Multiclass comparison of algorithms on test data for KDDCup 99. حسب Arati Behera (19864844)

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

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

Multiclass comparison of algorithms on test data for CIC-IoT23. حسب Arati Behera (19864844)

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

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

The genome coding scheme. حسب Wenbing Shi (5806160)

الموضوعات:

Hair removal preprocessing using the DullRazor algorithm before image generation. حسب Mujung Kim (22367223)

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

"…<p dir="ltr">The data in this repository accompanies the paper 'Efficient Event-based Delay Learning in Spiking Neural Networks'</p><p dir="ltr">The data relates to 4 benchmarks:</p><ol><li>Spiking Heidelberg Digits (SHD).…"

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

Relevant computed image data. حسب Zhaoyu Ku (20660546)

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

الموضوعات:

Summary of relevant works. حسب Bashir Olaniyi Sadiq (22478011)

Source Code and Dataset حسب Swathi Ganesan (14394924)

Algorithm training accuracy experiments. حسب Yingying Liu (360782)

The relevant code in the manuscript can be found in the supporting information data file. حسب Xini Fang (20861990)

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

Pseudo code. حسب 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. …"

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

Data generation algorithm. حسب Aluwani Ramalata (21262977)

USDA-ARS Tucson, Arizona 2014-2022 Data Reservoir of Field Experiments with Managed Honey Bee Colonies: Annotated Hive Frame Photos: Dataset I حسب 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.…"