Classification baseline performance. by Doaa Sami Khafaga (21463870)

Feature selection results. by Doaa Sami Khafaga (21463870)

ANOVA test result. by Doaa Sami Khafaga (21463870)

Summary of literature review. by Doaa Sami Khafaga (21463870)

Flow diagram of the proposed model. by Uğur Ejder (22683228)

“…<div><p>Machine learning models are increasingly applied to assisted reproductive technologies (ART), yet most studies rely on conventional algorithms with limited optimization. …”

Event-driven data flow processing. by Yixian Wen (12201388)

“…<div><p>This paper introduces a groundbreaking monitoring model tailored for sustainable trade activity surveillance, which synergistically integrates event-driven architecture with an intelligent decision tree. …”

Confusion matrix. by Yixian Wen (12201388)

“…<div><p>This paper introduces a groundbreaking monitoring model tailored for sustainable trade activity surveillance, which synergistically integrates event-driven architecture with an intelligent decision tree. …”

Parameter settings. by Yixian Wen (12201388)

“…<div><p>This paper introduces a groundbreaking monitoring model tailored for sustainable trade activity surveillance, which synergistically integrates event-driven architecture with an intelligent decision tree. …”

Dynamic resource allocation process. by Yixian Wen (12201388)

“…<div><p>This paper introduces a groundbreaking monitoring model tailored for sustainable trade activity surveillance, which synergistically integrates event-driven architecture with an intelligent decision tree. …”

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…Importantly, this strategy locates samples adaptively on the transition between facies which improves the performance of conventional \emph{<i>MPS</i>} algorithms. In conclusion, this work shows that preferential sampling can contribute in \emph{<i>MPS</i>} even at very small sampling regimes and, as a corollary, demonstrates that prior models (obtained form a training image) can be used effectively not only to simulate non-sensed variables of the field, but to decide where to measure next.…”

Data_Sheet_1_A Data-Driven Framework for Identifying Intensive Care Unit Admissions Colonized With Multidrug-Resistant Organisms.docx by Çaǧlar Çaǧlayan (12253934)

“…</p>Materials and Methods<p>Leveraging data from electronic healthcare records and a unique MDRO universal screening program, we developed a data-driven modeling framework to predict MRSA, VRE, and CRE colonization upon intensive care unit (ICU) admission, and identified the associated socio-demographic and clinical factors using logistic regression (LR), random forest (RF), and XGBoost algorithms. …”

Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png by Minjin Guo (22751300)

“…</p>Methods<p>To address these challenges, we propose a novel AI-driven framework that incorporates two key methodological innovations: CardioSpectra, a structured sparse inference model, and Risk-Stratified Exertional Embedding (RSEE), a domain-specific representation learning strategy. …”