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Supervised or unsupervised classification is the main objective of pattern recognition. The statistical approach is the most popular approach that is practised among the several frameworks where pattern recognition is initially formulated. In the recent past, the neural network technique and the methodology scheme from the statistical learning theory have garnered the attention of people. It requires proper attention to deal with the design of the recognition system. There are several issues associated with the design of the recognition system. They are the pattern class definition, sensing environment and representation extraction and selection of features, cluster analysis, classifier design, learning, and choosing the training and test samples. There is no solution to the general issue of recognizing complex patterns associated with arbitrary patterns. Data mining, web searching, and retrieval of multimedia are the various emerging applications that require proper and effective regulation techniques. The main purpose of this paper is to give a detailed overview of the various methods that can be used in the different stages of the pattern recognition system. The paper also aims to figure out the research topics in the application that can be highlighted in this challenging field.

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References

  1. Bhamare DP, Suryawanshi P. Review on Reliable Pattern Recognition with Machine Learning Techniques. Fuzzy Information and Engineering, 2018; 10(3): 362–377. https://doi.org/10.1080/16168658.2019.1611030.
    DOI  |   Google Scholar
  2. Armengol E, Boixader D, Grimaldo,F. Special Issue on Pattern Recognition Techniques in Data Mining. Pattern Recognition Letters, 2017; 93: 1–2. https://doi.org/10.1016/j.patrec.2017.02.014
    DOI  |   Google Scholar
  3. Kumar S, Gao X, Welch I. A machine learning based web spam filtering approach. In 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), 2016.
    DOI  |   Google Scholar
  4. Ermushev S, Balashov A. A Complex Machine Learning Technique for Ground Target Detection and Classification. Int J Appl Eng Res., 2017; 11(1): 158–161.
     Google Scholar
  5. Wu J, Yu Y, Huang C, Yu K. Deep multiple instance learning for image classification and auto-annotation. Computer Vision and Pattern Recognition, 2015. https://doi.org/10.1109/cvpr.2015.7298968.
    DOI  |   Google Scholar
  6. Notton VG, Kalogirou S. Machine learning methods for solar radiation forecasting: a review. Renew Energ., 2017; 105: 569–582.
    DOI  |   Google Scholar
  7. Tajbakhsh N, Suzuki K. Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs. Pattern Recognit., 2017; 63: 476–486.
    DOI  |   Google Scholar
  8. Aginako N, Echegaray G, Martínez-Otzeta J. Iris matching by means of machine learning paradigms: a new approach to dissimilarity computation. Pattern Recognit Lett., 2017; 91: 60–64.
    DOI  |   Google Scholar
  9. Saii MM. Classification of Pattern Recognition Techniques Used Deep Learning and Machine Learning. International Journal of Computer Science Trends and Technology (IJCST), 2019; 7(3): 165-173.
     Google Scholar
  10. Omarov B, Cho YI. Machine learning based pattern recognition and classification framework development. In 2017 17th International Conference on Control, Automation and Systems (ICCAS 2017). Ramada Plaza, Jeju, Korea, 2017.
    DOI  |   Google Scholar
  11. Ushmani A. Machine Learning Pattern Matching. International Journal of Computer Science Trends and Technology (IJCST), 2019; 7(2): 4-7.
     Google Scholar
  12. Chen S, Pande A, Mohapatra P. Sensor-assisted facial recognition: an enhanced biometric authentication system for smartphones. In Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, 2014.
    DOI  |   Google Scholar
  13. Yan Z, Zhan Y, Peng Z, Liao S, Shinagawa Y, Zhang S, Metaxas DN, Zhou X. Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition. IEEE Transactions on Medical Imaging, 2016; 35(5): 1332–1343. https://doi.org/10.1109/tmi.2016.2524985.
    DOI  |   Google Scholar
  14. Monté-Rubio G, Falcón C, Pomarol-Clotet E. A comparison of various MRI feature types for characterizing whole brain anatomical differences using linear pattern recognition methods. NeuroImage, 2018; 178: 753-768.
    DOI  |   Google Scholar
  15. Silasai O, Khowfa W. The Study on Using Biometric Authentication on Mobile Device. NU. International Journal of Science, 2020; 17(1): 90-110.
     Google Scholar
  16. Findling R, Hölzl M, Mayrhofer RM. Mobile match-on-card authentication using offline-simplified models with gait and face biometrics. IEEE Trans Mob Comput., 2018; 17(11): 2578-2590.
    DOI  |   Google Scholar
  17. Nair HH, Amte GS, Todase NB, Dandekar PR. Face detection and recognition in smartphones. International Journal of Advance Research and Development, 2018; 3(4): 177-182.
     Google Scholar
  18. Xi K, Hu J, Han F. Mobile device access control: an improved correlation-based face authentication scheme and its java me application. Concurr Comp Pract Exp, 2012; 24: 1066-1085.
    DOI  |   Google Scholar
  19. Zhu X, Wang Z, Lin P, Ma Z, Yu Z. Algorithm and Technology Application of Image Recognition Based on Artificial Intelligence. Journal of Physics: Conference Series, 2021; 2136(012062): 1-6.
    DOI  |   Google Scholar
  20. Jangapally T, Hiwarkar T. Performance Analysis of Pattern Recognition Algorithms Using Artificial Neural Networks. International Research Journal of Modernization in Engineering Technology and Science, 2020; 2(7): 1501-1508.
     Google Scholar
  21. Veena S, Shankari T, Sowmiya S, Varsha M. A Survey on Tools Used For Machine Learning. International Journal of Engineering Applied Sciences and Technology, 2020; 4(9): 116-119.
    DOI  |   Google Scholar