Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Majdi Beseiso"'
Publikováno v:
The Journal of Supercomputing. 78:12278-12309
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::870a642072148f86cf1242dd627f4899
https://doi.org/10.1007/s11227-022-04357-y
https://doi.org/10.1007/s11227-022-04357-y
Autor:
Gulshan Kumar, Majdi Beseiso
Publikováno v:
Journal of Intelligent & Fuzzy Systems. 40:11341-11354
This paper presents a fuzzy computational approach for selecting project portfolio by combining fuzzy logic, Quality Function Deployment (QFD) and Genetic algorithm (GA) approaches with the consideration of prioritized selection criteria as per objec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::64d6e0847eb4d23242df3467cd160be0
https://doi.org/10.3233/jifs-202506
https://doi.org/10.3233/jifs-202506
Publikováno v:
Journal of Computing in Higher Education. 33:727-746
E-learning is gradually gaining prominence in higher education, with universities enlarging provision and more students getting enrolled. The effectiveness of automated essay scoring (AES) is thus holding a strong appeal to universities for managing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::51f8e2ebd0e70a9f71b24c36ceee28db
https://doi.org/10.1007/s12528-021-09283-1
https://doi.org/10.1007/s12528-021-09283-1
Some classic machine translation (MT) Evaluation methods, such as the bilingual evaluation understudy score (BLEU), have notably underperformed in evaluating machine translations for morphologically rich languages like Arabic. However, the recent rem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee2f62ded88f82732f5f0e9c2fb6a0f7
https://zenodo.org/record/7178879
https://zenodo.org/record/7178879
Autor:
Haytham H. Elmousalami, Majdi Beseiso
Publikováno v:
ACM Transactions on Asian and Low-Resource Language Information Processing. 19:1-17
Social media data is unstructured data where these big data are exponentially increasing day to day in many different disciplines. Analysis and understanding the semantics of these data are a big challenge due to its variety and huge volume. To addre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e97f88b6567864de2e1342c21c73707
https://doi.org/10.1145/3360016
https://doi.org/10.1145/3360016
Publikováno v:
Expert Systems. 39
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8cb0553ebb6589a3826e88c08bb9c8c4
https://doi.org/10.1111/exsy.12879
https://doi.org/10.1111/exsy.12879
Autor:
Majdi Beseiso
Publikováno v:
Recent Patents on Computer Science. 12:142-147
Background: Social media texts are often highly unstructured in accordance with the presence of hashtags, emojis and URLs occurring in abundance. Thus, a sentiment or emotion analysis on these kinds of texts becomes very difficult. The difficulty inc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::95990c28508b68192171fba3b2bf0db3
https://doi.org/10.2174/2213275911666181119112645
https://doi.org/10.2174/2213275911666181119112645
Autor:
Majdi Beseiso
Publikováno v:
DATA
The automated essay scoring (AES) has significant importance in machine grading of student essays particularly in standardized exams like the Graduate Record Examination (GRE). However, some issues in AES have remained unsolved over the past several
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::854e421b346513735cd71f59ba51aab3
https://doi.org/10.1145/3460620.3460630
https://doi.org/10.1145/3460620.3460630
Autor:
Saleh Alzahrani, Majdi Beseiso
Publikováno v:
International Journal of Advanced Computer Science and Applications. 11
Automated Essay Scoring (AES) is one of the most challenging problems in Natural Language Processing (NLP). The significant challenges include the length of the essay, the presence of spelling mistakes affecting the quality of the essay and represent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a8ad2f8a08cda2c1641462dda603639
https://doi.org/10.14569/ijacsa.2020.0111027
https://doi.org/10.14569/ijacsa.2020.0111027
Autor:
Majdi Beseiso
Publikováno v:
ICAAI
Due to the lack of sentiment analysis for Arabic social platforms (like twitter and Facebook), this paper presents a new approach to extract lexical features based on the semantically aspects (topics) which has been extracted using Latent Dirichlet A
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::89da50295a53cabc7e66a6c4f4061554
https://doi.org/10.1145/3369114.3369123
https://doi.org/10.1145/3369114.3369123