Zobrazeno 1 - 10
of 132
pro vyhledávání: '"Naijie Gu"'
Publikováno v:
Complex & Intelligent Systems, Vol 9, Iss 2, Pp 1415-1437 (2022)
Abstract In the field of preference-based evolutionary multiobjective optimization, optimization algorithms are required to search for the Pareto optimal solutions preferred by the decision maker (DM). The reference point is a type of techniques that
Externí odkaz:
https://doaj.org/article/171ff3913e584d908be6262aa5d65f31
Publikováno v:
Applied Artificial Intelligence, Vol 35, Iss 12, Pp 876-892 (2021)
This paper proposes a deep learning-based model to predict stock price movements. The proposed model is composed of a deep belief network (DBN) to learn the latent feature representation from stock prices, and a long short-term memory (LSTM) network
Externí odkaz:
https://doaj.org/article/860fad83a7f24fbd823e467fc22e2ed4
Autor:
Qianqian Yu, Naijie Gu
Publikováno v:
2023 IEEE 8th International Conference on Big Data Analytics (ICBDA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b994425c63509d53bd8495c0e9853608
https://doi.org/10.1109/icbda57405.2023.10104712
https://doi.org/10.1109/icbda57405.2023.10104712
Publikováno v:
2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6571fe2f3374b286e0cb1992b060bb96
https://doi.org/10.1109/saner56733.2023.00016
https://doi.org/10.1109/saner56733.2023.00016
Autor:
Diponkor Bala, Md. Shamim Hossain, Mohammad Alamgir Hossain, Md. Ibrahim Abdullah, Md. Mizanur Rahman, Balachandran Manavalan, Naijie Gu, Mohammad S. Islam, Zhangjin Huang
The monkeypox virus poses a new pandemic threat while we are still recovering from COVID-19. Despite the fact that monkeypox is not as lethal and contagious as COVID-19, new patient cases are recorded every day. If preparations are not made, a global
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a84afa2ba77de011b7761e54a760593
https://hdl.handle.net/10453/167388
https://hdl.handle.net/10453/167388
Publikováno v:
2022 International Conference on Networking and Network Applications (NaNA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2a675765bea8fb597579bade3ae1fdea
https://doi.org/10.1109/nana56854.2022.00039
https://doi.org/10.1109/nana56854.2022.00039
Publikováno v:
The Visual Computer.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a489528e6f1a19e6644fa83d07f685e7
https://doi.org/10.1007/s00371-022-02677-x
https://doi.org/10.1007/s00371-022-02677-x
Publikováno v:
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 31
This paper presents 6D-ViT, a transformer-based instance representation learning network, which is suitable for highly accurate category-level object pose estimation on RGB-D images. Specifically, a novel two-stream encoder-decoder framework is dedic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3296a91e5301465bcd202f798a224735
https://pubmed.ncbi.nlm.nih.gov/36315551
https://pubmed.ncbi.nlm.nih.gov/36315551
Publikováno v:
Applied Artificial Intelligence. 35:876-892
This paper proposes a deep learning-based model to predict stock price movements. The proposed model is composed of a deep belief network (DBN) to learn the latent feature representation from stock...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2b1198a5987c0d39c0e69ec047f2c941
https://doi.org/10.1080/08839514.2021.1942520
https://doi.org/10.1080/08839514.2021.1942520
Publikováno v:
Computers & Graphics. 95:115-122
3D human pose estimation from 2D detections is an ill-posed problem because multiple solutions may exist due to the inherent ambiguity and occlusion. In this paper, we propose a novel graph-based mixture density network (GMDN) to tackle the 2D-to-3D
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cc20e40cb69898c9bdbac5ba50ef7b28
https://doi.org/10.1016/j.cag.2021.01.010
https://doi.org/10.1016/j.cag.2021.01.010