Notations and definitions used in the algorithm. by Nazym Alimbekova (22766158)

Paired t-test for detecting the difference between DMFDEM errors and other types of DEM errors. by Qinghua Li (398885)

Entry Point byte frequency by Paul A. Gagniuc (1818325)

MD5 signatures from specific regions from a set of files by Paul A. Gagniuc (1818325)

Generation of a Position Weight Matrix (PWM) from a set of infected PE files by Paul A. Gagniuc (1818325)

Image 5_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…This study investigates whether a single webcam, combined with human pose estimation and deep learning algorithms, can automatically detect compensatory movements in persons with stroke performing a drinking task. …”

Image 4_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…This study investigates whether a single webcam, combined with human pose estimation and deep learning algorithms, can automatically detect compensatory movements in persons with stroke performing a drinking task. …”

Image 2_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…This study investigates whether a single webcam, combined with human pose estimation and deep learning algorithms, can automatically detect compensatory movements in persons with stroke performing a drinking task. …”

Image 1_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…This study investigates whether a single webcam, combined with human pose estimation and deep learning algorithms, can automatically detect compensatory movements in persons with stroke performing a drinking task. …”

Image 3_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…This study investigates whether a single webcam, combined with human pose estimation and deep learning algorithms, can automatically detect compensatory movements in persons with stroke performing a drinking task. …”

Data Sheet 1_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.pdf by Tim Unger (20457145)

“…This study investigates whether a single webcam, combined with human pose estimation and deep learning algorithms, can automatically detect compensatory movements in persons with stroke performing a drinking task. …”

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

“…We evaluate our method on a simple sequence detection task, as well as the Yin-Yang, Spiking Heidelberg Digits, Spiking Speech Commands and Braille letter reading datasets, demonstrating that our algorithm can optimise delays from suboptimal initial conditions and enhance classification accuracy compared to architectures without delays. …”

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…Additionally, 3,735 digital images at the leaf level were taken. Machine learning regression algorithms were trained to predict betalain accumulation from digital images, outperforming classic spectroradiometer measurements. …”

A Four-Wavelength Photoplethysmography dataset for non-invasive hemoglobin assessment by Liang yongbo (4822017)

“…Non-invasive blood glucose detection system based on conservation of energy method. …”