Chuangchuang Yuan, Tonghai Liu^*, Shuang Song, Fangyu Gao, Rui Zhang

Phyton-International Journal of Experimental Botany, Vol.92, No.4, pp. 1037-1058, 2023, DOI:10.32604/phyton.2023.025343

Abstract Plant species recognition is an important research area in image recognition in recent years. However, the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy. Therefore, ShuffleNetV2 was improved by combining the current hot concern mechanism, convolution kernel size adjustment, convolution tailoring, and CSP technology to improve the accuracy and reduce the amount of computation in this study. Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning. The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the… More >

Zijian Li¹, Chengying Chi^{1, *}, Yunyun Zhan^{2, *}

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 637-650, 2020, DOI:10.32604/cmc.2020.010265

Abstract The translation quality of neural machine translation (NMT) systems depends largely on the quality of large-scale bilingual parallel corpora available. Research shows that under the condition of limited resources, the performance of NMT is greatly reduced, and a large amount of high-quality bilingual parallel data is needed to train a competitive translation model. However, not all languages have large-scale and high-quality bilingual corpus resources available. In these cases, improving the quality of the corpora has become the main focus to increase the accuracy of the NMT results. This paper proposes a new method to improve the quality of data by… More >