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

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

, MSE, and MAE of the optimised algorithms. by Itxasne Antúnez Sáenz (22794696)

Folder with all data and algorithms by Jorge Servert Lerdo de Tejada (22290001)

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

Risk element category diagram. by Yao Hu (3479972)

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IRS-assisted system model. by Sarmad Sohaib (21273402)

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Convergence curve of the DBO algorithm. by 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. …”

Computational Domain: The computational domain was discretized using conformal tetrahedral elements, resulting in a total of 926K elements. by Martina Bukač (20161460)

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

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

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

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

“…Finally, a transformer diagnostic model based on SSA-LightGBM was constructed, and the ten fold cross validation method was used to verify the classification ability of the model. …”

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

Soft sensor modeling of steel pickling concentration based on IGEP algorithm by Li Wang (15202)

“…In this study, an indirect soft sensor model based on the improved gene expression programming (IGEP) algorithm has been constructed, leveraging easily measurable indexes from a large-scale dataset. …”

Mitochondrial toxic prediction of marine alga toxins using a predictive model based on feature coupling and ensemble learning algorithms by 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. by Tomokaze Shiratori (9635271)

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

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

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

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

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