Data Sheet 6_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf by Yuwen ShangGuan (22633190)

“…Missing data were handled using random forest algorithm, feature selection was performed using Boruta algorithm, and SMOTE technique addressed class imbalance. …”

Data Sheet 3_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf by Yuwen ShangGuan (22633190)

“…Missing data were handled using random forest algorithm, feature selection was performed using Boruta algorithm, and SMOTE technique addressed class imbalance. …”

Data Sheet 1_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf by Yuwen ShangGuan (22633190)

“…Missing data were handled using random forest algorithm, feature selection was performed using Boruta algorithm, and SMOTE technique addressed class imbalance. …”

Data Sheet 2_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf by Yuwen ShangGuan (22633190)

“…Missing data were handled using random forest algorithm, feature selection was performed using Boruta algorithm, and SMOTE technique addressed class imbalance. …”

Data Sheet 4_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf by Yuwen ShangGuan (22633190)

“…Missing data were handled using random forest algorithm, feature selection was performed using Boruta algorithm, and SMOTE technique addressed class imbalance. …”

Confusion matrix. by Yixian Wen (12201388)

“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”

Parameter settings. by Yixian Wen (12201388)

“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”

Dynamic resource allocation process. by Yixian Wen (12201388)

“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”

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

“…In this direction, the option of estimating the statistics of the model directly from the training image (performing a refined pattern search instead of simulating data) is a very promising.<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…”

Table 1_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.docx by Yuwen ShangGuan (22633190)

“…Missing data were handled using random forest algorithm, feature selection was performed using Boruta algorithm, and SMOTE technique addressed class imbalance. …”

Supplementary Material for: Development of an explainable machine learning model for cardiovascular-kidney-metabolic syndrome prediction based on dietary antioxidants in a national... by figshare admin karger (2628495)

“…Five machine learning algorithms were trained with rigorous hyperparameter optimization and evaluated comprehensively. …”

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)

“…RSEE projects heterogeneous input data into an exertion-conditioned latent space, aligning model predictions with observed physiological variance and mitigating false positives by explicitly modeling the overlap between athletic remodeling and subclinical pathology.…”

Data_Sheet_1_A Machine Learning Based Dose Prediction of Lutein Supplements for Individuals With Eye Fatigue.XLSX by Juntao Kan (432159)

Data_Sheet_1_Evaluation of nutritional status and clinical depression classification using an explainable machine learning method.docx by Payam Hosseinzadeh Kasani (13280397)

“…Various nutritional and dietary compounds have been suggested to be involved in the onset, maintenance, and severity of depressive disorders. …”

Image_1_Evaluation of nutritional status and clinical depression classification using an explainable machine learning method.JPEG by Payam Hosseinzadeh Kasani (13280397)

“…Various nutritional and dietary compounds have been suggested to be involved in the onset, maintenance, and severity of depressive disorders. …”

Image_2_Evaluation of nutritional status and clinical depression classification using an explainable machine learning method.JPEG by Payam Hosseinzadeh Kasani (13280397)

“…Various nutritional and dietary compounds have been suggested to be involved in the onset, maintenance, and severity of depressive disorders. …”