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

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

“…<div><p>Generalization from past experience is an important feature of intelligent systems. …”

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

The method for determining the chemical constituents of unknown materials in the algorithm. by Dong Hyeok Choi (12562048)

The genome coding scheme. by Wenbing Shi (5806160)

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Algorithm optimization experiment matrix. by Gabriel Galeote-Checa (20613878)

Comprehensive Re, F1 and Pre values of FcaNet-CycleGAN model. by Xiaojun Wang (50332)

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Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

Algorithm comparison experiment on the RSOD dataset. by Pingping Yan (462509)

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Source Code and Dataset by Swathi Ganesan (14394924)

Algorithm training accuracy experiments. by Yingying Liu (360782)

Scatter diagram of different principal elements. by Jizhong Wang (7441697)

Pseudo code. by Xuwei Liu (6034592)

“…The results showed that the mean squared errors on the training and testing sets were 1.2% and 1.1%, and the average absolute errors were 8.3% and 8.1%, respectively. …”

USDA-ARS Tucson, Arizona 2014-2022 Data Reservoir of Field Experiments with Managed Honey Bee Colonies: Annotated Hive Frame Photos: Dataset I by Vladimir Kulyukin (21983990)

“…,</p><p dir="ltr">2020_02_27_RR_Hive_Directions_IMG_2540_VK.PNG</p><p dir="ltr">2020_02_27_RR_Hive_Directions_IMG_2540_VK.xml</p><p dir="ltr">2020_02_27_RR_Hive_Directions_IMG_2540_VK.txt</p><p dir="ltr">Each PNG is annotated for the following categories:</p><p dir="ltr">(1) CappedHoneyCell </p><p dir="ltr">(2) CappedWorkerBroodCell</p><p dir="ltr"> (3) EmptyCombCell</p><p dir="ltr">(4) PollenCell</p><p dir="ltr"> (5) UncappedNectarCell</p><p dir="ltr"> (6) UncappedWorkerLarvaCell</p><p dir="ltr">(7) BeeHiveFrame</p><p dir="ltr">The counts on the number of annotated region of interest (ROI) images are as follows:</p><p dir="ltr">CappedHoneyCell: 19,723</p><p dir="ltr">CappedWorkerBroodCell: 21,456</p><p dir="ltr">EmptyCombCell: 20,655</p><p dir="ltr">PollenCell: 13,406</p><p dir="ltr">UncappedNectarCell: 11,009</p><p dir="ltr">UncappedWorkerLarvaCell: 18,283</p><p dir="ltr">BeeHiveFrame: 1001</p><p dir="ltr">Each such ROI can be extracted into a separate image and used in training machine learning algorithms.…”

Hyperparameters of self-supervised learning algorithms used in Experiments 1–5. by Lalit Pandey (13195488)

“…<p>Hyperparameters of self-supervised learning algorithms used in Experiments 1–5.</p>…”

An overview of the organization of the study program. by Marialuisa Gandolfi (8639238)

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Overview of the development of the biomarkers in the motor, exteroceptive, interoceptive, and cerebral domains. by Marialuisa Gandolfi (8639238)

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The schedule of enrolment, interventions, and assessments. by Marialuisa Gandolfi (8639238)

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Algorithm comparison experiment on the NWPU VHR-10 dataset. by Pingping Yan (462509)

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