Journal articles: 'Recognition'

1

Austin, Mary M. "Recognition, Recognition, Recognition." Diabetes Educator 30, no. 5 (September 2004): 710–12. http://dx.doi.org/10.1177/014572170403000504.

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Ounachad, Khalid, Mohamed Oualla, Abdelalim Sadiq, and Abdelghani Sohar. "Face Sketch Recognition: Gender Classification and Recognition." International Journal of Psychosocial Rehabilitation 24, no. 03 (February 18, 2020): 1073–85. http://dx.doi.org/10.37200/ijpr/v24i3/pr200860.

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Herzog, Benno. "Recognition in multicultural societies. Intergroup relations as second-order recognition." Revista Internacional de Sociología 73, no. 2 (April 27, 2015): e008. http://dx.doi.org/10.3989/ris.2013.07.21.

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Singh, Arunima, and Dr Ashok Kumar Sahoo. "Traffic Sign Recognition." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 122–26. http://dx.doi.org/10.31142/ijtsrd12783.

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Deshmukh, Sagar, Sanjay Rawat, and Shubhangi Patil. "Face Recognition Technology." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1612–13. http://dx.doi.org/10.31142/ijtsrd14331.

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Yadav, Rakeshkumar H., Brajgopal Agarwal, and Sheeba James. "Face Recognition System." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1815–18. http://dx.doi.org/10.31142/ijtsrd14453.

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Goud, N. Swapna, K. Revanth Reddy, and G. Alekhya G. S. Sucheta. "Facial Emoji Recognition." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1330–33. http://dx.doi.org/10.31142/ijtsrd23166.

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Shinde, Jyoti, Chaitali Rajput, and Prof Mrunal Shidore Prof Milind Rane. "Handwritten Digit Recognition." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (February 28, 2018): 608–11. http://dx.doi.org/10.31142/ijtsrd8384.

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Schweiger, Gottfried. "Recognition and poverty." Eidos 22 (January 1, 2015): 143–68. http://dx.doi.org/10.14482/eidos.22.5095.

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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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Tang, Liqi. "The Field of Intelligent Recognition that be Advance by Machine Learning." Highlights in Science, Engineering and Technology 7 (August 3, 2022): 246–50. http://dx.doi.org/10.54097/hset.v7i.1078.

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Over the past few years, there has been a sharp increase in software projects using artificial intelligence and machine learning. Although artificial intelligence (AI) and machine learning (ML) are often used interchangeably, they are not the same. With the development of artificial intelligence and machine learning, technologies have gradually become an indispensable part of people's life.). In this essay, the recognition pattern will be mentioned as well as optical character recognition (OCR), intelligent character recognition (ICR) and intelligent word recognition (IWR). And we can easily find that all these three kinds of recognitions have been completely integrated into our daily lives, and convenient our life a lot. The comparison of these three recognitions will also be mentioned in this essay, like what are the differences in performances and usages between them. What’s more, we also can learn the process of these three recognitions.

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Xu, Hui Hong, and Su Chun Gao. "Speaker Recognition Study Based on Optimized Baum-Welch Algorithm." Applied Mechanics and Materials 543-547 (March 2014): 2471–73. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2471.

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The speaker recognition is a sort of biological recognition technology according to person's sound to identify .The article based on vc platform implement speaker recognitions function using VQ and HMM technology. using genetic algorithm to improve the Baum-Welch algorithm.Trough experiment verificate that improved-arithmetic enhance recognition effect.

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Garst, John F. "Chiral recognition as directional recognition." Journal of the Chemical Society, Chemical Communications, no. 3 (1990): 211. http://dx.doi.org/10.1039/c39900000211.

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Sinnerbrink, Robert. "Recognitive Freedom: Hegel and the Problem of Recognition." Critical Horizons 5, no. 1 (February 19, 2004): 271–95. http://dx.doi.org/10.1163/1568516042653503.

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Hietamäki, Minna. "Attitudes and Ecumenical Recognition. How Do Attitudes Define, Facilitate, or Inhibit Ecumenical Advancement?" Exchange 51, no. 1 (June 8, 2022): 61–81. http://dx.doi.org/10.1163/1572543x-20221619.

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Abstract Ecumenical texts cultivate a rich language of attitudes referring to trust, commitment, love, faithfulness, openness, etc., but their role in ecumenical advancement has been taken for granted. This article discusses attitudes in ecumenical advancement by focusing on ecumenical recognition. Some recognition theories hold attitudes, specifically personifying attitudes, to be a precondition for recognition. Contemporary recognition theories offer a rich analysis of attitudes concerning subjects and objects of recognition, the direction, and the consequences of recognition. Based on an overview of recognitive attitudes this article aims to offer new insights on (ecumenical) attitudes and a deeper understanding of ecumenical recognition and some of its challenge. Some of the main points of discussion include recognition on the interpersonal level and its relation to institutional level recognition, the individual, corporate and institutional subjects and objects of recognition, and the variety of ways attitudes relate to recognition.

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Jiao, Chenlei, Binbin Lian, Zhe Wang, Yimin Song, and Tao Sun. "Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm." International Journal of Advanced Robotic Systems 17, no. 5 (September 1, 2020): 172988142094872. http://dx.doi.org/10.1177/1729881420948727.

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Object recognition is a prerequisite to control a soft gripper successfully grasping an unknown object. Visual and tactile recognitions are two commonly used methods in a grasping system. Visual recognition is limited if the size and weight of the objects are involved, whereas the efficiency of tactile recognition is a problem. A visual–tactile recognition method is proposed to overcome the disadvantages of both methods in this article. The design and fabrication of the soft gripper considering the visual and tactile sensors are implemented, where the Kinect v2 is adopted for visual information, bending and pressure sensors are embedded to the soft fingers for tactile information. The proposed method is divided into three steps: initial recognition by vision, detail recognition by touch, and a data fusion decision making. Experiments show that the visual–tactile recognition has the best results. The average recognition accuracy of the daily objects by the proposed method is also the highest. The feasibility of the visual–tactile recognition is verified.

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V, Prathama, and Thippeswamy G. "Age Invariant Face Recognition." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 971–76. http://dx.doi.org/10.31142/ijtsrd23572.

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PyarZaw, Kyi, and Zin Mar Kyu. "Myanmar WarningBoard Recognition System." International Journal of Research and Engineering 5, no. 8 (September 2018): 480–85. http://dx.doi.org/10.21276/ijre.2018.5.8.3.

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Bates, Jane. "Recognition." Nursing Standard 29, no. 44 (July 2015): 26–27. http://dx.doi.org/10.7748/ns.29.44.26.s26.

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Flay, Joseph C. "Recognition." International Studies in Philosophy 27, no. 1 (1995): 151. http://dx.doi.org/10.5840/intstudphil199527124.

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Chen, Christopher. "RECOGNITION." International Surgery 99, no. 2 (March 1, 2014): 97. http://dx.doi.org/10.9738/0020-8868-99.2.97.

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Bryant-Hampton, Linda, Ann Marie Walton, Tracy Carroll, and Laura Strickler. "Recognition." JONA: The Journal of Nursing Administration 40, no. 3 (March 2010): 121–23. http://dx.doi.org/10.1097/nna.0b013e3181d04137.

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&NA;. "Recognition." Journal for Nurses in Staff Development (JNSD) 27, no. 6 (2011): 304–5. http://dx.doi.org/10.1097/nnd.0b013e318238654f.

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Apple, David F. "Recognition." Spinal Cord 27, no. 6 (December 1989): 421. http://dx.doi.org/10.1038/sc.1989.66.

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Hardinge, Mervyn G., and Patricia K. Johnston. "Recognition." American Journal of Clinical Nutrition 70, no. 3 (September 1, 1999): 431s—432s. http://dx.doi.org/10.1093/ajcn/70.3.431s.

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Gutmann, L. "Recognition." Neurology 78, no. 2 (January 9, 2012): 148–49. http://dx.doi.org/10.1212/wnl.0b013e31823efc2e.

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Reider, Bruce. "Recognition." American Journal of Sports Medicine 46, no. 14 (November 30, 2018): 3337–39. http://dx.doi.org/10.1177/0363546518812396.

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Hartigan, J. A. "Recognition." Computational Statistics & Data Analysis 23, no. 1 (November 1996): 97–103. http://dx.doi.org/10.1016/s0167-9473(96)00023-0.

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Levey, David. "Recognition." English Academy Review 35, no. 1 (January 2, 2018): 132–35. http://dx.doi.org/10.1080/10131752.2018.1474638.

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Jurist, Elliot L. "Recognition." Owl of Minerva 26, no. 1 (1994): 59–65. http://dx.doi.org/10.5840/owl199426129.

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Nelson, Vere Heath. "Recognition." Ploughshares 50, no. 2 (June 2024): 176–86. http://dx.doi.org/10.1353/plo.2024.a932303.

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Abstract: The Summer 2024 Issue. Ploughshares is an award-winning journal of new writing. Since 1971, Ploughshares has discovered and cultivated the freshest voices in contemporary American literature, and now provides readers with thoughtful and entertaining literature in a variety of formats. Find out why the New York Times named Ploughshares “the Triton among minnows.” The Summer 2024 Issue, guest-edited by Rebecca Makkai, features prose by Dur e Aziz Amna, Ramona Ausubel, Peter Mountford, Khaddafina Mbabazi, DK Nnuro, and more.

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32

Dopkins, Stephen, Catherine Trinh Ngo, and Jese Sargent. "Exploring a recognition-induced recognition decrement." Memory & Cognition 34, no. 4 (June 2006): 839–53. http://dx.doi.org/10.3758/bf03193431.

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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 (November 2006): 1963–76. http://dx.doi.org/10.1109/jproc.2006.884094.

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34

Cowley, Elizabeth. "Recognition confidence, recognition accuracy and choice." Journal of Business Research 57, no. 6 (June 2004): 641–46. http://dx.doi.org/10.1016/s0148-2963(02)00307-7.

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35

Reetz, M. T. "Molecular recognition and stereotopic group recognition." Pure and Applied Chemistry 68, no. 6 (January 1, 1996): 1279–83. http://dx.doi.org/10.1351/pac199668061279.

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36

Wang, Man-Ying, and Chi-Le Ching. "Recognition intent and visual word recognition." Consciousness and Cognition 18, no. 1 (March 2009): 65–77. http://dx.doi.org/10.1016/j.concog.2008.10.004.

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37

Schyns, Philippe G. "Object Recognition: Complexity of Recognition Strategies." Current Biology 28, no. 7 (April 2018): R313—R315. http://dx.doi.org/10.1016/j.cub.2018.02.059.

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Zhao, Yuyang, and Eung-Joo Lee. "A Group Behavior Recognition Method Combining Scene Recognition and Behavior Recognition." Journal of Korea Multimedia Society 27, no. 3 (March 31, 2024): 410–19. http://dx.doi.org/10.9717/kmms.2024.27.3.410.

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39

Thủy, Đào Thị Lệ, Trinh Van Loan, and Nguyen Hong Quang. "GMM FOR EMOTION RECOGNITION OF VIETNAMESE." Journal of Computer Science and Cybernetics 33, no. 3 (March 20, 2018): 229–46. http://dx.doi.org/10.15625/1813-9663/33/3/11017.

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This paper presents the results of GMM-based recognition for four basic emotions of Vietnamese such as neutral, sadness, anger and happiness. The characteristic parameters of these emotions are extracted from speech signals and divided into different parameter sets for experiments. The experiments are carried out according to speaker-dependent or speaker-independent and content-dependent or content-independent recognitions. The results showed that the recognition scores are rather high with the case for which there is a full combination of parameters as MFCC and its first and second derivatives, fundamental frequency, energy, formants and its correspondent bandwidths, spectral characteristics and F0 variants. In average, the speaker-dependent and content-dependent recognition scrore is 89.21%. Next, the average score is 82.27% for the speaker-dependent and content-independent recognition. For the speaker-independent and content-dependent recognition, the average score is 70.35%. The average score is 66.99% for speaker-independent and content-independent recognition. Information on F0 has significantly increased the score of recognition

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40

Wang, Yu Tai, Jie Han, Xiao Qing Jiang, Jing Zou, and Hui Zhao. "Study of Speech Emotion Recognition Based on Prosodic Parameters and Facial Expression Features." Applied Mechanics and Materials 241-244 (December 2012): 1677–81. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1677.

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The present status of speech emotion recognition was introduced in the paper. The emotional databases of Chinese speech and facial expressions were established with the noise stimulus and movies evoking subjects' emtion. For different emotional states, we analyzed the single-mode speech emotion recognitions based the prosodic features and the geometric features of facial expression. Then, we discussed the bimodal emotion recognition by the use of Gaussian Mixture Model. The experimental results show that, the bimodal emotion recognition rate combined with facial expression is about 6% higher than the single model recognition rate merely using prosodic features.

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Et. al., Mihir Verma,. "Action Recognition Using Deep Learning And Cnn." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 11 (May 10, 2021): 818–24. http://dx.doi.org/10.17762/turcomat.v12i11.5967.

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Automated action recognition using Deep learning and CNN is playing a vital role in today‘s day to day society, it may be video action recognitions through cctv, or it may be the smart homes. Now day’s human actions are used in many devices to control them like HoloLens VR, for that recognition of action is important that why video recognition. This Paper represents practical, reliable, and generic systems for video-based human action recognition, technology of CNN network is used to recognize different layers of the video images features. These features are obtained by extracting the features from different layers that are through the CNN (Convolutional Neural Network).

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Adeyemi, Oladimeji, Martins Irhebhude, and Adeola Kolawole. "Speed Breakers, Road Marking Detection and Recognition Using Image Processing Techniques." Advances in Image and Video Processing 7, no. 5 (November 8, 2019): 30–42. http://dx.doi.org/10.14738/aivp.75.7205.

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This paper presents a image processing technique for speed breaker, road marking detection and recognition. An Optical Character Recognition (OCR) algorithm was used to recognize traffic signs such as “STOP” markings and a Hough transform was used to detect line markings which serves as a pre-processing stage to determine when the proposed technique does OCR or speed breaker recognition. The stopline inclusion serves as a pre-processing stage that tells the system when to perform stop marking recognition or speed breaker recognition. Image processing techniques was used for the processing of features from the images. Local Binary Pattern (LBP) was extracted as features and employed to train the Support Vector Machine (SVM) classifier for speed breaker recognition. Experimental results shows 79%, 100% “STOP” sign and speed breaker recognitions respectively. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.

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43

Horton, David L., and Timothy J. Pavlick. "Recognition failure, associative relatedness, and recognition memory." Bulletin of the Psychonomic Society 31, no. 5 (May 1993): 478–80. http://dx.doi.org/10.3758/bf03334968.

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44

Koh, Young-Sang. "Pattern-recognition receptors and recognition of pathogens." Journal of Medicine and Life Science 6, no. 3 (June 1, 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 cytokines such as interleukin-1β (lL-1β) and IL-18.

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Gangania, Priti, Sowmya Mishra, Shreshtha Garg, and Sonam Agarwal. "Handwriting Recognition System Using Optical Character Recognition." International Journal of Scientific Research in Computer Science and Engineering 6, no. 3 (June 30, 2018): 18–21. http://dx.doi.org/10.26438/ijsrcse/v6i3.1821.

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46

Kim, Weon-Goo. "Emotion Recognition using Robust Speech Recognition System." Journal of Korean institute of intelligent systems 18, no. 5 (October 25, 2008): 586–91. http://dx.doi.org/10.5391/jkiis.2008.18.5.586.

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47

Zhi-bin, LIU, Sôichi Yamane, Kazuki Tsuji, and ZHENG Zhe-min. "NESTMATE RECOGNITION AND KIN RECOGNITION IN ANTS." Insect Science 7, no. 1 (March 2000): 71–96. http://dx.doi.org/10.1111/j.1744-7917.2000.tb00344.x.

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48

Walker, L. L., and J. Malik. "When is scene recognition just texture recognition?" Journal of Vision 2, no. 7 (March 15, 2010): 255. http://dx.doi.org/10.1167/2.7.255.

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49

Bernard, Jean-Baptiste, Françoise Vitu-thibault, and Eric Castet. "Can crowded letter recognition predict word recognition?" Journal of Vision 16, no. 12 (September 1, 2016): 1113. http://dx.doi.org/10.1167/16.12.1113.

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50

Park, Heekyeong, Cheryl Abellanoza, and James D. Schaeffer. "Comparison of associative recognition versus source recognition." Neuroscience Letters 581 (October 2014): 52–56. http://dx.doi.org/10.1016/j.neulet.2014.08.024.

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Journal articles on the topic 'Recognition'

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