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

Conservation of Small Populations

Conservation of Small Populations

Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less...

You might also read

Related Articles

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

Sort by

Same author

Comprehensive evaluation of a tri-band MIMO antenna with SAR and MIMO performance for 5G devices.

Scientific reports·2026

Same author

Synergistic effects on biofuel yields and heating value during co-pyrolysis of sewage sludge and sugarcane residue under different blend ratios.

Scientific reports·2025

Same author

Highly efficient, perfect, large angular and ultrawideband solar energy absorber for UV to MIR range.

Scientific reports·2022

Same author

QoS Analysis for Cloud-Based IoT Data Using Multicriteria-Based Optimization Approach.

Computational intelligence and neuroscience·2022

Same author

Direct somatic embryogenesis of <i>Malaxis densiflora</i> (A. Rich.) Kuntze.

Journal, genetic engineering & biotechnology·2019

Same author

Retraction notice to "Anti-diabetic activity of Swertia corymbosa (Griseb.) Wight ex C.B. Clarke aerial parts extract in streptozotocin induced diabetic rats" [J. Ethnopharmacol. 151/3 (2014) 1175-1183].

Journal of ethnopharmacology·2017

Search research articles

Related Experiment Video

Updated: Jul 8, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Hybrid grey assisted whale optimization based machine learning for the COVID-19 prediction.

A Shyamala¹, S Murugeswari², G Mahendran²

¹Department of Electronics and Communication Engineering, Mohamed Sathak Engineering College, Kilakarai, Ramanathapuram, Chennai, Tamil Nadu, India.

Computer Methods in Biomechanics and Biomedical Engineering

|December 19, 2023

Summary

This study introduces a machine learning model for predicting COVID-19 outcomes. The hybrid grey assisted whale optimization algorithm-adaptive network-based fuzzy inference system (H-GAWOA-ANFIS) achieved excellent prediction accuracy, particularly for deceased cases.

Keywords:

Covid-19 death rate health issue machine learning model survival rate

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Related Experiment Videos

Last Updated: Jul 8, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Computational biology
Epidemiology
Artificial Intelligence

Background:

The COVID-19 pandemic has caused significant socio-economic disruption and mortality.
Rapid spread and lack of a definitive vaccine necessitate accurate prediction models.
Existing prediction methods may require improvement for accuracy and efficiency.

Purpose of the Study:

To develop and evaluate a machine learning scheme for predicting COVID-19 positive, negative, and deceased instances.
To enhance prediction accuracy using feature selection and a robust classification model.
To assess the model's performance against established metrics using real-world data.

Main Methods:

Data preprocessing involved removing redundant and missing values.
Feature selection was performed using the hybrid grey assisted whale optimization algorithm (H-GAWOA).
The adaptive network-based fuzzy inference system (ANFIS) classifier was employed for outcome prediction.

Main Results:

The proposed H-GAWOA-ANFIS model demonstrated superior performance with very low Mean Squared Error (MSE) values across all predictions.
Specifically, the model achieved an MSE of 0.00 for predicting deceased COVID-19 cases.
Performance metrics including MSE, Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared were analyzed.

Conclusions:

The H-GAWOA-ANFIS model significantly outperforms other approaches in predicting COVID-19 outcomes.
The proposed machine learning scheme offers a promising tool for epidemiological analysis and public health management.
Accurate prediction of COVID-19 cases, including mortality, can aid in resource allocation and intervention strategies.