The run time for each algorithm in seconds. by Edward Antonian (21453161)

“…The goal of this paper is to examine several extensions to KGR/GPoG, with the aim of generalising them a wider variety of data scenarios. The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. …”

Improve algorithm settings. by Hui Zhao (7395)

Subjects:

Comparison of different optimization algorithms. by Hang Zhao (143592)

Subjects: “…crayfish optimization algorithm…”

Kepler optimization algorithm. by Jiangli Yu (4601986)

Subjects:

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

Visualization of mean absolute SHAP values for conditional algorithm for each class. by Sadia Nazim (21440115)

Visualization of mean absolute SHAP values for Interventional algorithm for each class. by Sadia Nazim (21440115)

K-means++ clustering algorithm. by Zhen Zhao (159931)

“…Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Diagram of BILSTM-AEAM algorithm structure. by Hao Cao (2248066)

Subjects:

Optimization algorithms performance comparison across benchmark functions. by Sundreen Asad Kamal (20390818)

Effects of different algorithms on feature extraction of jamming signals. by Hui Zhao (7395)

Subjects:

The construction process of the improved data fusion model. by Tang Ruipeng (20141334)

Subjects:

Visualization of mean absolute SHAP values for conditional algorithm across all classes. by Sadia Nazim (21440115)

Visualization of mean absolute SHAP values for Interventional algorithm across all classes. by Sadia Nazim (21440115)

Summary of the metrics of the different trained algorithms that performed best in the Testing process for each dataset. by Jaime B. Matas-Bustos (21772430)

Summary of the metrics of the different trained algorithms based on mean and 95% confidence intervals (calculated using t-Student) for the Testing process in each dataset. by Jaime B. Matas-Bustos (21772430)

Schematic diagram of TSO-VMD-BILSTM-AEAM algorithm structure. by Hao Cao (2248066)

Subjects:

Notations and definitions used in the algorithm. by Nazym Alimbekova (22766158)

Flow of data processing and preparation. by Xuan Wang (55634)

Subjects:

Dynamic window based median filtering algorithm. by Li Yuan (102305)

“…Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. The model uses fixed K-mean algorithm for feature classification and optimizes median filtering algorithm using dynamic thresholding. …”