The performance of S-YOFEO model on MOT17. by Wenshun Sheng (21485393)

“…<div><p>A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed to address the issue of target ID transformation and loss caused by the increase of practical background complexity. …”

Five multi-target tracking evaluation indexes. by Wenshun Sheng (21485393)

“…<div><p>A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed to address the issue of target ID transformation and loss caused by the increase of practical background complexity. …”

Partial tracking results of MOT17 dataset. by Wenshun Sheng (21485393)

“…<div><p>A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed to address the issue of target ID transformation and loss caused by the increase of practical background complexity. …”

Improved detection layer. by Wenshun Sheng (21485393)

“…<div><p>A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed to address the issue of target ID transformation and loss caused by the increase of practical background complexity. …”

The performance of S-YOFEO model on MOT16. by Wenshun Sheng (21485393)

“…<div><p>A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed to address the issue of target ID transformation and loss caused by the increase of practical background complexity. …”

The matching process of EIOU. by Wenshun Sheng (21485393)

“…<div><p>A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed to address the issue of target ID transformation and loss caused by the increase of practical background complexity. …”

Comparison of Gene Discovery and Performance Evaluation with Baseline Methods. by Fatemeh Keikha (20917848)

Methodology block diagram. by Gahao Chen (21688843)

“…Six machine learning algorithms - Random Forest (RF), AdaBoost, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Tabular Prior-data Fitted Network version 2.0 (TabPFN-V2) - were implemented with five-fold cross-validation to optimize model hyperparameters. …”

From Baseline to Best Practice: An Advanced Feature Selection, Feature Resampling and Grid Search Techniques to Improve Injury Severity Prediction by Soukaina EL Ferouali (20602200)

“…As far as we know and as of right now, there hasn’t been much interest in supporting a fusion-based system that critically reviews machine learning techniques using grid search optimization, feature selection, and smote technique and examines how injury severity prediction is affected by road crashes.…”

Social media competitive intelligence frameworks. by Xingting Ju (20323838)

“…Second, we trained and compared the performance of six machine learning algorithms to find the optimal classifiers.</p><p>Findings</p><p>The study reveals significant shifts in social media engagement topics following the COVID-19 pandemic. …”

Top terms before and during the pandemic. by Xingting Ju (20323838)

“…Second, we trained and compared the performance of six machine learning algorithms to find the optimal classifiers.</p><p>Findings</p><p>The study reveals significant shifts in social media engagement topics following the COVID-19 pandemic. …”

LDA model tuning for the "Pre-pandemic" subset. by Xingting Ju (20323838)

“…Second, we trained and compared the performance of six machine learning algorithms to find the optimal classifiers.</p><p>Findings</p><p>The study reveals significant shifts in social media engagement topics following the COVID-19 pandemic. …”

Metrics of the predictive models. by Xingting Ju (20323838)

“…Second, we trained and compared the performance of six machine learning algorithms to find the optimal classifiers.</p><p>Findings</p><p>The study reveals significant shifts in social media engagement topics following the COVID-19 pandemic. …”

LDA model tuning for the "Pandemic" subset. by Xingting Ju (20323838)

“…Second, we trained and compared the performance of six machine learning algorithms to find the optimal classifiers.</p><p>Findings</p><p>The study reveals significant shifts in social media engagement topics following the COVID-19 pandemic. …”

An overview of DDGWizard. by Mingkai Wang (9650918)

“…D: Develop a prediction model using the XGBoost [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013783#pcbi.1013783.ref044" target="_blank">44</a>] algorithm based on the optimal features. E: Evaluate the developed model and compare it with other representative prediction methods using the identical cross-validation sets, test set, S669 dataset [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013783#pcbi.1013783.ref045" target="_blank">45</a>], and p53 dataset [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013783#pcbi.1013783.ref025" target="_blank">25</a>].…”

ARISCAT score. by Britta Trautwein (22086711)

“…A reliable prediction algorithm based on machine learning holds great potential to improve postoperative outcomes.…”

Postoperative visit. by Britta Trautwein (22086711)

“…A reliable prediction algorithm based on machine learning holds great potential to improve postoperative outcomes.…”

Quality of recovery-9 questionnaire. by Britta Trautwein (22086711)

“…A reliable prediction algorithm based on machine learning holds great potential to improve postoperative outcomes.…”

Examples of model selection methods for the PHMM. by Tianshu Li (1527088)

Scale Production of a Stretchable Fiber Triboelectric Nanogenerator in Customizable Textile for Human Motion Recognition by Yifan Zu (20231556)

“…Furthermore, customizable textiles (e.g., wrist support and socks) that conform perfectly to the human body have been knitted from the F-TENGs as warps or wefts, which are able to monitor human motion signals. Together with an optimized machine learning algorithm, five human motions (stand, slow walk, normal walk, run, and jump) can be analyzed with a precision of up to 99%. …”