openalexMendeley DataCited by 0
Data for “Detection of geospatial Anomalies and structural risks in geological layers through 3D gradient analysis using Machine Learning”
Also available via: Mendeley Data
Also available via: Mendeley Data
Also available via: Mendeley Data
Choosing IEEE Machine Learning Projects is one of the best decisions engineering students can make to enhance their technical knowledge and prepare for successful careers in artificial intelligence and data science. With research-oriented implementations, practical applications,…
Also available via: Mendeley Data
This archive contains the data and MATLAB code used to reproduce the analyses, model evaluations, tables, and figures for the manuscript: “Evaluating the cross-lake transferability limits of machine learning models for Sentinel-3 inland water Chlorophyll-a retrieval” The study ev…
Si Zhu, Nobuyoshi Komai, Shihao Zhang, Shigenobu Ogata
This archive provides the reproducibility materials associated with the manuscript “Wide-Range Predictions of Hydrogen-Dependent Vacancy Diffusion in Nickel from a near-DFT-Accurate Machine-Learning Potential.” It contains the numerical data underlying the manuscript figures and…
Cafer Mert Yeşilkanat, Uğur Kölemen
This deposit is the complete, fully deterministic computational pipeline that reproduces every quantitative result, table, and figure of the associated article by C. M. Yeşilkanat and U. Kölemen. No physical indentation experiments were performed. A physics-informed Monte Carlo f…