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

“…Decoding from functional magnetic resonance imaging data revealed that solutions from the SF&GPI algorithm were activated on test tasks in visual and prefrontal cortex. …”

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

“…The data is stored in JSON format under the following labels:</p><ul><li><b>"kannada text"</b> – Kannada algorithmic statement</li><li><b>"text"</b> – English algorithmic statement</li><li><b>"code"</b> – Expected Python output</li></ul><h4><b>Dataset Highlights: -</b></h4><ul><li><b>Triplet Structure:</b> Kannada algorithmic statements, their English translations, and Python code.…”

Performance of algorithms outside the training species. by Sven B. Gould (12237287)

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

Subjects:

The genome coding scheme. by Wenbing Shi (5806160)

Subjects:

A framework for improving localisation prediction algorithms. by Sven B. Gould (12237287)

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

“…This study applies and evaluates machine learning (ML) algorithms to predict LBW and identify its key risk factors in Bangladesh. …”

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

Subjects:

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." …”

Source Code and Dataset by Swathi Ganesan (14394924)

The run time for each algorithm in seconds. by Edward Antonian (21453161)

“…The goal of this paper is to examine several extensions to KGR/GPoG, with the aim of generalising them a wider variety of data scenarios. The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. …”

Comparative analysis of spike sorting using Uniform Manifold Approximation and Projection (UMAP) vs. feature-based methods in in vivo extracellular recordings. by Daniel Suárez-Barrera (22676654)

Subjects:

Research data for paper: Efficient Event-based Delay Learning in Spiking Neural Networks by 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).…”

S1 Data - by Wenbing Shi (5806160)

Subjects:

The structure of the data sample. by Wenbing Shi (5806160)

Subjects:

The principle of sample data augmentation. by Wenbing Shi (5806160)

Subjects:

GA pseudo-code. by Jianpeng Zhang (528185)

“…Genetic algorithm is utilized to solve and optimize the data, one-dimensional multi-scale convolutional neural network is combined with gated recurrent unit to improve the network intrusion detection model, and finally the detection and defense of industrial control network intrusion is completed. …”

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

Subjects:

Schematic diagram of elements in the delivery area. by Yingbo Mao (22522192)

Pseudo-code for the study design model. by Jianpeng Zhang (528185)

“…Genetic algorithm is utilized to solve and optimize the data, one-dimensional multi-scale convolutional neural network is combined with gated recurrent unit to improve the network intrusion detection model, and finally the detection and defense of industrial control network intrusion is completed. …”