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Updated: May 28, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Control chart pattern recognition using K-MICA clustering and neural networks.

Ataollah Ebrahimzadeh¹, Jalil Addeh, Zahra Rahmani

¹Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran. ataebrahim@yahoo.com

ISA Transactions

|November 1, 2011

Summary

This summary is machine-generated.

This study introduces a hybrid intelligent method (HIM) for accurate automatic recognition of control chart patterns (CCPs) in manufacturing. The novel approach achieves nearly 99.65% accuracy in identifying abnormal patterns.

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Area of Science:

Industrial Engineering
Artificial Intelligence
Statistical Process Control

Background:

Manufacturing processes increasingly require automated detection of abnormal control chart patterns (CCPs).
Existing methods may lack the accuracy and efficiency needed for real-time industrial applications.

Purpose of the Study:

To develop and evaluate a novel hybrid intelligent method (HIM) for the automatic recognition of common control chart patterns (CCPs).
To identify the most effective neural network classifier for CCP recognition within the proposed HIM.

Main Methods:

A hybrid intelligent method (HIM) combining a modified imperialist competitive algorithm (MICA) with K-means clustering for data preprocessing.
A classifier module employing various neural networks (MLP, PNN, RBFN) to determine pattern membership based on Euclidean distance from clusters.
Experimental evaluation to select the optimal neural network for CCP recognition.

Main Results:

The proposed HIM achieved a high recognition accuracy of approximately 99.65% for control chart patterns.
The study successfully identified the most effective neural network architecture for the classification task within the HIM.
The hybrid approach demonstrated superior performance in distinguishing between normal and various abnormal CCPs.

Conclusions:

The novel hybrid intelligent method (HIM) offers a highly accurate and effective solution for automatic control chart pattern recognition.
The integration of MICA-K-means clustering and advanced neural networks provides a robust framework for statistical process control.
This method has significant potential for enhancing quality control and reducing defects in manufacturing processes.