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Related Experiment Video

Updated: Jan 19, 2026

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Sensor-based occupancy detection using neutrosophic features fusion.

N S Fayed1, Mervat Abu-Elkheir1, E M El-Daydamony1

  • 1Department of Information Technology, Faculty of Computers and Information, Mansoura University, Egypt.

Heliyon
|September 19, 2019
PubMed
Summary

This study introduces a novel method for occupancy detection using fused data from heterogeneous sensors. The approach enhances accuracy by handling uncertain data with neutrosophic sets, improving energy savings and security.

Keywords:
Computer scienceFuzzy geneticHeterogeneous sensorsLDANeutrosophic setsOccupancy detectionRandom forestSensors data correlationsSensors data fusionWireless sensor networks

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Sensor Networks

Background:

  • Occupancy detection offers benefits like energy savings and enhanced security.
  • Sensor data often suffers from uncertainty and unreliability.
  • Existing methods may struggle with data imperfections.

Purpose of the Study:

  • To develop a robust binary occupancy detection method.
  • To address uncertainty and unreliability in sensor data.
  • To improve the accuracy of occupancy detection systems.

Main Methods:

  • A novel heterogeneous sensor data fusion method is proposed.
  • Neutrosophic sets are utilized to manage uncertain sensor data.
  • Sensor data correlations are employed to enhance reliability.

Main Results:

  • The proposed method demonstrates enhanced detection accuracy.
  • Experiments were conducted using Random Forest, LDA, and FUGE algorithms.
  • Fusion of heterogeneous sensor data improved system performance.

Conclusions:

  • The neutrosophic set-based data fusion method effectively handles sensor data uncertainty.
  • This approach significantly enhances the accuracy of binary occupancy detection.
  • The method provides a reliable solution for energy saving and security monitoring.