCA3) against 28 existing popular algorithms.

Main Results:

  • IBSCA3 demonstrated superior performance in classification accuracy and fitness values compared to most existing algorithms.
  • IBSCA3 achieved a competitive ranking (15th out of 19) for the number of features selected.
  • The proposed IBSCA versions, particularly IBSCA3, showed significant improvements over the basic BSCA.

Conclusions:

  • The enhanced IBSCA3 algorithm offers a powerful and effective approach for feature selection in machine learning and data mining.
  • The integration of Opposition Based Learning, Variable Neighborhood Search, Laplace distribution, and Refraction Learning significantly boosts algorithm performance.
  • IBSCA3 represents a state-of-the-art method for feature selection, providing a valuable tool for data analysis and model building.