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Related Experiment Video

Updated: Sep 29, 2025

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

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Coronavirus herd immunity optimizer to solve classification problems.

Mohammed Alweshah¹

¹Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan.

|March 21, 2022

Summary

This summary is machine-generated.

The coronavirus herd immunity optimizer (CHIO) algorithm enhances probabilistic neural network (PNN) classification accuracy. This novel CHIO-PNN approach achieved 90.3% accuracy on benchmark datasets, outperforming existing methods with faster convergence.

Keywords:

Classification problem Coronavirus herd immunity optimizer Data mining Metaheuristics Probabilistic neural network

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Area of Science:

Data Mining
Machine Learning
Computational Intelligence

Background:

Classification is a key data mining task for predicting categorical variables.
Probabilistic Neural Networks (PNN) are used for classification but can be computationally intensive.
Optimizing PNN performance is crucial for improving classification accuracy and efficiency.

Purpose of the Study:

To enhance the classification efficiency of Probabilistic Neural Networks (PNN) using the Coronavirus Herd Immunity Optimizer (CHIO) algorithm.
To evaluate the performance of the proposed CHIO-PNN approach on various benchmark datasets.
To compare the CHIO-PNN method against existing classification algorithms.

Main Methods:

The study integrates the CHIO algorithm to refine PNN weights, optimizing the search space for better solutions.
The PNN generates an initial solution, which is then improved by the CHIO algorithm.
The CHIO-PNN model was tested on 11 benchmark datasets.

Main Results:

The CHIO-PNN approach achieved a high overall classification rate of 90.3% across all tested datasets.
The proposed method demonstrated a faster convergence speed compared to other algorithms.
CHIO-PNN outperformed the standard PNN, Firefly Algorithm, African Buffalo Algorithm, and β-hill climbing.

Conclusions:

The CHIO-PNN hybrid model significantly improves classification accuracy and efficiency.
The CHIO algorithm effectively optimizes PNN performance for complex classification tasks.
This approach offers a promising advancement in data mining and machine learning classification techniques.