From machine learning to big data & knowledge engineering, submit your research. IET journal sponsored by the Chinese Association for Artificial Intelligence. Submit today
- CAAI Editorial Board
CAAI Transactions on Intelligence
Technology
- Journal of Sleep Research
Expand your reach: open access
papers typically receive 4x ...
- CAAI Editorial Board
Suchergebnisse
Suchergebnisse:
Vor 4 Tagen · Our contributions include the development of a unified model for multi-subject brain decoding, the creation of a lightweight deep learning model, and the demonstration of strong performance with limited data. Overall, MindFormer provides a new framework for understanding multi-subject brain decoding and common neural patterns. The model’s ability to leverage shared information across ...
Vor 5 Tagen · As we can observe in Fig. 7, in terms of FDR and SHD, using meta-learning algorithm (setting \(lr > 0\)) increases the performance of the algorithm compared to the decoupled NoTears. This again verifies the benefit of knowledge sharing enabled by our meta-learning algorithm. As the learning rate increases to sufficiently large (greater than 0.8 in this case), we can see that the algorithm ...
Vor einem Tag · In datasets where there are many more negative than positive instances, it has become common wisdom that the ROC-AUC is inflated and the PR-AUC should be used. The authors show that this is a misunderstanding: the ROC-AUC is invariant to class imbalance when the score distribution isn’t changed by the imbalance, in contrast to the PR-AUC. The ROC-AUC and its early retrieval area allow an ...
Vor 22 Stunden · Student performance prediction has been addressed using various approaches, with machine learning models being the most common in the literature. The effectiveness of these models hinges on the relevant features they are trained on, such as numerical/alphabetical grades and demographic information, among others [ 13 ].
Vor 5 Tagen · We have tested the performance of the proposed feature selection algorithm using five machine learning classifiers: Logistic Regression (LR), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes (NB) classifier, and Decision Tree (DT) classifier on four student performance datasets. The experimental results highlight the proposed method significantly decreases feature count by ...
Vor 4 Tagen · We have used the principal components analysis PCA technique to decrease the attributes of the datasets. The performance of these three classifiers‟ was compared using dataset parameters such as size, number of attributes, number of classes, class imbalance, and missing values. The accuracy, precision, recall, and F1-score of the classifiers ...
Vor 2 Tagen · Machine learning model monitoring measures how well your machine learning models perform a task during training and in real-time deployment. As machine learning engineers and data scientists, we define performance metrics such as accuracy, F1 score, Recall, etc., which compare the predictions of machine learning models with the known ...
ScaleOut's real-time digital twins use machine learning to analyze telemetry from devices. Run ML within digital twins to identify trends in telemetry streams and send alerts.
