Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Jiangshu Wei"'
Autor:
Yupeng Niu, Zhenghao Li, Ziao Chen, Wenyuan Huang, Jingxuan Tan, Fa Tian, Tao Yang, Yamin Fan, Jiangshu Wei, Jiong Mu
Publikováno v:
Heliyon, Vol 10, Iss 9, Pp e30373- (2024)
In the vanguard of oncological advancement, this investigation delineates the integration of deep learning paradigms to refine the screening process for Anticancer Peptides (ACPs), epitomizing a new frontier in broad-spectrum oncolytic therapeutics r
Externí odkaz:
https://doaj.org/article/2bf1bb47036c4baf98aa73c753f195a7
Publikováno v:
PeerJ Computer Science, Vol 10, p e1830 (2024)
Object detection based on deep learning has made great progress in the past decade and has been widely used in various fields of daily life. Model lightweighting is the core of deploying target detection models on mobile or edge devices. Lightweight
Externí odkaz:
https://doaj.org/article/961813b8a22a4e1c8950664338dedb44
Publikováno v:
PeerJ Computer Science, Vol 9, p e1665 (2023)
While convolutional operation effectively extracts local features, their limited receptive fields make it challenging to capture global dependencies. Transformer, on the other hand, excels at global modeling and effectively captures global dependenci
Externí odkaz:
https://doaj.org/article/f39ba90cd6d94fc5a6c8e62638c4b062
Publikováno v:
PeerJ Computer Science, Vol 9, p e1344 (2023)
The Transformer has achieved great success in many computer vision tasks. With the in-depth exploration of it, researchers have found that Transformers can better obtain long-range features than convolutional neural networks (CNN). However, there wil
Externí odkaz:
https://doaj.org/article/3bb482be8bcd4b5db0d67b8337ce339e
Publikováno v:
PeerJ Computer Science, Vol 9, p e1314 (2023)
Small object detection is one of the difficulties in the development of computer vision, especially in the case of complex image backgrounds, and the accuracy of small object detection still needs to be improved. In this article, we present a small o
Externí odkaz:
https://doaj.org/article/ce52a1b73f42442bada2f0bd2d05e42f
Publikováno v:
PeerJ Computer Science, Vol 8, p e948 (2022)
Cultivated land quality is related to the quality and safety of agricultural products and to ecological safety. Therefore, reasonably evaluating the quality of land, which is helpful in identifying its benefits, is crucial. However, most studies have
Externí odkaz:
https://doaj.org/article/395b224195a548899671492c9bf362c2
This is the code for improving the swinv2 model. The "experiment results" folder contains the experimental results. The improved models are located in the swin/models file path. The file "swinv2_transformer_Improved_block.py" is the model file that h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1d76b30104a65a78c249a97cd09c48e2
Publikováno v:
PeerJ. Computer science. 8
Cultivated land quality is related to the quality and safety of agricultural products and to ecological safety. Therefore, reasonably evaluating the quality of land, which is helpful in identifying its benefits, is crucial. However, most studies have
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b2860cfe3ca95063c16a036a2ba9ca4
https://pubmed.ncbi.nlm.nih.gov/35494807
https://pubmed.ncbi.nlm.nih.gov/35494807
Publikováno v:
Applied Soft Computing. 119:108582
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9889a12cc0be8b34d019675768a7c419
https://doi.org/10.1016/j.asoc.2022.108582
https://doi.org/10.1016/j.asoc.2022.108582
Autor:
Xiaolin Sun, Jiangshu Wei
Publikováno v:
Journal of Physics: Conference Series. 1437:012080
To reduce the time and energy needed by agricultural experts to evaluate maize diseases and improve the accuracy of image recognition of maize diseases, considering that the maize disease dataset is a small sample dataset, a method of image recogniti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7cf15d91e62f066a3b7abc3809aa2b81
https://doi.org/10.1088/1742-6596/1437/1/012080
https://doi.org/10.1088/1742-6596/1437/1/012080