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Related Concept Videos

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
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Updated: May 10, 2025

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Trust-driven approach to enhance early forest fire detection using machine learning.

Tayyab Khan1, Karan Singh2, Bhoopesh Singh Bhati1

  • 1Indian Institute of Information Technology Sonepat, Khewra, Haryana, India.

Scientific Reports
|April 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Universal Trust Model (UTM) for early forest fire detection (FFD) using wireless sensor networks and machine learning. The system enhances reliability and reduces detection time for effective forest fire prevention.

Keywords:
Forest fire detectionMachine learningSensorTrustWSN

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Area of Science:

  • Environmental Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Forest fires present significant threats to ecosystems and human communities.
  • Early detection is crucial for mitigating adverse environmental and climatic impacts.
  • Existing detection systems require enhancement in reliability and speed.

Purpose of the Study:

  • To develop a real-time Universal Trust Model (UTM) for early forest fire detection (FFD).
  • To improve the reliability and reduce the detection time of forest fire identification systems.
  • To integrate intelligent wireless sensor networks (WSN) with machine learning for robust fire detection.

Main Methods:

  • Implemented an intelligent WSN with clustered sensor nodes for extensive forest coverage.
  • Developed a UTM calculating trust ratings based on communication, energy, and data factors for sensor nodes.
  • Utilized a machine learning regression model analyzing temperature, humidity, and CO2 for enhanced detection precision.

Main Results:

  • The proposed UTM system demonstrated a high data processing rate.
  • Achieved a reduced time delay in fire detection compared to existing systems.
  • Experimental validation with 7200 samples confirmed the system's efficacy in early-stage forest fire detection.

Conclusions:

  • The UTM system offers a robust and accurate solution for early forest fire detection.
  • Combining trust mechanisms with machine learning significantly advances fire detection capabilities.
  • The system is a promising solution for prompt forest fire detection and prevention, especially under challenging conditions.