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Journal articles on the topic 'Pattern recognition applications'

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Author: Grafiati

Published: 4 June 2021

Last updated: 14 March 2023

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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

Grabusts, Peter. "POTENTIAL FUNCTION METHOD APPROACH TO PATTERN RECOGNITION APPLICATIONS." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 2 (June 15, 2017): 30. http://dx.doi.org/10.17770/etr2017vol2.2512.

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Potential function method was originally offered to solve the pattern recognition tasks, then it was generalized to a wider range of tasks, which were associated with the function approximation. Potential function method algorithms are based on the hypothesis of the nature of the function that separates sets according to different classes of patterns. Geometrical interpretation of pattern recognition task includes display of patterns in the form of vector in the space of input signal that allows to perceive the learning as approximation task. The paper describes the essence of potential function method and the learning procedure is shown that is based on practical application of potential methods. Pattern recognition applications with the help of examples of potential functions and company bankruptcy data analysis with the help of potential functions are given.

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3

Nezhad, Mohammad Saber Fallah. "Applications of Pattern Recognition Techniques in Social Science." Studies in Social Science & Humanities 2, no. 1 (January 2023): 31–35. http://dx.doi.org/10.56397/sssh.2023.01.06.

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Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition algorithms can be effectively applied for analysis of big data obtained from society to predict possible chaos in future or to identify required changes needed for society or changing the public policies. This technical note reviews some method of pattern recognition in society.

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4

PAPAKOSTAS, G. A., Y. S. BOUTALIS, D. E. KOULOURIOTIS, and B. G. MERTZIOS. "FUZZY COGNITIVE MAPS FOR PATTERN RECOGNITION APPLICATIONS." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 08 (December 2008): 1461–86. http://dx.doi.org/10.1142/s0218001408006910.

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A first attempt to incorporate Fuzzy Cognitive Maps (FCMs), in pattern classification applications is performed in this paper. Fuzzy Cognitive Maps, as an illustrative causative representation of modeling and manipulation of complex systems, can be used to model the behavior of any system. By transforming a pattern classification problem into a problem of discovering the way the sets of patterns interact with each other and with the classes that they belong to, we could describe the problem in terms of Fuzzy Cognitive Maps. More precisely, some FCM architectures are introduced and studied with respect to their pattern recognition abilities. An efficient novel hybrid classifier is proposed as an alternative classification structure, which exploits both neural networks and FCMs to ensure improved classification capabilities. Appropriate experiments with four well-known benchmark classification problems and a typical computer vision application establish the usefulness of the Fuzzy Cognitive Maps, in a pattern recognition research field. Moreover, the present paper introduces the use of more flexible FCMs by incorporating nodes with adaptively adjusted activation functions. This advanced feature gives more degrees of freedom in the FCM structure to learn and store knowledge, as needed in pattern recognition tasks.

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5

Kober, Vitaly, Tae Choi, Victor Diaz-Ramírez, and Pablo Aguilar-González. "Pattern Recognition: Recent Advances and Applications." Mathematical Problems in Engineering 2018 (November 15, 2018): 1–2. http://dx.doi.org/10.1155/2018/8510319.

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6

Ren, Dong, and Simon X. Yang. "Intelligent Pattern Recognition Technology and Applications." Intelligent Automation & Soft Computing 19, no. 4 (December 2013): 497–99. http://dx.doi.org/10.1080/10798587.2013.869107.

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7

Paolanti, Marina, and Emanuele Frontoni. "Multidisciplinary Pattern Recognition applications: A review." Computer Science Review 37 (August 2020): 100276. http://dx.doi.org/10.1016/j.cosrev.2020.100276.

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8

Lavine, Barry K. "Environmental applications of pattern recognition techniques." Chemometrics and Intelligent Laboratory Systems 15, no. 2-3 (August 1992): 219–30. http://dx.doi.org/10.1016/0169-7439(92)85011-q.

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9

Krasnoproshin, V. V., and V. A. Obraztsov. "Pattern Recognition: Theoretical Research Experience and Applications." Pattern Recognition and Image Analysis 31, no. 1 (January 2021): 163–71. http://dx.doi.org/10.1134/s1054661821010132.

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10

Silva, Francisco de Assis Tavares Ferreira da, Magno Prudêncio de Almeida Filho, Antonio Macilio Pereira de Lucena, and Alexandre Guirland Nowosad. "Pattern recognition on FPGA for aerospace applications." Research, Society and Development 10, no. 12 (September 14, 2021): e83101219181. http://dx.doi.org/10.33448/rsd-v10i12.19181.

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This paper presents a low power near real-time pattern recognition technique based on Mathematical Morphology-MM implemented on FPGA (Field Programmable Gate Array). The key to the success of this approach concerns the advantages of machine learning paradigm applied to the translation invariant template-matching operators from MM. The paper shows that compositions of simple elementary operators from Mathematical Morphology based on ELUTs (Elementary Look-Up Tables) are very suitable to embed in FPGA hardware. The paper also shows the development techniques regarding all mathematical modeling for computer simulation and system generating models applied for hardware implementation using FPGA chip. In general, image processing on FPGAs requires low-level description of desired operations through Hardware Description Language-HDL, which uses high complexity to describe image operations at pixel level. However, this work presents a reconfiguring pattern recognition device implemented directly in FPGA from mathematical modeling simulation under Matlab/Simulink/System Generator environment. This strategy has reduced the hardware development complexity. The device will be useful mainly when applied on remote sensing tasks for aerospace missions using passive or active sensors.

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11

Nicoliche, Caroline Yumi Nakiri, Gabriel Floriano Costa, Angelo Luiz Gobbi, Flavio Makoto Shimizu, and Renato Sousa Lima. "Pencil graphite core for pattern recognition applications." Chemical Communications 55, no. 32 (2019): 4623–26. http://dx.doi.org/10.1039/c9cc01595g.

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12

Nohsoh, Kazunori, and Shinji Ozawa. "Pattern Recognition and Image Processing and Applications." IEEJ Transactions on Electronics, Information and Systems 113, no. 12 (1993): 1038–43. http://dx.doi.org/10.1541/ieejeiss1987.113.12_1038.

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13

Strickland, J., B. Nenchev, and H. B. Dong. "Applications of pattern recognition for dendritic microstructures." IOP Conference Series: Materials Science and Engineering 861 (June 13, 2020): 012057. http://dx.doi.org/10.1088/1757-899x/861/1/012057.

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14

Vlachos, Ioannis K., and George D. Sergiadis. "Intuitionistic fuzzy information – Applications to pattern recognition." Pattern Recognition Letters 28, no. 2 (January 2007): 197–206. http://dx.doi.org/10.1016/j.patrec.2006.07.004.

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15

Hancock, Edwin, José Francisco Martínez-Trinidad, and Jesús Ariel Carrasco-Ochoa. "Advances in pattern recognition methodology and applications." Pattern Recognition Letters 34, no. 4 (March 2013): 359–60. http://dx.doi.org/10.1016/j.patrec.2012.11.001.

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16

Fred, Ana, and Maria De Marsico. "Advances in pattern recognition applications and methods." Neurocomputing 173 (January 2016): 1–2. http://dx.doi.org/10.1016/j.neucom.2015.07.081.

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17

Latorre Carmona, Pedro, J. Salvador Sánchez, and Ana L. N. Fred. "Advances in pattern recognition applications and methods." Neurocomputing 123 (January 2014): 1–2. http://dx.doi.org/10.1016/j.neucom.2013.03.010.

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18

Mikhailov, Alexei, and Mikhail Karavay. "Pattern Inversion as a Pattern Recognition Method for Machine Learning." Journal of Physics: Conference Series 2224, no. 1 (April 1, 2022): 012002. http://dx.doi.org/10.1088/1742-6596/2224/1/012002.

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Abstract Artificial neural networks use a lot of coefficients that take a great deal of computing power for their adjustment, especially if deep learning networks are employed. However, there exist coefficients-free extremely fast indexing-based technologies that work, for instance, in Google search engines, in genome sequencing, etc. The paper discusses the use of indexing-based methods for pattern recognition. It is shown that for pattern recognition applications such indexing methods replace with inverse patterns the fully inverted files, which are typically employed in search engines. Not only such inversion provides automatic feature extraction, which is a distinguishing mark of deep learning, but, unlike deep learning, pattern inversion supports almost instantaneous learning, which is a consequence of absence of coefficients. The paper discusses a pattern inversion formalism that makes use on a novel pattern transform and its application for unsupervised instant learning. Examples demonstrate a view-angle independent recognition of three-dimensional objects, such as cars, against arbitrary background, prediction of remaining useful life of aircraft engines, and other applications. In conclusion, it is noted that, in neurophysiology, the function of the neocortical mini-column has been widely debated since 1957. This paper hypothesizes that, mathematically, the cortical mini-column can be described as an inverse pattern, which physically serves as a connection multiplier expanding associations of inputs with relevant pattern classes.

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19

Zhao, Xing-Ming, Alioune Ngom, and Jin-Kao Hao. "Pattern recognition in bioinformatics." Neurocomputing 145 (December 2014): 1–2. http://dx.doi.org/10.1016/j.neucom.2014.06.035.

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20

Jeong, Gu-Min, Hyun-Sik Ahn, Sang-Il Choi, Nojun Kwak, and Chanwoo Moon. "Pattern recognition using feature feedback: Application to face recognition." International Journal of Control, Automation and Systems 8, no. 1 (February 2010): 141–48. http://dx.doi.org/10.1007/s12555-010-0118-7.

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21

Yin, Lo I., and Stephen M. Seltzer. "Qualitative XRF Analysis with Pattern Recognition." Advances in X-ray Analysis 33 (1989): 603–13. http://dx.doi.org/10.1154/s0376030800020073.

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AbstractIn many applications of energy-dispersive XRF analysis, quantitative information concerning the chemical composition of the samples is not required. Rather, one is interested in whether a given sample is similar to some reference material or whether the chemical composition is changing from one sample to the next. We have investigated the use of pattern-recognition techniques in such applications. It will be demonstrated with experimental data that the pattern-recognition approach is extremely simple and fast. It uses only a single parameter, the normalized correlation coefficient, and can be applied directly to raw data. The efficacy of the method is illustrated with Si(Li) spectra of geological and pigment samples, and proportional counter spectra of geological samples. The pattern-recognition method should be ideally suited for field XRF applications, and the algorithm can be easily implemented on a personal computer.

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22

BUI, Tien D. "Wavelet Analysis and its Applications to Pattern Recognition." Journal of the Visualization Society of Japan 21, no. 1Supplement (2001): 187–90. http://dx.doi.org/10.3154/jvs.21.1supplement_187.

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23

Ben Ayed, Ahmed. "Permission Request Pattern Recognition in Android Malware Applications." International Journal of Strategic Information Technology and Applications 8, no. 1 (January 2017): 37–49. http://dx.doi.org/10.4018/ijsita.2017010103.

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This article discusses that smartphone systems have known a huge evolution in terms of their capacities and functionalities. Therefore, they are used extensively for professional and personal work. Since smartphones became popular, cybercriminals and Malware developers have shown an extensive interest in the smartphone's system. Therefore, the protection of these devices is very important. Since Malware has to be granted some permissions to achieve its goals, the author believes those permissions could be a useful characteristic in helping detect malicious applications. However, the usefulness of such features is not yet confirmed. This research consists of an examination of three hundred eighty-seven different Android-Based Malware applications in order to determine if there is a permission request pattern. This article presents a complete analysis of permission requests in Android malicious applications using the Self-Organizing Maps.

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24

Sánchez, J. S. "PATTERN RECOGNITION AND IMAGE ANALYSIS IN CYBERNETIC APPLICATIONS." Cybernetics and Systems 35, no. 1 (January 2004): 1–2. http://dx.doi.org/10.1080/716100285.

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25

Chen, C. H., Seth Wolpert, A. John Mallinckrodt, and Susan McKay. "Neural Networks in Pattern Recognition and their Applications." Computers in Physics 7, no. 3 (1993): 288. http://dx.doi.org/10.1063/1.4823177.

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26

Gaouda, A. M., S. H. Kanoun, M. M. A. Salama, and A. Y. Chikhani. "Pattern recognition applications for power system disturbance classification." IEEE Transactions on Power Delivery 17, no. 3 (July 2002): 677–83. http://dx.doi.org/10.1109/tpwrd.2002.1022786.

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27

Gaouda, A. M., S. H. Kanoun, M. M. A. Salama, and A. Y. Chikhani. "Pattern Recognition Applications for Power System Disturbance Classification." IEEE Power Engineering Review 22, no. 1 (2002): 69–70. http://dx.doi.org/10.1109/mper.2002.4311687.

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28

Talaie, Afshad, and Jose A. Romagnoli. "Pattern Recognition Applications in Conducting Polymer Based Sensors." IFAC Proceedings Volumes 30, no. 9 (June 1997): 341–45. http://dx.doi.org/10.1016/s1474-6670(17)43179-x.

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29

Nedjah, Nadia, Luiza de Macedo Mourelle, Fernando Buarque, and Chao Wang. "New trends for pattern recognition: Theory and applications." Neurocomputing 265 (November 2017): 1–3. http://dx.doi.org/10.1016/j.neucom.2017.05.080.

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30

Liebe, C. C. "Pattern recognition of star constellations for spacecraft applications." IEEE Aerospace and Electronic Systems Magazine 7, no. 6 (June 1992): 34–41. http://dx.doi.org/10.1109/62.145117.

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31

Liebe, C. C. "Pattern recognition of star constellations for spacecraft applications." IEEE Aerospace and Electronic Systems Magazine 8, no. 1 (January 1993): 31–39. http://dx.doi.org/10.1109/62.180383.

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32

Doroshenko, Anna. "Applying Artificial Neural Networks In Construction." E3S Web of Conferences 143 (2020): 01029. http://dx.doi.org/10.1051/e3sconf/202014301029.

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Currently, artificial neural networks (ANN) are used to solve the following complex problems: pattern recognition, speech recognition, complex forecasts and others. The main applications of ANN are decision making, pattern recognition, optimization, forecasting, data analysis. This paper presents an overview of applications of ANN in construction industry, including energy efficiency and energy consumption, structural analysis, construction materials, smart city and BIM technologies, structural design and optimization, application forecasting, construction engineering and soil mechanics.

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33

Stringa, Luigi. "A VISUAL MODEL FOR PATTERN RECOGNITION." International Journal of Neural Systems 03, supp01 (January 1992): 31–39. http://dx.doi.org/10.1142/s0129065792000358.

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A general model for an optical recognition system capable of simultaneous recognition of patterns at different resolution levels is outlined. The model is based on two hierarchic stages of processing networks and presents interesting analogies with the human visual system. Illustrative applications and preliminary experimental results are also briefly discussed.

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34

Bianchini, Monica, and Franco Scarselli. "Pattern recognition in graphical domains." Neurocomputing 73, no. 1-3 (December 2009): 177–78. http://dx.doi.org/10.1016/j.neucom.2009.08.008.

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35

Gorelik, Natalia, Jaron Chong, and Dana J. Lin. "Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence." Seminars in Musculoskeletal Radiology 24, no. 01 (January 28, 2020): 38–49. http://dx.doi.org/10.1055/s-0039-3400266.

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AbstractArtificial intelligence (AI) has the potential to affect every step of the radiology workflow, but the AI application that has received the most press in recent years is image interpretation, with numerous articles describing how AI can help detect and characterize abnormalities as well as monitor disease response. Many AI-based image interpretation tasks for musculoskeletal (MSK) pathologies have been studied, including the diagnosis of bone tumors, detection of osseous metastases, assessment of bone age, identification of fractures, and detection and grading of osteoarthritis. This article explores the applications of AI for image interpretation of MSK pathologies.

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36

Emptoz, H., and M. Lamure. "A systemic approach to pattern recognition." Robotica 5, no. 2 (April 1987): 129–33. http://dx.doi.org/10.1017/s0263574700015095.

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SUMMARYWe suggest a new pretopological model for pattern recognition which was introduced to study complex economic systems. The model has its origin in the concept of “neighbour”, which is both primitive and fundamental in pattern recognition. Pretopology enables us to develop a perceptive and topological approach for patterns and to see that problems, apparently different, are in fact identical e.g. clustering and recognition, search of skeletons in image processing and search of an informative learning set. It should be noted that the suggeted model is more than a descriptive one.

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37

GUYON, I. "APPLICATIONS OF NEURAL NETWORKS TO CHARACTER RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 05, no. 01n02 (June 1991): 353–82. http://dx.doi.org/10.1142/s021800149100020x.

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Among the many applications that have been proposed for neural networks, character recognition has been one of the most successful. Compared to other methods used in pattern recognition, the advantage of neural networks is that they offer a lot of flexibility to the designer, i.e. expert knowledge can be introduced into the architecture to reduce the number of parameters determined by training by examples. In this paper, a general introduction to neural network architectures and learning algorithms commonly used for pattern recognition problems is given. The design of a neural network character recognizer for on-line recognition of handwritten characters is then described in detail.

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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 (June 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

Lin, Yo-Hsien, and Jong-Chen Chen. "Neuromolecularware and its application to pattern recognition." Expert Systems with Applications 36, no. 2 (March 2009): 2568–83. http://dx.doi.org/10.1016/j.eswa.2008.01.077.

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40

Ciora, Radu Adrian, and Carmen Mihaela Simion. "Industrial Applications of Image Processing." ACTA Universitatis Cibiniensis 64, no. 1 (November 1, 2014): 17–21. http://dx.doi.org/10.2478/aucts-2014-0004.

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Abstract The recent advances in sensors quality and processing power provide us with excellent tools for designing more complex image processing and pattern recognition tasks. In this paper we review the existing applications of image processing and pattern recognition in industrial engineering. First we define the role of vision in an industrial. Then a dissemination of some image processing techniques, feature extraction, object recognition and industrial robotic guidance is presented. Moreover, examples of implementations of such techniques in industry are presented. Such implementations include automated visual inspection, process control, part identification, robots control. Finally, we present some conclusions regarding the investigated topics and directions for future investigation

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41

Canals, V., C. F. Frasser, M. L. Alomar, A. Morro, A. Oliver, M. Roca, E. Isern, V. Martínez-Moll, E. Garcia-Moreno, and J. L. Rosselló. "Noise tolerant probabilistic logic for statistical pattern recognition applications." Integrated Computer-Aided Engineering 24, no. 4 (September 5, 2017): 351–65. http://dx.doi.org/10.3233/ica-170549.

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42

LI, Z. C., Y. Y. TANG, T. D. BUI, and C. Y. SUEN. "SHAPE TRANSFORMATION MODELS AND THEIR APPLICATIONS IN PATTERN RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 01 (March 1990): 65–94. http://dx.doi.org/10.1142/s021800149000006x.

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This paper presents linear and bilinear shape transformations including basic transformations, analyzes their geometric properties, and provides computer algorithms. The shape transformations can be used to simplify the recognition of Roman letters, Chinese characters and other pictorial patterns by normalizing their shapes to the standard forms. Important theoretical analyses have been performed to illustrate that the linear and bilinear transformations are applicable to computer recognition of digitized patterns. A number of pictorial examples have been computed to confirm the analyses and conclusions made.

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43

Martínez, Francisco, Ariel Carrasco, Joaquín Salas, and Gabriella Sanniti di Baja. "Pattern recognition applications in computer vision and image analysis." Pattern Recognition 48, no. 4 (April 2015): 1025–26. http://dx.doi.org/10.1016/j.patcog.2014.10.024.

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44

Lindon, J. C., E. Holmes, and J. K. Nicholson. "Pattern recognition methods and applications in biomedical magnetic resonance." Progress in Nuclear Magnetic Resonance Spectroscopy 39, no. 1 (July 2001): 1–40. http://dx.doi.org/10.1016/s0079-6565(00)00036-4.

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45

Awad, Maha, Fatma G. Hashad, Mustafa M. Abd Elnaby, Said E. El Khamy, Osama S. Faragallah, Alaa M. Abbas, Heba A. El-Khobby, et al. "Resolution enhancement of images for further pattern recognition applications." Optik 127, no. 1 (January 2016): 484–92. http://dx.doi.org/10.1016/j.ijleo.2015.08.122.

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46

Yin, Hujun, and Weilin Huang. "Adaptive nonlinear manifolds and their applications to pattern recognition." Information Sciences 180, no. 14 (July 2010): 2649–62. http://dx.doi.org/10.1016/j.ins.2010.04.004.

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47

Li, Y. "Applications of moment invariants to neurocomputing for pattern recognition." Electronics Letters 27, no. 7 (1991): 587. http://dx.doi.org/10.1049/el:19910370.

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48

KAZANTSEV, VLADIMIR S. "THE “KVAZAR” PACKAGE FOR PATTERN RECOGNITION AND ITS APPLICATIONS." International Journal of Software Engineering and Knowledge Engineering 03, no. 04 (December 1993): 439–44. http://dx.doi.org/10.1142/s0218194093000215.

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The package of applied programs named KVAZAR has been elaborated to be used for classification, diagnostic, predicative, experimental data analysis problems. The package may be used in medicine, biology, geology, economics, engineering and some other problems. The algorithmical base of the package is the method of pattern recognition, based on the linear inequalities and committee constructions. Other algorithms are used too. The package KVAZAR is intended to be used with IBM PC AT/XT. The range of processing data is bounded by 40,000 numbers.

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49

Liu, Weixiang, Nanning Zheng, and Qubo You. "Nonnegative matrix factorization and its applications in pattern recognition." Chinese Science Bulletin 51, no. 1 (January 2006): 7–18. http://dx.doi.org/10.1007/s11434-005-1109-6.

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50

Chefrour, Aida. "Incremental supervised learning: algorithms and applications in pattern recognition." Evolutionary Intelligence 12, no. 2 (February 2, 2019): 97–112. http://dx.doi.org/10.1007/s12065-019-00203-y.

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