Home ›
Entities
› academia
› Comparative Studies of Response Surface Methodology (RSM) and Predictive Capacity of Artificial Neural Network (ANN) on Mild Steel Corrosion Inhibition using Water Hyacinth as an Inhibitor
Comparative Studies of Response Surface Methodology (RSM) and Predictive Capacity of Artificial Neural Network (ANN) on Mild Steel Corrosion Inhibition using Water Hyacinth as an Inhibitor
Research article (Journal of Physics Conference Series, 2019) · cited 16× · AI/ML
Comparative Studies of Response Surface Methodology (RSM) and Predictive Capacity of Artificial Neural Network (ANN) on Mild Steel Corrosion Inhibition using Water Hyacinth as an Inhibitor
Summary
Comparative Studies of Response Surface Methodology (RSM) and Predictive Capacity of Artificial Neural Network (ANN) on Mild Steel Corrosion Inhibition using Water Hyacinth as an Inhibitor is a scholarly article[1].
Key Facts
Comparative Studies of Response Surface Methodology (RSM) and Predictive Capacity of Artificial Neural Network (ANN) on Mild Steel Corrosion Inhibition using Water Hyacinth as an Inhibitor's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
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). Comparative Studies of Response Surface Methodology (RSM) and Predictive Capacity of Artificial Neural Network (ANN) on Mild Steel Corrosion Inhibition using Water Hyacinth as an Inhibitor. Retrieved May 24, 2026, from https://4ort.xyz/entity/comparative-studies-of-response-surface-methodology-rsm-and-predictive-capacity-of-artificial-neural-network-ann-on-mild
MLA“Comparative Studies of Response Surface Methodology (RSM) and Predictive Capacity of Artificial Neural Network (ANN) on Mild Steel Corrosion Inhibition using Water Hyacinth as an Inhibitor.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/comparative-studies-of-response-surface-methodology-rsm-and-predictive-capacity-of-artificial-neural-network-ann-on-mild.
BibTeX@misc{4ortxyz_comparative-studies-of-response-surface-methodology-rsm-and-predictive-capacity-of-artificial-neural-network-ann-on-mild_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Comparative Studies of Response Surface Methodology (RSM) and Predictive Capacity of Artificial Neural Network (ANN) on Mild Steel Corrosion Inhibition using Water Hyacinth as an Inhibitor}}, year = {2026}, url = {https://4ort.xyz/entity/comparative-studies-of-response-surface-methodology-rsm-and-predictive-capacity-of-artificial-neural-network-ann-on-mild}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Comparative Studies of Response Surface Methodology (RSM) and Predictive Capacity of Artificial Neural Network (ANN) on Mild Steel Corrosion Inhibition using Water Hyacinth as an Inhibitor — https://4ort.xyz/entity/comparative-studies-of-response-surface-methodology-rsm-and-predictive-capacity-of-artificial-neural-network-ann-on-mild (retrieved 2026-05-24)
