Analysis of cost-effective sensors: Data Fusion approach used for Forest Fire Application

Verma, Neetu; Singh, Dinesh

doi:10.1016/j.matpr.2020.03.756

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…The authors [22] investigated fire sensor information fusion using a backpropagation neural network to ensure firefighter safety. Robots were used in the study to identify the remaining fires, lowering labor costs.…”

Section: Related Workmentioning

confidence: 99%

Multisensor Information Fusion for Condition Based Environment Monitoring

Reyana¹,

Vijayalakshmi²

2023

Intelligent Automation &Amp; Soft Computing

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Destructive wildfires are becoming an annual event, similar to climate change, resulting in catastrophes that wreak havoc on both humans and the environment. The result, however, is disastrous, causing irreversible damage to the ecosystem. The location of the incident and the hotspot can sometimes have an impact on early fire detection systems. With the advancement of intelligent sensor-based control technologies, the multi-sensor data fusion technique integrates data from multiple sensor nodes. The primary objective to avoid wildfire is to identify the exact location of wildfire occurrence, allowing fire units to respond as soon as possible. Thus to predict the occurrence of fire in forests, a fast and effective intelligent control system is proposed. The proposed algorithm with decision tree classification determines whether fire detection parameters are in the acceptable range and further utilizes a fuzzy-based optimization to optimize the complex environment. The experimental results of the proposed model have a detection rate of 98.3. Thus, providing real-time monitoring of certain environmental variables for continuous situational awareness and instant responsiveness.

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Section: Related Workmentioning

confidence: 99%

Multisensor Information Fusion for Condition Based Environment Monitoring

Reyana¹,

Vijayalakshmi²

2023

Intelligent Automation &Amp; Soft Computing

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“…These categories make multi-sensor information fusion more reliable and informative [56,57]. Such fusion has been widely used in the field of transportation [58][59][60][61][62][63][64][65][66][67], materials science [68][69][70][71][72][73][74][75][76][77], automation control systems [78,79], medicine [80][81][82][83][84][85], and agriculture [86][87][88][89][90][91][92][93][94][95]. The fusion strategy aims to create images with almost negligible distortion by maintaining the quality of the image in all aspects when compared with the original or source images [96,97].…”

Section: Introductionmentioning

confidence: 99%

Broad Learning System for Tackling Emerging Challenges in Face Recognition

Zhang¹,

Wang²

2023

Computer Modeling in Engineering &Amp; Sciences

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Face recognition has been rapidly developed and widely used. However, there is still considerable uncertainty in the computational intelligence based on human-centric visual understanding. Emerging challenges for face recognition are resulted from information loss. This study aims to tackle these challenges with a broad learning system (BLS). We integrated two models, IR 3 C with BLS and IR 3 C with a triplet loss, to control the learning process. In our experiments, we used different strategies to generate more challenging datasets and analyzed the competitiveness, sensitivity, and practicability of the proposed two models. In the model of IR 3 C with BLS, the recognition rates for the four challenging strategies are all 100%. In the model of IR 3 C with a triplet loss, the recognition rates are 94.61%, 94.61%, 96.95%, 96.23%, respectively. The experiment results indicate that the proposed two models can achieve a good performance in tackling the considered information loss challenges from face recognition.

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“…However, to use a WSN to detect a re in its early stages, existing environmental factors such as temperature, relative humidity, wind speed, gas detection, and so on must be known [22]. The location of the incident and the hotspot can sometimes have an impact on early re detection systems [13].…”

Section: Introductionmentioning

confidence: 99%

Multisensor Information Fusion and Optimization Technique for Wildfire Detection and Prevention in WSN Environment

Reyana

Vijayalakshmi

2022

Preprint

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Monitoring natural traits like temperature and humidity are imperative for evaluating climate change. Remote sensing applications in wireless sensor networks face a crucial issue in transmitting the information from sensors in a continuous period to the gateway nodes for processing. Correspondingly, sensor nodes are subject to low memory and processing limitations in concern to wireless sensor networks. Wildfires can be started intentionally or unintentionally. The result, however, is disastrous, causing irreversible damage to the ecosystem. With the advancement of intelligent sensor-based control technologies, data fusion and classification have become an essential component of the environmental monitoring system. To predict the occurrence of fire in forests, a fast and effective intelligent control system using Adaptive Decentralized Kalman Filter is proposed. The Decision tree classification can successfully solve problems on the side of the sink node. Thus the described Decision tree classification determines whether fire detection parameters are in the acceptable range and further utilizes a fuzzy-based optimization to improve the performance of the considered complex fusion environment. The experimental results of the proposed model have a detection rate of 98.3 %, demonstrating that the proposed technique is robust. Thus, providing real-time monitoring of certain environmental variables for continuous situational awareness and instant responsiveness.

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Analysis of cost-effective sensors: Data Fusion approach used for Forest Fire Application

Cited by 9 publications

References 11 publications

Multisensor Information Fusion for Condition Based Environment Monitoring

Multisensor Information Fusion for Condition Based Environment Monitoring

Broad Learning System for Tackling Emerging Challenges in Face Recognition

Multisensor Information Fusion and Optimization Technique for Wildfire Detection and Prevention in WSN Environment

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