Standout Papers

Data augmentation for deep-learning-based electroencephalography 2020 2026 2022 2024227
  1. Data augmentation for deep-learning-based electroencephalography (2020)
    Elnaz Lashgari, Uri Maoz et al. Journal of Neuroscience Methods

Immediate Impact

1 from Science/Nature 42 standout
Sub-graph 1 of 19

Citing Papers

Interpretable modulated differentiable STFT and physics-informed balanced spectrum metric for freight train wheelset bearing cross-machine transfer fault diagnosis under speed fluctuations
2024 Standout
Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations
2024 Standout
2 intermediate papers

Works of Uri Maoz being referenced

Data augmentation for deep-learning-based electroencephalography
2020 Standout
An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data
2019

Author Peers

Author Last Decade Papers Cites
Uri Maoz 431 63 101 96 30 707
Nicola Moccaldi 289 44 76 59 41 578
Brent J. Lance 538 75 128 79 23 700
Ji-Hoon Jeong 479 39 186 64 53 779
Matteo Spezialetti 287 74 51 50 32 641
Junfeng Gao 503 151 61 135 38 710
Yuliang Ma 403 24 61 70 41 623
Md. Kafiul Islam 448 19 77 96 31 660
Xiangguo Yan 510 24 72 113 27 648
Zhao Lv 386 29 76 51 57 631
Yufeng Ke 519 90 138 49 53 640

All Works

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2026