We present a four-pronged approach to build firefighter’s situationalawareness for the first time in the literature . We construct a series of deeplearning frameworks built on top of one another to enhance the safety, efficiency, and successful completion of rescue missions conducted by firefighters in emergency first response settings . We built a deep Q-learning-based agent,immune to stress-induced disorientation and anxiety, capable of making clearnavigation decisions based on the observed and stored facts in live-fire environments . To bring the designed system into usage by firefighters, we designed a physical structure where the processed results are used as inputs in the creation of an augmented reality . We used a low computational unsupervised learningtechnique called tensor decomposition to perform feature extraction to perform meaningful feature extraction in real-time. Finally, we used a high-performance tool called Tensor decompose to perform significant feature detection in real time. To help first responders to get back to safety, we also designed a virtual path planning feature that acts as a virtual guide to assist disoriented first responders in getting back to the rescue operation at hand . To help firefighters,we designed a real-to-be-trained system

Author(s) : Manish Bhattarai

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Keywords : designed - feature - real - time - firefighters -

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