Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Juniarta, Nyoman"'
Autor:
Ghor, Tayeb Abderrahmani, Agrawal, Esha, Alam, Mehwish, Alqawasmeh, Omar, D'amato, Claudia, Annane, Amina, Azzam, Amr, Berezovskyi, Andrew, Biswas, Russa, Bonduel, Mathias, Brabant, Quentin, Bucur, Cristina-iulia, Camossi, Elena, Carriero, Valentina Anita, Chari, Shruthi, Fraga, David Chaves, Ciroku, Fiorela, Cochez, Michael, Curien, Hubert, Cutrona, Vincenzo, Dandan, Rahma, Dess, Danilo, Di Carlo, Valerio, Djebri, Ahmed El Amine, Van Erp, Marieke, Falakh, Faiq Miftakhul, Izquierdo, Alba Fernndez, Futia, Giuseppe, Gangemi, Aldo, Gasperoni, Simone, Grall, Arnaud, Heling, Lars, Henri, Pierre, Herradi, Noura, Issa, Subhi, Jozashoori, Samaneh, Juniarta, Nyoman, Kaffee, Lucie-aime, Keles, Ilkcan, Khare, Prashant, Kovtun, Viktor, Leone, Valentina, Li, Siying, Lieber, Sven, Lisena, Pasquale, Makhalova, Tatiana, Marinucci, Ludovica, Minier, Thomas, Moreau, Benjamin, Loustaunau, Alberto Moya, Nandini, Durgesh, Ozdowska, Sylwia, De Moura, Amanda Pacini, Padhee, Swati, Palma, Guillermo, Jimnez, Pedro Del Pozo, Presutti, Valentina, Reda, Roberto, Rizza, Ettore, Rosales-mndez, Henry, Rudolph, Sebastian, Sack, Harald, Sciullo, Luca, Simanjuntak, Humasak, Stomeo, Carlo, Thanapalasingam, Thiviyan, Tietz, Tabea, Varanka, Dalia, Vidal, Maria-esther, Wolowyk, Michael, Zocholl, Maximilian
Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially available to any human as well as artificial intelligence that may want
Externí odkaz:
http://arxiv.org/abs/1903.12554
Autor:
Dina, Nasa Zata1 nasazatadina@vokasi.unair.ac.id, Juniarta, Nyoman2
Publikováno v:
DESIDOC Journal of Library & Information Technology. Mar2023, Vol. 43 Issue 2, p69-77. 9p.
Autor:
Dina, Nasa Zata1 nasazatadina@vokasi.unair.ac.id, Juniarta, Nyoman2
Publikováno v:
Library Philosophy & Practice. 11/2/2021, p1-11. 11p.
Publikováno v:
Proceedings of LREC 2022
13th International Conference on Language Resources and Evaluation (LREC 2022)
13th International Conference on Language Resources and Evaluation (LREC 2022), ELRA, Jun 2022, Marseille, France. pp.3969-3976
13th International Conference on Language Resources and Evaluation (LREC 2022)
13th International Conference on Language Resources and Evaluation (LREC 2022), ELRA, Jun 2022, Marseille, France. pp.3969-3976
International audience; We apply Formal Concept Analysis (FCA) to organize and to improve the quality of Démonette2, a French derivational database, through a detection of both missing and spurious derivations in the database. We represent each deri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::a4d8e384b2b12de677ddac71fa020039
https://hal.science/hal-03699463
https://hal.science/hal-03699463
Autor:
DINA, Nasa Zata, JUNIARTA, Nyoman
Publikováno v:
GeoJournal of Tourism & Geosites; 2022, Vol. 43 Issue 3, p872-877, 6p
Autor:
Bellanger, Clément, Hung, Jane, Juniarta, Nyoman, Leroux, Vincent, Maigret, Bernard, Napoli, Amedeo
Publikováno v:
[Technical Report] Inria Nancy-Grand Est. 2020
Many bacteria are acquiring more resistance to usual treatments worldwide, to the point that the possible advent of pathogens resistant to the entire current arsenal is a true concern. Therefore, there is an urgent need for finding new effective anti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::0e6861295cff1e2577ca611737567f42
https://hal.inria.fr/hal-02615395/file/document.pdf
https://hal.inria.fr/hal-02615395/file/document.pdf
Autor:
Juniarta, Nyoman
Publikováno v:
Computer Science [cs]. Université de Lorraine, 2019. English. ⟨NNT : 2019LORR0199⟩
Knowledge discovery in database (KDD) is a process which is applied to possibly large volumes of data for discovering patterns which can be significant and useful. In this thesis, we are interested in data transformation and data mining in knowledge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::966575dba80f7747b8170879a0c5a722
https://inria.hal.science/tel-02426034/document
https://inria.hal.science/tel-02426034/document
Publikováno v:
International Journal of Emerging Technologies in Learning; 2021, Vol. 16 Issue 17, p76-88, 13p
Publikováno v:
LEG@ECML-PKDD 2019-The third International Workshop on Advances in Managing and Mining Large Evolving Graphs
LEG@ECML-PKDD 2019-The third International Workshop on Advances in Managing and Mining Large Evolving Graphs, Sep 2019, Würzburg, Germany
LEG@ECML-PKDD 2019-The third International Workshop on Advances in Managing and Mining Large Evolving Graphs, Sep 2019, Würzburg, Germany
International audience; This paper presents a new method for feature selection where only relevant features are kept in the dataset and all other features are discarded. The proposed method uses tree decomposition heuristics to reveal subsets of high
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::410e7d084d416178db6351111c1f184f
https://hal.archives-ouvertes.fr/hal-02194229
https://hal.archives-ouvertes.fr/hal-02194229
In this paper we are studying the task of finding coherent-sign-changes biclusters in a binary matrix. This task can be applied to the interpretation of gene expression data, where such a bicluster represents a set of experiments that affect a set of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::f2b8858bec7c867af63bd2b958c2f291
https://hal.inria.fr/hal-02181600/file/nj_sfc19.pdf
https://hal.inria.fr/hal-02181600/file/nj_sfc19.pdf