Relevant bibliographies by topics / Artificail neural networks

Contents

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

Academic literature on the topic 'Artificail neural networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Artificail neural networks"

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CVS, Rajesh, and Nadikoppula Pardhasaradhi. "Analysis of Artificial Neural-Network." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 418–28. http://dx.doi.org/10.31142/ijtsrd18482.

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Sarwar, Abid. "Diagnosis of hyperglycemia using Artificial Neural Networks." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (2017): 606–10. http://dx.doi.org/10.31142/ijtsrd7045.

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CVS, Rajesh, and M. Padmanabham. "Basics and Features of Artificial Neural Networks." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (2018): 1065–69. http://dx.doi.org/10.31142/ijtsrd9578.

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Parks, Allen D. "Characterizing Computation in Artificial Neural Networks by their Diclique Covers and Forman-Ricci Curvatures." European Journal of Engineering Research and Science 5, no. 2 (2020): 171–77. http://dx.doi.org/10.24018/ejers.2020.5.2.1689.

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The relationships between the structural topology of artificial neural networks, their computational flow, and their performance is not well understood. Consequently, a unifying mathematical framework that describes computational performance in terms of their underlying structure does not exist. This paper makes a modest contribution to understanding the structure-computational flow relationship in artificial neural networks from the perspective of the dicliques that cover the structure of an artificial neural network and the Forman-Ricci curvature of an artificial neural network’s connections
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O., Sheeba, Jithin George, Rajin P. K., Nisha Thomas, and Thomas George. "Glaucoma Detection Using Artificial Neural Network." International Journal of Engineering and Technology 6, no. 2 (2014): 158–61. http://dx.doi.org/10.7763/ijet.2014.v6.687.

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Demiralay, Raziye. "Estimating of student success with artificial neural networks." New Trends and Issues Proceedings on Humanities and Social Sciences 03, no. 07 (2017): 21–27. http://dx.doi.org/10.18844/prosoc.v2i7.1980.

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Klyuchko, O. M. "APPLICATION OF ARTIFICIAL NEURAL NETWORKS METHOD IN BIOTECHNOLOGY." Biotechnologia Acta 10, no. 4 (2017): 5–13. http://dx.doi.org/10.15407/biotech10.04.005.

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Jung, Jisoo, and Ji Won Yoon. "Author Identification Using Artificial Neural Network." Journal of the Korea Institute of Information Security and Cryptology 26, no. 5 (2016): 1191–99. http://dx.doi.org/10.13089/jkiisc.2016.26.5.1191.

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Al-Abaid, Shaimaa Abbas. "Artificial Neural Network Based Image Encryption Technique." Journal of Advanced Research in Dynamical and Control Systems 12, SP3 (2020): 1184–89. http://dx.doi.org/10.5373/jardcs/v12sp3/20201365.

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Al-Rawi, Kamal R., and Consuelo Gonzalo. "Adaptive Pointing Theory (APT) Artificial Neural Network." International Journal of Computer and Communication Engineering 3, no. 3 (2014): 212–15. http://dx.doi.org/10.7763/ijcce.2014.v3.322.

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Dissertations / Theses on the topic "Artificail neural networks"

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Moreno, Barón Laura. "Avances en lenguas electrónicas voltamperométricas y su aplicación en la multideterminación de compuestos presentes en muestras complejas." Doctoral thesis, Universitat Autònoma de Barcelona, 2017. http://hdl.handle.net/10803/402232.

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La presente tesis doctoral he tenido como objetivo la aplicación de lenguas electrónicas en dos áreas concretas, la determinación de aminoácidos oxidables en matrices complejas (muestras de pienso animal), y la determinación de metales pesados en muestras de agua que contiene como interferentes otros metales a nivel de traza. Para ambos casos se ha establecido un protocolo de trabajo que ha consistido en la caracterización de sistema de medida mediante el estudio de la respuesta amperométrica y posterior el análisis de la señal a través de técnicas clásicas, (quimiometría PLS en el caso de
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Chambers, Mark Andrew. "Queuing network construction using artificial neural networks /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488193665234291.

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Ariza, Zambrano William Camilo 1989. "Controle ativo de vibrações usando redes neurais artificiais : Active vibration control using artificial neural networks." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/264927.

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Orientador: Alberto Luiz Serpa<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica<br>Made available in DSpace on 2018-08-23T23:04:02Z (GMT). No. of bitstreams: 1 ArizaZambrano_WilliamCamilo_M.pdf: 5789145 bytes, checksum: 151f5e3ef1780d5448a7073b85b4715f (MD5) Previous issue date: 2013<br>Resumo: Este trabalho tem como objetivo principal o estudo de um método de controle baseado no uso de redes neurais artificiais aplicado ao problema de controle de vibrações em estruturas flexíveis. Este trabalho centra-se no estudo do esquema de controle inverso
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BRUCE, WILLIAM, and OTTER EDVIN VON. "Artificial Neural Network Autonomous Vehicle : Artificial Neural Network controlled vehicle." Thesis, KTH, Maskinkonstruktion (Inst.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191192.

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This thesis aims to explain how a Artificial Neural Network algorithm could be used as means of control for a Autonomous Vehicle. It describes the theory behind the neural network and Autonomous Vehicles, and how a prototype with a camera as its only input can be designed to test and evaluate the algorithms capabilites, and also drive using it. The thesis will show that the Artificial Neural Network can, with a image resolution of 100 × 100 and a training set with 900 images, makes decisions with a 0.78 confidence level.<br>Denna rapport har som mal att beskriva hur en Artificiellt Neuronnatve
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Menneer, Tamaryn Stable Ia. "Quantum artificial neural networks." Thesis, University of Exeter, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286530.

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Dai, Jing. "Reservoir-computing-based, biologically inspired artificial neural networks and their applications in power systems." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47646.

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Computational intelligence techniques, such as artificial neural networks (ANNs), have been widely used to improve the performance of power system monitoring and control. Although inspired by the neurons in the brain, ANNs are largely different from living neuron networks (LNNs) in many aspects. Due to the oversimplification, the huge computational potential of LNNs cannot be realized by ANNs. Therefore, a more brain-like artificial neural network is highly desired to bridge the gap between ANNs and LNNs. The focus of this research is to develop a biologically inspired artificial neural netwo
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Varoonchotikul, Pichaid. "Flood forecasting using artificial neural networks /." Lisse : Balkema, 2003. http://www.e-streams.com/es0704/es0704_3168.html.

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Orr, Ewan. "Evolving Turing's Artificial Neural Networks." Thesis, University of Canterbury. Department of Physics and Astronomy, 2010. http://hdl.handle.net/10092/4620.

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Our project uses ideas first presented by Alan Turing. Turing's immense contribution to mathematics and computer science is widely known, but his pioneering work in artificial intelligence is relatively unknown. In the late 1940s Turing introduced discrete Boolean artificial neural networks and, it has been argued that, he suggested that these networks be trained via evolutionary algorithms. Both artificial neural networks and evolutionary algorithms are active fields of research. Turing's networks are very basic yet capable of complex tasks such as processing sequential input; consequently, t
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Xu, Shuxiang. "Neuron-adaptive neural network models and applications /." [Campbelltown, N.S.W. : The Author], 1999. http://library.uws.edu.au/adt-NUWS/public/adt-NUWS20030702.085320/index.html.

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Fraticelli, Chiara. "Λc reconstruction with artificial neural networks". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/19985/.

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Il rivelatore ALICE studia collisioni di ioni pesanti ultrarelativistici in modo da creare e di conseguenza studiare lo stato della materia chiamato plasma di quark e gluoni. Questo obiettivo risulta difficoltoso data la sua vita breve, quindi facciamo riferimento a misure indirette per la prova della sua esistenza. In questa tesi abbiamo sfruttato tecniche di machine learning per studiare il decadimento del barione charmato Λc per dedurre alcune sue proprietà. In particolare abbiamo usato il metodo delle reti neurali per ricavare tutte le informazioni possibili con la tecninca di un'analisi m
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Books on the topic "Artificail neural networks"

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Kwon, Seoyun J. Artificial neural networks. Nova Science Publishers, 2010.

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Artificial neural networks. McGraw-Hill, 1997.

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Cartwright, Hugh, ed. Artificial Neural Networks. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2239-0.

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Karayiannis, N. B., and A. N. Venetsanopoulos. Artificial Neural Networks. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4757-4547-4.

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Cartwright, Hugh, ed. Artificial Neural Networks. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0826-5.

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da Silva, Ivan Nunes, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, and Silas Franco dos Reis Alves. Artificial Neural Networks. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-43162-8.

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Koprinkova-Hristova, Petia, Valeri Mladenov, and Nikola K. Kasabov, eds. Artificial Neural Networks. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09903-3.

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Livingstone, David J., ed. Artificial Neural Networks. Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-101-1.

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Prieto, Alberto, ed. Artificial Neural Networks. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/bfb0035870.

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Braspenning, P. J., F. Thuijsman, and A. J. M. M. Weijters, eds. Artificial Neural Networks. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/bfb0027019.

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Book chapters on the topic "Artificail neural networks"

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Bell, Tony. "Artificial dendritic learning." In Neural Networks. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52255-7_37.

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Pratt, Ian. "Neural Networks." In Artificial Intelligence. Macmillan Education UK, 1994. http://dx.doi.org/10.1007/978-1-349-13277-5_10.

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Aggarwal, Charu C. "Neural Networks." In Artificial Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72357-6_7.

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Pérez Castaño, Arnaldo. "Neural Networks." In Practical Artificial Intelligence. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3357-3_11.

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Broese, Einar, and Hans Ulrich Löffler. "Artificial Neural Networks." In Continuum Scale Simulation of Engineering Materials. Wiley-VCH Verlag GmbH & Co. KGaA, 2005. http://dx.doi.org/10.1002/3527603786.ch7.

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Mallot, Hanspeter A. "Artificial Neural Networks." In Computational Neuroscience. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00861-5_4.

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Jin, Yaochu. "Artificial Neural Networks." In Advanced Fuzzy Systems Design and Applications. Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1771-3_3.

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Fullér, Robert. "Artificial neural networks." In Introduction to Neuro-Fuzzy Systems. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1852-9_2.

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Czogała, Ernest, and Jacek Łęski. "Artificial neural networks." In Fuzzy and Neuro-Fuzzy Intelligent Systems. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1853-6_3.

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Dracopoulos, Dimitris C. "Artificial Neural Networks." In Perspectives in Neural Computing. Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0903-7_4.

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Conference papers on the topic "Artificail neural networks"

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Yang, Zhun, Adam Ishay, and Joohyung Lee. "NeurASP: Embracing Neural Networks into Answer Set Programming." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/243.

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We present NeurASP, a simple extension of answer set programs by embracing neural networks. By treating the neural network output as the probability distribution over atomic facts in answer set programs, NeurASP provides a simple and effective way to integrate sub-symbolic and symbolic computation. We demonstrate how NeurASP can make use of a pre-trained neural network in symbolic computation and how it can improve the neural network's perception result by applying symbolic reasoning in answer set programming. Also, NeurASP can make use of ASP rules to train a neural network better so that a n
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Mehdizadeh, Nasser S., Payam Sinaei, and Ali L. Nichkoohi. "Modeling Jones’ Reduced Chemical Mechanism of Methane Combustion With Artificial Neural Network." In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-31186.

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The present work reports a way of using Artificial Neural Networks for modeling and integrating the governing chemical kinetics differential equations of Jones’ reduced chemical mechanism for methane combustion. The chemical mechanism is applicable to both diffusion and premixed laminar flames. A feed-forward multi-layer neural network is incorporated as neural network architecture. In order to find sets of input-output data, for adapting the neural network’s synaptic weights in the training phase, a thermochemical analysis is embedded to find the chemical species mole fractions. An analysis o
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Wołowiec, Tomasz, and Volodymyr Martyniuk. "POSSIBILITIES OF USING ARTIFICIAL RADIAL BASIS FUNCTION NEURAL NETWORKS FOR MODELING ECONOMIC PROCESSES." In 6th International Scientific Conference ERAZ - Knowledge Based Sustainable Development. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2020. http://dx.doi.org/10.31410/eraz.2020.239.

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The possibility of using artificial radial basis function neural networks for modeling of economic phenomena and processes is shown. The basic characteristics and parameters of an artificial radial basis function neural network are shown and the advantages of using this type of artificial neural networks for modeling economic phenomena and processes are emphasized. Using an artificial radial basis function neural network, together with official statistics for 2010-2017, the modeling of the influence caused by work efficiency indicators of the customs authorities of Ukraine on the indicators of
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Chen, Huiyuan, and Jing Li. "Learning Data-Driven Drug-Target-Disease Interaction via Neural Tensor Network." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/477.

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Precise medicine recommendations provide more effective treatments and cause fewer drug side effects. A key step is to understand the mechanistic relationships among drugs, targets, and diseases. Tensor-based models have the ability to explore relationships of drug-target-disease based on large amount of labeled data. However, existing tensor models fail to capture complex nonlinear dependencies among tensor data. In addition, rich medical knowledge are far less studied, which may lead to unsatisfied results. Here we propose a Neural Tensor Network (NeurTN) to assist personalized medicine trea
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Ahmadian, M. T., and A. Mobini. "Online Prediction of Plate Deformations Under External Forces Using Neural Networks." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-15844.

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Recently online prediction of plate deformations in modern systems have been considered by many researchers, common standard methods are highly time consuming and powerful processors are needed for online computation of deformations. Artificial neural networks have capability to develop complex, nonlinear functional relationships between input and output patterns based on limited data. A good trained network could predict output data very fast with acceptable accuracy. This paper describes the application of an artificial neural network to identify deformation pattern of a four-side clamped pl
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Xiradakis, N., and Y. G. Li. "Gas Turbine and Sensor Fault Diagnosis With Nested Artificial Neural Networks." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53570.

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Accurate gas turbine diagnosis relies on accurate measurements from sensors. Unfortunately, sensors are prone to degradation or failure during gas turbine operations. In this paper a stack of decentralised artificial neural networks are introduced and investigated as an approach to approximate the measurement of a failed sensor once it is detected. Such a system is embedded into a nested neural network system for gas turbine diagnosis. The whole neural network diagnostic system consists of a number of feedforward neural networks for engine component diagnosis, sensor fault detection and isolat
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Cheng, Dawei, Xiaoyang Wang, Ying Zhang, and Liqing Zhang. "Risk Guarantee Prediction in Networked-Loans." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/618.

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The guaranteed loan is a debt obligation promise that if one corporation gets trapped in risks, its guarantors will back the loan. When more and more companies involve, they subsequently form complex networks. Detecting and predicting risk guarantee in these networked-loans is important for the loan issuer. Therefore, in this paper, we propose a dynamic graph-based attention neural network for risk guarantee relationship prediction (DGANN). In particular, each guarantee is represented as an edge in dynamic loan networks, while companies are denoted as nodes. We present an attention-based graph
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Narodytska, Nina. "Formal Analysis of Deep Binarized Neural Networks." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/811.

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Understanding properties of deep neural networks is an important challenge in deep learning. Deep learning networks are among the most successful artificial intelligence technologies that is making impact in a variety of practical applications. However, many concerns were raised about `magical' power of these networks. It is disturbing that we are really lacking of understanding of the decision making process behind this technology. Therefore, a natural question is whether we can trust decisions that neural networks make. One way to address this issue is to define properties that we want a neu
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Gao, Yang, Hong Yang, Peng Zhang, Chuan Zhou, and Yue Hu. "Graph Neural Architecture Search." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/195.

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Graph neural networks (GNNs) emerged recently as a powerful tool for analyzing non-Euclidean data such as social network data. Despite their success, the design of graph neural networks requires heavy manual work and domain knowledge. In this paper, we present a graph neural architecture search method (GraphNAS) that enables automatic design of the best graph neural architecture based on reinforcement learning. Specifically, GraphNAS uses a recurrent network to generate variable-length strings that describe the architectures of graph neural networks, and trains the recurrent network with polic
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Asnaashari, Ahmad, and Isam Shahrour. "Analysis of Water Mains Failure Frequencies: Artificial Neural Networks Versus Poisson Regression, Case Study — Sanandaj-Iran." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-43402.

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This paper includes analysis of water mains failure in Sanandaj city (in Iran) using both the artificial neural networks and the Poisson regression. This issue constitutes a major challenge in Iranian water authorities for its environmental consequences and associated direct and indirect costs such as the lost of purified water and the social impacts. According to 10-years collected data in Sanandaj city, an artificial neural networks model is proposed for the prediction of failure in water mains network; the performances of this model are then compared to the conventional Poisson regression.
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Reports on the topic "Artificail neural networks"

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Sgurev, Vassil. Artificial Neural Networks as a Network Flow with Capacities. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.09.12.

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Keller, P. E. Artificial neural networks in medicine. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/10162484.

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Powell, Bruce C. Artificial Neural Network Analysis System. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada392390.

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Dawes, Robert L. BIOMASSCOMP: Artificial Neural Networks and Neurocomputers. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada200902.

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Karakowski, Joseph A., and Hai H. Phu. A Fuzzy Hypercube Artificial Neural Network Classifier. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada354805.

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Blough, D. K., and K. K. Anderson. A comparison of artificial neural networks and statistical analyses. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/10146489.

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Amidi, Erfan. A Search for top quark using artificial neural networks. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/1156358.

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Baud-Bovy, Gabriel. A gaze-addressing communication system using artificial neural networks. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6142.

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Powell, Jr., James Estes. Learning Memento archive routing with Character-based Artificial Neural Networks. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1477616.

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Coleman, Andre Michael. An Adaptive Landscape Classification Procedure using Geoinformatics and Artificial Neural Networks. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/971112.

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