[1] R. Lou, Z. Lv, S. Dang, T. Su, and X. Li, "Application of machine learning in ocean data," Multimedia systems, vol. 29, no. 3, pp. 1815-1824, 2023.
[2] U. Kanjir, H. Greidanus, and K. Oštir, "Vessel detection and classification from spaceborne optical images: A literature survey," Remote sensing of environment, vol. 207, pp. 1-26, 2018.
[3] R. Danovaro et al., "Ecological variables for developing a global deep-ocean monitoring and conservation strategy," Nature Ecology & Evolution, vol. 4, no. 2, pp. 181-192, 2020.
[4] L. Bo, X. Xiaoyang, W. Xingxing, and T. Wenting, "Ship detection and classification from optical remote sensing images: A survey," Chinese Journal of Aeronautics, vol. 34, no. 3, pp. 145-163, 2021.
[5] X. Chen, "AI and big data: Leveraging machine learning for advanced data analytics," Advances in Computer Sciences, vol. 7, no. 1, 2024.
[6] T. Yang, J. Chen, and N. Zhang, "AI-empowered maritime Internet of Things: A parallel-network-driven approach," IEEE Network, vol. 34, no. 5, pp. 54-59, 2020.
[7] A. Rawson and M. Brito, "A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis," Transport Reviews, vol. 43, no. 1, pp. 108-130, 2023.
[8] W. Samek, G. Montavon, S. Lapuschkin, C. J. Anders, and K.-R. Müller, "Explaining deep neural networks and beyond: A review of methods and applications," Proceedings of the IEEE, vol. 109, no. 3, pp. 247-278, 2021.
[9] Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, "A survey of convolutional neural networks: analysis, applications, and prospects," IEEE transactions on neural networks and learning systems, vol. 33, no. 12, pp. 6999-7019, 2021.
[10] M. H. Salem, Y. Li, Z. Liu, and A. M. AbdelTawab, "A transfer learning and optimized CNN based maritime vessel classification system," Applied Sciences, vol. 13, no. 3, p. 1912, 2023.
[11] F. Ucar and D. Korkmaz, "A novel ship classification network with cascade deep features for line-of-sight sea data," Machine Vision and Applications, vol. 32, no. 3, p. 73, 2021.
[12] M. H. Salem, Y. Li, and Z. Liu, "Transfer learning on efficientnet for maritime visible image classification," in 2022 7th international conference on signal and image processing (ICSIP), 2022: IEEE, pp. 514-520.
[13] L. A. Leonidas and Y. Jie, "Ship classification based on improved convolutional neural network architecture for intelligent transport systems," Information, vol. 12, no. 8, p. 302, 2021.
[14] S. Sabour, N. Frosst, and G. E. Hinton, "Dynamic routing between capsules," Advances in neural information processing systems, vol. 30, 2017.
[15] E. Teixeira, B. Araujo, V. Costa, S. Mafra, and F. Figueiredo, "Literature review on ship localization, classification, and detection methods based on optical sensors and neural networks," Sensors, vol. 22, no. 18, p. 6879, 2022.
[16] Z. Wang, J. Chen, and S. C. Hoi, "Deep learning for image super-resolution: A survey," IEEE transactions on pattern analysis and machine intelligence, vol. 43, no. 10, pp. 3365-3387, 2020.
[17] P. Satish, M. Srikantaswamy, and N. K. Ramaswamy, "A Comprehensive Review of Blind Deconvolution Techniques for Image Deblurring," Traitement du Signal, vol. 37, no. 3, 2020.
[18] R. Singh and S. Bansal, "A comparative study of image deblurring techniques," Journal of Computational and Theoretical Nanoscience, vol. 17, no. 9-10, pp. 4571-4579, 2020.
[19] L. Wang et al., "A review of methods for ship detection with electro-optical images in marine environments," Journal of Marine Science and Engineering, vol. 9, no. 12, p. 1408, 2021.
[20] J. Fu, J. Zhao, and F. Li, "Infrared sea-sky line detection utilizing self-adaptive Laplacian of Gaussian filter and visual-saliency-based probabilistic Hough transform," IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2021.
[21] D. Liang and Y. Liang, "Horizon detection from electro-optical sensors under maritime environment," IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 1, pp. 45-53, 2019.
[22] W. Yang, H. Li, J. Liu, S. Xie, and J. Luo, "A sea-sky-line detection method based on Gaussian mixture models and image texture features," International Journal of Advanced Robotic Systems, vol. 16, no. 6, p. 1729881419892116, 2019.
[23] R. Zebari, A. Abdulazeez, D. Zeebaree, D. Zebari, and J. Saeed, "A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction," Journal of Applied Science and Technology Trends, vol. 1, no. 1, pp. 56-70, 2020.
[24] S. Wu, X. Chen, C. Shi, J. Fu, Y. Yan, and S. Wang, "Ship detention prediction via feature selection scheme and support vector machine (SVM)," Maritime Policy & Management, vol. 49, no. 1, pp. 140-153, 2022.
[25] L. R. Abreu, I. S. Maciel, J. S. Alves, L. C. Braga, and H. L. Pontes, "A decision tree model for the prediction of the stay time of ships in Brazilian ports," Engineering Applications of Artificial Intelligence, vol. 117, p. 105634, 2023.
[26] F. Zhuang et al., "A comprehensive survey on transfer learning," Proceedings of the IEEE, vol. 109, no. 1, pp. 43-76, 2020.
[27] X. Shan, D. Zhao, M. Pan, D. Wang, and L. Zhao, "Sea–Sky line and its nearby ships detection based on the motion attitude of visible light sensors," Sensors, vol. 19, no. 18, p. 4004, 2019.
[28] C. Lin, W. Chen, and H. Zhou, "Multi-visual feature saliency detection for sea-surface targets through improved sea-sky-line detection," Journal of Marine Science and Engineering, vol. 8, no. 10, p. 799, 2020.
[29] A. Zhou, W. Xie, and J. Pei, "Infrared maritime target detection using the high order statistic filtering in fractional Fourier domain," Infrared Physics & Technology, vol. 91, pp. 123-136, 2018.
[30] Y. Li, P. Gao, B. Tang, Y. Yi, and J. Zhang, "Double feature extraction method of ship-radiated noise signal based on slope entropy and permutation entropy," Entropy, vol. 24, no. 1, p. 22, 2021.
[31] H. Yang, L.-l. Li, G.-h. Li, and Q.-r. Guan, "A novel feature extraction method for ship-radiated noise," Defence Technology, vol. 18, no. 4, pp. 604-617, 2022.
[32] X. Xu, X. Zhang, and T. Zhang, "Multi-scale SAR ship classification with convolutional neural network," in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, IEEE, pp. 4284-4287, 2021.
[33] T. Lu, B. Han, and F. Yu, "Detection and classification of marine mammal sounds using AlexNet with transfer learning," Ecological Informatics, vol. 62, p. 101277, 2021.
[34] M. Liang, R. W. Liu, S. Li, Z. Xiao, X. Liu, and F. Lu, "An unsupervised learning method with convolutional auto-encoder for vessel trajectory similarity computation," Ocean Engineering, vol. 225, p. 108803, 2021.
[35] X. Du, Y. Sun, Y. Song, H. Sun, and L. Yang, "A Comparative Study of Different CNN Models and Transfer Learning Effect for Underwater Object Classification in Side-Scan Sonar Images," Remote Sensing, vol. 15, no. 3, p. 593, 2023.
[36] L. A. Yates, Z. Aandahl, S. A. Richards, and B. W. Brook, "Cross validation for model selection: a review with examples from ecology," Ecological Monographs, vol. 93, no. 1, p. e1557, 2023.
[37] X. Wang, G. Li, X.-P. Zhang, and Y. He, "Ship detection in SAR images via local contrast of Fisher vectors," IEEE Transactions on geoscience and remote sensing, vol. 58, no. 9, pp. 6467-6479, 2020.
[38] S. Pawan and J. Rajan, "Capsule networks for image classification: A review," Neurocomputing, vol. 509, pp. 102-120, 2022.
[39] Z. Lv, H. Ding, L. Wang, and Q. Zou, "A convolutional neural network using dinucleotide one-hot encoder for identifying DNA N6-methyladenine sites in the rice genome," Neurocomputing, vol. 422, pp. 214-221, 2021.
[40] E. Gundogdu, B. Solmaz, V. Yücesoy, and A. Koc, "Marvel: A large-scale image dataset for maritime vessels," in Computer Vision–ACCV 2016: 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part V 13, 2017: Springer, pp. 165-180.
[41] A. Ali-Gombe and E. Elyan, "MFC-GAN: Class-imbalanced dataset classification using multiple fake class generative adversarial network," Neurocomputing, vol. 361, pp. 212-221, 2019.
[42] H. Jabbari and N. Bigdeli, "New conditional generative adversarial capsule network for imbalanced classification of human sperm head images," Neural Computing and Applications, vol. 35, no. 27, pp. 19919-19934, 2023.
[43] H. Jabbari and N. Bigdeli, "A new hierarchical algorithm based on CapsGAN for imbalanced image classification," IET Image Processing, vol. 18, no. 1, pp. 194-210, 2024.
[44] H. Jabbari and N. Bigdeli, "A New Capsule Generative Adversarial Network for Imbalanced Classification of Human Sperm Images," Journal of Modeling in Engineering, vol. 21, no. 73, pp. 279-294, 2023.
[45] جباری، حامد و بیگدلی، نوشین، "طراحی و ارزیابی یک شبکه عصبی کپسولی جدید برای طبقهبندی نامتوازن تصاویر"، مجله ماشین بینایی و پردازش تصویر، دوره 9، شماره 1، صفحه 15-1، فروردین 1401