A Review on Pattern Recognition Using Machine Learning

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Advances in Mechanical Engineering

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

Machine learning (ML) techniques have gained remarkable attention in past two decades including many fields like computer vision, information retrieval, and pattern recognition. This paper presents a literature review on pattern recognition of various applications like signal processing, agriculture sector, healthcare sector, signature recognition, and different model analysis using ML techniques. The focus of our survey is at the ML techniques, classification techniques and deep learning model, and improves the accuracy rate for the automatic decision making algorithms.

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References

  1. Weng Y, **a C (2020) A new deep learning-based handwritten character recognition system on mobile computing devices. Mob Netw Appl 25:402–411

    Google Scholar 

  2. Du M, Liu N, Hu X (2020) Techniques for interpretable machine learning. Commun ACM 63:68–77

    Google Scholar 

  3. Latif J, **ao C, Imran A, Tu S (2019) Medical imaging using MLand Deep Learning Algorithms: a review. In: International conference on computing, mathematics and engineering technologies, pp 1–6

    Google Scholar 

  4. Bravo F, Shaposhnik Y (2020) Mining optimal policies: a pattern recognition approach to model analysis. INFORMS J Optim 1–22

    Google Scholar 

  5. Emmert-Streib F, Dehmer M (2019) A MLPerspective on personalized medicine: an atomized, comprehensive knowledge base with ontology for pattern recognition. Mach Learn Know Ext 1:149–156

    Google Scholar 

  6. Mshir S, Kaya M (2020) Signature recognition using machine learning. IEEE, pp 1–4

    Google Scholar 

  7. Morales A, Fierrez J, Vera-Rodriguez R, Tolosana R (2019) Sensitive nets: learning agnostic representations with application to face images. IEEE

    Google Scholar 

  8. Zhou Y, Chen C, Ni J, Ni G, Li M, Xu G, Cavanaugh J, Cheng M, Lemos S (2020) EMG signal processing for hand motion pattern recognition using MLAlgorithms. Arch Orthop 17–26

    Google Scholar 

  9. Davarzani S, Nagahi M, Tidwell M, Smith BK (2020) Pattern recognition using MLfor corn and soybean yield prediction. In: Proceedings of the 2020 IISE annual conference, pp 1–6

    Google Scholar 

  10. Wang Y, Yan J, Sun Q, Li J, Yang Z (2019) A MobileNets convolutional neural network for GIS partial discharge pattern recognition in the ubiquitous power internet of things context: optimization, comparison, and application. IEEE Access 7:150226–150236

    Google Scholar 

  11. Toraman S, Girgin M, Ustundag B, Turkoglu G (2019) Classification of the likelihood of colon cancer with MLtechniques using FTIR signals obtained from plasma. Turkish J Electr Eng Comput Sci 27:1765–1779

    Google Scholar 

  12. Nicholson AA, Densmore M, McKinnon MC, Neufeld RWJ, Frewen PA, Théberge J, Jetly R, Donald Richardson J, Lanius RA (2018) MLmultivariate pattern analysis predicts classification of posttraumatic stress disorder and its dissociative subtype: a multimodal neuroimaging approach. Psychol Med 1–11

    Google Scholar 

  13. Melati D, Grinberg Y, Dezfouli MK, Janz S, Cheben P, Schmid JH, Sanchez-Postigo A, Xu D-X (2019) Map** the global design space of nanophotonic components using MLpattern recognition. Nat Commun 10:1–9

    Google Scholar 

  14. von Rueden L, Mayer S, Garcke J, Bauckhage C, Schuecker J (2019) Informed ML—towards a taxonomy of explicit integration of knowledge into machine learning In: IEEE, pp 1–8

    Google Scholar 

  15. Li G, Li J, Ju Z, Sun Y, Kong J (2019) A novel feature extraction method for MLBased on surface electromyography from healthy brain

    Google Scholar 

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Correspondence to Preeti Saini .

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Saini, P., Kaur, J., Lamba, S. (2021). A Review on Pattern Recognition Using Machine Learning. In: Manik, G., Kalia, S., Sahoo, S.K., Sharma, T.K., Verma, O.P. (eds) Advances in Mechanical Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-0942-8_58

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  • DOI: https://doi.org/10.1007/978-981-16-0942-8_58

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0941-1

  • Online ISBN: 978-981-16-0942-8

  • eBook Packages: EngineeringEngineering (R0)

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