Data-Driven Development of Heterogeneous Catalysts for Propane Dehydrogenation with Machine Learning and Metaheuristic Optimization by Jungmok Oh (14117464)

“…Recent advances in data-driven approaches using the machine learning (ML) method have enabled the discovery of high-performance materials. …”

Data-Driven Development of Heterogeneous Catalysts for Propane Dehydrogenation with Machine Learning and Metaheuristic Optimization by Jungmok Oh (14117464)

“…Recent advances in data-driven approaches using the machine learning (ML) method have enabled the discovery of high-performance materials. …”

List of data tables. by Mukhtar Ijaiya (18935122)

“…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …”

Flow chart of data source inclusion. by Mukhtar Ijaiya (18935122)

“…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …”

Predictive model-building process. by Mukhtar Ijaiya (18935122)

“…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …”

Comparison of models performance metrics. by Mukhtar Ijaiya (18935122)

“…By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. …”

Workflow overview to reconstruct integrated host-virus genome-scale models and detect promising compounds with an antiviral activity. by Nantia Leonidou (14833068)

Data Sheet 1_Real-world data-driven early warning system for risk-stratified liver injury in hospitalized COVID-19 patients—Machine learning models for clinical decision support.do... by Yuanguo Xiong (20135991)

“…Thirteen distinct machine learning (ML) algorithms were trained and benchmarked to construct an optimal risk stratification framework. …”

Image 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.jpeg by Zhenze Zhang (22011422)

“…Missing data (<30%) were imputed using random forest. The cohort was split into training (75%) and internal testing (25%) sets, with hyperparameter optimization via 5-fold cross-validation. …”

Table 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.docx by Zhenze Zhang (22011422)

“…Missing data (<30%) were imputed using random forest. The cohort was split into training (75%) and internal testing (25%) sets, with hyperparameter optimization via 5-fold cross-validation. …”