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Inference of Recyclable Objects with Convolutional Neural Networks paper
⭐ 63.2
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Paper

Inference of Recyclable Objects with Convolutional Neural Networks

00f347e4d3b9d5503027041e52c6430eec8b419c
Free2AITools Nexus Index
63.2
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 70
P: Popularity 45
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Population growth in the last decades has resulted in the production of about 2.01 billion tons of municipal waste per year. The current waste management systems are not capable of providing adequate solutions for the disposal and use of these wastes. Recycling and reuse have proven to be a solution to the problem, but large-scale waste segregation is a tedious task and on a small scale it depends on public awareness. This research used convolutional neural networks and computer vision to dev...

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Semantic Scholar 4 Citations
Paper Information Summary
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Registry ID 00f347e4d3b9d5503027041e52c6430eec8b419c
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{00f347e4d3b9d5503027041e52c6430eec8b419c,
  title = {Inference of Recyclable Objects with Convolutional Neural Networks Paper},
  howpublished = {\url{https://api.semanticscholar.org/00f347e4d3b9d5503027041e52c6430eec8b419c}},
  note = {Accessed via Free2AITools.}
}
APA Style
Inference of Recyclable Objects with Convolutional Neural Networks [Paper]. Free2AITools. https://api.semanticscholar.org/00f347e4d3b9d5503027041e52c6430eec8b419c

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 70
Popularity (P) 45
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Inference of Recyclable Objects with Convolutional Neural Networks: Authority (A:70), Popularity (P:45), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"Population growth in the last decades has resulted in the production of about 2.01 billion tons of municipal waste per year. The current waste management systems are not capable of providing adequate solutions for the disposal and use of these wastes. Recycling and reuse have proven to be a solution to the problem, but large-scale waste segregation is a tedious task and on a small scale it depends on public awareness. This research used convolutional neural networks and computer vision to dev..."

❝ Cite Node 

@article{Inference,
  title={Inference of Recyclable Objects with Convolutional Neural Networks},
  note={Indexed by Free2AITools}
}

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πŸ“Š Research Signals

πŸ“ˆ4CitationsSemantic Scholar
πŸ›οΈ70AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈinfrastructure opsField

🏷️ Research Topics

vision models
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πŸ†” Identity & Source

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

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citations
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