Folder with all data and algorithms by Jorge Servert Lerdo de Tejada (22290001)

Comparison of different optimization algorithms. by Hang Zhao (143592)

Subjects: “…crayfish optimization algorithm…”

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

Subjects:

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”

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. …”

Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

“…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”

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). …”

Flowchart of DQN algorithm. by Jiandong Qiu (20389944)

The run time for each algorithm in seconds. by Edward Antonian (21453161)

“…The goal of this paper is to examine several extensions to KGR/GPoG, with the aim of generalising them a wider variety of data scenarios. The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. …”

Phases of proposed algorithm. by Muhammad Abdullah (3357692)

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

Top-rated mode of transport based on the SEU algorithm (SEU scores from 0 to 40). by Johannes Weyer (13392175)

Risk element category diagram. by Yao Hu (3479972)

“…This article extracted features related to risk assessment, such as weather factors, airport facility inspections, and security check results, and conducted qualitative and quantitative analysis on these features to generate a datable risk warning weight table. This article used these data to establish an LSTM model, which trained LSTM to identify potential risks and provide early warning by learning patterns and trends in historical data. …”

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. …”