Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Sanaz Jabbari"'
Publikováno v:
IASSIST Quarterly, Vol 46, Iss 1 (2022)
Data Documentation Initiative-Lifecycle (DDI-L) introduced a robust metadata model to support the capture of questionnaire content and flow, and encouraged through support for versioning and provenancing, objects such as BasedOn for the reuse of exis
Externí odkaz:
https://doaj.org/article/d57fc3d6d909431eafe94758139caf03
Publikováno v:
2022 IEEE International Conference on Big Data (Big Data).
Autor:
Abbas Salihi, Osama K. Abou-Zied, Goran Qader Othman, Sanaz Jabbari, Amitis Naskhi, Asaad Abdulwahed B. Al-Asady, Majid Sharifi, Mojtaba Falahati, Shang Ziyad Abdulqadir, Anwarul Hasan, Soyar Sari, Keivan Akhtari, Falah Mohammad Aziz
Publikováno v:
International Journal of Nanomedicine. 14:8433-8444
Aims Different kinds of vitamins can be used as promising candidates to mitigate the structural changes of proteins and associated cytotoxicity stimulated by NPs. Therefore, the structural changes of α-syn molecules and their associated cytotoxicity
Data Documentation Initiative-Lifecycle (DDI-L) introduced a robust metadata model to support the capture of questionnaire content and flow, and encouraged through support for versioning and provenancing, objects such as BasedOn for the reuse of exis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b9c007d063d18e2418b7e063a68374f
Publikováno v:
Text, Speech and Dialogue ISBN: 9783540873907
TSD
TSD
In this paper, we propose an empirical Bayesian method for determining whether a word is used out of context. We suggest we can treat a word's context as a multinomially distributed random variable, and this leads us to a simple and direct Bayesian h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b89a461db017fb493e831b89d4c94ec
https://doi.org/10.1007/978-3-540-87391-4_15
https://doi.org/10.1007/978-3-540-87391-4_15
Publikováno v:
ACL
This paper describes the largest scale annotation project involving the Enron email corpus to date. Over 12,500 emails were classified, by humans, into the categories "Business" and "Personal", and then sub-categorised by type within these categories