Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Salman Salloum"'
Publikováno v:
CAAI Transactions on Intelligence Technology, Vol 8, Iss 4, Pp 1425-1443 (2023)
Abstract In this study, an observation points‐based positive‐unlabeled learning algorithm (hence called OP‐PUL) is proposed to deal with positive‐unlabeled learning (PUL) tasks by judiciously assigning highly credible labels to unlabeled samp
Externí odkaz:
https://doaj.org/article/bbc5dc27378d43ff9ecf6f563511928e
Publikováno v:
CAAI Transactions on Intelligence Technology, Vol 8, Iss 2, Pp 500-517 (2023)
Abstract In this paper, an Observation Points Classifier Ensemble (OPCE) algorithm is proposed to deal with High‐Dimensional Imbalanced Classification (HDIC) problems based on data processed using the Multi‐Dimensional Scaling (MDS) feature extra
Externí odkaz:
https://doaj.org/article/9a38f78e33244f7fafdb335f0f1b8dc6
Autor:
Kuanishbay Sadatdiynov, Laizhong Cui, Lei Zhang, Joshua Zhexue Huang, Salman Salloum, Mohammad Sultan Mahmud
Publikováno v:
Digital Communications and Networks, Vol 9, Iss 2, Pp 450-461 (2023)
Handling the massive amount of data generated by Smart Mobile Devices (SMDs) is a challenging computational problem. Edge Computing is an emerging computation paradigm that is employed to conquer this problem. It can bring computation power closer to
Externí odkaz:
https://doaj.org/article/537f6234107942b892831a0fa801ac8f
Autor:
Mohammad Sultan Mahmud, Joshua Zhexue Huang, Salman Salloum, Tamer Z. Emara, Kuanishbay Sadatdiynov
Publikováno v:
Big Data Mining and Analytics, Vol 3, Iss 2, Pp 85-101 (2020)
Computer clusters with the shared-nothing architecture are the major computing platforms for big data processing and analysis. In cluster computing, data partitioning and sampling are two fundamental strategies to speed up the computation of big data
Externí odkaz:
https://doaj.org/article/3fc66f78b01c4f7e9c34f86beb1835a3
Publikováno v:
Journal of Big Data, Vol 6, Iss 1, Pp 1-28 (2019)
Abstract Data scientists need scalable methods to explore and clean big data before applying advanced data analysis and mining algorithms. In this paper, we propose the RSP-Explore method to enable data scientists to iteratively explore big data on s
Externí odkaz:
https://doaj.org/article/5200465654254b3ba3734531c8276d83
Publikováno v:
IEEE Access, Vol 7, Pp 3675-3693 (2019)
In order to enable big data analysis when data volume goes beyond the available computing resources, we propose a new method for big data analysis. This method uses only a few random sample data blocks of a big data set to obtain approximate results
Externí odkaz:
https://doaj.org/article/00afc742ec3445af88051cfff772ccd4
Publikováno v:
CAAI Transactions on Intelligence Technology.
Autor:
Salman Salloum, Tamer Z. Emara, Mohammad Sultan Mahmud, Kuanishbay Sadatdiynov, Joshua Zhexue Huang
Publikováno v:
Big Data Mining and Analytics. 3:85-101
Computer clusters with the shared-nothing architecture are the major computing platforms for big data processing and analysis. In cluster computing, data partitioning and sampling are two fundamental strategies to speed up the computation of big data
Publikováno v:
IEEE Transactions on Industrial Informatics. 15:5846-5854
With the ever-increasing volume of data, alternative strategies are required to divide big data into statistically consistent data blocks that can be used directly as representative samples of the entire data set in big data analysis. In this paper,
Autor:
Salman Salloum, Joshua Zhexue Huang
Publikováno v:
2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC).