Zobrazeno 1 - 10
of 8 821
pro vyhledávání: '"Data Classification"'
Autor:
More, Anjali, Rana, Dipti
Publikováno v:
International Journal of Pervasive Computing and Communications, 2022, Vol. 20, Issue 4, pp. 525-541.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJPCC-02-2022-0034
Autor:
Xu Pengru, Zhou Junhui, Kausar Nasreen, Lin Chunlei, Lu Qianqian, Ghaderpour Ebrahim, Pamucar Dragan, Zadeh Ardashir M.
Publikováno v:
Demonstratio Mathematica, Vol 57, Iss 1, Pp 339-350 (2024)
Wearable sensors (WS) play a vital role in health assistance to improve the patient monitoring process. However, the existing data collection process faces difficulties in error corrections, rehabilitation, and training validations. Therefore, the da
Externí odkaz:
https://doaj.org/article/62d48a41305b4b29b4bd3c848ffee115
Autor:
Phan Anh Phong, Le Van Thanh
Publikováno v:
Tạp chí Khoa học, Vol 53, Iss 3A, Pp 5-15 (2024)
This paper proposes a method to enhance the effectiveness of classifying imbalanced data. The main contribution of the method is integrating the K-means clustering algorithm and the minority oversampling technique VCIR to generate synthetic samples t
Externí odkaz:
https://doaj.org/article/65f1e4fa5a394c75865bbff7cd519028
Publikováno v:
AIMS Mathematics, Vol 9, Iss 8, Pp 22366-22392 (2024)
We considered a convex bilevel optimization problem when the outer level problem was to find a minimizer of a strongly convex function over the set of solutions of the inner level problem which was in the form of minimization of the sum of a convex d
Externí odkaz:
https://doaj.org/article/e1772dca09df421d867fb86f8b67cf83
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 10, Pp 102248- (2024)
In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose a novel oversampling method named FCM-KSMOTE. The algorithm initially performs a density-based fuzzy clustering on the data, then ite
Externí odkaz:
https://doaj.org/article/c1791d6a953c4f828bbfa714c1127ea0
Publikováno v:
AIMS Mathematics, Vol 9, Iss 7, Pp 17504-17530 (2024)
Learning from imbalanced data is a challenging task in the machine learning field, as with this type of data, many traditional supervised learning algorithms tend to focus more on the majority class while damaging the interests of the minority class.
Externí odkaz:
https://doaj.org/article/23cf291005224d8d951cab92b08a252c
Publikováno v:
Compiler, Vol 13, Iss 1, Pp 11-18 (2024)
Sleep is a fundamental aspect of human life, accounting for approximately one-third of our existence and playing a crucial role in the restoration of physical health and overall quality of life. However, poor sleep quality can interfere with these cr
Externí odkaz:
https://doaj.org/article/23947e71c2444ecb873a32f1ce82b6f4
Publikováno v:
Gong-kuang zidonghua, Vol 50, Iss 7, Pp 130-135, 146 (2024)
Currently, the construction of intelligent mines is facing problems such as incomplete data standards, difficulty in integrating heterogeneous data from multiple sources, and the need to improve sharing mechanisms. Establishing a unified intelligent
Externí odkaz:
https://doaj.org/article/3778bd20a05b4c59a044f04330b43a6e
Publikováno v:
International Journal of Applied Mathematics and Computer Science, Vol 34, Iss 1, Pp 149-165 (2024)
When constructing a new data classification algorithm, relevant quality indices such as classification accuracy (ACC) or the area under the receiver operating characteristic curve (AUC) should be investigated. End-users of these algorithms are intere
Externí odkaz:
https://doaj.org/article/659f1d906d5c47f79ace6aa7f5d084bd
Autor:
Puntita Sae-jia, Suthep Suantai
Publikováno v:
AIMS Mathematics, Vol 9, Iss 4, Pp 8476-8496 (2024)
In this paper, we propose a new accelerated algorithm for solving convex bilevel optimization problems using some fixed point and two-step inertial techniques. Our focus is on analyzing the convergence behavior of the proposed algorithm. We establish
Externí odkaz:
https://doaj.org/article/9733db00d7014feeaa0be9ec5855ce4c