[1] V. Dankan Gowda, A. PolaJ. R. Hershey, Z. Chen, J. Le Roux, and S. Watanabe, “Deep clustering: discriminative embeddings for segmentation andseparation,” in International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022
[2] Z. Wang, J. Le Roux, and J. R. Hershey, “Multi-channel Deep Clustering: Discriminative spectral and spatial embeddings for speaker-independent speech separation,” in International Conference
on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023.
[3] Akhtar, M.S., Ekbal, A., Cambria, E.: How intense are you? predicting intensities of emotions and entiments using stacked ensemble [application notes]. IEEE Computational Intelligence Magazine 15(1), 64–75 (2020)
[4] M. Alajeely, R. Doss, and A. Ahmad, “Routing Protocols in Opportunistic Networks – A Survey,” IETE Tech. Rev., vol. 35, no. 4, pp. 369–387, 2018.
[5] K. Ahmad, M. Fathima, M. S. Hossen, J. Ahamed, and K. A. Bin Ahmad, “Opportunistic Networks: An Empirical Research of Routing Protocols and Mobility Models,” SN Comput. Sci., vol. 4, no. 5, p. 652, 2023.
[6] Alhussan, A., M. Talaat, F., El-kenawy, E.S., Abdelhamid, A., Ibrahim, A., Khafaga, D., Alnaggar, M.: Facial expression recognition model depending on optimized support vector machine. Computers, Materials and Continua 76, 499–515 (06 2023).
https://doi.org/10.32604/cmc.2023.039368
[7] R. Dalal, M. Khari, J. P. Anzola, and V. Garcia-Diaz, “Proliferation of Opportunistic Routing: A Systematic Review,” IEEE Access, vol. 10, pp. 5855–5883, 2022.
[8] Li, H., Wang, N., Yang, X., Wang, X., Gao, X.: Towards semi-supervised deep facial expression recognition with an adaptive confidence margin. 2022 IEEE/CVFConference on Computer Vision and Pattern Recognition (CVPR) pp. 4156–4165 (2022), https://api.semanticscholar.org/CorpusID:247618790
[9] Li, S., Deng, W.: Reliable crowdsourcing and deep locality-preserving learning for unconstrained facial expression recognition. IEEE Transactions on Image Processing 28(1), 356–370 (2019). https://doi.org/10.1109/TIP.2018.2868382
[10] Liu, F.: Artificial intelligence in emotion quantification : A prospective verview. CAAI Artificial Intelligence Research 3, 9150040 (2024). https://doi.org/10.26599/AIR.2024.9150040, https://www.sciopen.com/article/10.
26599/AIR.2024.9150040
[11] S. Esmaeili and J. Ghasemi, “A location-aware covert communication protocol in inter-environmental communication applications,” Alexandria Eng. J., vol. 123, pp. 592–609, 2025.
[12] Liu, F., Wang, H.Y., Shen, S.Y., Jia, X., Hu, J.Y., Zhang, J.H., Wang, X.Y., Lei,Y., Zhou, A.M., Qi, J.Y., Li, Z.B.: Opo-fcm: A computational affection based occpad-ocean federation cognitive modeling approach. IEEE Transactions on Computational Social Systems 10(4), 1813–1825 (2023). https://doi.org/10.1109/
TCSS.2022.3199119
[13] E. H. Houssein, M. R. Saad, Y. Djenouri, G. Hu, A. A. Ali, and H. Shaban, “Metaheuristic algorithms and their applications in wireless sensor networks: review, open issues, and challenges,” Cluster Comput., vol. 27, no. 10, pp. 13643–13673, 2024.
[14] D. Bahrepour, N. Evaznia, and T. Khodabakhshi, “A New Resource Allocation Method Based on PSO in Cloud Computing,” Int. J. Web Res., vol. 7, no. 2, pp. 13–21, 2024.
[15] Liu, H., Cai, H., Lin, Q., Li, X., Xiao, H.: Adaptive multilayer perceptual attention network for facial expression recognition. IEEE Transactions on Circuits and Systems for Video Technology 32(9), 6253–6266 (2022)
[16] N. Evaznia, R. Ebrahimi, and D. Bahrepour, “An Energy-Aware Approach to Virtual Machine Consolidation Using Classification and the Dragonfly Algorithm in Cloud Data Centers,” J. Inf. Syst. Telecommun., vol. 12, no. 48, pp. 280–290, 2025.
[17] Ngwe, J.L., Lim, K.M., Lee, C.P., Ong, T.S., Alqahtani, A.: Patt-lite: Lightweight patch and attention mobilenet for challenging facial expression recognition. IEEE Access 12, 79327–79341 (2024). https://doi.org/10.1109/ACCESS.2024.3407108
[18] S. Chaurasia and K. Kumar, “EEMOR: Energy Eflicient Metaheuristic Opportunistic Routing Protocol for WSNs.,” Adhoc Sens. Wirel. Networks, vol. 55, 2023.
[19] M. Sharifi Sani, S. Iranmanesh, H. Salarian, F. Tubbal, and R. Raad, “Optimizing Energy Efficiency in Opportunistic Networks: A Heuristic Approach to Adaptive Cluster-Based Routing Protocol,” Information, vol. 15, no. 5, p. 283, 2024.
[20] Y. Zing and N. Zhao, “Routing revolution: strategic applications of meta-heuristic AI in wireless sensor networks—a comprehensive survey,” Multimed. Tools Appl., vol. 84, no. 35, pp. 44605–44646, 2025.
[21] S. Chaurasia, K. Kumar, and A. K. Kamboj, “EHRP-WSN: Energy-Efficient Hyperheuristic Routing Protocol for Wireless Sensor Networks,” AEU - Int. J. Electron. Commun., vol. 202, p. 156044, 2025.
[22] J. M. Belman-Flores, D. A. Rodríguez-Valderrama, S. Ledesma, J. J. García-Pabón, D. Hernández, and D. M. Pardo-Cely, “A Review on Applications of Fuzzy Logic Control for Refrigeration Systems,” Appl. Sci., vol. 12, no. 3, p. 1302, 2022.
[23] Qian, B., Chen, H., Xu, Y., Wen, Y., Li, H., Xie, Y., Feng, D.D., Kim, J., Bi, L.,Xu, X., He, X., Sheng, B.: Deep contour attention learning for scleral deformation from oct images. The Visual Computer pp. 1–16 (2024)
[24] She, J., Hu, Y., Shi, H., Wang, J., Shen, Q., Mei, T.: Dive into ambiguity: Latent distribution mining and pairwise uncertainty estimation for facial expression recognition. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6244–6253 (2021). https://doi.org/10.1109/CVPR46437.2021.00618
[25]Amirzdeh, M., Hosseini Moradi, S. A., & Ghobadi, N. (2023). Real Time Detection of Multi-Rotor Unmanned Aerial Vehicle Using YOLOv5 Optimized Algorithm. Journal of Advanced Defense Science & Technology, 14(1), 11-22.
[26] M. F. Khan, E. A. Felemban, S. Qaisar, and S. Ali, “Performance Analysis on Packet Delivery Ratio and End-to-End Delay of Different Network Topologies in Wireless Sensor Networks (WSNs),” in 2013 IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks, IEEE, pp. 324–329, 2013.
