Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Eli Shnaider"'
Publikováno v:
Computers, Vol 13, Iss 3, p 62 (2024)
Association rule learning is a machine learning approach aiming to find substantial relations among attributes within one or more datasets. We address the main problem of this technology, which is the excessive computation time and the memory require
Externí odkaz:
https://doaj.org/article/d216282bbd1441568f39d696d7954e9e
Publikováno v:
Bioinformatics and Biology Insights, Vol 17 (2023)
In this study, we introduce an artificial intelligent method for addressing the batch effect of a transcriptome data. The method has several clear advantages in comparison with the alternative methods presently in use. Batch effect refers to the disc
Externí odkaz:
https://doaj.org/article/1f505f06818042a388748d760ad69e7d
Publikováno v:
Soft Computing. 27:7813-7829
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d9fe62fc99402185a81f2d4b3da04abd
https://doi.org/10.1007/s00500-023-08049-4
https://doi.org/10.1007/s00500-023-08049-4
Publikováno v:
Fuzzy Sets and Systems. 449:100-119
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd988a94b9b6723deb2dbfda679ce347
https://doi.org/10.1016/j.fss.2022.01.011
https://doi.org/10.1016/j.fss.2022.01.011
Publikováno v:
International Journal of Information Technology & Decision Making. 21:109-141
In this study, we introduce a data mining method to identify biased and/or misleading outlooks for future performance of various factors, such as income, corporate profits, production, countries’ GDP, etc. The method consists of several components.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5587c3ff785915541b3be9aee5c45441
https://doi.org/10.1142/s0219622021500516
https://doi.org/10.1142/s0219622021500516
Publikováno v:
International Journal of Intelligent Systems. 36:2267-2291
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::04ae7f68c8b96239e9d5b7729cbf07db
https://doi.org/10.1002/int.22378
https://doi.org/10.1002/int.22378
Publikováno v:
Journal of Intelligent & Fuzzy Systems. 40:117-129
This study introduces computerized model for evaluation of corporate performance for companies traded in the main world stock markets. The main contribution of this study is to utilize a “Soft Regression” modeling tool, which is a soft computing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0a3c2d237b4bc9597770404511a28d5
https://doi.org/10.3233/jifs-190109
https://doi.org/10.3233/jifs-190109
In this study we introduce an artificial intelligent method for addressing the batch effect of a transcriptome data. The method has several clear advantages in comparison to the alternative methods presently in use. Batch effect refers to the discrep
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::612f0bca928c3f540cae581639938503
https://doi.org/10.21203/rs.3.rs-1364951/v1
https://doi.org/10.21203/rs.3.rs-1364951/v1
Autor:
Eli Shnaider, Arthur Yosef
Publikováno v:
Computational Economics. 55:211-230
Very often, model-building professionals are facing a very difficult choice of selecting relevant variable/s from a set of several similar variables. All those variables are supposedly representing the same factor but are measured differently. They a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f17364244c008175bc9f6f97d206c0da
https://doi.org/10.1007/s10614-019-09889-9
https://doi.org/10.1007/s10614-019-09889-9
Decision Support System Based on Fuzzy Logic for Assessment of Expected Corporate Income Performance
Publikováno v:
International Journal of Computational Intelligence and Applications. 20
This study presents a decision-support method to estimate the next year performance of corporate Operating Income Margin (OIM). It is based on a unique combination of cross-section model and the rules-based evaluation mechanism. The estimate is done
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::accf12b85394de4f42b284aa61ea3bde
https://doi.org/10.1142/s1469026821500097
https://doi.org/10.1142/s1469026821500097