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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"

Chudi Wu, Z Chen

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

Research Data for “Two-Fraction Kinetic Modelling of Ethyl Levulinate Production from Oil Palm Empty Fruit Bunches via Dimethyl Carbonate-Assisted Ethanolysis

heriyanti heriyanti

This dataset contains the experimental and processed data supporting the study entitled “Two-Fraction Kinetic Modelling of Ethyl Levulinate Production from Oil Palm Empty Fruit Bunches via Dimethyl Carbonate-Assisted Ethanolysis.” The dataset includes temperature- and time-depend…

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openalexMendeley Data2026-07-23

Quasi-static compression test dataset of sinusoidally-corrugated multi-cell PLA+ energy absorbers fabricated by fused deposition modeling (FDM)

Ahmed Saber

This dataset provides experimental quasi-static axial compression data for 40 FDM-printed sinusoidally-corrugated multi-cell (SCMC) PLA+ energy absorbers generated through Latin Hypercube Sampling (LHS), along with data for a simple tube without corrugation (STWC) and a nominal S…

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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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openalexMendeley Data2026-07-23

IEEE Machine Learning Projects

Takeoff 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,…

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