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Smart city article details
| Title | Integrating Transfer Learning With Neutrosophic Weighted Extreme Learning Machine For Violence Detection In Smart Cities |
|---|---|
| ID_Doc | 32082 |
| Authors | Khaytboeva N.; Bakhvalov S.; Denisovich V.; Zakieva R. |
| Year | 2025 |
| Published | International Journal of Neutrosophic Science, 25, 1 |
| DOI | http://dx.doi.org/10.54216/IJNS.250136 |
| Abstract | Neutrosophic logic extends conventional and fuzzy logic (FL) by integrating the concepts of indeterminacy, truth, and falsity, enabling for a further extensive management of uncertainty. In classical binary logic, a statement can be either true or false. FL extends this by adding degree of truth, where a statement is partially true or false. The smart city technology shown to be an effective solution to the problems regarding improved urbanization. The practical applications of a smart city technology to video surveillance relies on the ability of processing and gathering large quantities of live urban data. Violence detection is considered as a major challenge in smart city monitoring. The required computational power is substantial due to the large volume of video data gathered from the extensive camera network. As a result, the algorithm based on handcrafted features utilizing video and image processing fails to provide a promising solution. Deep Learning (DL) and Deep Neural Networks (DNNs) models are more reliable to handle these data. In this study, we introduce a Transfer Learning with Neutrosophic Weighted Extreme Learning Machine for Violence Detection (TL-NWELMVD) technique in smart cities. The TL-NWELMVD technique aims to recognize the presence of the violence in the smart city environment. In the TL-NWELMVD technique, the features can be extracted using SE-RegNet model. To enhance the performance of the TL-NWELMVD technique, a hyperparameter optimizer using monarch butterfly optimization (MBO) is involved. Finally, the NWELM classifier is applied for the identification of violence in the smart city environment. To investigate the accomplishment of the TL-NWELMVD technique, a widespread investigational outcome is involved. The simulation results portrayed that the TL-NWELMVD technique gains better performance compared to other models. © 2025, American Scientific Publishing Group (ASPG). All rights reserved. |
| Author Keywords | Fuzzy Logic; Membership Function; Monarch Butterfly Optimization; Neutrosophic Set; Transfer Learning; Violence Detection |
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