Data Sheet 1_Enhanced vehicle routing for medical waste management via hybrid deep reinforcement learning and optimization algorithms.pdf by Norhan Khallaf (20710673)

Data Sheet 3_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 2_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 4_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 6_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.docx by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 1_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.pdf by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Sparse canonical-correlation analysis (sCCA) of TC scores with reinforcement-learning and sensorimotor-control variables. by Huu Hoang (9188747)

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Performance comparison of ML algorithms across multiple evaluation metrics. by Muhammad Tausif (12385892)

Data Sheet 1_Development of a novel artificial intelligence algorithm for interpreting fetal heart rate and uterine activity data in cardiotocography.docx by Rohit Pardasani (22253656)

“…</p>Conclusion<p>This study demonstrates the successful development of a novel AI algorithm utilizing FHR and UA data to analyze and interpret fetal tracing events and parameters. …”

S1 Data - Text intelligent correction in English translation: A study on integrating models with dependency attention mechanism by Yutong Liu (4027994)

Registered and fused point cloud data using improved ICP algorithm. by Mengyuan Yan (14200322)

Reference set of diverse topological and structural variants of Ig domains from the SCOP b.1 Superfamily. (see supplement file S7 Data, for the corresponding spreadsheet). It inclu... by Caesar Tawfeeq (21087272)

Convergence curves for all algorithms. by Genliang Li (5816264)

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Parameter Settings for competitive algorithms. by Genliang Li (5816264)

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Data Sheet 1_Efficient tree species classification using machine and deep learning algorithms based on UAV-LiDAR data in North China.docx by Hanyu Zhang (1425250)

“…</p>Methods<p>UAV-LiDAR point clouds of Populus alba, Populus simonii, Pinus sylvestris, and Pinus tabuliformis from 12 sample plots, 2,622 tree in total, were obtained in North China, training and testing sets were constructed through data pre-processing, individual tree segmentation, feature extraction, Non-uniform Grid and Farther Point Sampling (NGFPS), and then four tree species were classified efficiently by two machine learning algorithms and two deep learning algorithms.…”

Data Sheet 1_Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea).docx by Degninou Yehadji (3451511)

In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons by Owen Powell (16328658)

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Schematic diagram of TSO-VMD-BILSTM-AEAM algorithm structure. by Hao Cao (2248066)

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Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)

Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)