Data_Sheet_2_Development and Validation of a Novel Risk Prediction Model Using Recursive Feature Elimination Algorithm for Acute-on-Chronic Liver Failure in Chronic Hepatitis B Pat... by Mingxue Yu (10273496)

Proposed reinforcement learning architecture. by Enoch Solomon (21416703)

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The images shown correspond the downscaled images an agent receives in Unity environment. by Enoch Solomon (21416703)

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Average success rate (%) of the test experiment on ViZDoom and Unity environments. by Enoch Solomon (21416703)

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Natural language processing for automated quantification of bone metastases reported in free-text bone scintigraphy reports by Olivier Q. Groot (9370461)

“…The aim of this study was to develop a natural language processing (NLP) algorithm for binary classification (single metastasis versus two or more metastases) in bone scintigraphy reports of patients undergoing surgery for bone metastases.…”

Models’ performance without optimization. by Muhammad Usman Tariq (11022141)

“…Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. …”

Iteration curve of the optimization process. by Meijun Shang (22806461)

“…The load-bearing mechanism of the proposed steel platform was analyzed theoretically, and finite element analysis (FEA) was employed to evaluate the stresses and deflections of key members. A particle swarm optimization (PSO) algorithm was integrated with the FEA model to optimize the cross-sectional dimensions of the primary beams, secondary beams, and foundation boxes, achieving a balance between load-bearing capacity and cost efficiency. …”

Data_Sheet_1_Prediction of patient choice tendency in medical decision-making based on machine learning algorithm.pdf by Yuwen Lyu (14330781)

“…In conjunction with the study's data characteristics, three ML classification algorithms, decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM), were used to predict patients' medical decision-making tendency, and the performance of the three types of algorithms was compared.…”

The relationship between the prevalence of PE and failure rate of each diagnostic strategy. by Geert-Jan Geersing (331049)

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Forest plot of failure rate and efficiency of the diagnostic strategies across healthcare settings. by Geert-Jan Geersing (331049)

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The relationship between the prevalence of PE and efficiency of each diagnostic strategy. by Geert-Jan Geersing (331049)

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Patient characteristics across different healthcare settings. by Geert-Jan Geersing (331049)

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Sensitivity and specificity of diagnostic strategies across healthcare settings. by Geert-Jan Geersing (331049)

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Comparative analysis of DDcGAN-GSOM’s True Positive Rate, False Alarm Rate, and Specificity. by Nanavath Kiran Singh Nayak (22184565)

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Architecture of proposed system model. by Nanavath Kiran Singh Nayak (22184565)

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F1-score analysis of DDcGAN-GSOM with existing methods. by Nanavath Kiran Singh Nayak (22184565)

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Accuracy analysis of DDcGAN-GSOM with existing techniques. by Nanavath Kiran Singh Nayak (22184565)

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Network simulation of the proposed method. by Nanavath Kiran Singh Nayak (22184565)

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Simulation parameters of SDN network. by Nanavath Kiran Singh Nayak (22184565)

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Generator loss of the proposed method. by Nanavath Kiran Singh Nayak (22184565)

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