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Towards a NIR Spectroscopy ensemble learning technique competing with the standard ASTM-CFR: An optimal boosting and bagging extreme learning machine algorithms for gasoline octane number prediction
Research article (Optik, 2022) · cited 13× · AI/ML
Towards a NIR Spectroscopy ensemble learning technique competing with the standard ASTM-CFR: An optimal boosting and bagging extreme learning machine algorithms for gasoline octane number prediction
Summary
Towards a NIR Spectroscopy ensemble learning technique competing with the standard ASTM-CFR: An optimal boosting and bagging extreme learning machine algorithms for gasoline octane number prediction is a scholarly article[1].
Key Facts
Towards a NIR Spectroscopy ensemble learning technique competing with the standard ASTM-CFR: An optimal boosting and bagging extreme learning machine algorithms for gasoline octane number prediction's instance of is recorded as scholarly article[2].
References
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Towards a NIR Spectroscopy ensemble learning technique competing with the standard ASTM-CFR: An optimal boosting and bagging extreme learning machine algorithms for gasoline octane number prediction. Retrieved May 24, 2026, from https://4ort.xyz/entity/towards-a-nir-spectroscopy-ensemble-learning-technique-competing-with-the-standard-astm-cfr-an-optimal-boosting-and-bagg
MLA“Towards a NIR Spectroscopy ensemble learning technique competing with the standard ASTM-CFR: An optimal boosting and bagging extreme learning machine algorithms for gasoline octane number prediction.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/towards-a-nir-spectroscopy-ensemble-learning-technique-competing-with-the-standard-astm-cfr-an-optimal-boosting-and-bagg.
BibTeX@misc{4ortxyz_towards-a-nir-spectroscopy-ensemble-learning-technique-competing-with-the-standard-astm-cfr-an-optimal-boosting-and-bagg_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Towards a NIR Spectroscopy ensemble learning technique competing with the standard ASTM-CFR: An optimal boosting and bagging extreme learning machine algorithms for gasoline octane number prediction}}, year = {2026}, url = {https://4ort.xyz/entity/towards-a-nir-spectroscopy-ensemble-learning-technique-competing-with-the-standard-astm-cfr-an-optimal-boosting-and-bagg}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Towards a NIR Spectroscopy ensemble learning technique competing with the standard ASTM-CFR: An optimal boosting and bagging extreme learning machine algorithms for gasoline octane number prediction — https://4ort.xyz/entity/towards-a-nir-spectroscopy-ensemble-learning-technique-competing-with-the-standard-astm-cfr-an-optimal-boosting-and-bagg (retrieved 2026-05-24)
