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Titlebook: Generative Intelligence and Intelligent Tutoring Systems; 20th International C Angelo Sifaleras,Fuhua Lin Conference proceedings 2024 The E

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樓主: 孵化
41#
發(fā)表于 2025-3-28 16:56:08 | 只看該作者
https://doi.org/10.1007/978-1-4612-3232-2gineering, Industrial Engineering, and Economics. The results show that representing data in windows of time spanning 3 previous semesters, in conjunction with the LSTM-based algorithm for binary classification, yields the best results, achieving a precision of 0.838.
42#
發(fā)表于 2025-3-28 21:48:53 | 只看該作者
43#
發(fā)表于 2025-3-29 01:24:06 | 只看該作者
44#
發(fā)表于 2025-3-29 03:32:05 | 只看該作者
45#
發(fā)表于 2025-3-29 07:39:42 | 只看該作者
Assessing Cognitive Workload of Aircraft Pilots Through Face Temperature and facial muscle temperatures, alongside facial landmark points. The implications of these findings extend beyond mere academic curiosity, offering valuable insights into the physiological repercussions of workload. Moreover, they hold promise for enhancing aviation safety protocols and optimizing
46#
發(fā)表于 2025-3-29 14:20:05 | 只看該作者
MonaCoBERT: Monotonic Attention Based ConvBERT for?Knowledge TracingCoBERT achieves remarkable performance on most benchmark datasets. In addition, we used a classical test-theory-based embedding strategy to reflect the difficulty degree of knowledge concepts. We conducted ablation studies and further analysis to explain the remarkable performance of our model quant
47#
發(fā)表于 2025-3-29 18:08:06 | 只看該作者
Detection of Pre-error States in Aircraft Pilots Through Machine Learningf the FLANKER dataset using various models revealed the superiority of the transformer model, with notable reductions in false negatives and a final F1 score of 0.610. Moving beyond typical study conclusions, our objective extends to assessing model applicability in a secondary domain—evaluating the
48#
發(fā)表于 2025-3-29 22:06:38 | 只看該作者
Analysis of?Machine Learning Models for?Academic Performance Predictiongineering, Industrial Engineering, and Economics. The results show that representing data in windows of time spanning 3 previous semesters, in conjunction with the LSTM-based algorithm for binary classification, yields the best results, achieving a precision of 0.838.
49#
發(fā)表于 2025-3-30 02:59:09 | 只看該作者
Simplifying Decision Tree Classification Through the?AutoDTrees Web Application and?Services can be evaluated by using the k-fold cross-validation and presenting detailed metrics. Users are then able to save the pre-trained model and reuse it for predicting unclassified instances or visualizing the Decision Tree. AutoDTrees was evaluated in terms of user experience using the System Usabil
50#
發(fā)表于 2025-3-30 07:31:08 | 只看該作者
Generative Intelligence and Intelligent Tutoring Systems20th International C
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