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Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things.

Ali M Al Shahrani1, Madani Abdu Alomar2, Khaled N Alqahtani3

  • 1Faculty of Computer Studies, Arab Open University, Riyadh 11681, Saudi Arabia.

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|January 8, 2023
PubMed
Summary
This summary is machine-generated.

A new artificial neural network algorithm enhances industrial automation by improving control and monitoring. This ESSANN approach significantly boosts performance metrics like accuracy and network bandwidth while reducing operational costs.

Keywords:
Internet of Things (IoT)elaborative stepwise stacked artificial neural networks (ESSANN) algorithmindustrial automationindustrial environmentleast absolute shrinkage and selection operator (LASSO)machine learningprincipal component analysis (PCA)robotics

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Area of Science:

  • Industrial Automation
  • Machine Learning
  • Robotics

Background:

  • Industrial automation leverages robotics and software for operational efficiency.
  • Integration of IoT and machine learning enhances smart features in industrial processes.
  • Organizations are increasingly adopting automation to mitigate risks and inefficiencies of traditional methods.

Purpose of the Study:

  • To develop an advanced algorithm for improved control and monitoring in industrial automation.
  • To enhance the performance of industrial automation systems using artificial neural networks.

Main Methods:

  • An elaborative stepwise stacked artificial neural network (ESSANN) algorithm was developed.
  • Data preprocessing and feature extraction using Principal Component Analysis (PCA).
  • Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO).

Main Results:

  • The ESSANN algorithm demonstrated significant improvements across key performance indicators.
  • Achieved high accuracy (98%), precision (98.95%), and recall (95.02%).
  • Showcased enhanced network bandwidth (97%), scalability (96%), and reduced delay (52%) and packet loss (53%).

Conclusions:

  • The proposed ESSANN algorithm effectively improves industrial automation control and monitoring.
  • The system offers substantial gains in efficiency, accuracy, and cost-effectiveness compared to traditional methods.
  • The ESSANN approach represents a significant advancement in smart industrial automation.