[1] Islam, M.R., Lima, A.A., Das, S.C., Mridha, M.F., Prodeep, A.R., Watanobe, Y.: A comprehensive survey on the process, methods, evaluation, and challenges of feature selection. IEEE Access 10, 99595–99632 (2022)

[2] Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE Conference on Neural Networks IV, Piscataway, NJ (1995)

[3] Hossain, M.M., et al.: Particle swarm optimized fuzzy CNN with quantitative feature fusion for ultrasound image quality identification. In: IEEE J. Transl. Eng. Health Med. 10, 1–12, 1800712 (2022)

[4] Mousavi, S.M.H., Victorovich, L.V., Ilanloo, A., Mirinezhad, S.Y.: Fatty liver level recognition using PSO (PSO) image segmentation and analysis. In: 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, Republic of Islamic, pp. 237–245 (2022)

[5] Razmah, M., Prabha, R., Divya, B., Sridevi, S., Naveen, A.: LSTM method for human activity recognition of video using PSO algorithm. In: 2022 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, India pp. 1–6 (2022)

[6] Li, J., Lin, L., Luo, L., Lin, S., Wang, J.: PSO-ELM optimization algorithm for gastroscopic image classification. In: 2022 16th ICME International Conference on Complex Medical Engineering (CME), Zhongshan, China, pp. 336–340 (2022)

[7] Song, G., Zuo, Z.: SVM license plate recognition method based on PSO algorithm. In: 2022 4th International Academic Exchange Conference on Science and Technology Innovation (IAECST), Guangzhou, China, pp. 1065–1068 (2022)

[8] Zarei, M.A., Nickfarjam, A.M.: PSO-based procedure to find number of clusters and better initial centroids for K-means algorithm: image segmentation as case study. In: 2023 6th International Conference on Pattern Recognition and Image Analysis (IPRIA), Qom, Iran, Republic of Islamic, pp. 1–4 (2023)

[9] Jia, W., Sun, M., Lian, J., et al.: Feature dimensionality reduction: a review. Complex Intell. Syst. 8, 2663–2693 (2022)

[10] Tao, J., Zhang, R.: Intelligent feature selection using GA and neural network optimization for real-time driving pattern recognition. IEEE Trans. Intell. Transp. Syst. 23(8), 12665–12674 (2022)

[11] Lu, S.: Intelligent vehicle trajectory tracking and identification technology based on multi-fitness genetic algorithm. In: 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), Dalian, China, pp. 1183–1185 (2022)

[12] Gharraf, H.S., Cansever, G., Ahmed, A.S.: Image filtering of impulsive noise using biologically inspired algorithms. In: 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, pp. 58–65 (2022)

[13] Reffad, A., Mebarkia, K.: Motor imagery hand movements recognition using SVM classifier and genetic algorithm optimization. In: 2022 19th International Multi-Conference on Systems, Signals and Devices (SSD), Sétif, Algeria, pp. 1125–1129 (2022)

[14] Huang, H., Cheng, H., Wang, T., Fu, T., Zou, C.: Hole-finding learning strategy for a robot assembly with keyed circular peg. IEEE Access 11, 21488–21497 (2023)

[15] Yu, C., Liu, Y., Zhang, W., Zhang, X., Zhang, Y., Jiang, X.: Foreign objects identification of transmission line based on improved YOLOv7. IEEE Access 11, 51997–52008 (2023)

[16] Jia, W., Zhao, D., Ding, L.: An optimized RBF neural network algorithm based on partial least squares and genetic algorithm for classification of small sample. Appl. Soft Comput. 48, 373–384 (2016)