@Article{cmc.2023.036435,
AUTHOR = {Abdul Rehman, Dongsun Kim, Anand Paul},
TITLE = {Convolutional Neural Network Model for Fire Detection in Real-Time Environment},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {77},
YEAR = {2023},
NUMBER = {2},
PAGES = {2289--2307},
URL = {http://www.techscience.com/cmc/v77n2/54786},
ISSN = {1546-2226},
ABSTRACT = {Disasters such as conflagration, toxic smoke, harmful gas or chemical leakage, and many other catastrophes in the
industrial environment caused by hazardous distance from the peril are frequent. The calamities are causing massive
fiscal and human life casualties. However, Wireless Sensors Network-based adroit monitoring and early warning
of these dangerous incidents will hamper fiscal and social fiasco. The authors have proposed an early fire detection
system uses machine and/or deep learning algorithms. The article presents an Intelligent Industrial Monitoring
System (IIMS) and introduces an Industrial Smart Social Agent (ISSA) in the Industrial SIoT (ISIoT) paradigm.
The proffered ISSA empowers smart surveillance objects to communicate autonomously with other devices. Every
Industrial IoT (IIoT) entity gets authorization from the ISSA to interact and work together to improve surveillance
in any industrial context. The ISSA uses machine and deep learning algorithms for fire-related incident detection
in the industrial environment. The authors have modeled a Convolutional Neural Network (CNN) and compared
it with the four existing models named, FireNet, Deep FireNet, Deep FireNet V2, and Efficient Net for identifying
the fire. To train our model, we used fire images and smoke sensor datasets. The image dataset contains fire, smoke,
and no fire images. For evaluation, the proposed and existing models have been tested on the same. According to
the comparative analysis, our CNN model outperforms other state-of-the-art models significantly.},
DOI = {10.32604/cmc.2023.036435}
}