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The Development of Symbolic Expressions for Fire Detection with Symbolic Classifier Using Sensor Fusion Data.

Nikola Anđelić1, Sandi Baressi Šegota1, Ivan Lorencin1

  • 1Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel artificial intelligence (AI) system using genetic programming symbolic classifier (GPSC) to enhance fire detection accuracy. The AI model, trained on balanced datasets, achieved superior performance in identifying fire alarms.

Keywords:
fire-alarmgenetic programmingoversampling methodssymbolic classifierundersampling methods

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Fire Safety Engineering

Background:

  • Traditional fire detection systems rely on sensing smoke, heat, or flame, with limitations in detecting fires early or in complex environments.
  • Integrating diverse sensor data (temperature, pressure, humidity) requires advanced processing for comprehensive fire detection.
  • Existing fire detection methods can be improved by incorporating artificial intelligence (AI) for higher accuracy and reliability.

Purpose of the Study:

  • To develop a simple AI system capable of high-accuracy fire detection using fused sensor data.
  • To create a symbolic expression-based fire detection model using genetic programming symbolic classifier (GPSC).
  • To evaluate the effectiveness of various data balancing techniques on an imbalanced fire detection dataset.

Main Methods:

  • Implemented a genetic programming symbolic classifier (GPSC) algorithm to generate symbolic expressions for fire detection.
  • Applied data balancing techniques including random undersampling/oversampling, Near Miss-1, ADASYN, SMOTE, and Borderline SMOTE to address dataset imbalance.
  • Utilized random hyperparameter search and 5-fold cross-validation to optimize the GPSC model and evaluate its performance using accuracy, AUC, precision, recall, and F1-score.

Main Results:

  • The SMOTE (Synthetic Minority Over-sampling Technique) method yielded the best performance among the tested balancing techniques.
  • The optimized AI model achieved excellent classification metrics: Accuracy (ACC) of 0.998±4.79×10-5, AUC of 0.998±4.79×10-5, Precision of 0.999±5.32×10-5, Recall of 0.998±4.26×10-5, and F1-score of 0.998±4.796×10-5.
  • A specific symbolic expression demonstrating high classification accuracy was identified and validated on the original dataset.

Conclusions:

  • A simple AI system based on GPSC can significantly enhance fire detection accuracy, serving as a valuable supplementary tool.
  • Data balancing techniques, particularly SMOTE, are crucial for improving the performance of AI models on imbalanced fire detection datasets.
  • The developed symbolic expression-based model offers a interpretable and highly accurate approach to fire detection.