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.…”

Feature selection using the Boruta algorithm. by Guang Tu (22054865)

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Proposed genetic algorithm for feature selection. by Shirin Dehghan (19837936)

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

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

Illustration of the training algorithm for the sPHMM. by Tianshu Li (1527088)

Exploring the achievements and forecasting of SDG 3 using machine learning algorithms: Bangladesh perspective. by Md. Maeen Molla (20827041)

Types of machine learning algorithms. by Md. Merajul Islam (12646837)

“…Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.…”

The diagram of the LSTM neural network. by Mohd Sakib (20565228)

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Fitness curves for each algorithm. by Minan Tang (20389941)

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Ultrafast Hydrogen Detection System Using Vertical Thermal Conduction Structure and Neural Network Prediction Algorithm Based on Sensor Response Process by Ruilin Yang (151028)

“…The fabricated sensor exhibits rapid response, wide detection range, and wide operating temperature, while showing good long-term stability and excellent selectivity. Meanwhile, the model significantly enhanced the detection speed by enabling hydrogen concentration prediction using only the initial 40 data points (sampling frequency of 100 Hz) from the sensor response before the sensor completes the entire response process. …”

Ultrafast Hydrogen Detection System Using Vertical Thermal Conduction Structure and Neural Network Prediction Algorithm Based on Sensor Response Process by Ruilin Yang (151028)

“…The fabricated sensor exhibits rapid response, wide detection range, and wide operating temperature, while showing good long-term stability and excellent selectivity. Meanwhile, the model significantly enhanced the detection speed by enabling hydrogen concentration prediction using only the initial 40 data points (sampling frequency of 100 Hz) from the sensor response before the sensor completes the entire response process. …”

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

Spatial variables selected for LULC modeling. by Firdissa Sadeta Tiye (21222102)

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(A) Model 1: Crossover of genes screened by SVM-RFE and Degree algorithms. (B) Model 2: Crossover of genes screened by SVM-RFE and Betweenness algorithms. (C-D) Model 1 and Model 2... by Qingcong Zheng (11169094)

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Performance metrics of various algorithms. by Pritam Chakraborty (9261302)

“…Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. …”

Algorithm comparison - top performances. by Pritam Chakraborty (9261302)

“…Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. …”

Ranking of features for each algorithm. by Pritam Chakraborty (9261302)

“…Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. …”

Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

UpSet diagram illustrating the overlap of DNBs selected by our method and top-ranked genes by the 13 baseline centrality-based ranking algorithms. by Fatemeh Keikha (20917848)