Relevant bibliographies by topics / Neural networks (Computer science)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Neural networks (Computer science)'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Neural networks (Computer science)"

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Mijwel, Maad M., Adam Esen, and Aysar Shamil. "Overview of Neural Networks." Babylonian Journal of Machine Learning 2023 (August 11, 2023): 42–45. http://dx.doi.org/10.58496/bjml/2023/008.

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Since it was confirmed and verified that the human nervous system consists of individual cells, which were later called neurons, and it was discovered that these cells connect with each other to form an extensive communication network, a large number of possibilities have been opened for application in multiple disciplines in areas of knowledge. Neural Networks are created to perform tasks such as pattern recognition, classification, regression, and many other functions that serve humans and are an essential component in the field of machine learning and artificial intelligence. In computer sc
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Cottrell, G. W. "COMPUTER SCIENCE: New Life for Neural Networks." Science 313, no. 5786 (July 28, 2006): 454–55. http://dx.doi.org/10.1126/science.1129813.

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Li, Xiao Guang. "Research on the Development and Applications of Artificial Neural Networks." Applied Mechanics and Materials 556-562 (May 2014): 6011–14. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6011.

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Intelligent control is a class of control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. In computer science and related fields, artificial neural networks are computational models inspired by animals’ central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. Like other
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Schöneburg, E. "Neural networks hunt computer viruses." Neurocomputing 2, no. 5-6 (July 1991): 243–48. http://dx.doi.org/10.1016/0925-2312(91)90027-9.

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Turega, M. A. "Neural Networks." Computer Journal 35, no. 3 (June 1, 1992): 290. http://dx.doi.org/10.1093/comjnl/35.3.290.

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Widrow, Bernard, David E. Rumelhart, and Michael A. Lehr. "Neural networks." Communications of the ACM 37, no. 3 (March 1994): 93–105. http://dx.doi.org/10.1145/175247.175257.

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Begum, Afsana, Md Masiur Rahman, and Sohana Jahan. "Medical diagnosis using artificial neural networks." Mathematics in Applied Sciences and Engineering 5, no. 2 (June 4, 2024): 149–64. http://dx.doi.org/10.5206/mase/17138.

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Medical diagnosis using Artificial Neural Networks (ANN) and computer-aided diagnosis with deep learning is currently a very active research area in medical science. In recent years, for medical diagnosis, neural network models are broadly considered since they are ideal for recognizing different kinds of diseases including autism, cancer, tumor lung infection, etc. It is evident that early diagnosis of any disease is vital for successful treatment and improved survival rates. In this research, five neural networks, Multilayer neural network (MLNN), Probabilistic neural network (PNN), Learning
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Yen, Gary G., and Haiming Lu. "Hierarchical Rank Density Genetic Algorithm for Radial-Basis Function Neural Network Design." International Journal of Computational Intelligence and Applications 03, no. 03 (September 2003): 213–32. http://dx.doi.org/10.1142/s1469026803000975.

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In this paper, we propose a genetic algorithm based design procedure for a radial-basis function neural network. A Hierarchical Rank Density Genetic Algorithm (HRDGA) is used to evolve the neural network's topology and parameters simultaneously. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies highlighted in literature. In addition, the rank-density based fitness assignment technique is used to optimize the performance and topology of the evolved neural network to deal with the confliction between the traini
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Cavallaro, Lucia, Ovidiu Bagdasar, Pasquale De Meo, Giacomo Fiumara, and Antonio Liotta. "Artificial neural networks training acceleration through network science strategies." Soft Computing 24, no. 23 (September 9, 2020): 17787–95. http://dx.doi.org/10.1007/s00500-020-05302-y.

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AbstractThe development of deep learning has led to a dramatic increase in the number of applications of artificial intelligence. However, the training of deeper neural networks for stable and accurate models translates into artificial neural networks (ANNs) that become unmanageable as the number of features increases. This work extends our earlier study where we explored the acceleration effects obtained by enforcing, in turn, scale freeness, small worldness, and sparsity during the ANN training process. The efficiency of that approach was confirmed by recent studies (conducted independently)
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Kumar, G. Prem, and P. Venkataram. "Network restoration using recurrent neural networks." International Journal of Network Management 8, no. 5 (September 1998): 264–73. http://dx.doi.org/10.1002/(sici)1099-1190(199809/10)8:5<264::aid-nem298>3.0.co;2-o.

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Dissertations / Theses on the topic "Neural networks (Computer science)"

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Landassuri, Moreno Victor Manuel. "Evolution of modular neural networks." Thesis, University of Birmingham, 2012. http://etheses.bham.ac.uk//id/eprint/3243/.

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It is well known that the human brain is highly modular, having a structural and functional organization that allows the different regions of the brain to be reused for different cognitive processes. So far, this has not been fully addressed by artificial systems, and a better understanding of when and how modules emerge is required, with a broad framework indicating how modules could be reused within neural networks. This thesis provides a deep investigation of module formation, module communication (interaction) and module reuse during evolution for a variety of classification and prediction
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Sloan, Cooper Stokes. "Neural bus networks." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119711.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 65-68).<br>Bus schedules are unreliable, leaving passengers waiting and increasing commute times. This problem can be solved by modeling the traffic network, and delivering predicted arrival times to passengers. Research att
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Khan, Altaf Hamid. "Feedforward neural networks with constrained weights." Thesis, University of Warwick, 1996. http://wrap.warwick.ac.uk/4332/.

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The conventional multilayer feedforward network having continuous-weights is expensive to implement in digital hardware. Two new types of networks are proposed which lend themselves to cost-effective implementations in hardware and have a fast forward-pass capability. These two differ from the conventional model in having extra constraints on their weights: the first allows its weights to take integer values in the range [-3,3] only, whereas the second restricts its synapses to the set {-1,0,1} while allowing unrestricted offsets. The benefits of the first configuration are in having weights w
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Zaghloul, Waleed A. Lee Sang M. "Text mining using neural networks." Lincoln, Neb. : University of Nebraska-Lincoln, 2005. http://0-www.unl.edu.library.unl.edu/libr/Dissertations/2005/Zaghloul.pdf.

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Thesis (Ph.D.)--University of Nebraska-Lincoln, 2005.<br>Title from title screen (sites viewed on Oct. 18, 2005). PDF text: 100 p. : col. ill. Includes bibliographical references (p. 95-100 of dissertation).
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Hadjifaradji, Saeed. "Learning algorithms for restricted neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0016/NQ48102.pdf.

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Cheung, Ka Kit. "Neural networks for optimization." HKBU Institutional Repository, 2001. http://repository.hkbu.edu.hk/etd_ra/291.

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Ahamed, Woakil Uddin. "Quantum recurrent neural networks for filtering." Thesis, University of Hull, 2009. http://hydra.hull.ac.uk/resources/hull:2411.

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The essence of stochastic filtering is to compute the time-varying probability densityfunction (pdf) for the measurements of the observed system. In this thesis, a filter isdesigned based on the principles of quantum mechanics where the schrodinger waveequation (SWE) plays the key part. This equation is transformed to fit into the neuralnetwork architecture. Each neuron in the network mediates a spatio-temporal field witha unified quantum activation function that aggregates the pdf information of theobserved signals. The activation function is the result of the solution of the SWE. Theincorpor
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Williams, Bryn V. "Evolutionary neural networks : models and applications." Thesis, Aston University, 1995. http://publications.aston.ac.uk/10635/.

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The scaling problems which afflict attempts to optimise neural networks (NNs) with genetic algorithms (GAs) are disclosed. A novel GA-NN hybrid is introduced, based on the bumptree, a little-used connectionist model. As well as being computationally efficient, the bumptree is shown to be more amenable to genetic coding lthan other NN models. A hierarchical genetic coding scheme is developed for the bumptree and shown to have low redundancy, as well as being complete and closed with respect to the search space. When applied to optimising bumptree architectures for classification problems the GA
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De, Jongh Albert. "Neural network ensembles." Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/50035.

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Thesis (MSc)--Stellenbosch University, 2004.<br>ENGLISH ABSTRACT: It is possible to improve on the accuracy of a single neural network by using an ensemble of diverse and accurate networks. This thesis explores diversity in ensembles and looks at the underlying theory and mechanisms employed to generate and combine ensemble members. Bagging and boosting are studied in detail and I explain their success in terms of well-known theoretical instruments. An empirical evaluation of their performance is conducted and I compare them to a single classifier and to each other in terms of accuracy
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Lee, Ji Young Ph D. Massachusetts Institute of Technology. "Information extraction with neural networks." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111905.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 85-97).<br>Electronic health records (EHRs) have been widely adopted, and are a gold mine for clinical research. However, EHRs, especially their text components, remain largely unexplored due to the fact that they must be de-identified prior to any medical investigation. Existing systems for de-identification rely on manual rules or features, which are time-consuming to develop and fine-tun
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Books on the topic "Neural networks (Computer science)"

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Dominique, Valentin, and Edelman Betty, eds. Neural networks. Thousand Oaks, Calif: Sage Publications, 1999.

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1931-, Taylor John, and UNICOM Seminars, eds. Neural networks. Henley-on-Thames: A. Waller, 1995.

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1948-, Vandewalle J., and Roska T, eds. Cellular neural networks. Chichester [England]: Wiley, 1993.

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Bischof, Horst. Pyramidal neural networks. Mahwah, NJ: Lawrence Erlbaum Associates, 1995.

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Kwon, Seoyun J. Artificial neural networks. Hauppauge, N.Y: Nova Science Publishers, 2010.

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Hoffmann, Norbert. Simulating neural networks. Wiesbaden: Vieweg, 1994.

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Maass, Wolfgang, 1949 Aug. 21- and Bishop Christopher M, eds. Pulsed neural networks. Cambridge, Mass: MIT Press, 1999.

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Caudill, Maureen. Understanding neural networks: Computer explorations. Cambridge, Mass: MIT Press, 1993.

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Hu, Xiaolin, and P. Balasubramaniam. Recurrent neural networks. Rijek, Crotia: InTech, 2008.

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Baram, Yoram. Nested neural networks. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1988.

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Book chapters on the topic "Neural networks (Computer science)"

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ElAarag, Hala. "Neural Networks." In SpringerBriefs in Computer Science, 11–16. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4893-7_3.

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Siegelmann, Hava T. "Recurrent neural networks." In Computer Science Today, 29–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/bfb0015235.

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Yan, Wei Qi. "Convolutional Neural Networks and Recurrent Neural Networks." In Texts in Computer Science, 69–124. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4823-9_3.

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Ertel, Wolfgang. "Neural Networks." In Undergraduate Topics in Computer Science, 221–56. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-299-5_9.

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Ertel, Wolfgang. "Neural Networks." In Undergraduate Topics in Computer Science, 245–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58487-4_9.

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Feldman, Jerome A. "Neural Networks and Computer Science." In Opportunities and Constraints of Parallel Computing, 37–38. New York, NY: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-9668-0_10.

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Kruse, Rudolf, Christian Borgelt, Christian Braune, Sanaz Mostaghim, and Matthias Steinbrecher. "General Neural Networks." In Texts in Computer Science, 37–46. London: Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-7296-3_4.

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Kruse, Rudolf, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, and Pascal Held. "General Neural Networks." In Texts in Computer Science, 37–46. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5013-8_4.

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Kruse, Rudolf, Sanaz Mostaghim, Christian Borgelt, Christian Braune, and Matthias Steinbrecher. "General Neural Networks." In Texts in Computer Science, 39–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-42227-1_4.

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Betti, Alessandro, Marco Gori, and Stefano Melacci. "Foveated Neural Networks." In SpringerBriefs in Computer Science, 63–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90987-1_4.

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Conference papers on the topic "Neural networks (Computer science)"

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Doncow, Sergey, Leonid Orbachevskyi, Valentin Birukow, and Nina V. Stepanova. "Artificial Kohonen's neural networks for computer capillarometry." In Optical Information Science and Technology, edited by Andrei L. Mikaelian. SPIE, 1998. http://dx.doi.org/10.1117/12.304962.

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Nowak, Jakub, Marcin Korytkowski, and Rafał Scherer. "Classification of Computer Network Users with Convolutional Neural Networks." In 2018 Federated Conference on Computer Science and Information Systems. IEEE, 2018. http://dx.doi.org/10.15439/2018f321.

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Shastri, Bhavin J., Volker Sorger, and Nir Rotenberg. "In situ Training of Silicon Photonic Neural Networks: from Classical to Quantum." In CLEO: Science and Innovations. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_si.2023.sm4j.1.

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Photonic neural networks perform ultrafast inference operations but are trained on slow computers. We highlight on-chip network training enabled by silicon photonics. We introduce quantum photonic neural networks and discuss the role of weak nonlinearities.
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Dias, L. P., J. J. F. Cerqueira, K. D. R. Assis, and R. C. Almeida. "Using artificial neural network in intrusion detection systems to computer networks." In 2017 9th Computer Science and Electronic Engineering (CEEC). IEEE, 2017. http://dx.doi.org/10.1109/ceec.2017.8101615.

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Araújo, Georger, and Célia Ralha. "Computer Forensic Document Clustering with ART1 Neural Networks." In The Sixth International Conference on Forensic Computer Science. ABEAT, 2011. http://dx.doi.org/10.5769/c2011011.

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Wang, Huiran, and Ruifang Ma. "Optimization of Neural Networks for Network Intrusion Detection." In 2009 First International Workshop on Education Technology and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/etcs.2009.102.

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Eilermann, Sebastian, Christoph Petroll, Philipp Hoefer, and Oliver Niggemann. "3D Multi-Criteria Design Generation and Optimization of an Engine Mount for an Unmanned Air Vehicle Using a Conditional Variational Autoencoder." In Computer Science Research Notes. University of West Bohemia, Czech Republic, 2024. http://dx.doi.org/10.24132/csrn.3401.22.

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One of the most promising developments in computer vision in recent years is the use of generative neural networks for functionality condition-based 3D design reconstruction and generation. Here, neural networks learn dependencies between functionalities and a geometry in a very effective way. For a neural network the functionalities are translated in conditions to a certain geometry. But the more conditions the design generation needs to reflect, the more difficult it is to learn clear dependencies. This leads to a multi criteria design problem due various conditions, which are not considered
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Sakas, D. P., D. S. Vlachos, T. E. Simos, Theodore E. Simos, and George Psihoyios. "Fuzzy Neural Networks for Decision Support in Negotiation." In INTERNATIONAL ELECTRONIC CONFERENCE ON COMPUTER SCIENCE. AIP, 2008. http://dx.doi.org/10.1063/1.3037115.

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"Speech Emotion Recognition using Convolutional Neural Networks and Recurrent Neural Networks with Attention Model." In 2019 the 9th International Workshop on Computer Science and Engineering. WCSE, 2019. http://dx.doi.org/10.18178/wcse.2019.06.044.

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Čajić, Elvir, Irma Ibrišimović, Alma Šehanović, Damir Bajrić, and Julija Ščekić. "Fuzzy Logic And Neural Networks For Disease Detection And Simulation In Matlab." In 9th International Conference on Computer Science, Engineering and Applications. Academy & Industry Research Collaboration Center, 2023. http://dx.doi.org/10.5121/csit.2023.132302.

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This paper investigates the integration of fuzzy logic and neural networks for disease detection using the Matlab environment. Disease detection is key in medical diagnostics, and the combination of fuzzy logic and neural networks offers an advanced methodology for the analysis and interpretation of medical data. Fuzzy logic is used for modeling and resolving uncertainty in diagnostic processes, while neural networks are applied for indepth processing and analysis of images relevant to disease diagnosis. This paper demonstrates the development and implementation of a simulation system in Matla
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Reports on the topic "Neural networks (Computer science)"

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Markova, Oksana, Serhiy Semerikov та Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, травень 2018. http://dx.doi.org/10.31812/0564/2250.

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The role of neural network modeling in the learning сontent of special course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities – future IT-specialists and directed to breaking the gap between theoretic computer science and it’s applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic “Neural network and pattern recognition” of the sp
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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3178.

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The authors of the given article continue the series presented by the 2018 paper “Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot”. This time, they consider mathematical informatics as the basis of higher engineering education fundamentalization. Mathematical informatics deals with smart simulation, information security, long-term data storage and big data management, artificial intelligence systems, etc. The authors suggest studying basic principles of mathematical informatics by applying cloud-oriented means of various levels including those traditio
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Grossberg, Stephen. Instrumentation for Scientific Computing in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics. Fort Belvoir, VA: Defense Technical Information Center, October 1987. http://dx.doi.org/10.21236/ada189981.

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Semerikov, Serhiy O., Illia O. Teplytskyi, Yuliia V. Yechkalo, and Arnold E. Kiv. Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2648.

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The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet ad
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Farhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, December 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.

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We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network’s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. W
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Willson. L51756 State of the Art Intelligent Control for Large Engines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 1996. http://dx.doi.org/10.55274/r0010423.

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Computers have become a vital part of the control of pipeline compressors and compressor stations. For many tasks, computers have helped to improve accuracy, reliability, and safety, and have reduced operating costs. Computers excel at repetitive, precise tasks that humans perform poorly - calculation, measurement, statistical analysis, control, etc. Computers are used to perform these type of precise tasks at compressor stations: engine / turbine speed control, ignition control, horsepower estimation, or control of complicated sequences of events during startup and/or shutdown. For other task
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Modlo, Yevhenii O., Serhiy O. Semerikov, Ruslan P. Shajda, Stanislav T. Tolmachev, and Oksana M. Markova. Methods of using mobile Internet devices in the formation of the general professional component of bachelor in electromechanics competency in modeling of technical objects. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3878.

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The article describes the components of methods of using mobile Internet devices in the formation of the general professional component of bachelor in electromechanics competency in modeling of technical objects: using various methods of representing models; solving professional problems using ICT; competence in electric machines and critical thinking. On the content of learning academic disciplines “Higher mathematics”, “Automatic control theory”, “Modeling of electromechanical systems”, “Electrical machines” features of use are disclosed for Scilab, SageCell, Google Sheets, Xcos on Cloud in
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SAINI, RAVINDER, AbdulKhaliq Alshadid, and Lujain Aldosari. Investigation on the application of artificial intelligence in prosthodontics. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2022. http://dx.doi.org/10.37766/inplasy2022.12.0096.

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Review question / Objective: 1. Which artificial intelligence techniques are practiced in dentistry? 2. How AI is improving the diagnosis, clinical decision making, and outcome of dental treatment? 3. What are the current clinical applications and diagnostic performance of AI in the field of prosthodontics? Condition being studied: Procedures for desktop designing and fabrication Computer-aided design (CAD/CAM) in particular have made their way into routine healthcare and laboratory practice.Based on flat imagery, artificial intelligence may also be utilized to forecast the debonding of dental
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Johansen, Richard, Alan Katzenmeyer, Kaytee Pokrzywinski, and Molly Reif. A review of sensor-based approaches for monitoring rapid response treatments of cyanoHABs. Engineer Research and Development Center (U.S.), July 2023. http://dx.doi.org/10.21079/11681/47261.

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Water quality sensors are dynamic and vary greatly both in terms of utility and data acquisition. Data collection can range from single-parameter and one-dimensional to highly complex multiparameter spatiotemporal. Likewise, the analytical and statistical approaches range from relatively simple (e.g., linear regression) to more complex (e.g., artificial neural networks). Therefore, the decision to implement a particular water quality monitoring strategy is dependent upon many factors and varies widely. The purpose of this review was to document the current scientific literature to identify and
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Seginer, Ido, James Jones, Per-Olof Gutman, and Eduardo Vallejos. Optimal Environmental Control for Indeterminate Greenhouse Crops. United States Department of Agriculture, August 1997. http://dx.doi.org/10.32747/1997.7613034.bard.

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Increased world competition, as well as increased concern for the environment, drive all manufacturing systems, including greenhouses, towards high-precision operation. Optimal control is an important tool to achieve this goal, since it finds the best compromise between conflicting demands, such as higher profits and environmental concerns. The report, which is a collection of papers, each with its own abstract, outlines an approach for optimal, model-based control of the greenhouse environment. A reliable crop model is essential for this approach and a significant portion of the effort went i
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