Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Response Surface Methodology

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Hybrid Zones

Hybrid Zones

Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.

Residuals and Least-Squares Property

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

Regression Analysis

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

You might also read

Related Articles

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

Sort by

Same author

Optimized multi-modal action recognition using multi-agent systems and adaptive temporal attention.

Scientific reports·2026

Same author

Heart rate optimizer: a novel bio-inspired metaheuristic algorithm.

Scientific reports·2026

Same author

Multi-objective optimization for 3D heterogeneous WSN deployment using an enhanced Genghis Khan shark algorithm.

Scientific reports·2026

Same author

Enhancing credit card fraud detection with a hybrid approach using machine and deep learning.

Scientific reports·2026

Same author

AFoCo: Ambiguous Focus and Correction for Semi-Supervised Medical Image Segmentation.

IEEE transactions on neural networks and learning systems·2025

Same author

SMENN-hybrid: an efficient technique combining the synthetic minority oversampling technique with ensemble learning for diabetes prediction.

Scientific reports·2025

Search research articles

Related Experiment Video

Updated: Jun 1, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

A hybrid Harris Hawks Optimization with Support Vector Regression for air quality forecasting.

Essam H Houssein¹, Meran Mohamed², Eman M G Younis²

¹Faculty of Computers and Information, Minia University, Minia, Egypt. essam.halim@mu.edu.eg.

Scientific Reports

|January 17, 2025

Summary

This study introduces the Harris Hawks Optimization-Support Vector Regression (HHO-SVR) model for accurate air quality forecasting, specifically predicting Particulate Matter levels. The HHO-SVR model demonstrates superior performance compared to existing methods.

Keywords:

Air Quality Harris Hawks Optimization (HHO)Metaheuristics Particulate Matter Support Vector Regression (SVR)

More Related Videos

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Related Experiment Videos

Last Updated: Jun 1, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

Environmental Science
Computer Science
Data Science

Background:

Accurate air quality forecasting is crucial for public health and environmental monitoring.
Existing forecasting models face challenges in precision and efficiency.

Purpose of the Study:

To develop and evaluate a novel hybridized model for predicting Particulate Matter ([Formula: see text]) levels.
To enhance the accuracy and reliability of air quality forecasting.

Main Methods:

A hybridized model combining Support Vector Regression (SVR) with Harris Hawks Optimization (HHO) was developed (HHO-SVR).
The model was trained and tested using five datasets from the Environmental Protection Agency's Downscaler Model (DS).
Performance was assessed using metrics including Mean Absolute Percentage Error (MAPE), Average, Standard Deviation (SD), Best Fit, Worst Fit, and CPU time.

Main Results:

The proposed HHO-SVR model significantly outperformed recently published models such as GWO, SSA, HGSO, BMO, WOA, and MRFO.
The HHO-SVR model achieved superior results, establishing it as the optimal forecasting model.
Key performance metrics confirmed the model's efficacy in predicting Particulate Matter levels.

Conclusions:

The hybridized HHO-SVR model offers a highly effective approach for air quality forecasting.
This novel method provides a significant advancement in predicting Particulate Matter concentrations.
The study highlights the potential of optimization algorithms combined with machine learning for environmental applications.