Diabetes prediction using optimized machine learning algorithms with grid search and Bayesian optimization. by Muhammad Rizwan Khurshid (20613973)

Scatter plots of actual and forecasted values of different optimization algorithms for BiLSTM models in corn and wheat datasets. by Kaixuan Sun (4739976)

LSTM algorithm for cyber attack prediction in IoMT. by Mohanad Faeq Ali (21354273)

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. …”

Maple code for algorithm 3. by Peter Thompson (165468)

“…This new type of identifiability can be determined using our new algorithms, as is demonstrated by applications to various published biomedical models. …”

Comparative analysis of off-policy PPO algorithm and on-policy algorithm on the a) CSMAR and b) MorningStar datasets. by Cai Yuanqing (22513351)

Flow diagram of cuckoo search algorithm. by Muhammad Sadiq (9449915)

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Improved random forest algorithm. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

K-means++ clustering algorithm. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Machine learning hyperparameters used for this study. by Shaik Ahamed Fayaz (19859670)

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Simulated results of different DL algorithms using ISBI2016 dataset in FedAvg approach. by Shuvo Biswas (21728782)

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Simulated results of different DL algorithms using ISBI2017 dataset in FedAvg approach. by Shuvo Biswas (21728782)

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Simulated results of different DL algorithms using ISBI2017 dataset in a centralized approach. by Shuvo Biswas (21728782)

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Flow chart of the six hybrid ET models and a pure machine learning model using random forest (RF) algorithms. by Yeonuk Kim (4589488)

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Type II error rate from estimation of simulated outcomes using the EE algorithm. by Alex Stivala (8356257)

Type I error rate from estimation of simulated outcomes using the EE algorithm. by Alex Stivala (8356257)