Volume 25 , Issue 1 , PP: 405-417, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Nigora Khaytboeva 1 * , Sergey Bakhvalov 2 , Veronika Denisovich 3 , Rafina Zakieva 4
Doi: https://doi.org/10.54216/IJNS.250136
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.
Violence Detection , Transfer Learning , Monarch Butterfly Optimization , Membership Function , Neutrosophic Set , Fuzzy Logic
[1] Abobala, M., 2020. n-Cyclic Refined Neutrosophic Algebraic Systems Of Sub-Indeterminacies, An Application To Rings and Modules. International Journal of Neutrosophic Science, 12, pp.81-95.
[2] Abobala, M,. "Classical Homomorphisms Between Refined Neutrosophic Rings and Neutrosophic Rings", International Journal of Neutrosophic Science, Vol. 5, pp. 72-75, 2020.
[3] Alhamido, R., and Abobala, M., "AH-Substructures in Neutrosophic Modules", International Journal of Neutrosophic Science, Vol. 7, pp. 79-86 , 2020.
[4] Hatip, A., and Olgun, N., " On Refined Neutrosophic R-Module", International Journal of Neutrosophic Science, Vol. 7, pp.87-96, 2020.
[5] Ibrahim, M.A., Agboola, A.A.A, Badmus, B.S., and Akinleye, S.A., "On Refined Neutrosophic Vector Spaces I", International Journal of Neutrosophic Science, Vol. 7, pp. 97-109, 2020.
[6] Ye, L., Wang, L., Ferdinando, H., Seppänen, T. and Alasaarela, E., 2020. A video-based DT–SVM school violence detecting algorithm. Sensors, 20(7), p.2018.
[7] Baba, M., Gui, V., Cernazanu, C. and Pescaru, D., 2019. A sensor network approach for violence detection in smart cities using deep learning. Sensors, 19(7), p.1676.
[8] Pujol, F.A., Mora, H. and Pertegal, M.L., 2020. A soft computing approach to violence detection in social media for smart cities. Soft Computing, 24(15), pp.11007-11017.
[9] Ullah, F.U.M., Ullah, A., Muhammad, K., Haq, I.U. and Baik, S.W., 2019. Violence detection using spatiotemporal features with 3D convolutional neural network. Sensors, 19(11), p.2472.
[10] Jain, A. and Vishwakarma, D.K., 2020, August. Deep NeuralNet for violence detection using motion features from dynamic images. In 2020 third international conference on smart systems and inventive technology (ICSSIT) (pp. 826-831). IEEE.
[11] Dalal, S., Lilhore, U.K., Sharma, N., Arora, S., Simaiya, S., Ayadi, M., Almujally, N.A. and Ksibi, A., 2024. Improving smart home surveillance through YOLO model with transfer learning and quantization for enhanced accuracy and efficiency. PeerJ Computer Science, 10, p.e1939.
[12] Bakhshi, A., García-Gómez, J., Gil-Pita, R. and Chalup, S., 2023. Violence detection in real-life audio signals using lightweight deep neural networks. Procedia Computer Science, 222, pp.244-251.
[13] Khan, M., El Saddik, A., Gueaieb, W., De Masi, G. and Karray, F., 2024. VD-Net: An Edge Vision-Based Surveillance System for Violence Detection. IEEE Access, 12, pp.43796-43808.
[14] Shoaib, M., Ullah, A., Abbasi, I.A., Algarni, F. and Khan, A.S., 2023. Augmenting the Robustness and efficiency of violence detection systems for surveillance and non-surveillance scenarios. IEEE Access, 11, pp.123295-123313.
[15] Elakiya, V., Puviarasan, N. and Aruna, P., 2024. Detection of violence using mosaicking and DFE-WLSRF: Deep feature extraction with weighted least square with random forest. Multimedia Tools and Applications, 83(14), pp.40873-40908.
[16] Jaafar, N. and Lachiri, Z., 2023. Multimodal fusion methods with deep neural networks and meta-information for aggression detection in surveillance. Expert Systems with Applications, 211, p.118523.
[17] Debnath, S., 2021. Application of intuitionistic neutrosophic soft sets in decision making based on game theory. International Journal of Neutrosophic Science, 14(2), pp.83-97.
[18] Wang, Y., Ma, W., Song, L. and Cai, Z., 2024. HCSMBO: A hybrid cat swarm and monarch butterfly optimization algorithm for energy consumption optimization in industrial internet of things. Alexandria Engineering Journal, 102, pp.279-289.
[19] Taş, F., Topal, S. and Smarandache, F., 2018. Clustering neutrosophic data sets and neutrosophic valued metric spaces. Symmetry, 10(10), p.430.
[20] Akbulut, Y., Şengür, A., Guo, Y. and Smarandache, F., 2017. A novel neutrosophic weighted extreme learning machine for imbalanced data set. Symmetry, 9(8), p.142.
[21] https://www.kaggle.com/datasets/yassershrief/hockey-fight-vidoes
[22] Aldehim, G., Asiri, M.M., Aljebreen, M., Mohamed, A., Assiri, M. and Ibrahim, S.S., 2023. Tuna swarm algorithm with deep learning enabled violence detection in smart video surveillance systems. IEEE Access.
