JETI Admin2
Abstract
Globally and at varying frequencies, a multitude of natural catastrophes have occurred, including earthquakes, fire accidents, wildfires, floods, tsunamis, and volcanic activity. These events have caused buildings and other man-made infrastructure to collapse. Finding and locating victims in an emergency is one of the most difficult tasks, particularly when the victims are buried under debris. It is imperative that disaster relief techniques evolve in the modern day. Even though advances have been made in vital sign, image and signal processing, and machine learning-based disaster relief techniques, more work has to be done, particularly in regions like Africa where building collapses are frequent occurrences. This work uses a non-line-of-sight human detection signal dataset to improve classification and localization following building collapses. Cyclic. This work uses a non-line-of-sight human detection signal dataset to improve classification and localization following building collapses. After 23,552 instances have been examined, dimensionality reduction is achieved using recursive feature elimination. The reduced dataset's support vector machine classification produces an amazing 82.76% accuracy rate. Comparative evaluations with cutting-edge methods show how successful the proposed approach is; these methods improve the theoretical foundations and practical applications of search and rescue operations. With regard to practical methods for victim prediction, identification, and localization following structural collapses, the findings have significant implications for search and rescue teams.
References
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