The actual training effect of four algorithms on original photo data (All images are original images taken by the author). by Xiaojun Wang (50332)

Summary of the metrics of the different trained algorithms based on mean and 95% confidence intervals (calculated using t-Student) for the Testing process in each dataset. by Jaime B. Matas-Bustos (21772430)

The training process and validation process of SE-MAConvLSTM and its variants on two datasets. by Rong Zhu (172093)

Subjects:

ATF-DF-WA Algorithm Experimental Code by jinhao zhong (20616116)

“…<p dir="ltr">The provided code files serve the following purposes:</p><ul><li><b>Preprocess.py</b> – This script handles text preprocessing tasks, such as cleaning and preparing raw text data for further processing.</li><li><b>txtvec.py</b> – This script is responsible for text vectorization, converting processed text into numerical representations suitable for machine learning algorithms.…”

Flowchart diagram illustrating the data analysis process using machine learning models. by Isabela Bittencourt Basso (14178739)

Subjects:

Reshaping Industrial Maintenance with Machine Learning: Fouling Control Using Optimized Gaussian Process Regression by Francesco Negri (12992994)

“…The algorithm’s predictive capabilities are validated on five distinct data sets representing the evolution of the column’s pressure differential, a key indicator of fouling. …”

The process of FL. by Pan Wang (198097)

Subjects:

Ablation comparison experimental data. by Tengfei Ma (597633)

Performance of the U-Net model with CellProfiler post-processing for detection and area determination of respiratory organoids on new data. by Anna Demchenko (18449439)

Evaluation metrics with Q-learning on ML-latest-small. by Muhammad Waqar (500788)

Subjects:

Data Sheet 1_Exploring immune-inflammation markers in psoriasis prediction using advanced machine learning algorithms.pdf by Li Yang (6520)

“…Subsequently, nine classification algorithms were developed using the processed training set, including random forest, neural networks, XGBoost, k-nearest neighbors, gradient boosting, logistic regression, naïve Bayes, AdaBoost, and SVMs. …”

Example measurement and analysis units for landscape elements. by Jaewon Han (8660295)

Subjects:

Algorithm steps for house price prediction model. by Xuan Wang (55634)

“…The research problem is addressed by introducing the House Price Evaluation Model (HPEM), which utilizes a hybrid deep learning network for analyzing multi-source geographic data. …”

DeepGlobe Land Cover Classification Examples: Shows satellite images processed by classification algorithms, with land cover types such as agricultural land, forests, urban areas,... by Mingzhen Lu (21782047)

Risk element category diagram. by Yao Hu (3479972)

“…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. …”

Data Sheet 1_Person-based design and evaluation of MIA, a digital medical interview assistant for radiology.pdf by Kerstin Denecke (11534035)

Agent learning of FilmTrust dataset across various parameters. by Muhammad Waqar (500788)

Subjects:

Snapshot of Q-table at some time instance for Q-learning. by Muhammad Waqar (500788)

Subjects:

Evaluation results obtained with Q-learning on ML-100K. by Muhammad Waqar (500788)

Subjects:

Box plot of ablation experiment data. by Tengfei Ma (597633)