Thursday, January 22, 2026
Space & Astronomy
21 min read

Optimizing Deep Learning Architectures for Intelligent Structural Space Scheduling

Nature
January 18, 20264 days ago
Research on deep learning architecture optimization method for intelligent scheduling of structural space

AI-Generated Summary
Auto-generated

The provided article content is a list of references, not a news article. It does not contain information about a specific news event, key developments, or outcomes. Therefore, a summary cannot be generated.

Mi, B. & Yi, F. A review: development of named entity recognition (ner) technology for aeronautical information intelligence. Artif. Intell. Rev. 1, 1 (2022). Hernandez-Lareda, F. & Auccahuasi, W. Implementation of a customized named entity recognition (ner) model in document categorization. 2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS) (2024). Chavan, T. & Patil, S. Named entity recognition (ner) for news articles. Int. J. Adv. Res. Eng. Technol. 1, 1 (2024). Khouya, N., Retbi, A. & Bennani, S. Enriching ontology with named entity recognition (ner) integration. ACR (2024). Yossy, E., Suhartono, D., Trisetyarso, A. & Budiharto, W. Question classification of university admission using named-entity recognition (ner). International Conference on Information Technology, Computer, and Electrical Engineering (2023). Bade, G., Kolesnikova, O. & Oropeza, J. The role of named entity recognition (ner): Survey. Int. J. Comput. Org. Trends 1, 1 (2024). Zhang, Z. et al. E-ner: Evidential deep learning for trustworthy named entity recognition. Annual Meeting of the Association for Computational Linguistics (2023). Mo, Y. et al. mcl-ner: Cross-lingual named entity recognition via multi-view contrastive learning. AAAI Conference on Artificial Intelligence (2023). Ushio, A. & Camacho-Collados, J. T-ner: An all-round python library for transformer-based named entity recognition. Conference of the European Chapter of the Association for Computational Linguistics (2022). Ray, A. T., Pinon-Fischer, O. J., Mavris, D., White, R. T. & Cole, B. F. aerobert-ner: Named-entity recognition for aerospace requirements engineering using bert. AIAA SCITECH 2023 Forum (2023). Au, T. W. T., Cox, I. & Lampos, V. E-ner—an annotated named entity recognition corpus of legal text. NLLP (2022). Yu, J. et al. S-ner: A concise and efficient span-based model for named entity recognition. Italian National Conference on Sensors (2022). Hu, Y. et al. Improving large language models for clinical named entity recognition via prompt engineering. J. Am. Med. Inform. Assoc. 1, 1 (2023). Jarrar, M. et al. Wojoodner 2024: The second arabic named entity recognition shared task. ARABICNLP (2024). Zhou, W., Zhang, S., Gu, Y., Chen, M. & Poon, H. Universalner: Targeted distillation from large language models for open named entity recognition. International Conference on Learning Representations (2023). Zaratiana, U., Tomeh, N., Holat, P. & Charnois, T. Gliner: Generalist model for named entity recognition using bidirectional transformer. North American Chapter of the Association for Computational Linguistics (2023). Shen, Y. et al. Promptner: Prompt locating and typing for named entity recognition. Annual Meeting of the Association for Computational Linguistics (2023). Shen, Y. et al. Diffusionner: Boundary diffusion for named entity recognition. Annual Meeting of the Association for Computational Linguistics (2023). Ding, N. et al. Few-nerd: A few-shot named entity recognition dataset. Annual Meeting of the Association for Computational Linguistics (2021). Xie, T., Li, Q., Zhang, Y., Liu, Z. & Wang, H. Self-improving for zero-shot named entity recognition with large language models. North American Chapter of the Association for Computational Linguistics (2023). Qu, X. et al. A survey on arabic named entity recognition: Past, recent advances, and future trends. IEEE Trans. Knowl. Data Eng. 1, 1 (2023). Vendé, B., Barberousse, A. & Ruphy, S. From 2015 to 2023, eight years of empirical research on research integrity: a scoping review. Res. Integr. Peer Rev. 10, 5 (2025). Adjovi, I. S. M. A worldwide itinerary of research ethics in science for a better social responsibility and justice: a bibliometric analysis and review. Front. Res. Metr. Anal. 10, 1504937 (2025). Sizo, A., Lino, A., Rocha, Á. & Reis, L. P. Defining quality in peer review reports: a scoping review. Knowl. Inf. Syst. 1, 1–48 (2025). Hu, Z. et al. The global research landscape and future trends in healthcare total quality management. Arch. Public Health 82, 193 (2024). Endalamaw, A. et al. A scoping review of continuous quality improvement in healthcare system: conceptualization, models and tools, barriers and facilitators, and impact. BMC Health Serv. Res. 24, 487 (2024). Bierbaum, M. et al. The integration of quality improvement and implementation science methods and frameworks in healthcare: a systematic review. BMC Health Serv. Res. 25, 558 (2025). Chen, J. et al. Learning in-context learning for named entity recognition. Annual Meeting of the Association for Computational Linguistics (2023). Budi, I. & Suryono, R. R. Application of named entity recognition method for Indonesian datasets: a review. Bull. Electric. Eng. Inform. 1, 1 (2023). Jarrar, M. et al. Wojoodner 2023: The first arabic named entity recognition shared task. ARABICNLP (2023). Darji, H., Mitrović, J. & Granitzer, M. German bert model for legal named entity recognition. International Conference on Agents and Artificial Intelligence (2023). Yu, J., Bohnet, B. & Poesio, M. Named entity recognition as dependency parsing. Annual Meeting of the Association for Computational Linguistics (2020). Deng, J. et al. Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition 248–255 (IEEE, 2009). Kayed, M., Anter, A. & Mohamed, H. Classification of garments from fashion mnist dataset using cnn lenet-5 architecture. In 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE) 238–243 (IEEE, 2020). Tong, K. & Wu, Y. Rethinking pascal-voc and ms-coco dataset for small object detection. J. Vis. Commun. Image Represent. 93, 103830 (2023). Benbrahim, H. & Behloul, A. Fine-tuned xception for image classification on tiny imagenet. In 2021 International Conference on Artificial Intelligence for Cyber Security Systems and Privacy (AI-CSP) 1–4 (IEEE, 2021). Arslan, S. Application of bilstm-crf model with different embeddings for product name extraction in unstructured turkish text. Neural Comput. Appl. 36, 8371–8382 (2024). Silva-Rodriguez, J., Chakor, H., Kobbi, R., Dolz, J. & Ayed, I. B. A foundation language-image model of the retina (flair): Encoding expert knowledge in text supervision. Med. Image Anal. 99, 103357 (2025). Zhu, P., Yuan, Y. & Chen, L. Electra-based graph network model for multi-hop question answering. J. Intell. Inf. Syst. 61, 819–834 (2023). Zhang, H., Xiang, J., Guo, X., Wang, L. & Wang, X. A redundant relation reduced bidirectional extraction framework based on spanbert for relational triple extraction. In International Conference on Intelligent Computing 83–95 (Springer, 2024). Hussain, A., Usman, M., Zaman, F., Ibrahim, T. & Dawood, A. Symmetry analysis, closed-form invariant solutions and dynamical wave structures of the benney-luke equation using optimal system of lie subalgebras. Chin. J. Phys. 84, 66–88 (2023).

Rate this article

Login to rate this article

Comments

Please login to comment

No comments yet. Be the first to comment!
    Deep Learning Architecture Optimization for Intelligent Scheduling