Leveraging Supervised Machine Learning Algorithms for System Suitability Testing of Mass Spectrometry Imaging Platforms by Russell R. Kibbe (15352075)

“…However, mass spectrometry imaging (MSI) analyses present added complexity since both chemical and spatial information are measured. Herein, we employ various machine learning algorithms and a novel quality control mixture to classify the working conditions of an MSI platform. …”

Fusion of gridded satellite and earth-observed daily precipitation data in the United States using tree-based ensemble learning algorithms by Georgia Papacharalampous (4399993)

“…The same procedures often rely on the application of machine and statistical learning regression algorithms in spatial settings. …”

Computational complexity of various algorithms in the pipeline. by Ali M. S. Alfosool (12936678)

Large-scale comparison of machine and statistical learning algorithms for blending gridded satellite and earth-observed precipitation data by Georgia Papacharalampous (4399993)

ROC curve and unsupervised machine learning approach on testing the spatial uniformness of 25 highly-dense rectangles. by Shuting Liao (9524506)

Data for "Machine Learning-based Prediction of Passive Gears from Vessel Tracking Data in Small-Scale Multi-gear Fisheries" by Pamela Lattanzi (16454871)

Supplementary file 1_Modelling the spatial distribution of total nitrogen and phosphorus stock in dryland terrestrial ecosystems of China using machine learning algorithms.docx by Bin Liu (5899)

ROC curve and unsupervised machine learning approach ontesting the spatial uniformness of [R1-R2] rectangles. by Shuting Liao (9524506)

Understanding patterns and potential drivers of forest diversity in northeastern China using machine-learning algorithms by Weixue Luo (6070040)

The application of machine learning in the integration and optimization of natural protected areas by Qiuyan Liang (21585203)

“…However, traditional optimization methods have certain limitations. Machine learning, as an efficient analysis algorithm, has been applied to many fields to process high-dimensional data due to its advantages in multidimensional classification problems. …”

A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms by Xingan Chen (11334186)

Data Sheet 1_Hybrid machine learning algorithms accurately predict marine ecological communities.pdf by Luciana Erika Yaginuma (10477013)

“…Data was analyzed by means of a hybrid machine learning (ML) approach, which combines unsupervised and supervised methods. …”

Image1_A comparative study for landslide susceptibility assessment using machine learning algorithms based on grid unit and slope unit.TIF by Niandong Deng (14036877)

“…Three typical machine learning models, including random forest forest by penalizing attributes (FPA) and rotation forest were merged by random subspace algorithm. …”

DataSheet1_A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks.docx by Brandon Smith (68546)

DataSheet1_A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks.docx by Brandon Smith (68546)

Figure 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies by Yeman Brhane Hagos (17885432)

“…Bone texture and structural heterogeneity were investigated using an autoencoder-based machine learning method (Supplementary Materials and Methods). …”

Estimation of net ecosystem carbon exchange at climate sites by combing remote sensing data and FLUXNET2015 data with machine learning algorithms by Wenqiang Zhang (13245471)

“…In this study, we first optimized the hyperparameters and input variables of the ML model based on the adaptive genetic algorithm. Then, we developed 566 random forest (RF)-based NEE estimation models by the strategy of spatial leave-out-one cross- validation (SLOOCV). …”

China’s annual forest age dataset at a 30-m spatial resolution from 1986 to 2022 by Rong Shang (17306089)

DataSheet1_Performances of Machine Learning Algorithms in Predicting the Productivity of Conservation Agriculture at a Global Scale.pdf by Yang Su (521862)

“…Our approach covers the comparison of 12 different machine learning algorithms, model training, tuning with cross-validation, testing, and global projection of results. …”

Image_1_Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species.JPEG by Jia-Wei Tang (3561386)

“…According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). …”