توسعه الگوریتم یادگیری عمیق به منظور تشخیص و طبقه‌بندی هوشمند گونه‌های ماهی کپور

Anderson, J., Jensen, D., Gunderson, J. & Zhuiko, M. (2008). A Field Guide to Fish Invaders of the Great Lakes Region: Non-native Fish and Native Look-a-Likes. University of Minnesota Sea Grant Program.

Asian Carp: Key to Identification. (2002). National Invasive Species Council materials, from https:// digitalcommons. unl.edu/ natlinvasive/7.

Baldominos, A., Saez, Y. & Isasi, P. (2018). Evolutionary convolutional neural networks: An application to handwriting recognition. Neurocomputing, 283:38-52.

Banan, A., Nasiri, A., & Taheri-Garavand, A. (2020). Deep learning-based appearance features extraction for automated carp species identification. Aquacultural Engineering, 89, p.102053.

Boureau, Y.L., Ponce, J. & LeCun, Y. (2010). A theoretical analysis of feature pooling in visual recognition. In Proceedings of the 27th international conference on machine learning (ICML-10), pp. 111-118.

Chambah, M., Semani, D., Renouf. A., Courtellemont, P. & Rizzi, A. (2004). Underwater color constancy: enhancement of automatic live fish recognition. Proceedings of SPIE / IS&T Electronic Imaging, Jan 18-22, San Jose, California, USA.

Dayrat, B. (2005). Towards integrative taxonomy. Biological Journal of the Linnean Society, 85:407–415.

Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P. & Bengio, S. (2010). Why does unsupervised pre-training help deep learning. Journal of Machine Learning Research, 11(Feb): 625-660.

Fischer, J. (2014). Fish identification tools for biodiversity and fisheries assessments: review and guidance for decision-makers. FAO Fisheries and Aquaculture Technical Paper, 585, p.I.

Food and Agriculture Organization (FAO). (1997). FAO Database on Introduced Aquatic Species, from http://www. fao.org/fishery/dias/en.

Food and Agriculture Organization (FAO). (2018). Fishery and aquaculture statistics, from http://www. fao.org/ fishery/nems/41266/en.

Gaber, T., Tharwat, A., Hassanien, A.E. & Snasel, V. (2016). Biometric cattle identification
approach based on Weber's local descriptor and AdaBoost classifier. Computers and Electronincs in Agriculture, 122, 55–66.

Ghazi, M.M., Yanikoglu, B. & Aptoula, E. 2017. Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing, 235: 228-35.

Girshick, R., Donahue, J., Darrell, T. & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580-587.

Hasanpour Mati Kalaei, S.H. & Saadati, R. (2016). A review of the applications of convolutional neural network and in-depth learning in computer vision. Third National Conference on Electrical and Computer Engineering Distributed Systems and Smart Grids, Kashan, IRAN. (In Farsi).

Hernández-Serna, A. & Jiménez-Segura, L.F. (2014). Automatic identification of species with neural networks. PeerJ, 2, p.e563.

Hu, J., Li, D., Duan, Q., Han, Y., Chen, G. & Si, X. (2012). Fish species classification by color, texture and multi-class support vector machine using computer vision. Computers and Electronincs in Agriculture, 88, 133–140.

Iran Fisheries Organization. (2017). Iran Fisheries Organization statistical yearbook. Tehran, IRAN. (In Farsi).

Javanmardi, S. & Zare Chahooki, M.A. (2018). Refining large scale image annotation via transfer learning in deep convolutional neural network. Machine Vision and Image Processing, 5(1), 39-52. In Farsi.

Jiménez-Gamero, I., Dorado, G., Muñoz-Serrano, A., Analla, M. & Alonso-Moraga, A. (2006).DNA microsatellites to ascertain pedigree-recorded information in a selecting nucleus of Murciano-Granadina dairy goats. Small Ruminant Research, 65(3), 266–273.

Khalifa, N.E.M., Taha, M.H.N. & Hassanien, A.E. (2018) Aquarium Family Fish Species Identification System Using Deep Neural Networks. In International Conference on Advanced Intelligent Systems and Informatics, vol 845. Springer, Cham.

Labatut, V. & Cherifi, H. (2012). Accuracy measures for the comparison of classifiers. arXiv preprint arXiv, 1207.3790.

Lee, D.J., Schoenberger, R., Shiozawa, D., Xu, X. & Zhan, P. (2004). Contour matching for a fish recognition and migration monitoring system. Proceedings of the SPIE optics east, Two and Three-Dimensional Vision Systems for Inspection, Control, and Metrology II, Oct 25-28, Philadelphia, PA, USA.

Li, L. & Hong, J. (2014). Identification of fish species based on image processing and statistical analysis research. In: 2014 IEEE International Conference on Mechatronics and Automation, August, IEEE, pp. 1155–1160.

Li, S., Liu, G., Tang, X., Lu, J. & Hu, J. (2017). An ensemble deep convolutional neural network model with improved DS evidence fusion for bearing fault diagnosis. Sensors, 17(8), p.1729.

Lim, K., Jang, W.D., & Kim, C.S. (2017). Background subtraction using encoder-decoder structured convolutional neural network. In 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 1-6.

Liu, B., Zhang, X., Gao, Z. & Chen, L. (2017). Weld defect images classification with VGG16-Based neural network. In International Forum on Digital TV and Wireless Multimedia Communications, Singapore, Springer; 215-223.

Lu, Y.C., & Kuo, Y.F. (2019). Identifying species of common sea fish harvested by longliner using deep convolutional neural networks. In American Society of Agricultural and Biological Engineers Annual International Meeting, p. 1.

Rova, A., Mori, G., & Dill, L.M. (2007). One fish, two fish, butterfish, trumpeter: Recognizing fish in underwater video. Proceedings of the IAPR Conference on Machine Vision Applications, May 16-18, Tokyo, Japan.

Rusk, C.P., Blomeke, C.R., Balschweid, M.A., Elliot, S., & Baker, D. (2006). An evaluation of retinal imaging technology for 4-h beef and sheep identification. The Journal of Extension, 44(5), 1–33.

Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). Deepfruits: A fruit detection system using deep neural networks. Sensors, 16(8):1222.

Simonyan, K. & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv, 1409.1556.

 Sokolova, M. & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4): 427–437.

Storbeck, F. & Daan, B. (2001). Fish species recognition using computer vision and a neural network. Fisheries Research, 51(1), 11–15.

Tharwat, A. (2016). Linear vs. quadratic discriminant analysis classifier: a tutorial. International Journal of Applied Pattern Recognition, 3(2), 145–180.

Tharwat, A., Hemedan, A.A., Hassanien, A.E. & Gabel, T. (2018). A biometric-based model for fish species classification.  Fisheries research, 204: 324-336.

Tung, C., Hsieh, C.L. & Kuo, Y.F. (2017). Sea fish identification using convolutional neural network. In American Society of Agricultural and Biological Engineers Annual International Meeting,  p. 1.

Wu, R., Yan, S., Shan, Y., Dang, Q. & Sun, G. (2015). Deep image: Scaling up image recognition.  arXiv preprint arXiv, 1501.02876.

Zhang, Z., Niu, Z., & Zhao, S. (2011). Identification of freshwater fish species based on computer vision. Transations of the Chinese Society of Agricultural Engineering, 27(11): 388–392.

Zeiler, M.D. (2013). Hierarchical convolutional deep learning in computer vision. Ph.D dissertation, New York University, New York.