Rough sets and their variants
Mahmut Dirik
Abstract
Fire is a natural disaster that poses a profound existential threat to humanity. It has traditionally been fought with conventional methods, which, unfortunately, are often fraught with limitations and potential environmental damage. Given these limitations, there is an urgent need for research into ...
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Fire is a natural disaster that poses a profound existential threat to humanity. It has traditionally been fought with conventional methods, which, unfortunately, are often fraught with limitations and potential environmental damage. Given these limitations, there is an urgent need for research into novel firefighting methods. Sound wave-based firefighting systems, an emerging solution, show promising potential in this regard.The current study uses an extensive data set derived from numerous experimental trials of sound-wave-based firefighting. Based on this extensive dataset, we have developed a sound wave technology-based fire suppression model that includes five different fuzzy logic methods: Fuzzy Rough Set (FRS), Fuzzy K-Nearest Neighbors (FNN), Fuzzy Ownership K-Nearest Neighbors (FONN), Fuzzy-Rough K-Nearest Neighbors (FRNN), and Vaguely Quantified K-Nearest Neighbors (VQNN).The main objective of these models is to accurately distinguish between the extinguished and non-extinguished states of a flame. This classification is based on a number of intrinsic model parameters, such as the type of fuel, the size of the flame, the decibel level, the frequency, the airflow, and the distance.To evaluate the classification effectiveness of the models, a number of statistical methods were used, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Kappa Statistics (KP), and Mean Square Error (MSE).Our analysis yielded promising results, with the models FRS, FNN, FONN, FRNN, and VQNN achieving classification accuracies of 93.12%, 96.66%, 95.56%, 96.35%, and 96.89%, respectively. These results confirm the high accuracy of the proposed model in classifying fire data and underline its practical applicability.
