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              <text>Real-Time Fire Detection Through the Analysis of Surveillance Videos</text>
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              <text>Email alerts; Forest fire detection; Real-time; Speaker alarm; YOLOv5</text>
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              <text>The Forest Fire Detection System is an intelligent system that can detect forest fires and alert authorities in real-time. It uses a YOLOv5 deep learning algorithm to process live video feeds captured by a web camera which is trained with the sizable dataset of inputs to locate the fire accurately, making it an ideal choice for real-time fire detection in the forest. Upon detecting a fire, the system sends an email alert to a designated email address, containing a picture of the fire and location information. The email alert system is built using the standard SMTP protocol, which ensures that the message is delivered to the recipient in a timely and reliable manner. The system is also equipped with a speaker that triggers an alarm upon detecting a fire. The alarm is designed to alert people in the vicinity of the fire so that they can take the necessary action. It is activated using the Pygame library, a collection of Python modules specifically crafted for game development across multiple platforms. Overall, the Forest Fire Detection System is a fast, efficient, and accurate system that can help prevent the spread of forest fires. It is an intelligent system that can detect fires quickly and send alerts to authorities, giving them the information they need to take the necessary action to control the fire. The system is built using a web camera, a computer, and a speaker, making it easy to install and maintain.   2024 IEEE.</text>
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              <text>Vigneshwaran T.; Velammal B.L.</text>
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              <text>TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1109/TQCEBT59414.2024.10545050" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/TQCEBT59414.2024.10545050&lt;/a&gt;
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              <text>ISBN: 979-835038427-7</text>
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              <text>Vigneshwaran T., Christ University, School of Engineering and Technology, Department of Cse, Bengaluru, India; Velammal B.L., Anna University, College of Engineering Guindy, Department of Cse, Chennai, India</text>
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