Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

Data Sheet 1_Spatial prediction of ground substrate thickness in shallow mountain area based on machine learning model.pdf by Xiaosong Zhu (11721357)

“…</p>Methods<p>This study utilizes six machine learning algorithms—Gradient Boosting Machine (GB), Random Forest (RF), AdaBoost Regressor (AB), Neural Network (NN), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN)—to predict ground substrate thickness. …”

Spatial power spectrum of compact sound source. by Chuanxi Xing (20141665)

Supplementary file 1_3D colored point cloud classification of a deep-sea cold-water coral and sponge habitat using geometric features and machine learning algorithms.docx by Salem Morsy (10840104)

“…Three unsupervised (k-means (KM), fuzzy c-means (FCM), and Gaussian mixture model (GMM)) and three supervised (decision tree (DT), random forest (RF), and linear discriminant analysis (LDA)) machine learning (ML) algorithms were compared and assessed for accuracy and reliability. …”

Data Sheet 1_Convolutional neural networks decode finger movements in motor sequence learning from MEG data.docx by Aleksey Zabolotniy (22211695)

“…We also compared LF-CNN to existing deep learning architectures such as EEGNet, FBCSP-ShallowNet, and VGG19.…”