Zobrazeno 1 - 10
of 2 637
pro vyhledávání: '"Noisy Data"'
Publikováno v:
AppliedMath, Vol 4, Iss 2, Pp 561-569 (2024)
This paper presents a novel deep-learning network designed to detect intervals of jump discontinuities in single-variable piecewise smooth functions from their noisy samples. Enhancing the accuracy of jump discontinuity estimations can be used to fin
Externí odkaz:
https://doaj.org/article/0b0f45feb61f4bba98face1ec36ac3c0
Publikováno v:
Proceedings of the International Conference on Applied Innovations in IT, Vol 12, Iss 1, Pp 123-127 (2024)
A novel approach has been introduced to estimate the parameters of exponential and DN distributions during the rejection testing of electronic devices, accompanied by a detailed procedure for its implementation. This innovative method enhances noise
Externí odkaz:
https://doaj.org/article/d412550d5afc490d8650ba230662f360
Publikováno v:
In Information Sciences August 2024 677
Publikováno v:
IEEE Access, Vol 12, Pp 140643-140659 (2024)
We present an ensemble learning-based data cleaning approach (touted as ELDC) capable of identifying and pruning anomaly data. ELDC is characterized in that an ensemble of base models can be trained directly with the noisy in-sample data and can dyna
Externí odkaz:
https://doaj.org/article/7e4c9f38266b4fc7ac50bdf76a2f947d
Publikováno v:
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-18 (2024)
Abstract Background Convolutional neural network-based image processing research is actively being conducted for pathology image analysis. As a convolutional neural network model requires a large amount of image data for training, active learning (AL
Externí odkaz:
https://doaj.org/article/3d6ed6f9465845ff8438ece192681dde
Publikováno v:
IEEE Access, Vol 12, Pp 54382-54396 (2024)
The healthcare fraud detection field is constantly evolving and faces significant challenges, particularly when addressing imbalanced data issues. Previous studies mainly focused on traditional machine learning (ML) techniques, often struggling with
Externí odkaz:
https://doaj.org/article/1fbffb8d46b841b291bd76d99dba9f8b
Publikováno v:
Mathematics, Vol 12, Iss 20, p 3229 (2024)
Federated Learning (FL) enables decentralized data utilization while maintaining edge user privacy, but it faces challenges due to statistical heterogeneity. Existing approaches address client drift and data heterogeneity issues. However, real-world
Externí odkaz:
https://doaj.org/article/b23a9f5f1c2f4d36991260d04811f0a0
Publikováno v:
Tongxin xuebao, Vol 44, Pp 198-212 (2023)
Blockchain provides a trusted channel for information sharing in an untrusted network.However, blockchain ledgers are public and transparent.Hence, the sensitive data stored in plaintext on the blockchain will compromise user privacy.Considering priv
Externí odkaz:
https://doaj.org/article/b20860af075c46be80f3cf71986e4983
Publikováno v:
Sensors, Vol 24, Iss 13, p 4268 (2024)
The Broad Learning System (BLS) has demonstrated strong performance across a variety of problems. However, BLS based on the Minimum Mean Square Error (MMSE) criterion is highly sensitive to label noise. To enhance the robustness of BLS in environment
Externí odkaz:
https://doaj.org/article/dbc01454fb0d496c998e163a7d579daa
Publikováno v:
IEEE Access, Vol 11, Pp 138095-138107 (2023)
Attribute reduction, often referred to as feature selection, is a vital step in data preprocessing aimed at eliminating unnecessary attributes and enhancing the efficiency of classification models. Intuitionistic fuzzy sets are widely acknowledged fo
Externí odkaz:
https://doaj.org/article/0b76305b55ce4f319ab37924badb1e87