openalexMendeley Data2026-07-23Cited by 0

IEEE Machine Learning Projects

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, and industry-focused learning, these projects help students develop valuable real-world skills. Takeoff Edu Group offers comprehensive IEEE Machine Learning Projects with expert guidance, complete documentation, and end-to-end project support, enabling students to achieve academic excellence and build a strong foundation for future career opportunities.

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openalexMendeley Data2026-07-23

Data and code for "Evaluating the cross-lake transferability limits of machine learning models for Sentinel-3 inland water Chlorophyll-a retrieval"

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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…

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openalexMendeley Data2026-07-23

Data for: Wide-Range Predictions of Hydrogen-Dependent Vacancy Diffusion in Nickel from a near-DFT-Accurate Machine-Learning Potential

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…

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openalexMendeley Data2026-07-23

Physics-Informed Machine Learning Framework for Cross-Family Classification and Inverse Identification of Superconducting Materials from Simulated Nanoindentation Responses — Reproducible Pipeline

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…

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