Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Xishuang Dong"'
Publikováno v:
Applied Sciences, Vol 14, Iss 9, p 3826 (2024)
Fairness Artificial Intelligence (AI) aims to identify and mitigate bias throughout the AI development process, spanning data collection, modeling, assessment, and deployment—a critical facet of establishing trustworthy AI systems. Tackling data bi
Externí odkaz:
https://doaj.org/article/77d7c280907b42e28479894f30d06d23
Autor:
Xishuang Dong, Lijun Qian
Publikováno v:
Machine Learning with Applications, Vol 10, Iss , Pp 100428- (2022)
Misinformation refers to inaccurate information created to misguide the readers. It spreads on social platforms like Twitter with various presentations such as fake news and rumors that usually contain numbers, categorical information, texts, images,
Externí odkaz:
https://doaj.org/article/f2f1b8df95a84d14a025e70d89b679d6
Publikováno v:
PLoS ONE, Vol 17, Iss 11, p e0276250 (2022)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff for facilitating a diagnosi
Externí odkaz:
https://doaj.org/article/0976f7fea7724b31833823a30144ba20
Publikováno v:
BMC Bioinformatics, Vol 19, Iss S17, Pp 75-84 (2018)
Abstract Background Electronic Medical Record (EMR) comprises patients’ medical information gathered by medical stuff for providing better health care. Named Entity Recognition (NER) is a sub-field of information extraction aimed at identifying spe
Externí odkaz:
https://doaj.org/article/4868c0c2b38e4341928579771b5a2862
Publikováno v:
PLoS ONE, Vol 14, Iss 5, p e0216046 (2019)
Specific entity terms such as disease, test, symptom, and genes in Electronic Medical Record (EMR) can be extracted by Named Entity Recognition (NER). However, limited resources of labeled EMR pose a great challenge for mining medical entity terms. I
Externí odkaz:
https://doaj.org/article/ddc50afbe0d34d03a6f84fab00b613a5
Publikováno v:
Applied Sciences, Vol 10, Iss 21, p 7541 (2020)
Social media is a popular platform for information sharing. Any piece of information can be spread rapidly across the globe at lightning speed. The biggest challenge for social media platforms like Twitter is how to trust news shared on them when the
Externí odkaz:
https://doaj.org/article/f0485e6816e44fd2bc51a3b49a13586d
Publikováno v:
IEEE Transactions on Emerging Topics in Computational Intelligence. 6:941-956
To extract knowledge from the large data collected by edge devices, a traditional cloud-based approach that requires data upload may not be feasible due to communication bandwidth limitations as well as privacy and security concerns of end-users. A n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cf383381ab994df8c7d627c900446e0d
https://doi.org/10.1109/tetci.2021.3111636
https://doi.org/10.1109/tetci.2021.3111636
Publikováno v:
2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::707c97e24388927ac182964c58d3a110
https://doi.org/10.1109/idsta55301.2022.9923040
https://doi.org/10.1109/idsta55301.2022.9923040
Publikováno v:
2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6c2ad0b46bdd1a68ed6ef81635f7cfac
https://doi.org/10.1109/idsta55301.2022.9923043
https://doi.org/10.1109/idsta55301.2022.9923043
Publikováno v:
IEEE/ACM transactions on computational biology and bioinformatics.
Cell type identification from single-cell transcriptomic data is a common goal of single-cell RNA sequencing (scRNAseq) data analysis. Deep neural networks have been employed to identify cell types from scRNAseq data with high performance. However, i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30ec8f1f09470c0621ae19bd58021e76
https://pubmed.ncbi.nlm.nih.gov/35536811
https://pubmed.ncbi.nlm.nih.gov/35536811