Results 21 to 30 of about 2,368,157 (325)

Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees

Proceedings on Privacy Enhancing Technologies, 2023
Applying machine learning (ML) to sensitive domains requires privacy protection of the underlying training data through formal privacy frameworks, such as differential privacy (DP). Yet, usually, the privacy of the training data comes at the cost of the resulting ML models' utility. One reason for this is that DP uses one uniform privacy budget epsilon
Franziska Boenisch, Christopher Mühl, Roy Rinberg, Jannis Ihrig, Adam Dziedzic   +4 more
openaire   +2 more sources

Machine learning model for malaria risk prediction based on mutation location of large-scale genetic variation data

Journal of Big Data, 2022
In recent malaria research, the complexity of the disease has been explored using machine learning models via blood smear images, environmental, and even RNA-Seq data. However, a machine learning model based on genetic variation data is still required to
Kah Yee Tai, Jasbir Dhaliwal
doaj   +1 more source

Machine Learning for Predicting Individual Severity of Blepharospasm Using Diffusion Tensor Imaging

Frontiers in Neuroscience, 2021
Accumulating diffusion tensor imaging (DTI) evidence suggests that white matter abnormalities evaluated by local diffusion homogeneity (LDH) or fractional anisotropy (FA) occur in patients with blepharospasm (BSP), both of which are significantly ...
Gang Liu, Gang Liu, Yanan Gao, Yanan Gao, Ying Liu, Ying Liu, Yaomin Guo, Yaomin Guo, Zhicong Yan, Zhicong Yan, Zilin Ou, Zilin Ou, Linchang Zhong, Chuanmiao Xie, Jinsheng Zeng, Jinsheng Zeng, Weixi Zhang, Weixi Zhang, Kangqiang Peng, Qingwen Lv   +19 more
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Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method

NeuroImage, 2021
Resting-state functional connectivity (RSFC) can be used for mapping large-scale human brain networks during rest. There is considerable interest in distinguishing the individual-shared and individual-specific components in RSFC for the better ...
Xuetong Wang, Qiongling Li, Yan Zhao, Yirong He, Baoqiang Ma, Zhenrong Fu, Shuyu Li   +6 more
doaj   +1 more source

Influence of Individual Differences in fMRI-Based Pain Prediction Models on Between-Individual Prediction Performance

Frontiers in Neuroscience, 2018
Decoding subjective pain perception from functional magnetic resonance imaging (fMRI) data using machine learning technique is gaining a growing interest. Despite the well-documented individual differences in pain experience and brain responses, it still
Qianqian Lin, Qianqian Lin, Linling Li, Linling Li, Jia Liu, Jia Liu, Weixiang Liu, Weixiang Liu, Gan Huang, Gan Huang, Zhiguo Zhang, Zhiguo Zhang   +11 more
doaj   +1 more source

Reconstructing lost BOLD signal in individual participants using deep machine learning

Nature Communications, 2020
Signal loss in blood oxygen level‐dependent (BOLD) fMRI can lead to misinterpretation of findings. The authors trained a deep learning model to reconstruct compromised BOLD signal in datasets from healthy participants and in patients whose scans suffered
Yuxiang Yan, Louisa Dahmani, Jianxun Ren, Lunhao Shen, Xiaolong Peng, Ruiqi Wang, Changgeng He, Changqing Jiang, Chen Gong, Ye Tian, Jianguo Zhang, Yi Guo, Yuanxiang Lin, Shijun Li, Meiyun Wang, Luming Li, Bo Hong, Hesheng Liu   +17 more
doaj   +1 more source

Getting personal with epigenetics: towards individual-specific epigenomic imputation with machine learning

Nature Communications, 2023
Epigenetic modifications are dynamic mechanisms involved in the regulation of gene expression. Unlike the DNA sequence, epigenetic patterns vary not only between individuals, but also between different cell types within an individual.
Alex Hawkins-Hooker, Giovanni Visonà, Tanmayee Narendra, Mateo Rojas-Carulla, Bernhard Schölkopf, Gabriele Schweikert   +5 more
doaj   +1 more source

Verifying Individual Fairness in Machine Learning Models

CoRR, 2020
An extended version of the paper accepted at UAI 2020, 12 pages, code is available at https://github.com/philips-george/ifv-uai ...
Philips George John, Deepak Vijaykeerthy, Diptikalyan Saha   +2 more
openaire   +3 more sources

Machine learning for automatic prediction of the quality of electrophysiological recordings [PDF]

, 2013
The quality of electrophysiological recordings varies a lot due to technical and biological variability and neuroscientists inevitably have to select “good” recordings for further analyses.
AB Wiltschko, BT Priest, C Mathes, CG Galizia, Dominique Martinez, F Franke, H Lei, Jean-Pierre Rospars, Johannes Reisert, M Asmild, MS Lewicki, R Friedrich, R Kohavi, S Panzeri, S Takahashi, SB Wilson, Shereen Elbanna, Sylvia Anton, T Nowotny, Thomas Nowotny, Y Saeys   +20 more
core   +8 more sources

Determining university’s readiness to implement AI technologies for personalizing educational paths

Вестник университета, 2023
The key issue of the article is to determine readiness of HEIs to implement artificial intelligence and machine learning technologies for personalization of students’ individual educational trajectories (hereinafter – IET).
V. S. Starostin, K. A. Arzhanova, D. V. Dolgopolov, A. D. Ditrikh   +3 more
doaj   +1 more source