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Paper

A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil

002f845ec474602ba7f6b6358900a794ff328b46

Free2AITools Nexus Index

68.4

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 87

P: Popularity 64

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Thermal conductivity is a specific thermal property of soil which controls the exchange of thermal energy. If predicted accurately, the thermal conductivity of soil has a significant effect on geothermal applications. Since the thermal conductivity is influenced by multiple variables including soil type and mineralogy, dry density, and water content, its precise prediction becomes a challenging problem. In this study, novel computational approaches including hybridisation of two metaheuristic...

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Semantic Scholar 130 Citations

Paper Information Summary
Entity Passport
Registry ID	002f845ec474602ba7f6b6358900a794ff328b46
License	ArXiv
Provider	semantic_scholar

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Academic & Research Attribution

BibTeX

@misc{002f845ec474602ba7f6b6358900a794ff328b46,
  title = {A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil Paper},
  howpublished = {\url{https://api.semanticscholar.org/002f845ec474602ba7f6b6358900a794ff328b46}},
  note = {Accessed via Free2AITools.}
}

APA Style

A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil [Paper]. Free2AITools. https://api.semanticscholar.org/002f845ec474602ba7f6b6358900a794ff328b46

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⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 87

Popularity (P) 64

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil: Authority (A:87), Popularity (P:64), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"Thermal conductivity is a specific thermal property of soil which controls the exchange of thermal energy. If predicted accurately, the thermal conductivity of soil has a significant effect on geothermal applications. Since the thermal conductivity is influenced by multiple variables including soil type and mineralogy, dry density, and water content, its precise prediction becomes a challenging problem. In this study, novel computational approaches including hybridisation of two metaheuristic..."

❝ Cite Node

@article{A,
  title={A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil},
  note={Indexed by Free2AITools}
}

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📊 Research Signals

📈130CitationsSemantic Scholar

🏛️87AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

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📦Data Source: semantic_scholar

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

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downloads: 0
citations: 130

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