KFU. Card publication. Integrating Transfer Learning with Neutrosophic Weighted Extreme Learning Machine for Violence Detection in Smart Cities

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INTEGRATING TRANSFER LEARNING WITH NEUTROSOPHIC WEIGHTED EXTREME LEARNING MACHINE FOR VIOLENCE DETECTION IN SMART CITIES

Form of presentation

Articles in international journals and collections

Year of publication

2025

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Authors, employees of KFU

Bakhvalov Sergey Yurevich, author

Bibliographic description in the original language

Khaytboeva N, Bakhvalov S, Denisovich V, Integrating Transfer Learning with Neutrosophic Weighted Extreme Learning Machine for Violence Detection in Smart Cities//International Journal of Neutrosophic Science. - 2025. - Vol.25, Is.1. - P.405-417.

Annotation

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.

Keywords

Violence Detection , Transfer Learning , Monarch Butterfly Optimization , Membership Function , Neutrosophic Set , Fuzzy Logic

The name of the journal

International Journal of Neutrosophic Science

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201575938&doi=10.54216%2fIJNS.250136&partnerID=40&md5=1720ade6775685ff00235ff7fbc7e73a

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=303526&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Bakhvalov Sergey Yurevich	ru_RU
dc.date.accessioned	2025-01-01T00:00:00Z	ru_RU
dc.date.available	2025-01-01T00:00:00Z	ru_RU
dc.date.issued	2025	ru_RU
dc.identifier.citation	Khaytboeva N, Bakhvalov S, Denisovich V, Integrating Transfer Learning with Neutrosophic Weighted Extreme Learning Machine for Violence Detection in Smart Cities//International Journal of Neutrosophic Science. - 2025. - Vol.25, Is.1. - P.405-417.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=303526&p_lang=2	ru_RU
dc.description.abstract	International Journal of Neutrosophic Science	ru_RU
dc.description.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.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Violence Detection	ru_RU
dc.subject	Transfer Learning	ru_RU
dc.subject	Monarch Butterfly Optimization	ru_RU
dc.subject	Membership Function	ru_RU
dc.subject	Neutrosophic Set	ru_RU
dc.subject	Fuzzy Logic	ru_RU
dc.title	Integrating Transfer Learning with Neutrosophic Weighted Extreme Learning Machine for Violence Detection in Smart Cities	ru_RU
dc.type	Articles in international journals and collections	ru_RU