ergonomics
发表于 2025-3-21 19:04:12
书目名称Artificial Intelligence in Medicine影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0162485<br><br> <br><br>书目名称Artificial Intelligence in Medicine影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0162485<br><br> <br><br>书目名称Artificial Intelligence in Medicine网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0162485<br><br> <br><br>书目名称Artificial Intelligence in Medicine网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0162485<br><br> <br><br>书目名称Artificial Intelligence in Medicine被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0162485<br><br> <br><br>书目名称Artificial Intelligence in Medicine被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0162485<br><br> <br><br>书目名称Artificial Intelligence in Medicine年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0162485<br><br> <br><br>书目名称Artificial Intelligence in Medicine年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0162485<br><br> <br><br>书目名称Artificial Intelligence in Medicine读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0162485<br><br> <br><br>书目名称Artificial Intelligence in Medicine读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0162485<br><br> <br><br>
粗糙滥制
发表于 2025-3-21 23:46:42
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建筑师
发表于 2025-3-22 02:07:51
Gary R. Hudes MD,Jessie Schol RNsicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways.
orient
发表于 2025-3-22 06:33:15
Geistesgeschichtliche Faschismusdiagnosen,nd new smaller models were trained, achieving a performance as good as the initial ones. Despite the susceptibility of all models to adversarial attacks, adversarial training enabled them to preserve significantly higher results, so it can be a valuable approach to provide a more robust driver drowsiness detection.
Ferritin
发表于 2025-3-22 12:24:36
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Bombast
发表于 2025-3-22 13:13:28
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deficiency
发表于 2025-3-22 17:54:00
Causal Discovery with Missing Data in a Multicentric Clinical Studysicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways.
MAUVE
发表于 2025-3-22 21:38:53
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detectionnd new smaller models were trained, achieving a performance as good as the initial ones. Despite the susceptibility of all models to adversarial attacks, adversarial training enabled them to preserve significantly higher results, so it can be a valuable approach to provide a more robust driver drowsiness detection.
indigenous
发表于 2025-3-23 03:49:39
Computational Evaluation of Model-Agnostic Explainable AI Using Local Feature Importance in Healthcaocal feature importances) as features and the output of the prediction problem (labels) again as labels. We evaluate the method based a real-world tabular electronic health records dataset. At the end, we answer the research question: “How can we computationally evaluate XAI Models for a specific prediction model and dataset?”.
Focus-Words
发表于 2025-3-23 07:20:40
Batch Integrated Gradients: Explanations for Temporal Electronic Health RecordsRecords (EHRs), we see patient records can be stored in temporal sequences. Thus, we demonstrate Batch-Integrated Gradients in producing explanations over a temporal sequence that satisfy proposed properties corresponding to XAI for EHR data.