Home › Entities › academia › Recurrent neural network (RNN) and long short-term memory neural network (LSTM) based data-driven methods for identifying cohesive zone law parameters of nickel-modified carbon nanotube reinforced sintered nano-silver adhesives

Recurrent neural network (RNN) and long short-term memory neural network (LSTM) based data-driven methods for identifying cohesive zone law parameters of nickel-modified carbon nanotube reinforced sintered nano-silver adhesives

Research article (Materials Today Communications, 2024) · cited 29× · AI/ML
academia openalex:W4394987374
Press Enter · cited answer in seconds

Recurrent neural network (RNN) and long short-term memory neural network (LSTM) based data-driven methods for identifying cohesive zone law parameters of nickel-modified carbon nanotube reinforced sintered nano-silver adhesives

Summary

Recurrent neural network (RNN) and long short-term memory neural network (LSTM) based data-driven methods for identifying cohesive zone law parameters of nickel-modified carbon nanotube reinforced sintered nano-silver adhesives is a scholarly article[1].

Key Facts

  • Recurrent neural network (RNN) and long short-term memory neural network (LSTM) based data-driven methods for identifying cohesive zone law parameters of nickel-modified carbon nanotube reinforced sintered nano-silver adhesives's instance of is recorded as scholarly article[2].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

📑 Cite this page

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.

APA 4ort.xyz Knowledge Graph. (2026). Recurrent neural network (RNN) and long short-term memory neural network (LSTM) based data-driven methods for identifying cohesive zone law parameters of nickel-modified carbon nanotube reinforced sintered nano-silver adhesives. Retrieved May 24, 2026, from https://4ort.xyz/entity/recurrent-neural-network-rnn-and-long-short-term-memory-neural-network-lstm-based-data-driven-methods-for-identifying-co
MLA “Recurrent neural network (RNN) and long short-term memory neural network (LSTM) based data-driven methods for identifying cohesive zone law parameters of nickel-modified carbon nanotube reinforced sintered nano-silver adhesives.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/recurrent-neural-network-rnn-and-long-short-term-memory-neural-network-lstm-based-data-driven-methods-for-identifying-co.
BibTeX @misc{4ortxyz_recurrent-neural-network-rnn-and-long-short-term-memory-neural-network-lstm-based-data-driven-methods-for-identifying-co_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Recurrent neural network (RNN) and long short-term memory neural network (LSTM) based data-driven methods for identifying cohesive zone law parameters of nickel-modified carbon nanotube reinforced sintered nano-silver adhesives}}, year = {2026}, url = {https://4ort.xyz/entity/recurrent-neural-network-rnn-and-long-short-term-memory-neural-network-lstm-based-data-driven-methods-for-identifying-co}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Recurrent neural network (RNN) and long short-term memory neural network (LSTM) based data-driven methods for identifying cohesive zone law parameters of nickel-modified carbon nanotube reinforced sintered nano-silver adhesives — https://4ort.xyz/entity/recurrent-neural-network-rnn-and-long-short-term-memory-neural-network-lstm-based-data-driven-methods-for-identifying-co (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/recurrent-neural-network-rnn-and-long-short-term-memory-neural-network-lstm-based-data-driven-methods-for-identifying-co · Last refreshed: