Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Rachid El ayachi"'
Publikováno v:
Jordanian Journal of Computers and Information Technology, Vol 10, Iss 4, Pp 453-468 (2024)
In the era of digital journalism, the classification of Arabic news presents a significant challenge due to the complex nature of the language and the vast diversity of content. This study introduces a novel multichannel deep learning model, CLGNet,
Externí odkaz:
https://doaj.org/article/4d53a06d03944230b44000b42915df8d
Publikováno v:
Journal of Information and Organizational Sciences, Vol 47, Iss 2, Pp 423-443 (2023)
Search engines are now the main source for information retrieval due to the huge expansion of data on the internet over the last ten years. Providing users with the most relevant results for their queries poses a significant challenge for search engi
Externí odkaz:
https://doaj.org/article/9832d7e10ef441efa584089e8be5f055
A number of benefits have been reported for computer-based assessments over traditional paper-based exams, both in terms of IT support for question development, reduced distribution and test administration costs, and automated support. Possible for t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2659::5d854ec772d6cfc1646342db174dd830
https://zenodo.org/record/8137226
https://zenodo.org/record/8137226
Publikováno v:
Journal of Information Technology Research. 15:1-17
The field of information retrieval (IR) is an important area in computer science, this domain helps us to find information that we are interested in from an important volume of information. A search engine is the best example of the application of in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::703506cc52157b07079acf90183e5558
https://doi.org/10.4018/jitr.299930
https://doi.org/10.4018/jitr.299930
In the field of machine learning, hyperparameter tuning is a technique used to determine which learning algorithms have the most effective parameters. Using the Grid Search strategy as part of the machine learning algorithms, we suggest a few differe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ff94b2517de8f0ac5507c8c59bc4a895
https://doi.org/10.21203/rs.3.rs-2615380/v1
https://doi.org/10.21203/rs.3.rs-2615380/v1
Recently, people became more dependent on online social networks with the increasing use and the rapid development of information technology. Those environments constitute an important area where users interact and create communication ties to mainta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8693dbb2c8e0b518837a0325df689539
https://zenodo.org/record/7388475
https://zenodo.org/record/7388475
Publikováno v:
Journal of Engineering Research. 11:100061
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::64998d2eef842c0595f8bf9df75cade8
https://doi.org/10.1016/j.jer.2023.100061
https://doi.org/10.1016/j.jer.2023.100061
Publikováno v:
International Journal of Scientific Research in Computer Science, Engineering and Information Technology. :208-215
Extraction of itemset frequent is an important theme in Datamining. Several algorithm have been developed based on Apriori algorithm during the last decades. This paper deals with the FP- tree and Titanic algorithms. FP-Tree is an improvement to the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a4d84ed4b639bf10f7b799592f7f49e8
https://doi.org/10.32628/cseit206537
https://doi.org/10.32628/cseit206537
Publikováno v:
Business Intelligence ISBN: 9783031064579
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::85a4bcad9d8842a1c7b6f5032b026c48
https://doi.org/10.1007/978-3-031-06458-6_12
https://doi.org/10.1007/978-3-031-06458-6_12
Autor:
Younes Ed-Doughmi, Rachid El Ayachi
Publikováno v:
Intelligent Sustainable Systems ISBN: 9789811663086
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::11a6bd1bdb6acc5be6809452d1a12409
https://doi.org/10.1007/978-981-16-6309-3_14
https://doi.org/10.1007/978-981-16-6309-3_14