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

الموضوعات:

The parameter setting of the evolutionary neural network. حسب Wenbing Shi (5806160)

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The genome coding scheme. حسب Wenbing Shi (5806160)

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Evaluation indexes of different methods on the test set. حسب Wenbing Shi (5806160)

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The comparison of different prediction methods on the test set. حسب Wenbing Shi (5806160)

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

Algorithm testing. حسب Ziad M. Ali (12516532)

"…To address the parameter estimation problem, a novel metaheuristic algorithm, the chaotic honey-badger algorithm, is developed and evaluated. …"

The accuracy of anchor-free algorithms using test-set. حسب Olarewaju Mubashiru Lawal (14710251)

"…<p>The accuracy of anchor-free algorithms using test-set.</p>…"

The accuracy of anchor-based algorithms using test-set. حسب Olarewaju Mubashiru Lawal (14710251)

"…<p>The accuracy of anchor-based algorithms using test-set.</p>…"

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

"…Our method supports multiple spikes per neuron and introduces a delay learning algorithm that can, in contrast to previous methods, also be applied to recurrent Spiking Neural Networks. …"

Algorithm comparison test results. حسب Hang Ren (1312905)

Algorithm performance test results. حسب Yue Guo (695495)

Coarse grain contents and chronology, correlation test using SLICK algorithm حسب Yanan Li (10488293)

"…<p dir="ltr">This archive contains :coarse grain contents and chronology, and the correlation test using SLICK algorithm from</p><p dir="ltr">Li, Y., Donnelly, J. …"

Pseudo code. حسب Xuwei Liu (6034592)

"…In addition, the determination coefficients on the training and testing sets were 95.1% and 92.8%, and the accuracy was 92.3% and 91.4%. …"

TIR-Learner v3: New generation TE annotation program for identifying TIRs حسب Tianyu Lu (18856792)

"…The old TIR suffers from slow execution on large genomes due to intense I/O operations and less efficient algorithms, it also lacks maintainability due to legacy dependency issues. …"

The structure of the NANEAT model corresponds to the optimal species genome. حسب Wenbing Shi (5806160)

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The structural mutation of neuroevolution. حسب Wenbing Shi (5806160)

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The CGO risk prediction process based on data augmentation and neuroevolution. حسب Wenbing Shi (5806160)

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

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The analysis of feature importance. حسب Wenbing Shi (5806160)

الموضوعات: