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首页> 外文期刊>International Journal of Advanced Networking and Applications >Fire Detection Using Support Vector Machine in Wireless Sensor Network and Rescue Using Pervasive Devices
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Fire Detection Using Support Vector Machine in Wireless Sensor Network and Rescue Using Pervasive Devices

机译：在无线传感器网络中使用支持向量机进行火灾探测并使用普适设备进行救援

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In the recent days, environment is polluted day by day due to various factors. One of the causes is smokes during themassive fires. Using Wireless Sensor Networks (WSN) fire can be detected earlier and also initiate the rescue operationbefore it becomes fire. In this paper, we will examine the possibility Support Vector Machine (SVM) for detecting theFire which has large data sets. It is achieved by using the sketch of classes distribution which is obtained by usingMinimum Enclosing Ball(MEB). This approach has many distinctive advantages on dealing with large data setsparticularly forest fire data sets. Also, the Support Vector Machine has gained profound interest among the researchersbecause of its accuracy and the same is extremely important in this Forest Fire context as the cost of misclassificationusing a classifier is very high. Hence, this approach using multi class Support Vector Machine shows a higher accuracy indetecting the Forest Fire. The experimental result also shows a better accuracy in predicting the Forest Fire. Further, therescue process will be initiated through the pervasive devices which are placed around the fire sensational area. Thisprocess will suppress the fire sensation and protect the field from the fire. We are including an architectural levelprocedure for implementing the rescue process.
机译：近年来，由于各种因素，环境每天都在被污染。原因之一是在大火中冒烟。使用无线传感器网络（WSN）可以更早地检测到火灾，并在火灾发生之前启动救援操作。在本文中，我们将研究支持向量机（SVM）用于检测具有大数据集的火灾的可能性。它是通过使用类的分布图来实现的，该类的分布图是通过使用最小封闭球（MEB）获得的。这种方法在处理大型数据集（尤其是森林火灾数据集）方面具有许多独特的优势。同样，支持向量机由于其准确性而在研究人员中引起了浓厚的兴趣，在森林火灾的背景下，由于使用分类器进行错误分类的成本非常高，因此支持向量机非常重要。因此，这种使用多类支持向量机的方法在检测森林大火方面显示出更高的准确性。实验结果还显示出森林火灾预测的准确性更高。此外，救援过程将通过放置在火灾引起轰动区域周围的渗透性装置来启动。此过程将抑制火势并保护火场。我们包括用于实施救援过程的体系结构级过程。

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