FireDS-IoT: A Fire Detection System for Smart Home Based on IoT Data Analytics

2018 ◽  
Author(s):  
Sourav Kumar Bhoi ◽  
Sanjaya Kumar Panda ◽  
Biranchi Narayan Padhi ◽  
Manash Kumar Swain ◽  
Balabhadrah Hembram ◽  
...  
Keyword(s):  
Data Analytics ◽  
Smart Home ◽  
Detection System ◽  
Fire Detection ◽  
Home Based ◽  
Fire Detection System

scholarly journals Implementasi Smart Home Pada Pendeteksi Dini Kebakaran Menggunakan Forward Chaining

2020 ◽  
Vol 4 (02) ◽  
pp. 63-71
Author(s):  
Irawan Dwi Wahyono ◽  
Mochammad Bagus Priyantono
Keyword(s):  
Artificial Intelligence ◽  
Smart Home ◽  
Detection System ◽  
Fire Detection ◽  
Forward Chaining ◽  
Sensor Devices ◽  
Fire Detection System

Fire is a disaster that can occur due to human negligence. So we need a system that functions to minimize the occurrence of fires by having a working concept to detect fires. This study aims to develop a fire detection system using the forward chaining method. In this detection system applying Artificial Intelligence where there are parameters of temperature, gas, the presence of fire, and the presence of water. This system also applies the Smart Home concept to detect fires early where there are sensor devices used by DHT 11, FLAME and MQ2. the data obtained from the sensor will be processed by the NodemCU Esp-8266 microcontroller. If there is an indication that caused a fire, the system immediately sends a warning via telegram. The results of this study obtained a precision of .94%, recall 93.6% and an accuracy of 96%.

scholarly journals Fire Detection System Using Machine Learning

2021 ◽  
Vol 1916 (1) ◽  
pp. 012209
Author(s):  
A Arul ◽  
R S Hari Prakaash ◽  
R Gokul Raja ◽  
V Nandhalal ◽  
N Sathish Kumar
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Fire Detection ◽  
Fire Detection System

scholarly journals A TBB-CUDA Implementation for Background Removal in a Video-Based Fire Detection System

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Fan Wang ◽  
Xiao Jiang ◽  
Xiao Peng Hu
Keyword(s):  
Data Transfer ◽  
Detection System ◽  
Gaussian Model ◽  
Fire Detection ◽  
Distribution Model ◽  
Memory Bandwidth ◽  
Limited Memory ◽  
Background Removal ◽  
Fire Detection System ◽  
Gaussian Distribution Model

This paper presents a parallel TBB-CUDA implementation for the acceleration of single-Gaussian distribution model, which is effective for background removal in the video-based fire detection system. In this framework, TBB mainly deals with initializing work of the estimated Gaussian model running on CPU, and CUDA performs background removal and adaption of the model running on GPU. This implementation can exploit the combined computation power of TBB-CUDA, which can be applied to the real-time environment. Over 220 video sequences are utilized in the experiments. The experimental results illustrate that TBB+CUDA can achieve a higher speedup than both TBB and CUDA. The proposed framework can effectively overcome the disadvantages of limited memory bandwidth and few execution units of CPU, and it reduces data transfer latency and memory latency between CPU and GPU.

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