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

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

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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Code used for data analysis in this study. حسب Nourin Sultana (22339463)

Distribution of prediction errors across 30 runs for DKASC and solar I datasets. حسب Yaopeng Han (22622718)

The optimal number of clusters identified using the GMM clustering algorithm. حسب Dinh K. Tang (20469082)

Optimal number of clusters identified using the GMM clustering algorithm. حسب Dinh K. Tang (20469082)

Prediction of natural frequencies by the RF surrogate model for the PLA specimen with 30% infill ratio. حسب Jin Zhang (53297)

Impact of multiunit merging on neural information encoding. حسب Daniel Suárez-Barrera (22676654)

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Codes for "<b>A coherent power-load optimization algorithm for wind-farm-level yaw control considering wake effects via deep neural network</b>" حسب Yize Wang (19535173)

"…<p dir="ltr">Codes for "<b>A coherent power-load optimization algorithm for wind-farm-level yaw control considering wake effects via deep neural network</b>"</p>…"

code_4 حسب Olga Ovtsarenko (20432033)

The genetic algorithm. حسب Viktoria Zemliak (19500335)

Proposed genetic algorithm for feature selection. حسب Shirin Dehghan (19837936)

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General model performance for different algorithms. حسب Binghan Xue (21253235)

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Temporal response patterns determined by Dirichlet Process-based clustering algorithm across amplitudes and stimuli. حسب Dinh K. Tang (20469082)

The structure of genetic algorithm (GA). حسب Ali Akbar Moosavi (17769033)

"…The statistical assessment indicated that PSO-NNs with the lowest RMSE and MAPE as well as the highest correlation coefficient (R) value provided the most accurate and robust prediction of K_fs. The prediction models ranked as PSO-NNs (R = 0.958; RMSE = 0.343; MAPE = 9.47), GA-NNs (R = 0.949; RMSE = 0.404; MAPE = 11.83), MLPNNs (R = 0.933; RMSE = 0.426; MAPE = 12.13), RBFNNs (R = 0.926; RMSE = 0.452; MAPE = 14.30), and MLR (R = 0.675; RMSE = 0.685; MAPE = 22.54) in terms of their performances for the test data set. …"

Code for "Dataset of binocularly coded steady-state visual evoked potentials recorded with an augmented reality headset" حسب Yufeng Ke (16539882)

The folder containing the code is provided as supportive documentation associated with this paper. حسب Quxi Kuang (22493270)

Algorithm steps for house price prediction model. حسب Xuan Wang (55634)

"…The network integrates the attention mechanism with spatial feature extraction, and a bat optimization algorithm is used to improve explainability and accuracy. …"