Published February 28, 2025 | Version CC-BY-NC-ND 4.0
Journal article Open

AI Pattern Recognition and its Features

Authors/Creators

  • 1. Individual Researcher, Edison, NJ, USA.

Description

Abstract: Pattern recognition is one of the most fundamental aspects of artificial intelligence (AI) and machine learning (ML). It plays a pivotal role in tasks such as classification, clustering, regression, and anomaly detection. The ability to detect patterns and regularities from large datasets is critical for decision-making processes, automation, and developing intelligent systems. This article aims to provide an in-depth exploration of pattern recognition, its key features, utilities, and current challenges. It also examines the diverse applications of pattern recognition across industries such as healthcare, finance, and robotics, emphasizing its role in the future of AI.

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Additional details

Identifiers

DOI
10.35940/ijeat.C4562.14030225
EISSN
2249-8958

Dates

Accepted
2025-02-15
Manuscript received on 11 October 2024 | First Revised Manuscript received on 19 October 2024 | Second Revised Manuscript received on 28 December 2024 | Manuscript Accepted on 15 February 2025 | Manuscript published on 28 February 2025.

References

  • KUMAR, AJAY. "Neural Network Based Detection of Local Textile Defects." Pattern Recognition (2003): n. pag. Print. https://doi.org/10.1016/S0031-3203(03)00005-0
  • Finidori, Helene. "A Pattern LAnguage for Systemic Transformation (PLAST) - (Re)Generative of Commons ." PURPLSOC. The Workshop 2014. Designing Lively Scenarios With the Pattern Approach of Christopher Alexander (2015): 58–86. Print. (PDF) A Pattern LAnguage for Systemic Transformation (PLAST) - (re)Generative of Commons
  • Suzuki, E. (2002) 'Undirected discovery of interesting exception rules', International Journal of Pattern Recognition and Artificial Intelligence, Vol. 16, No. 8, pp.1065–1086. https://doi.org/10.1142/S0218001402002155
  • Ying Xie, T. Johnsten, V. V. Raghavan and K. Ramachandran, "On discovering "potentially useful" patterns from databases," 2006 IEEE International Conference on Granular Computing, Atlanta, GA, USA, 2006, pp. 494-497, DOI: https://doi.org/10.1109/GRC.2006.1635848
  • bansal, prashant. "Single Sign ON with SAML and Its Implementation." International Journal of Engineering Research and Technology, 2024. https://www.ijert.org/research/single-sign-on-with-saml-and-itsimplementation-IJERTV13IS090012.pdf
  • R. Singh, T. Johnsten, V. Raghavan and Y. Xie, "An efficient algorithm for discovering positive and negative patterns," 2009 IEEE International Conference on Granular Computing, Nanchang, China, 2009, pp. 507- 512, DOI: https://doi.org/10.1109/GRC.2009.5255068
  • Justine Zhang, Jonathan Chang, Cristian Danescu-Niculescu-Mizil, Lucas Dixon, Yiqing Hua, Dario Taraborelli, and Nithum Thain. 2018. Conversations Gone Awry: Detecting Early Signs of Conversational Failure. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1350– 1361, Melbourne, Australia. Association for Computational Linguistics. https://doi.org/10.48550/arXiv.1805.05345 https://doi.org/10.18653/v1/P18-1125
  • Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, Diyi Yang; Can Large Language Models Transform Computational Social Science? Computational Linguistics 2024; 50 (1): 237–291. doi: https://doi.org/10.1162/coli_a_00502
  • Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark Riedl, and Yejin Choi. 2022. Reframing Human-AI Collaboration for Generating Free-Text Explanations. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 632–658, Seattle, United States. Association for Computational Linguistics. https://doi.org/10.48550/arXiv.2112.08674 https://doi.org/10.18653/v1/2022.naacl-main.47
  • Hanna M. Wallach, Iain Murray, Ruslan Salakhutdinov, and David Mimno. 2009. Evaluation methods for topic models. In Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09). Association for Computing Machinery, New York, NY, USA, 1105–1112. https://doi.org/10.1145/1553374.1553515
  • Das, S., S, S., M, A., & Jayaram, S. (2021). Deep Learning Convolutional Neural Network for Defect Identification and Classification in Woven Fabric. https://doi.org/10.54105/ijainn.b1011.041221
  • Dhruvil Parmar, Mit Suthar, Dhaval Bhoi, Pattern Recognition in Games. (2020). In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 7S, pp. 6–8). https://doi.org/10.35940/ijitee.g1002.0597s20
  • Farooq, M., & Khan, M. H. (2019). Pattern Recognition in Digital Images using Fractals. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 2, pp. 3180–3183). https://doi.org/10.35940/ijeat.b4229.129219
  • Priyatharshini, Dr. R., Ram. A.S, A., Sundar, R. S., & Nirmal, G. N. (2019). Real-Time Object Recognition using Region-based Convolution Neural Network and Recursive Neural Network. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 4, pp. 2813– 2818). https://doi.org/10.35940/ijrte.d8326.118419.
  • Dutta, D., Halder, T., Penchala, A., Krishna, K. V., Prashnath, G., & Chakravarty, D. (2024). A Case Study on Image Co-Registration of Hyper Spectral and Dual (L & S) Band SAR Data and Ore Findings Over Zewar Mines, India. In International Journal of Emerging Science and Engineering (Vol. 12, Issue 6, pp. 17–25). https://doi.org/10.35940/ijese.a8055.12060524