Relevant bibliographies by topics / Recognition

Academic literature on the topic 'Recognition'

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Published: 4 June 2021
Last updated: 31 August 2024

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Journal articles on the topic "Recognition"

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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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Dissertations / Theses on the topic "Recognition"

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Chen, Qian. "Scanning probe recognition microscopy recognition strategies /." Diss., Connect to online resource - MSU authorized users, 2007.

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Thesis (Ph. D.)--Michigan State University. Dept. of Electrical & Computer Engineering, 2007.
Title from PDF t.p. (viewed on Apr. 21, 2009) Includes bibliographical references (p. 123-129). Also issued in print.
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An, Kyung Hee. "Concurrent Pattern Recognition and Optical Character Recognition." Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc332598/.

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The problem of interest as indicated is to develop a general purpose technique that is a combination of the structural approach, and an extension of the Finite Inductive Sequence (FI) technique. FI technology is pre-algebra, and deals with patterns for which an alphabet can be formulated.
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Goure, Devin Russell. "Contesting Recognition: A Critique of Hegelian Theories of Recognitive Freedom." Oberlin College Honors Theses / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin1274107371.

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Tran, Thao, and Nathalie Tkauc. "Face recognition and speech recognition for access control." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-39776.

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This project is a collaboration with the company JayWay in Halmstad. In order to enter theoffice today, a tag-key is needed for the employees and a doorbell for the guests. If someonerings the doorbell, someone on the inside has to open the door manually which is consideredas a disturbance during work time. The purpose with the project is to minimize thedisturbances in the office. The goal with the project is to develop a system that uses facerecognition and speech-to-text to control the lock system for the entrance door. The components used for the project are two Raspberry Pi’s, a 7 inch LCD-touch display, aRaspberry Pi Camera Module V2, a external sound card, a microphone and speaker. Thewhole project was written in Python and the platform used was Amazon Web Services (AWS)for storage and the face recognition while speech-to-text was provided by Google.The system is divided in three functions for employees, guests and deliveries. The employeefunction has two authentication steps, the face recognition and a random generated code that needs to be confirmed to avoid biometric spoofing. The guest function includes the speech-to-text service to state an employee's name that the guest wants to meet and the employee is then notified. The delivery function informs the specific persons in the office that are responsiblefor the deliveries by sending a notification.The test proves that the system will always match with the right person when using the facerecognition. It also shows what the threshold for the face recognition can be set to, to makesure that only authorized people enters the office.Using the two steps authentication, the face recognition and the code makes the system secureand protects the system against spoofing. One downside is that it is an extra step that takestime. The speech-to-text is set to swedish and works quite well for swedish-speaking persons.However, for a multicultural company it can be hard to use the speech-to-text service. It canalso be hard for the service to listen and translate if there is a lot of background noise or ifseveral people speak at the same time.
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Dimitrov, Emanuil. "Fingerprints recognition." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-5522.

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Nowadays biometric identification is used in a variety of applications-administration, business and even home. Although there are a lot of biometric identifiers, fingerprints are the most widely spread due to their acceptance from the people and the cheap price of the hardware equipment. Fingerprint recognition is a complex image recognition problem and includes algorithms and procedures for image enhancement and binarization, extracting and matching features and sometimes classification. In this work the main approaches in the research area are discussed, demonstrated and tested in a sample application. The demonstration software application is developed by using Verifinger SDK and Microsoft Visual Studio platform. The fingerprint sensor for testing the application is AuthenTec AES2501.

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Cooke, Jason W. B. "Chirality recognition." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306575.

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Davis, James W. "Gesture recognition." Honors in the Major Thesis, University of Central Florida, 1994. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/126.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.
Bachelors
Arts and Sciences
Computer Science
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OLIVEIRA, MARCELO LUNA GONCALVES DE. "HANDWRITING RECOGNITION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1995. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8738@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Neste trabalho é proposta uma metodologia de processamento da imagem associada a uma rede neural de perceptrons multicamadas, que é capaz de segmentar e reconhecer caracteres manuscritos cursivos. Esta técnica é robusta quanto à mudança na escala e translação dos caracteres, ligeiras variações na forma do caracter e ruído provocado pro tremores na mão do escritor. Pode ainda tornar-se robusta quanto à rotação, dependendo da escolha dos Descritores de Fourier. O método aproveita a existência de características geométricas e topológicas ou padrões de linhas. Estes componentes são fundamentais na construção da letra. São descritos pré-processamentos, que produzem os esqueletos dos caracteres, tais como algoritmos de afinamento e alisamento heurístico, filtragem zonal para atenuação de retas horizontais e verticais, detecção de contornos, extração heurística de características e a computação dos Descritores de Fourier representantes dos padrões de linha formadores dos caracteres. Após sua extração, as características são combinadas à entrada da rede neural de modo que cada combinação é reconhecida como pertencente a um determinado caracter. Para completar, os resultados do reconhecimento são combinados de modo a eliminar a interseção de classes proveniente das combinações comuns a vários caracteres. Esta metodologia procura a segmentação e o reconhecimento da forma de caracteres manuscritos, sem utilizar qualquer análise de contexto, o que naturalmente pode aumentar sua eficiência.
This work introduces an image processing methodology that, associated with a multi-level neural network of perceptrons, is able to isolate and recognize cursive handwritten characters. The character isolation technique makes use of fundamental geometric and topological aspectos of the characters. The work describe procedures to extract the characters skeletons, such as thinning and smoothing heuristic algorithms, zoned filtering to attenuate horizontal and vertical lines, contour detection, heuristic extraction of characteristics and the computation of Fourier Descriptors representing the line patterns, that compose the characters. After character extraction, its combined characteristics are presented to a neural network in order to allow recognition (identification). Finally, the results of the character identification are combined to avoid classification intersections, due to common aspects in a number of characters. The introduced methodology concerns only with the segmentation and form identification of the characters. It does not adress any context analysis.
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Childers, Jason C. "Peripheral Recognition." ScholarWorks@UNO, 2014. http://scholarworks.uno.edu/td/1853.

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Perception greatly affects the way we experience and understand the world. Using self-reflective research processes and data collection, I explore how art can subjectively re-present data and what this means for research and knowledge. The artworks through which I discuss these notions are Self Checkout 2013, Bibliography of Virtual Consciousness: Uniform Resource Locator Volumes 1-12 (BOVC:URL 1-12), and Observation Box. Self Checkout 2013 is composed of all of my receipts from 2013. They not only record my transactions, but also re-present data from which one can make inferences regarding my life—my consumer identity, my needs, my desires, etc. BOVC:URL 1-12 re-presents my web history and suggests a reflection on the relationships between physical realities, virtual realities, and the consciousness that mediates experience between them. These forms of data are analyzed by me and through audience participation in Observation Box in an attempt to construct multi-perspectival knowledges from art.
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Chuchilina, L. M., and I. E. Yeskov. "Speech recognition." Thesis, Видавництво СумДУ, 2008. http://essuir.sumdu.edu.ua/handle/123456789/15995.

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Books on the topic "Recognition"

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1972-, Barriball Anna, Musgrave David 1973-, Ashton Edwina 1965-, Mackintosh David 1966-, Roberts Catsou, Morrisey Simon, and Steeds Lucy, eds. Recognition. Bristol: Arnolfini, 2003.

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McLaughlin, Janice, Peter Phillimore, and Diane Richardson, eds. Contesting Recognition. London: Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230348905.

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Akata, Zeynep, Andreas Geiger, and Torsten Sattler, eds. Pattern Recognition. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71278-5.

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Roman-Rangel, Edgar, Ángel Fernando Kuri-Morales, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, and José Arturo Olvera-López, eds. Pattern Recognition. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77004-4.

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Feng, Jianjiang, Junping Zhang, Manhua Liu, and Yuchun Fang, eds. Biometric Recognition. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86608-2.

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Kisenwether, Elizabeth, and Mark Gagnon. Opportunity Recognition. 2455 Teller Road, Thousand Oaks California 91320: SAGE Publications, Inc., 2021. http://dx.doi.org/10.4135/9781071867761.

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Engelen, Andreas, Clara von Gagern, and Monika Engelen. Opportunity Recognition. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-34955-4.

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Bauckhage, Christian, Juergen Gall, and Alexander Schwing, eds. Pattern Recognition. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92659-5.

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Wallraven, Christian, Qingshan Liu, and Hajime Nagahara, eds. Pattern Recognition. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02444-3.

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Wallraven, Christian, Qingshan Liu, and Hajime Nagahara, eds. Pattern Recognition. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02375-0.

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Book chapters on the topic "Recognition"

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Benson, Edward. "Recognition." In The Law of Industrial Conflict, 41–46. London: Palgrave Macmillan UK, 1988. http://dx.doi.org/10.1007/978-1-349-08802-7_4.

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Longhofer, Jeffrey. "Recognition." In A-Z of Psychodynamic Practice, 151–54. London: Macmillan Education UK, 2015. http://dx.doi.org/10.1007/978-1-137-03387-1_60.

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Szeliski, Richard. "Recognition." In Texts in Computer Science, 575–640. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-935-0_14.

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Furia, Paolo. "Recognition." In Lecture Notes in Morphogenesis, 447–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51324-5_103.

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Buhmann, M. D., Prem Melville, Vikas Sindhwani, Novi Quadrianto, Wray L. Buntine, Luís Torgo, Xinhua Zhang, et al. "Recognition." In Encyclopedia of Machine Learning, 829. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_704.

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Ritvo, Ariella Riva, Fred R. Volkmar, Karen M. Lionello-Denolf, Trina D. Spencer, James Todd, Nurit Yirmiya, Maya Yaari, et al. "Recognition." In Encyclopedia of Autism Spectrum Disorders, 2531. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_101156.

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Mather, George. "Recognition." In Foundations of Sensation and Perception, 259–73. 4th ed. London: Psychology Press, 2022. http://dx.doi.org/10.4324/9781003335481-8.

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Civitarese, Giuseppe. "Recognition." In On Arrogance, 115–30. London: Routledge, 2024. http://dx.doi.org/10.4324/9781032669427-5.

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Davies, Bronwyn. "Recognition." In Entanglement in the World’s Becoming and the Doing of New Materialist Inquiry, 87–107. New York : Routledge, 2021.: Routledge, 2020. http://dx.doi.org/10.4324/9781003037477-7.

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Weik, Martin H. "recognition." In Computer Science and Communications Dictionary, 1429. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_15664.

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Conference papers on the topic "Recognition"

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Isberg, Thomas A., and G. Michael Morris. "Rotation-Invariant image recognition at low light levels." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.tur4.

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It has been recently demonstrated1 that fast, reliable image recognition can be accomplished using a commercially available 2-D photon counting detector and position computing electronics. In this paper the method of circular harmonic function expansion for coherent rotation-invariant image recognition2 is extended to the case of photon-limited image recognition. Theory for rotation-invariant filtering with incoherent illumination is presented and applied to photon-limited image recognition. Low light level input images are cross correlated with the square modulus of a single circular harmonic component of a high light level reference image stored in computer memory. The mean value of the correlation signal is found to be invariant with respect to rotation of the input image. The experimental results for the correlation signals for various input images are presented. Histograms of the correlation signal are shown and compared with theoretical predictions for the probability density functions. It is demonstrated that reliable image recognition, independent of the rotational orientation of the input, is possible with as few as 5000 detected photoevents.
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Iskra, Andrej, and Helena Gabrijelčič Tomc. "Analysis of answer times in recognition of facial images." In 11th International Symposium on Graphic Engineering and Design. University of Novi Sad, Faculty of technical sciences, Department of graphic engineering and design, 2022. http://dx.doi.org/10.24867/grid-2022-p21.

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In visual interaction between people, faces are the element that most attracts observers, so facial images are also an important element of communication between people. The aspect of observing and memorizing facial images has often been the subject of research. When observing facial images, there are two important parameters: the dimensions of the facial images and the observation time. Both of them have a great influence on recognition performance. In our research, we set three different dimensions of face images (small, medium, and large) and four observation times for the observation test (1, 2, 4, and 8 seconds). Since the results of face image recognition success in terms of these parameters have already been reported in other studies, we focused on the observation times for face images in the recognition process. We hypothesized that when participants looked the facial image quickly (short answer times), they were more likely to be convinced of the correctness of the answer and, consequently, fewer false recognitions would occur. In contrast, when participants looked at the face image for a longer time during the recognition process, they hesitated more, and the rate of incorrect recognition was higher because they were not completely sure of their answer. We found that in all 12 cases, the average duration of correct answers was shorter than that of incorrect answers. According to the time distribution of the duration of all answers, we set two limits (2 and 3 seconds) and analyzed the wrong answers according to these time limits. We found that with longer answer times, the proportion of incorrect recognition increased strongly. This is particularly noticeable in the tests where participants observed the facial images for a longer time (8 seconds) and could therefore memorize them better.
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Negoita, Alexandru, George Suciu, Svetlana Segarceanu, and Dan Trufin. "SPEECH RECOGNITION SYSTEM." In eLSE 2021. ADL Romania, 2021. http://dx.doi.org/10.12753/2066-026x-21-095.

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Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, is a capability which enables a program to process human speech into a written format. While it's commonly confused with voice recognition, speech recognition focuses on the translation of speech from a verbal format to a text one whereas voice recognition just seeks to identify an individual user's voice. Speech recognition applications are becoming more and more useful nowadays. Various interactive speech aware applications are available in the market. But they are usually meant for and executed on the traditional general-purpose computers. With growth in the needs for embedded computing and the demand for emerging embedded platforms, it is required that the speech recognition systems (SRS) are available on them too. Speech recognition systems emerge as efficient alternatives for such devices where typing becomes difficult attributed to their small screen limitations. The paper aims to test a speech recognition system that can be used for a human-machine interaction through speech. The goal is to allow the machine to recognize a set of instructions sent by the user through the voice signal. An automatic speech recognition system will be tested in order to identify words that belong to a limited vocabulary. It will be implemented by engaging a deep neural network (DNN). The construction of the network will be done with the help of the Tensorflow library, which provides support for the development of artificial intelligence algorithms. The system will be tested out on a non-homogeneous group of people, because it is desirable to develop a voice recognition system, independent of the speaker.
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McCormick, John, Adam Nash, Steph Hutchison, Kim Vincs, Saeid Nahavandi, and Douglas Creighton. "Recognition." In VRIC '14: Virtual Reality International Conference - Laval Virtual 2014. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2617841.2620707.

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Metze, Florian, Anton Batliner, Florian Eyben, Tim Polzehl, Björn Schuller, and Stefan Steidl. "Emotion recognition using imperfect speech recognition." In Interspeech 2010. ISCA: ISCA, 2010. http://dx.doi.org/10.21437/interspeech.2010-202.

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Sethy, Abhisek, Ajit Kuma Raut, and Soumya Ranjan Nayak. "Face Recognition Based Automated Recognition System." In 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2022. http://dx.doi.org/10.1109/confluence52989.2022.9734135.

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Keren, Sarah, Avigdor Gal, and Erez Karpas. "Goal Recognition Design - Survey." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/675.

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Goal recognition is the task of recognizing the objective of agents based on online observations of their behavior. Goal recognition design (GRD), the focus of this survey, facilitates goal recognition by the analysis and redesign of goal recognition models. In a nutshell, given a model of a domain and a set of possible goals, a solution to a GRD problem determines: (1) to what extent do actions performed by an agent reveal the agent’s objective? and (2) what is the best way to modify the model so that the objective of an agent can be detected as early as possible? GRD answers these questions by offering a solution for assessing and minimizing the maximal progress of any agent before recognition is guaranteed. This approach is relevant to any domain in which efficient goal recognition is essential and in which the model can be redesigned. Applications include intrusion detection, assisted cognition, computer games, and human-robot collaboration. This survey presents the solutions developed for evaluation and optimization in the GRD context, a discussion on the use of GRD in a variety of real-world applications, and suggestions of possible future avenues of GRD research.
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Matoba, Osamu, Takuya Murata, Kouichi Nitta, and Takeaki Yoshimura. "Recognition Property of Quantum Character Recognition Algorithm." In 2006 IEEE LEOS Annual Meeting. IEEE, 2006. http://dx.doi.org/10.1109/leos.2006.279126.

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Kabasakal, Burak, and Emre Sumer. "Gender recognition using innovative pattern recognition techniques." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404306.

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Lamiroy, B., S. Picard, and P. Gros. "Combining Local Recognition Methods for Better Recognition." In British Machine Vision Conference 2000. British Machine Vision Association, 2000. http://dx.doi.org/10.5244/c.14.74.

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Reports on the topic "Recognition"

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Steves, Michelle, Brian Stanton, Mary Theofanos, Dana Chisnell, and Hannah Wald. Camera Recognition. National Institute of Standards and Technology, March 2013. http://dx.doi.org/10.6028/nist.ir.7921.

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Koch, Mark William. Recognition using gait. Office of Scientific and Technical Information (OSTI), September 2007. http://dx.doi.org/10.2172/922087.

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Wells, III, and William M. Statistical Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada270887.

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Basri, Ronen. Recognition by Prototypes. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada271687.

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Pentland, Alex P. Recognition by Parts. Fort Belvoir, VA: Defense Technical Information Center, August 1987. http://dx.doi.org/10.21236/ada461783.

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Jacobs, David W. Grouping for Recognition. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada216716.

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Ballas, James A. Recognition of Environmental Sounds. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada214942.

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Kozaitis, Samuel P. Automated Optical Target Recognition. Fort Belvoir, VA: Defense Technical Information Center, December 1994. http://dx.doi.org/10.21236/ada292397.

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Shyne, Scott S. Neural Waveform Recognition System. Fort Belvoir, VA: Defense Technical Information Center, November 1992. http://dx.doi.org/10.21236/ada262444.

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10

Shashua, Amnon. Algebraic Functions for Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 1994. http://dx.doi.org/10.21236/ada276803.

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