Soft Computation Technique based Fire Evacuation System

Santhosh, K. V.; Mohanty, Preeti

doi:10.17485/ijst/2015/v8i24/79989

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…In these transformation domains, the data are very well-segregated and show well-organized clusters [10][11][12]21]. Furthermore, K-Nearest Neighbour (KNN), Naïve Bayes (NB), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Decision Tree (DT), and Support Vector Machine (SVM) analyses were used with different kernels and multilayer perceptron (MLP)-based classifiers for achieving superior classification performance over the considered dataset of sixteen types of fire smoke [13,[22][23][24]. The MLP-based classifier, trained using 2320 training data samples in the SPCA transformed analysis space domain, outperformed all the other transformation spaces considered and achieved 'all correct' classification accuracy of the 80 test samples belonging to the six classes of fire.…”

Section: Refmentioning

confidence: 99%

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ID2S4FH: A Novel Framework of Intelligent Decision Support System for Fire Hazards

Kumar

Rajput

Shvetsov

et al. 2023

Fire

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Modern societies and industrial sectors are serviced through storage and distribution centres (SDCs) such as supermarkets, malls, warehouses, etc. Large quantities of supplies are stocked here, e.g., food grains, clothes, shoes, pharmaceuticals, electronics, plastics, edible oils, electrical wires/equipment, petroleum products, painting materials, etc. Fires due to the burning of these materials are categorized into six classes, viz., Class A, Class B, Class C, Class D, Class K, and Class F. A fire is extinguished better when the right type of fire retardant is used. A thumb rule on firefighting also says, “never fight a fire if you do not know what is burning”. In this paper, we have proposed an Intelligent Decision Support System (ID2S4FH) to generate a real-time ‘fire-map’ of such SDCs during a fire hazard. We have interfaced six tin-oxide-based gas sensor elements, a temperature and humidity sensor, and a particulate matter (PM) sensor with microcontrollers to capture the real-time signature patterns of the ambient air. We burned sixteen different types of materials belonging to six classes of fire and created a dataset consisting of 2400 samples. The sensor array responses were then pre-processed and analysed using various classifiers trained in different analysis space domains. Among the classifiers, four classifiers achieved ‘all correct’ identification of the fire classes of 80 unknown test samples, and the lowest mean squared error (MSE) achieved was 2.81 × 10−3. During a fire hazard, our proposed ID2S4FH can generate real-time fire maps of SDCs and help firefighters to extinguish the fire using the appropriate fire retardant.

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Section: Refmentioning

confidence: 99%

“…Furthermore, we used many popular classifiers such as KNN, NB, LR, DT, SVM, and MLP. Further details of these classifiers can be found in the literature [9,13,[21][22][23][24]. Among these popular classifiers, the MLP-based classifier outperforms the other types of classifiers.…”

Section: Contextual Background Of Data Pre-processing and Classifiersmentioning

confidence: 99%