Leveraging AI for Automated Customs Document Processing: A Case Study on AI-Powered Document Intelligence
Keywords:
artificial intelligence, machine learning, document intelligence, customs clearanceAbstract
Customs document processing is emerged as a groundbreaking approach to address inefficiencies in international trade logistics after the integration of artificial intelligence. This Research paper explores the application of ai power document intelligence Specially using machine learning techniques for document classification, entity recognition, and automated indexing. This study evaluates the extent to which Azure AI driven automation services enhances the accuracy, efficiency, and scalability of customs documentation workflows.
Downloads
References
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, Aug. 2013.
Sivaraman, Hariprasad. (2020). Integrating Large Language Models for Automated Test Case Generation in Complex Systems.
S. Kumari, "Kanban and Agile for AI-Powered Product Management in Cloud-Native Platforms: Improving Workflow Efficiency Through Machine Learning-Driven Decision Support Systems", Distrib Learn Broad Appl Sci Res, vol. 5, pp. 867-885, Aug. 2019
Singu, Santosh Kumar. "Real-Time Data Integration: Tools, Techniques, and Best Practices." ESP Journal of Engineering & Technology Advancements 1.1 (2021): 158-172.
S. Kumari, "AI-Powered Cloud Security for Agile Transformation: Leveraging Machine Learning for Threat Detection and Automated Incident Response ", Distrib Learn Broad Appl Sci Res, vol. 6, pp. 467-488, Oct. 2020
S. Kumari, "AI-Powered Cybersecurity in Agile Workflows: Enhancing DevSecOps in Cloud-Native Environments through Automated Threat Intelligence ", J. Sci. Tech., vol. 1, no. 1, pp. 809-828, Dec. 2020.
Sivaraman, Hariprasad. (2020). Intelligent Deployment Orchestration Using ML for Multi-Environment CI/CD Pipelines.
S. Kumari, "Cloud Transformation and Cybersecurity: Using AI for Securing Data Migration and Optimizing Cloud Operations in Agile Environments", J. Sci. Tech., vol. 1, no. 1, pp. 791-808, Oct. 2020.
Singu, Santosh Kumar. "Designing scalable data engineering pipelines using Azure and Databricks." ESP Journal of Engineering & Technology Advancements 1.2 (2021): 176-187.
L. Deng and D. Yu, “Deep learning: Methods and applications,” Foundations and Trends in Signal Processing, vol. 7, no. 3–4, pp. 197–387, 2014.
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” in Proceedings of the IEEE Symposium on Security and Privacy (SP), San Jose, CA, USA, 2016, pp. 582–597.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” in Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA, 2015, pp. 1–15.
T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in Proceedings of the International Conference on Learning Representations (ICLR), Scottsdale, AZ, USA, 2013, pp. 1–12.
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,” in Proceedings of the International Conference on Machine Learning (ICML), Pittsburgh, PA, USA, 2006, pp. 369–376.
H. Sak, A. W. Senior, and F. Beaufays, “Long short-term memory recurrent neural network architectures for large scale acoustic modeling,” in Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), Singapore, 2014, pp. 338–342.
A. Geva, K. Jacobsen, and N. Shazeer, “Transformer feed-forward layers are key-value memories,” in Proceedings of the International Conference on Learning Representations (ICLR), Virtual Conference, 2021, pp. 1–13.
K. Cho et al., “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 2014, pp. 1724–1734.
H. H. Bui, S. Venkatesh, and G. West, “Policy recognition in the abstract hidden Markov model,” Journal of Artificial Intelligence Research, vol. 17, pp. 451–499, 2002.
L. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Montreal, Canada, 2014, pp. 3104–3112.
