This article aims to provide a concise overview of diverse methodologies employed at different stages of a pattern recognition system, highlighting contemporary research challenges and applications in this dynamic field. The integration of machine learning techniques has played a pivotal role in converging pattern recognition frameworks in academic literature. The process relies heavily on supervised or unsupervised categorization methods to achieve its objectives, with a notable focus on statistical approaches. More recently, there is a growing emphasis on incorporating neural network methodologies and insights from statistical learning theory. Designing an effective recognition system necessitates careful consideration of various factors, including pattern representation, pattern class definition, feature extraction, sensing environment, feature selection, classifier learning and design, cluster analysis, test and training sample selection, and performance assessment.
Keywords
Pattern Recognition, Machine Learning, Data Mining, Feature Selection, Test and Training Sample Selection, Pattern Class Definition.
Shuaiyi, K. Wang, L. Zhang, and B. Wang, “Process-Oriented heterogeneous graph learning in GNN-Based ICS anomalous pattern recognition,”
Pattern Recognit., vol. 141, no. 109661, p. 109661, 2023.
M. Umair et al., “A multi-layer holistic approach for cursive text recognition,” Appl. Sci. (Basel), vol. 12, no. 24, p. 12652, 2022.
G. Lyu, K. Liu, A. Zhu, S. Uchida, and B. K. Iwana, “Corrigendum to ‘FETNet: Feature erasing and transferring network for scene text removal’:
Pattern recognition volume 140 (2023) 109531,” Pattern Recognit., vol. 141, no. 109581, p. 109581, 2023.
C. Zhang and M. van der Baan, “Microseismic signal reconstruction from strong complex noise using low-rank structure extraction and dual
convolutional neural networks,” IEEE Trans. Neural Netw. Learn. Syst., vol. PP, 2023.
D. Zhang and G. Lu, “3D Fingerprint Identification System,” in 3D Biometrics, New York, NY: Springer New York, 2013, pp. 217–230.
S. Ayub, N. Singh, Md. Z. Hussain, M. Ashraf, D. K. Singh, and A. Haldorai, “Hybrid approach to implement multi‐robotic navigation system
using neural network, fuzzy logic, and bio‐inspired optimization methodologies,” Computational Intelligence, vol. 39, no. 4, pp. 592–606, Sep.
2022, doi: 10.1111/coin.12547.
K. Dhawan, S. P. R, and N. R K, “Identification of traffic signs for advanced driving assistance systems in smart cities using deep learning,”
Multimed. Tools Appl., pp. 1–16, 2023.
A. H and A. R, “Artificial Intelligence and Machine Learning for Enterprise Management,” 2019 International Conference on Smart Systems
and Inventive Technology (ICSSIT), Nov. 2019, doi: 10.1109/icssit46314.2019.8987964.
A. Haldorai, A. Ramu, and S. Murugan, “Cognitive Radio Communication and Applications for Urban Spaces,” Computing and Communication
Systems in Urban Development, pp. 161–183, 2019, doi: 10.1007/978-3-030-26013-2_8.
C. Chien, M. Seiler, F. Eitel, T. Schmitz-Hübsch, F. Paul, and K. Ritter, “Prediction of high and low disease activity in early MS patients using
multiple kernel learning identifies importance of lateral ventricle intensity,” Mult. Scler. J. Exp. Transl. Clin., vol. 8, no. 3, p.
20552173221109770, 2022.
A. Haldorai and U. Kandaswamy, Intelligent Spectrum Handovers in Cognitive Radio Networks. Springer International Publishing, 2019. doi:
10.1007/978-3-030-15416-5.
R. A. R. Ashfaq, X.-Z. Wang, J. Z. Huang, H. Abbas, and Y.-L. He, “Fuzziness based semi-supervised learning approach for intrusion detection
system,” Inf. Sci. (Ny), vol. 378, pp. 484–497, 2017.
K. S. Umadevi, K. S. Thakare, S. Patil, R. Raut, A. K. Dwivedi, and A. Haldorai, “Dynamic hidden feature space detection of noisy image set
by weight binarization,” Signal, Image and Video Processing, vol. 17, no. 3, pp. 761–768, Aug. 2022, doi: 10.1007/s11760-022-02284-2.
A. Haldorai and U. Kandaswamy, “Supervised Machine Learning Techniques in Intelligent Network Handovers,” EAI/Springer Innovations in
Communication and Computing, pp. 135–154, 2019, doi: 10.1007/978-3-030-15416-5_7.
K. Hu, W. Yang, and X. Gao, “Microcalcification diagnosis in digital mammography using extreme learning machine based on hidden Markov
tree model of dual-tree complex wavelet transform,” Expert Syst. Appl., vol. 86, pp. 135–144, 2017.
G. Fagherazzi, C. Bour, and A. Ahne, “Emulating a virtual digital cohort study based on social media data as a complementary approach to
traditional epidemiology: When, what for, and how?,” Diabet. Epidemiol. Manag., vol. 7, no. 100085, p. 100085, 2022.
D. Wadhera and E. D. Capaldi-Phillips, “A review of visual cues associated with food on food acceptance and consumption,” Eat. Behav., vol.
15, no. 1, pp. 132–143, 2014.
M. Korbmacher et al., “Brain-wide associations between white matter and age highlight the role of fornix microstructure in brain ageing,” Hum.
Brain Mapp., vol. 44, no. 10, pp. 4101–4119, 2023.
J. Hu, E. Goodman, K. Seo, Z. Fan, and R. Rosenberg, “The hierarchical fair competition (HFC) framework for sustainable evolutionary
algorithms,” Evol. Comput., vol. 13, no. 2, pp. 241–277, Summer 2005.
L. Schülen, M. Mikhailenko, E. S. Medeiros, and A. Zakharova, “Solitary states in complex networks: impact of topology,” arXiv [nlin.PS],
2022.
H. . Anandakumar, R. Arulmurugan, and C. C. Onn, “Big Data Analytics for Sustainable Computing,” Mobile Networks and Applications, vol.
24, no. 6, pp. 1751–1754, Oct. 2019, doi: 10.1007/s11036-019-01393-6.
N. Gradojevic, R. Gençay, and D. Kukolj, “Option pricing with modular neural networks,” IEEE Trans. Neural Netw., vol. 20, no. 4, pp. 626–
637, 2009.
A. Haldorai, A. Ramu, and S. Murugan, “Smart Sensor Networking and Green Technologies in Urban Areas,” Computing and Communication
Systems in Urban Development, pp. 205–224, 2019, doi: 10.1007/978-3-030-26013-2_10.
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Teshome Debushe Adugna
Teshome Debushe Adugna
Department of Information Technology, College of Engineering, Mattu University, Ethiopia.
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Cite this article
Teshome Debushe Adugna, Arulmurugan Ramu and Anandakumar Haldorai, “A Review of Pattern Recognition and Machine Learning”, Journal of Machine and Computing, pp. 210-220, January 2024. doi: 10.53759/7669/jmc202404020.
