Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Feature selection algorithm. by Mahmoud Zeydabadinezhad (12289570)

“…Our analysis pipeline included pre-processing steps, feature extraction from both time and frequency domains, a voting algorithm for selecting features, and model training and validation. …”

CEC2017 basic functions. by Tengfei Ma (597633)

“…Specifically, it achieves faster iteration speeds across four different environments, with the planned path length after escaping local optima being shortened by an average of 7.55175 m (16.291%) compared to other optimization algorithms. These results confirm OP-ZOA’s enhanced optimization capability, significantly improving both escape efficiency from local optima and solution reliability.…”

F1-scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

“…Empirical evaluations conducted on multiple benchmark datasets demonstrate that the proposed method outperforms classical anomaly detection algorithms while surpassing conventional model averaging techniques based on minimizing standard loss functions. …”

Summary of results of naïve Bayes algorithms. by Christopher E. Niemczak (8586861)

“…Algorithms trained without auditory variables as features were statistically worse (p < .001) in both the primary measure of area under the curve (0.82/0.78) and the secondary measure of accuracy (72.3%/74.5%) for the Gaussian and kernel algorithms respectively.…”

Optimum feature set for classification using both core and margin information utilizing GLCM, GRLM, and GLSZM texture methods and SVM-RBF classification algorithm. by Laurentius O. Osapoetra (9931759)

CEC2017 test function test results. by Tengfei Ma (597633)

“…Specifically, it achieves faster iteration speeds across four different environments, with the planned path length after escaping local optima being shortened by an average of 7.55175 m (16.291%) compared to other optimization algorithms. These results confirm OP-ZOA’s enhanced optimization capability, significantly improving both escape efficiency from local optima and solution reliability.…”

Statistical results of various algorithms. by ZeSheng Lin (20501356)

“…Finally, a novel Levy flight was implemented to promote the diversity of whale distribution. Results from experiments confirm that the enhanced WOA algorithm outperforms the standard WOA algorithm in terms of both fitness value and convergence speed. …”

Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

Iteration curves of different algorithms. by Tengfei Ma (597633)

“…Specifically, it achieves faster iteration speeds across four different environments, with the planned path length after escaping local optima being shortened by an average of 7.55175 m (16.291%) compared to other optimization algorithms. These results confirm OP-ZOA’s enhanced optimization capability, significantly improving both escape efficiency from local optima and solution reliability.…”

Flowchart of OP-ZOA algorithm. by Tengfei Ma (597633)

“…Specifically, it achieves faster iteration speeds across four different environments, with the planned path length after escaping local optima being shortened by an average of 7.55175 m (16.291%) compared to other optimization algorithms. These results confirm OP-ZOA’s enhanced optimization capability, significantly improving both escape efficiency from local optima and solution reliability.…”

Images of partial benchmark functions. by ZeSheng Lin (20501356)

“…Finally, a novel Levy flight was implemented to promote the diversity of whale distribution. Results from experiments confirm that the enhanced WOA algorithm outperforms the standard WOA algorithm in terms of both fitness value and convergence speed. …”

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

“…Each algorithm return a set of coarsened <i>variables</i> (a grouping of species into three groups) and a <i>model</i> that uses these variables to predict the function. …”

Fitness function parameters. by Ankita Sharma (3183054)

Multi-scale detection of hierarchical community architecture in structural and functional brain networks by Arian Ashourvan (6685232)

“…To address this limitation, we exercise a multi-scale extension of a common community detection technique, and we apply the tool to synthetic graphs and to graphs derived from human neuroimaging data, including structural and functional imaging data. …”

Table_1_Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models.DOCX by Shakiru A. Alaka (9302864)

“…We evaluate the predictive accuracy of machine-learning algorithms for predicting functional outcomes in acute ischemic stroke patients after endovascular treatment.…”

Acc epoch loss curve of IPCA algorithm. by Lu Lin (201289)

Procedure of the DCT-based EIT algorithm. by Rongqing Chen (249906)

“…To demonstrate the increased interpretability of the EIT image through structural prior in the DCT-based approach, the DCT-based reconstructions were compared with reconstructions from a widely applied one-step Gauss-Newton solver with background prior and from the advanced GREIT algorithm. …”

Power consumption for SHA256 software algorithm. by Maytham S. Jabor (15853046)

“…When simulating both approaches, the energy consumed when replacing functions by hardware decreases up to 63%.…”

Comparison of scores obtained by our interpenetration and scoring algorithm (ISA) and ROSETTA for a subset of structures. by Kevin Sawade (16726527)

“…However, our algorithm was 1000 times faster than pyROSETTA (both algorithms have been parallelized on a per-structure basis using the Python package joblib [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010531#pcbi.1010531.ref069" target="_blank">69</a>]).…”