Training and validation with class, object and box loss for YOLOv5 models with nine classes. by Kim Bjerge (14797213)

Conceptual representation of one realisation of the bootstrap algorithm. by Luke R. Lloyd-Jones (6718186)

Development and testing of an ARSD algorithm: Read, Understand, Learn, & Excel (Kucheria et al., 2019) by Priya Kucheria (5647163)

“…Currently, no tools exist to detect a reader’s use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.…”

FBD-SV-2024 by Zi-Wei Sun (19992142)

“…<p dir="ltr">The Flying Bird object Dataset in Surveillance Video (FBD-SV-2024) for surveillance video flying bird object detection algorithm development and performance evaluation.…”

Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

DataSheet1_A novel dataset and deep learning object detection benchmark for grapevine pest surveillance.pdf by Giorgio Checola (20418239)

“…Assisted by entomologists, we performed the annotation process, trained, and compared the performance of two state-of-the-art object detection algorithms: YOLOv8 and Faster R-CNN. …”

DataSheet_1_A comprehensive review of methods based on deep learning for diabetes-related foot ulcers.docx by Jianglin Zhang (4386475)

Supplementary Material for: The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis by He C. (2839727)

“…<b><i>Objective:</i></b> To use an individual participant data meta-analysis to evaluate the accuracy of two PHQ-9 diagnostic algorithms for detecting major depression and compare accuracy between the algorithms and the standard PHQ-9 cutoff score of ≥10. …”

Summary of results of naïve Bayes algorithms. by Christopher E. Niemczak (8586861)

“…Algorithms trained without auditory variables as features were statistically worse (p < .001) in both the primary measure of area under the curve (0.82/0.78) and the secondary measure of accuracy (72.3%/74.5%) for the Gaussian and kernel algorithms respectively.…”

Data_Sheet_1_A prospective multicenter clinical research study validating the effectiveness and safety of a chest X-ray-based pulmonary tuberculosis screening software JF CXR-1 bui... by Yang Yang (45629)

Data_Sheet_2_Application of machine learning algorithms to identify people with low bone density.docx by Rongxuan Xu (18445503)

Data_Sheet_1_Application of machine learning algorithms to identify people with low bone density.docx by Rongxuan Xu (18445503)

Image_3_Detecting glaucoma from multi-modal data using probabilistic deep learning.tif by Xiaoqin Huang (251036)

Image_4_Detecting glaucoma from multi-modal data using probabilistic deep learning.tif by Xiaoqin Huang (251036)

Image_2_Detecting glaucoma from multi-modal data using probabilistic deep learning.tif by Xiaoqin Huang (251036)

Image_1_Detecting glaucoma from multi-modal data using probabilistic deep learning.jpeg by Xiaoqin Huang (251036)

Table_1_Detecting glaucoma from multi-modal data using probabilistic deep learning.docx by Xiaoqin Huang (251036)

Data_Sheet_1_Automatic speech analysis for detecting cognitive decline of older adults.pdf by Lihe Huang (19350508)

Data_Sheet_1_Development of a deep learning model for automated detection of calcium pyrophosphate deposition in hand radiographs.docx by Thomas Hügle (177824)

Data_Sheet_1_Radiomics analysis of pancreas based on dual-energy computed tomography for the detection of type 2 diabetes mellitus.PDF by Wei Jiang (14986)