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). Unified CNN-LSTM for keyhole status prediction in PAW based on spatial-temporal features. Retrieved May 24, 2026, from https://4ort.xyz/entity/unified-cnn-lstm-for-keyhole-status-prediction-in-paw-based-on-spatial-temporal-features
MLA“Unified CNN-LSTM for keyhole status prediction in PAW based on spatial-temporal features.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/unified-cnn-lstm-for-keyhole-status-prediction-in-paw-based-on-spatial-temporal-features.
BibTeX@misc{4ortxyz_unified-cnn-lstm-for-keyhole-status-prediction-in-paw-based-on-spatial-temporal-features_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Unified CNN-LSTM for keyhole status prediction in PAW based on spatial-temporal features}}, year = {2026}, url = {https://4ort.xyz/entity/unified-cnn-lstm-for-keyhole-status-prediction-in-paw-based-on-spatial-temporal-features}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Unified CNN-LSTM for keyhole status prediction in PAW based on spatial-temporal features — https://4ort.xyz/entity/unified-cnn-lstm-for-keyhole-status-prediction-in-paw-based-on-spatial-temporal-features (retrieved 2026-05-24)
