Deep learning optimisation algorithms for snatch theft detection / Nurul Farhana Mohamad Zamri ...[et al.]

• Main Authors: Mohamad Zamri, Nurul Farhana (Author), Md Tahir, Nooritawati (Author), Megat Ali, Megat Syahirul Amin (Author), Khirul Ashar, Nur Dalila (Author)
• Format: Book
• Published: UiTM Press, 2022-04.
• Subjects: Article; PeerReviewed
• Online Access: Link Metadata
• Notes: https://ir.uitm.edu.my/id/eprint/63168/1/63168.pdf; Deep learning optimisation algorithms for snatch theft detection / Nurul Farhana Mohamad Zamri ...[et al.]. (2022) Journal of Electrical and Electronic Systems Research (JEESR) <https://ir.uitm.edu.my/view/publication/Journal_of_Electrical_and_Electronic_Systems_Research_=28JEESR=29/>, 20: 5. pp. 34-40. ISSN 1985-5389
• Keywords: Neural networks (Computer science); Detectors. Sensors. Sensor networks; Applications of electronics

Learning algorithms related to deep learning use bells and whistles, called hyperparameters. Hence, this study conducted numerical analysis, specifically backpropagation gradients and gradient-based optimization for snatch-theft detection. Here, snatch theft images and augmented images were used to perform the experimental study to determine the optimum hyperparameter values. Next, the value of epoch and learning rate was obtained after careful analysis based on each training option. Results achieved showed that epoch value of 20 and learning rate corresponding to 0.0001 was the optimum values. Findings from this study can be used as a practical guide in determining the importance of the most optimum hyperparameters.