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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc

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樓主: radionuclides
31#
發(fā)表于 2025-3-27 00:15:33 | 只看該作者
32#
發(fā)表于 2025-3-27 01:58:47 | 只看該作者
Interpretable EHR Disease Prediction System Based on?Disease Experts and?Patient Similarity Graph (Ding the data collected from electronic health records (EHRs) to predict future events or patient outcomes in the healthcare industry. Though these models already proficiently capture sequence data and provide invaluable insights and treatment solutions for patients, it would be desirable to further
33#
發(fā)表于 2025-3-27 05:35:15 | 只看該作者
Meteorological Data Based Detection of?Stroke Using Machine Learning TechniquesNetworks for detecting days with a greater probability of stroke incidence in the region of Transylvania, Romania. Being the first to address this problem in Romania, the study contributes to previous research by employing Machine Learning approaches and applying them to meteorological data that als
34#
發(fā)表于 2025-3-27 12:27:23 | 只看該作者
35#
發(fā)表于 2025-3-27 14:27:48 | 只看該作者
ProTeM: Unifying Protein Function Prediction via?Text Matching. However, finetuning a pretrained protein language model for diverse downstream tasks requires annotated protein data tailored to each task. To avoid the redundant individual finetuning, we propose a methodology that unifies various .tein function prediction tasks via .xt .atching (named .). This m
36#
發(fā)表于 2025-3-27 20:54:05 | 只看該作者
37#
發(fā)表于 2025-3-27 22:04:55 | 只看該作者
Unveiling the?Potential of?Synthetic Data in?Sports Science: A Comparative Study of?Generative Methoin scenarios involving invasive data collection. To address this limitation, we explored the idea of generating synthetic time-series data from a constrained dataset of five athletes, including daily metrics such as sleep quality, mood, training load (Foster load), and an indicator of the intrinsic
38#
發(fā)表于 2025-3-28 03:35:37 | 只看該作者
39#
發(fā)表于 2025-3-28 10:15:43 | 只看該作者
Advancing Free-Breathing Cardiac Cine MRI: Retrospective Respiratory Motion Correction Via Kspace-anfically for high-quality correction of respiratory motion, a prevalent challenge in cardiac cine MRI. Respiratory motion, caused by the natural movement of the thorax and diaphragm during breathing, often results in artifacts that can significantly degrade image quality. By leveraging dual-domain co
40#
發(fā)表于 2025-3-28 14:16:42 | 只看該作者
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