Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Fuchuan Ni"'
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
Detecting and controlling tea pests promptly are crucial for safeguarding tea production quality. Due to the insufficient feature extraction ability of traditional CNN-based methods, they face challenges such as inaccuracy and inefficiency of detecti
Externí odkaz:
https://doaj.org/article/4383fed6a0b641e2be25694c2e8dba2b
Publikováno v:
Plant Methods, Vol 17, Iss 1, Pp 1-13 (2021)
Abstract Background Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach d
Externí odkaz:
https://doaj.org/article/0b2833e3e2a348c9946e14de07ddb35b
Publikováno v:
Frontiers in Plant Science, Vol 13 (2022)
Peach diseases seriously affect peach yield and people’s health. The precise identification of peach diseases and the segmentation of the diseased areas can provide the basis for disease control and treatment. However, the complex background and im
Externí odkaz:
https://doaj.org/article/fb293bec272249e0ba8b51f127e05a83
Publikováno v:
Frontiers in Bioengineering and Biotechnology, Vol 8 (2020)
Identifying drug-disease associations is integral to drug development. Computationally prioritizing candidate drug-disease associations has attracted growing attention due to its contribution to reducing the cost of laboratory screening. Drug-disease
Externí odkaz:
https://doaj.org/article/fc965ad2e104402da51d60c05d05ef4b
Publikováno v:
International Journal of Distributed Sensor Networks, Vol 11 (2015)
Genetic algorithm is easy to fall into local optimal solution. Simulated annealing algorithm may accept nonoptimal solution at a certain probability to jump out of local optimal solution. On the other hand, lack of communication among genes in MapRed
Externí odkaz:
https://doaj.org/article/a8203922e4444c7398684636569576d7
Publikováno v:
Computer Science and Education ISBN: 9789819924486
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f443f81296d3eb1872d959ceee633257
https://doi.org/10.1007/978-981-99-2449-3_13
https://doi.org/10.1007/978-981-99-2449-3_13
Publikováno v:
Information Sciences. 578:750-761
Zero-shot learning (ZSL) realizes unseen object recognition by transferring knowledge from seen classes to unseen classes under common semantic space assumption, such as attribute space and semantic word vector space. Previous works used seen image f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee161473052fe9aefa8cf2125511713e
https://doi.org/10.1016/j.ins.2021.06.085
https://doi.org/10.1016/j.ins.2021.06.085
Publikováno v:
World Wide Web. 24:157-173
In recent years, social networks have surged in popularity as one of the main applications of the Internet. One key aspect of social network research is exploring important unobserved network information which is not explicitly represented. This stud
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dfddc1bfca5ad98081b8e5d971632040
https://doi.org/10.1007/s11280-020-00837-4
https://doi.org/10.1007/s11280-020-00837-4
Publikováno v:
Lecture Notes in Electrical Engineering ISBN: 9789811694226
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d7833da2459ad7af90155025f9606e8a
https://doi.org/10.1007/978-981-16-9423-3_16
https://doi.org/10.1007/978-981-16-9423-3_16
Publikováno v:
Plant Methods
Plant Methods, Vol 17, Iss 1, Pp 1-13 (2021)
Plant Methods, Vol 17, Iss 1, Pp 1-13 (2021)
Background Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3fcb8cf0cbcd0e3d92b552ae9438940f
https://doi.org/10.1186/s13007-021-00736-3
https://doi.org/10.1186/s13007-021-00736-3