Model-relevant variable data for external patients. by Lei Gao (6566)

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Data and code resources. by Sam Hall-McMaster (10343795)

“…Decoding from functional magnetic resonance imaging data revealed that solutions from the SF&GPI algorithm were activated on test tasks in visual and prefrontal cortex. …”

Structure of the Kuhn-Munkres Algorithm. by Qingnan Ji (22662198)

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Relevant data underlying the findings described in manuscript. by Lin Qi (128224)

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Temporal response patterns determined by the GMM clustering algorithm across varying primary stimulus parameters (amplitude or onset ramp velocity) of all test stimuli. by Dinh K. Tang (20469082)

Research data for paper: Efficient Event-based Delay Learning in Spiking Neural Networks by Balázs Mészáros (16890225)

“…</li></ol><p dir="ltr">The data was generated and analysed with the code available on GitHub at https://github.com/mbalazs98/deventprop/</p><p dir="ltr">results.py contains all test accuracies shown in figures 4-7. …”

Comparison of grayscale conversion algorithms. by Tianke Fang (22648035)

Comparison of noise reduction algorithms. by Tianke Fang (22648035)

Algorithm testing. by Ziad M. Ali (12516532)

“…The accuracy of the proposed equivalent circuits is demonstrated on two solar cells/modules, RTC-F and MSX-60, showing equal or better performance than the standard PV_DDM equivalent circuit. Further testing on a commercial solar panel under different irradiance and temperature conditions confirms the applicability of the proposed models. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

The genome coding scheme. by Wenbing Shi (5806160)

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Algorithm testing environment settings. by Hua Song (113155)

Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)

Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)

Definitions of relevant SSA parameters. by Yeping Shi (14279118)

“…The average response time of the ammonia sensor in the chamber is 13 s slower than that of the sensor directly exposed to the gas being measured, while the average recovery time is 19 s faster. In tests comparing the performance of the SSA-BPNN, support vector machine (SVM), and random forest (RF) models, the SSA-BPNN achieves a 99.1% classification accuracy, better than the SVM and RF models. …”

The relevant data file for this article. by Linsha Liu (20378293)

Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

The speciation of ANEAT model evolution. by Wenbing Shi (5806160)

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The fitness of ANEAT model evolution. by Wenbing Shi (5806160)

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The fitness of NANEAT model evolution. by Wenbing Shi (5806160)

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