FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz by Faizan Ali (21689861)

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Steps in the proposed Apriori algorithm. by Yongkang Ding (21807539)

“…Then, frequent item sets were extracted through the association rule mining algorithm, association rules were generated, and the key factors affecting the physical performance of athletes were analyzed. …”

Execution Steps of the EOF Algorithm. by Zezhi Lin (22521663)

User options and main steps of the generative algorithm. by Diana C. de Oliveira (9220729)

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Comparison of softsign function and tanh function. by Peihan Wen (6324315)

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Skeletonisation algorithm. by Jordan Hembrow (16908051)

Great machine learning performance with multiple classification algorithms. by Sydney C. Weiser (15383531)

Parameterization of the feature selection algorithm. by Chenyi Zhu (9383370)

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Comparison of metaheuristic algorithm parameterization. by Chenyi Zhu (9383370)

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The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

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Adaptive H-REMD simulation algorithm. by Danial Pourjafar-Dehkordi (7165523)

Step by step workflow for PRLF model training and data scoring. by John Prindle (10774286)

Schematic diagram of the TEAPS algorithm. by Yoshiaki Kariya (13767985)

“…To efficiently optimize BSR objective functions in g-LBFGS step, the objective function is changed in each subprocess as shown.…”

The losses of the training process before and after the optimization of loss function. by Peihan Wen (6324315)

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Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements by Pascal Wang (10130612)

“…In `main.py`, the parameters for the TAMS algorithm are specified (trajectory time, time step, score function, number of particles, type of score threshold, maximum number of iterations, noise level etc.). …”

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

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IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

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Table 12_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…For instance, our results revealed genes with important functions for each trait, such as SERPINA14 (plays a key role for the endometrial epithelium) identified for AFC, HSPG2 (associated with morphological development and tissue differentiation) identified for BW, among others.…”

Table 10_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…For instance, our results revealed genes with important functions for each trait, such as SERPINA14 (plays a key role for the endometrial epithelium) identified for AFC, HSPG2 (associated with morphological development and tissue differentiation) identified for BW, among others.…”

Table 15_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…For instance, our results revealed genes with important functions for each trait, such as SERPINA14 (plays a key role for the endometrial epithelium) identified for AFC, HSPG2 (associated with morphological development and tissue differentiation) identified for BW, among others.…”