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Zeitschriftenartikel zum Thema „Pattern recognition“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 25. Juli 2025

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1

Mikhailov, Alexei. "Indexing-Based Pattern Recognition." Advanced Materials Research 403-408 (November 2011): 5254–59. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.5254.

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The paper discusses the mathematics of pattern indexing and its applications to recognition of visual patterns and classification of objects that are represented by objects-properties matrices. It is shown that (a) pattern indexes can be represented by collections of inverted patterns, (b) solutions to pattern classification problems can be found as intersections of inverted patterns and, thus, matching of original patterns avoided.

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2

Klein, Freada Kapor, and Ana Díaz-Hernández. "Pattern recognition." XRDS: Crossroads, The ACM Magazine for Students 20, no. 4 (2014): 20–23. http://dx.doi.org/10.1145/2604991.

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3

Rutkove, S. B. "Pattern recognition." Neurology 61, no. 4 (2003): 585–86. http://dx.doi.org/10.1212/01.wnl.0000078930.98769.11.

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4

Heidelbaugh, Joel J. "Pattern Recognition." Primary Care: Clinics in Office Practice 42, no. 4 (2015): xiii—xiv. http://dx.doi.org/10.1016/j.pop.2015.09.002.

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5

Lavine, Barry K. "Pattern Recognition." Critical Reviews in Analytical Chemistry 36, no. 3-4 (2006): 153–61. http://dx.doi.org/10.1080/10408340600969411.

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6

Dixon, Jennifer. "Pattern recognition." Lancet 360, no. 9343 (2002): 1418. http://dx.doi.org/10.1016/s0140-6736(02)11396-1.

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7

Holmström, Lasse, and Petri Koistinen. "Pattern recognition." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 4 (2010): 404–13. http://dx.doi.org/10.1002/wics.99.

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8

Narayan, Ritushree, and Puja Mishra. "Pattern Recognition of Jharkhand Tribal Language." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 267–71. http://dx.doi.org/10.31142/ijtsrd10854.

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9

Huang, K. Y., K. S. Fu, S. W. Cheng, and Z. S. Lin. "Syntactic pattern recognition and Hough transformation for reconstruction of seismic patterns." GEOPHYSICS 52, no. 12 (1987): 1612–20. http://dx.doi.org/10.1190/1.1442278.

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Hierarchical syntactic pattern recognition and the Hough transformation are proposed for automatic recognition and reconstruction of seismic patterns in seismograms. In the first step, the patterns are hierarchically decomposed or recognized into single patterns, straight‐line patterns, or hyperbolic patterns, using syntactic pattern recognition. In the second step, the Hough transformation technique is used for reconstruction, pattern by pattern. The system of syntactic seismic pattern recognition includes envelope generation, a linking process in the seismogram, segmentation, primitive recog

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10

Cheeney, R. F. "Pattern recognition and pattern analysis." Geological Magazine 122, no. 2 (1985): 203–4. http://dx.doi.org/10.1017/s0016756800031150.

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11

Dutta Majumder, D. "Fuzzy sets in pattern recognition, image analysis and automatic speech recognition." Applications of Mathematics 30, no. 4 (1985): 237–54. http://dx.doi.org/10.21136/am.1985.104148.

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12

Koyama, Motoaki. "Pattern recognition apparatus with retrieval reference patterns." Journal of the Acoustical Society of America 95, no. 1 (1994): 592. http://dx.doi.org/10.1121/1.408283.

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13

Hill, Andrea A., and Gretchen E. Diehl. "Identifying the Patterns of Pattern Recognition Receptors." Immunity 49, no. 3 (2018): 389–91. http://dx.doi.org/10.1016/j.immuni.2018.08.027.

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14

Kim, E. E. "Pattern Recognition Neuroradiology." Journal of Nuclear Medicine 54, no. 3 (2012): 491. http://dx.doi.org/10.2967/jnumed.112.116202.

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15

HAYASHI, Takaya, Tohru NAKAMURA, and Akinori TAKAOKA. "Pattern recognition receptors." Japanese Journal of Clinical Immunology 34, no. 5 (2011): 329–45. http://dx.doi.org/10.2177/jsci.34.329.

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16

Deepa, Dr S. T., and Praneetha V. "Iris Pattern Recognition." IOSR Journal of Computer Engineering 18, no. 04 (2016): 43–50. http://dx.doi.org/10.9790/0661-1804024350.

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17

Stentiford, Frederick W. M. "Pattern recognition system." Journal of the Acoustical Society of America 91, no. 1 (1992): 546. http://dx.doi.org/10.1121/1.402660.

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18

Davis, J. Wade. "Statistical Pattern Recognition." Technometrics 45, no. 4 (2003): 368. http://dx.doi.org/10.1198/tech.2003.s172.

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19

Craig, James C., and Paul M. Evans. "Vibrotactile pattern recognition." Journal of the Acoustical Society of America 79, S1 (1986): S73. http://dx.doi.org/10.1121/1.2023367.

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20

Chin, G. J. "BIOCHEMISTRY: Pattern Recognition." Science 310, no. 5751 (2005): 1091e—1093e. http://dx.doi.org/10.1126/science.310.5751.1091e.

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21

Griffin, William A. "Affect Pattern Recognition." Marriage & Family Review 34, no. 1-2 (2002): 139–63. http://dx.doi.org/10.1300/j002v34n01_07.

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22

Takahashi, Satoshi, and Shigeki Sagayama. "Pattern recognition method." Journal of the Acoustical Society of America 105, no. 3 (1999): 1450. http://dx.doi.org/10.1121/1.426677.

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23

MacFie, H. J. H. "Chemical Pattern Recognition." Analytica Chimica Acta 193 (1987): 410–11. http://dx.doi.org/10.1016/s0003-2670(00)86201-7.

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24

Gordon, Siamon. "Pattern Recognition Receptors." Cell 111, no. 7 (2002): 927–30. http://dx.doi.org/10.1016/s0092-8674(02)01201-1.

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25

Lavine, B. K. "Multivariate pattern recognition." TrAC Trends in Analytical Chemistry 13, no. 8 (1994): 340–41. http://dx.doi.org/10.1016/0165-9936(94)87009-8.

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26

Inkinen, Sami J. "Pattern-Recognition Transforms." Signal Processing 35, no. 1 (1994): 100–102. http://dx.doi.org/10.1016/0165-1684(94)90201-1.

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27

Wong, Kum Thong, Chong Tin Tan, and Tchoyoson Lim. "Beyond Pattern Recognition." Neuroimaging Clinics of North America 33, no. 1 (2023): 225–33. http://dx.doi.org/10.1016/j.nic.2022.07.018.

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28

Sipe, Michael A. "Statistical Pattern Recognition." Pattern Analysis & Applications 4, no. 1 (2001): 71–72. http://dx.doi.org/10.1007/s100440170028.

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29

Mahadevia, Soham. "Pattern Recognition Neuroradiology." Academic Radiology 19, no. 10 (2012): 1305–6. http://dx.doi.org/10.1016/j.acra.2012.03.030.

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30

Tate, A. R., and S. Howells. "Pattern recognition analysis." NMR in Biomedicine 11, no. 4-5 (1998): 147. http://dx.doi.org/10.1002/(sici)1099-1492(199806/08)11:4/5<147::aid-nbm533>3.0.co;2-#.

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31

Rorvig, Mark E. "Pattern Recognition Engineering." Information Processing & Management 31, no. 2 (1995): 257–58. http://dx.doi.org/10.1016/0306-4573(95)80046-v.

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32

Solus, Dávid, Ľuboš Ovseník, and Ján Turán. "Microchip Pattern Recognition Based on Optical Correlator." Acta Electrotechnica et Informatica 17, no. 2 (2017): 38–42. http://dx.doi.org/10.15546/aeei-2017-0014.

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33

Kumar, B. V. K. V., M. Savvides, and Chunyan Xie. "Correlation Pattern Recognition for Face Recognition." Proceedings of the IEEE 94, no. 11 (2006): 1963–76. http://dx.doi.org/10.1109/jproc.2006.884094.

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34

Koh, Young-Sang. "Pattern-recognition receptors and recognition of pathogens." Journal of Medicine and Life Science 6, no. 3 (2009): 148–51. http://dx.doi.org/10.22730/jmls.2009.6.3.148.

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The mammalian host has evolved innate immune strategies to detect and respond to rapidly to diverse group of microorganisms. Microbial recognition is achieved by pattern-recognition receptors (PRRs) such as toll-like receptors (TLRs) and Nod-like receptor (NLRs). PRRs detect pathogen-associated molecular patterns (PAMPs) and activate downstream signalling pathways. NLRs have been proposed thal these cytosolic proteins regulate the cysteine protease caspase-1 within a multiprotein complex known as the inflammasome. Activation of caspase-1 leads to the cleavage and activation of proinflammatory

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35

Brauer, Donna J. "A Method for Pattern Recognition." Research and Theory for Nursing Practice 20, no. 4 (2006): 277–90. http://dx.doi.org/10.1891/rtnp-v20i4a004.

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Although pattern is a dominant concept in nursing science, only Newman’s method for recognizing pattern has been fully articulated and widely used in research about the human health experience. This article proposes an alternative, less costly method to facilitate research with larger numbers of participants in clinical settings. Cluster analysis, a quasi-quantitative technique, and content analysis were combined to produce a technique for recognizing patterns of person–environment interaction. Results from two studies with persons experiencing a highly variable chronic illness, rheumatoid art

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36

Tan, Z., B. S. Hepburn, C. Tucker, and M. K. Ali. "Pattern recognition using chaotic neural networks." Discrete Dynamics in Nature and Society 2, no. 4 (1998): 243–47. http://dx.doi.org/10.1155/s1026022698000211.

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Pattern recognition by chaotic neural networks is studied using a hyperchaotic neural network as model. Virtual basins of attraction are introduced around unstable periodic orbits which are then used as patterns. Search for periodic orbits in dynamical systems is treated as a process of pattern recognition. The role of synapses on patterns in chaotic networks is discussed. It is shown that distorted states having only limited information of the patterns are successfully recognized.

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37

Singh, Chetanpal. "Machine Learning in Pattern Recognition." European Journal of Engineering and Technology Research 8, no. 2 (2023): 63–68. http://dx.doi.org/10.24018/ejeng.2023.8.2.3025.

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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 en

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38

POTAPOV, ALEXEI B., and M. K. ALI. "PATTERN RECOGNITION WITH HAMILTONIAN DYNAMICS." International Journal of Modern Physics C 12, no. 05 (2001): 751–58. http://dx.doi.org/10.1142/s0129183101001948.

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We consider pattern recognition schemes that are based upon Hamiltonian dynamical system. Different oscillatory modes are used for storing and encoding patterns, and the effect of resonance is used for determining the most excited mode. We also propose a new technique for pattern orthogonalization resorting to hidden dimensions. Numerical experiments confirm high storage capacity and absence of false memories for the proposed system. Hamiltonian systems may be important as classical analogs of quantum computing systems or quantum neural networks.

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39

Kaur, Manpreet. "A Review on Pattern Recognition Using Genetic Algorithms." International journal of Emerging Trends in Science and Technology 04, no. 05 (2017): 5213–20. http://dx.doi.org/10.18535/ijetst/v4i5.16.

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40

G.Vijaya, Lakshmi, and N.Sharada. "Artificial Intelligence based Pattern Recognition." International Journal of Engineering and Management Research 9, no. 2 (2019): 29–32. https://doi.org/10.31033/ijemr.9.2.4.

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Artificial intelligence based pattern recognition is one of the most important tools in process control to identify process problems. The objective of this study was to evaluate the relative performance of a feature-based Recognizer compared with the raw data-based recognizer. The study focused on recognition of seven commonly researched patterns plotted on the quality chart. The artificial intelligence based pattern recognizer trained using the three selected statistical features resulted in significantly better performance compared with the raw data-based recognizer

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41

Liu, Yu Min, Hao Fei Zhou, and Shuai Zhang. "A MSVM Quality Pattern Recognition Model for Dynamic Process." Applied Mechanics and Materials 433-435 (October 2013): 555–61. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.555.

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Quality abnormal pattern recognition for dynamic process is the key problem to achieve the online quality control and diagnose of automatic production. Firstly, this paper analyzed the quality patterns of dynamic process. Secondly, we established recognition model of quality recognition in dynamic process using MSVM and compared the SVM recognition accuracy of different kernel functions for different quality patterns. Simulation experiment indicates that different SVM classifiers should choose specified kernel functions to recognition quality patterns. At last, we established MSVM recognition

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42

Porter, Tim, and Monique Pavel. "Fundamentals of Pattern Recognition." Mathematical Gazette 74, no. 467 (1990): 100. http://dx.doi.org/10.2307/3618903.

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43

Dutt, Vinita, Vikas Chaudhry, and Imran Khan. "Pattern Recognition: an Overview." American Journal of Intelligent Systems 2, no. 1 (2012): 23–27. http://dx.doi.org/10.5923/j.ajis.20120201.04.

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44

Kalyadin, N. I. "Constructivization in pattern recognition." Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki, no. 2 (April 2008): 188–93. http://dx.doi.org/10.20537/vm080263.

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45

Yakubovich, V. A. "Machines Learning Pattern Recognition." Vestnik St. Petersburg University, Mathematics 54, no. 4 (2021): 384–94. http://dx.doi.org/10.1134/s106345412104021x.

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46

Hoenig, Melanie P., and Stewart H. Lecker. "Pattern Recognition versus Pathogenesis." Clinical Journal of the American Society of Nephrology 17, no. 4 (2022): 585–87. http://dx.doi.org/10.2215/cjn.16061221.

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47

Jogi, Joycee, Varsha Sharma, Anju Nayak, Vandana Gupta, Poonam Shakya, and Smita Bordoloi. "Pattern Recognition Receptors (PRRs)." International Journal of Current Microbiology and Applied Sciences 7, no. 09 (2018): 3400–3406. http://dx.doi.org/10.20546/ijcmas.2018.709.422.

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48

Tao, Y., Z. Chen, and C. L. Griffis. "Chick feather pattern recognition." IEE Proceedings - Vision, Image, and Signal Processing 151, no. 5 (2004): 337. http://dx.doi.org/10.1049/ip-vis:20040730.

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49

Gebran, M., and F. Paletou. "Pattern recognition in spectra." Journal of Physics: Conference Series 869 (June 2017): 012075. http://dx.doi.org/10.1088/1742-6596/869/1/012075.

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50

Mikhailov, A. M. "Pattern recognition by indexing." Automation and Remote Control 73, no. 4 (2012): 717–24. http://dx.doi.org/10.1134/s0005117912040108.

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