Novel S-Box rendered by the suggested algorithm. by Nayeem Ahmad Khan (11727377)

Subjects:

Feature selection using Boruta algorithm. by Shayla Naznin (13014015)

“…Feature selection was performed using the Boruta algorithm and model performance was evaluated by comparing accuracy, precision, recall, F1 score, MCC, Cohen’s Kappa and AUROC.…”

The structure of genetic algorithm (GA). by Ali Akbar Moosavi (17769033)

“…Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. …”

Characteristics of training algorithms. by Tuan Anh Nguyen (121944)

“…The model is designed with three inputs and one output, trained using data derived from a high-performance robust controller for Electric Power Steering (EPS) systems. …”

DMTD algorithm. by Yunhu Huang (21402795)

“…Finally, we conducted comparative tests of the manual driving, intelligent driving algorithm (ITOR, STON), and the algorithms proposed in this paper, EITO, using real line data from the Yizhuang Line of Beijing Metro (YLBS). …”

Phases of proposed algorithm. by Muhammad Abdullah (3357692)

“…An enhanced Bingzhen and Weimin model-based decision-making algorithm is developed to support intelligent diagnosis. …”

The PBFT consensus algorithm. by Keyan Abdul-Aziz Mutlaq (17833696)

Robust control scheme. by Tuan Anh Nguyen (121944)

“…The model is designed with three inputs and one output, trained using data derived from a high-performance robust controller for Electric Power Steering (EPS) systems. …”

Overview of the Cell2Spatial algorithm. by Huamei Li (8815955)

“…SC and spatial transcriptomics (ST) data were standardized using <i>SCTransform</i> in Seurat, with cell-type-specific genes identified through a modified entropy-based method (Step 1). …”

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. …”

Algorithm: CL-selector. by Zhejun Sun (9873500)

Local Means Thresholding Algorithm shows high robustness. by Araven Tiroumalechetty (17827252)

Algorithm: MAML with CL-selector. by Zhejun Sun (9873500)

Algorithm: Pre-fine-tuning. by Zhejun Sun (9873500)

Algorithm: CL-selector initialization. by Zhejun Sun (9873500)

Algorithm flowchart of OFEO. by Wenshun Sheng (21485393)

“…Again, addressing the limitations of traditional Kalman filtering in nonlinear motion trajectory prediction, a novel adaptive forgetting Kalman filter algorithm (FSA) is devised to enhance the precision of model prediction and the effectiveness of parameter updates to adjust to the uncertain movement speed and trajectory of pedestrians in real scenarios. …”