CPU usage of benchmark function: (a) CPU Usage during the Execution of Benchmark Functions F21-F30, (b) CPU Usage during the Execution of Benchmark Functions F31-F40 and (c) CPU Us... by Ruyi Dong (9038174)

Iteration curve of benchmark function: (a) Convergence curves of the average best fitness for functions F21-F30, (b) Convergence curves of the average best fitness for functions F3... by Ruyi Dong (9038174)

AODV algorithm for route selection at node destination. by Tibebu Legesse (21030014)

Flowchart of the general algorithm for one trial. by Junying Wang (509009)

Computational and resource efficiency of different algorithms. by Rui Li (4631)

Pipeline of the interactor algorithm. by Jose Cleydson F. Silva (8685594)

Flowchart of MOSRS algorithm. by Vahid Goodarzimehr (21343124)

Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large. by Panfei Li (22379086)

“…<p>Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large.…”

EASP-AODV algorithm for route discovery at the intermediate node. by Tibebu Legesse (21030014)

Functional enrichment and correlation analysis of hub DE-SRGs. by Xihao Shen (20347942)

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Comparison of different algorithms. by Jiandong Qiu (20389944)

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

“…<p>(A), (B) Algorithm performance, evaluated over 50 simulated datasets generated as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012590#pcbi.1012590.g001" target="_blank">Fig 1</a> with <i>N</i> = 3 true groups, 900 samples and 10% simulated measurement noise. …”

Code snippets to add user-specific custom functionality in PyNoetic. by Gursimran Singh (575288)

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The training process for PV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for AV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for MV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The accuracy of different algorithms for TMD on SHL and HTC datasets. by Rui Li (4631)

Modular architecture design of PyNoetic showing all its constituent functions. by Gursimran Singh (575288)

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Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

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