Zobrazeno 1 - 10
of 2 177
pro vyhledávání: '"vae"'
Autor:
Sivalingan H
Publikováno v:
Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ, Vol 15, Iss 4, Pp 125-140 (2024)
Anomaly detection in computer vision is crucial, and manual identification of irregularities in videos is resource-intensive. Autonomous systems are essential for efficiently analysing and detecting anomalies in diverse video datasets. Video surveill
Externí odkaz:
https://doaj.org/article/fdd2c1b86b1141c8b73d6d12be9f1807
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract The variational autoencoder (VAE) architecture has significant advantages in predictive image generation. This study proposes a novel RFCNN-βVAE model, which combines residual-connected fully connected neural networks with VAE to handle mul
Externí odkaz:
https://doaj.org/article/c49193c54d504893bb624691f1c8d34e
Autor:
Faleh Alshameri, Ran Xia
Publikováno v:
Big Data Mining and Analytics, Vol 7, Iss 3, Pp 718-729 (2024)
Anomaly detection is one of the many challenging areas in cybersecurity. The anomaly can occur in many forms, such as fraudulent credit card transactions, network intrusions, and anomalous imageries or documents. One of the most common challenges in
Externí odkaz:
https://doaj.org/article/e5bbb3ec49a04a9bad70a5633d6c835a
Publikováno v:
Underground Space, Vol 17, Iss , Pp 226-245 (2024)
We conducted a study to evaluate the potential and robustness of gradient boosting algorithms in rock burst assessment, established a variational autoencoder (VAE) to address the imbalance rock burst dataset, and proposed a multilevel explainable art
Externí odkaz:
https://doaj.org/article/43bd135836774e6a8f26fd3bd4cabfb7
Autor:
Tong Zhao, Yi Cui, Taoyun Ji, Jiejian Luo, Wenling Li, Jun Jiang, Zaifen Gao, Wenguang Hu, Yuxiang Yan, Yuwu Jiang, Bo Hong
Publikováno v:
NeuroImage, Vol 304, Iss , Pp 120946- (2024)
The electroencephalogram (EEG) exhibits characteristics of complexity and strong randomness. Existing deep learning models for EEG typically target specific objectives and datasets, with their scalability constrained by the size of the dataset, resul
Externí odkaz:
https://doaj.org/article/73ffdbaafd1a44899c419ba86be563c1
Autor:
Rathod Hiral Yashwantbhai, Haresh Dhanji Chande, Sachinkumar Harshadbhai Makwana, Payal Prajapati, Archana Gondalia, Pinesh Arvindbhai Darji
Publikováno v:
Measurement: Sensors, Vol 36, Iss , Pp 101401- (2024)
With the proliferation of smart wearables, motion wristbands provide a wealth of data essential for comprehending the dynamic nature of health. However, outlier detection is typically necessary due to the presence of unknown outliers in their multidi
Externí odkaz:
https://doaj.org/article/f1b6e5996c6445cf90138948e396e846
Autor:
Peng Han, Xiangliang Zhang
Publikováno v:
International Journal of Crowd Science, Vol 8, Iss 2, Pp 95-99 (2024)
Disease-gene association, an important problem in the biomedical area, can be used to early intervene the treat of deadly diseases. Recently, models based on graph convolutional networks (GCNs) have outperformed previous linear models on predicting t
Externí odkaz:
https://doaj.org/article/433f523c1b194c51bb094eaeb00d9154
Publikováno v:
Results in Engineering, Vol 23, Iss , Pp 102504- (2024)
Accurate wind power prediction is critical for efficient grid management and the integration of renewable energy sources into the power grid. This study presents an effective deep-learning approach that improves short-term wind power forecasting accu
Externí odkaz:
https://doaj.org/article/721e9dd10236449e925de32a119933b1
Autor:
Yinzhu Jin, Rory McDaniel, N. Joseph Tatro, Michael J. Catanzaro, Abraham D. Smith, Paul Bendich, Matthew B. Dwyer, P. Thomas Fletcher
Publikováno v:
Frontiers in Computer Science, Vol 6 (2024)
Many deep generative models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), learn an immersion mapping from a standard normal distribution in a low-dimensional latent space into a higher-dimensional data space. As
Externí odkaz:
https://doaj.org/article/27a48a21b24043779d3800b7b6875e1f