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

Convergence curve of the DBO algorithm. حسب Ma Haohao (22177538)

"…The improved Dung Beetle Optimization algorithm, Back Propagation Neural Network, Finite Element Analysis, and Response Surface Methodology provide a strong guarantee for the selection of robot polishing process parameters. …"

Top-rated mode of transport based on the SEU algorithm (SEU scores from 0 to 40). حسب Johannes Weyer (13392175)

Scatter diagram of different principal elements. حسب Jizhong Wang (7441697)

Numbers of iterations (#Ite) and eigenvalue calculations (#Eig) in the DC algorithm on the random and chain graph datasets. حسب Tomokaze Shiratori (9635271)

الموضوعات:

The comparison of results from the proposed architecture and state-of-the-art algorithms on the UNSW-B15, NSL-KDD and KDDCUP’99 dataset. حسب Abdullah Asım Yılmaz (20715311)

Comparison of results based on CycleGAN method. حسب Xiaojun Wang (50332)

Element model generation method with geometric distribution errors حسب Yiqi Liu (21357815)

"…The product surface geometric distribution error is directly attached to the element nodes of the product ideal element model using the error surface reconstruction method and the replacement algorithm of the element node vector height based on the product’s point cloud data. …"

Model-Based Clustering of Categorical Data Based on the Hamming Distance حسب Raffaele Argiento (647076)

"…<p>A model-based approach is developed for clustering categorical data with no natural ordering. …"

Mitochondrial toxic prediction of marine alga toxins using a predictive model based on feature coupling and ensemble learning algorithms حسب Guangyin Jia (21075537)

"…By comparing 8 machine learning algorithms and using a weighted soft voting method to integrate the two optimal algorithms, we established 108 prediction models and identified the best ensemble learning model MACCS_LK for screening and defining its application domain. …"

Computation time as a function of the sample size on the chain graph dataset. حسب Tomokaze Shiratori (9635271)

الموضوعات:

Log-likelihood as a function of the number of selected edges on the chain graph dataset (black dashed line: The true number of edges). حسب Tomokaze Shiratori (9635271)

الموضوعات:

Computation time as a function of the sample size on the random graph dataset. حسب Tomokaze Shiratori (9635271)

الموضوعات:

F1 score of edges selected through cross-validation on the chain graph dataset. حسب Tomokaze Shiratori (9635271)

الموضوعات:

Log-likelihood as a function of the number of selected edges on the random graph dataset (black dashed line: The true number of edges). حسب Tomokaze Shiratori (9635271)

الموضوعات:

Examples of ground-truth graph structures with (<i>p</i>, <i>n</i>_≠0) = (10, 10). حسب Tomokaze Shiratori (9635271)

الموضوعات:

Number of edges selected through cross-validation on the chain graph dataset. حسب Tomokaze Shiratori (9635271)

الموضوعات:

Number of edges selected through cross-validation on the random graph dataset. حسب Tomokaze Shiratori (9635271)

الموضوعات:

Graphs of the regularization terms. حسب Tomokaze Shiratori (9635271)

الموضوعات:

F1 score of a given number of selected edges on the chain graph dataset. حسب Tomokaze Shiratori (9635271)

الموضوعات: