International Journal For Multidisciplinary Research

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Pattern Recognition in Embedded Systems for Event Occurrences

Author(s) Abhishek Deepak Urunkar, Aarti Hemant Tirmare
Country India
Abstract PR (pattern recognition) typically includes interaction with humans and other complicated processes in the real world, embedded systems are ideal candidates. A typical PR application often considered the more perceptual branch of AI, responds to external events that the system detects through physical sensors or input devices by activating actuators or displaying relevant information. To explore the embedded recognition system and apply the deep learning algorithm to face detection, the deep learning-based Convolutional Neural Network (CNN) suggests two deep face detection methods. These are presented to use the deep learning algorithm. This was done to make it possible for us to use the deep learning algorithm for face detection. Because of this, to analyze the built-in face recognition system and applied the deep learning algorithm to the process of identifying faces. In addition, to do both things simultaneously. OMTCNN's training accuracy is 85.14%, higher than the unimproved algorithm. Accuracy of the recognition and calculation acceleration modules boosts embedded system face detection and identification performance. Embedded deep learning recognition is helpful.
Keywords Embedded system, deep learning, convolutional neural networks, pattern recognition, event occurrence management network
Field Engineering
Published In Volume 5, Issue 6, November-December 2023
Published On 2023-11-12
Cite This Pattern Recognition in Embedded Systems for Event Occurrences - Abhishek Deepak Urunkar, Aarti Hemant Tirmare - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.8620
DOI https://doi.org/10.36948/ijfmr.2023.v05i06.8620
Short DOI https://doi.org/gs4xtb
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