Zobrazeno 1 - 10
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pro vyhledávání: '"Hidri, Minyar Sassi"'
Autor:
Hidri, Minyar Sassi
Publikováno v:
Interdisciplinary Journal of Information, Knowledge, and Management, Volume 19, 2024, pp. 010
This study contributes to the literature by considering the difference in vocabulary used to express document content and information needs. Users are integrated into all research phases in order to provide them with relevant information adapted to t
Externí odkaz:
http://arxiv.org/abs/2405.15791
Publikováno v:
International Journal of Computing and Digital Systems, 15, 1103-1117, 2024
The new age of digital growth has marked all fields. This technological evolution has impacted data flows which have witnessed a rapid expansion over the last decade that makes the data traditional processing unable to catch up with the rapid flow of
Externí odkaz:
http://arxiv.org/abs/2404.12505
Publikováno v:
2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET)
To succeed in a Big Data strategy, you have to arm yourself with a wide range of data skills and best practices. This strategy can result in an impressive asset that can streamline operational costs, reduce time to market, and enable the creation of
Externí odkaz:
http://arxiv.org/abs/2309.08362
Publikováno v:
2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET)
Any company's human resources department faces the challenge of predicting whether an applicant will search for a new job or stay with the company. In this paper, we discuss how machine learning (ML) is used to predict who will move to a new job. Fir
Externí odkaz:
http://arxiv.org/abs/2309.08333
Diversification of DB applications highlighted the limitations of relational database management system (RDBMS) particularly on the modeling plan. In fact, in the real world, we are increasingly faced with the situation where applications need to han
Externí odkaz:
http://arxiv.org/abs/1904.12344
In this study, we propose a new statical approach for high-dimensionality reduction of heterogenous data that limits the curse of dimensionality and deals with missing values. To handle these latter, we propose to use the Random Forest imputation's m
Externí odkaz:
http://arxiv.org/abs/1707.00351
Publikováno v:
Conf\'erence Internationale Francophone sur la Science de Donn\'ees - Les 23\`emes Rencontres annuelles de la Soci\'et\'e Francophone de Classification (AAFD & SFC), Marrakech, Maroc, pp. 37-42, 2016
When it comes to cluster massive data, response time, disk access and quality of formed classes becoming major issues for companies. It is in this context that we have come to define a clustering framework for large scale heterogeneous data that cont
Externí odkaz:
http://arxiv.org/abs/1707.00297
Autor:
Arfaoui, Olfa, Hidri, Minyar Sassi
Publikováno v:
The 5th International Conference on Web and Information Technologies (ICWIT), pp. 51-60, 2013
The need for discovering knowledge from XML documents according to both structure and content features has become challenging, due to the increase in application contexts for which handling both structure and content information in XML data is essent
Externí odkaz:
http://arxiv.org/abs/1504.04031
Publikováno v:
The 5th International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA), pp. 87-94, 2013
Moved by the need increased for modeling of the fuzzy data, the success of the systems of exact generation of summary of data, we propose in this paper, a new approach of generation of summary from fuzzy data called Fuzzy-SaintEtiQ. This approach is
Externí odkaz:
http://arxiv.org/abs/1310.7829
Publikováno v:
In Procedia Computer Science 2018 126:224-233