Code and data for the paper "Investigating machine learning algorithms to classify label-free images of pancreatic neuroendocrine neoplasms" by Noelle Madyson Daigle (22120114)

“…<p dir="ltr">Code and data for analysis detailed in the paper "Investigating machine learning algorithms to classify label-free images of pancreatic neuroendocrine neoplasms." …”

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

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

Machine learning algorithm performance evaluated by ROC curves. by Man Wang (110466)

Machine learning from 21 gly - DEGs filter core gene. by Ziyang Liu (242055)

Performance of various biclustering algorithms on FilmTrust dataset with Q-learning. by Muhammad Waqar (500788)

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Performance of machine learning algorithms. by Wenxue Liu (6877754)

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

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Performance of various biclustering algorithms on ML-100K dataset with Q- learning. by Muhammad Waqar (500788)

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Performance of various biclustering algorithms on ML-latest-small dataset with Q-learning. by Muhammad Waqar (500788)

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The genome coding scheme. by Wenbing Shi (5806160)

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

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

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

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rbpTransformer model using multiple cores. by Ahmet Gürhanlı (21602042)

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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>" by 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>…”

Evaluation metrics with Q-learning on ML-latest-small. by Muhammad Waqar (500788)

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code_4 by Olga Ovtsarenko (20432033)

Data and code availability: Machine Learning on systematically curated data reveals key determinants of magnetic hyperthermia performance by Edgar Vega (21981590)

“…The CatBoost algorithm emerged as the most effective model, achieving the lowest mean absolute error (20.92 W/g) and root mean squared error (39.41 W/g), along with a high coefficient of determination (R² = 0.98). …”

Agent learning of FilmTrust dataset across various parameters. by Muhammad Waqar (500788)

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