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Letteratura scientifica selezionata sul tema "Artificial neural networks"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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Articoli di riviste sul tema "Artificial neural networks"

1

N, Vikram. "Artificial Neural Networks." International Journal of Research Publication and Reviews 4, no. 4 (2023): 4308–9. http://dx.doi.org/10.55248/gengpi.4.423.37858.

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Partridge, Derek, Sarah Rae, and Wen Jia Wang. "Artificial Neural Networks." Journal of the Royal Society of Medicine 92, no. 7 (1999): 385. http://dx.doi.org/10.1177/014107689909200723.

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3

Moore, K. L. "Artificial neural networks." IEEE Potentials 11, no. 1 (1992): 23–28. http://dx.doi.org/10.1109/45.127697.

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4

Dalton, J., and A. Deshmane. "Artificial neural networks." IEEE Potentials 10, no. 2 (1991): 33–36. http://dx.doi.org/10.1109/45.84097.

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5

Yoon, Youngohc, and Lynn Peterson. "Artificial neural networks." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 23, no. 1 (1992): 55–57. http://dx.doi.org/10.1145/134347.134362.

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6

MAKHOUL, JOHN. "Artificial Neural Networks." INVESTIGATIVE RADIOLOGY 25, no. 6 (1990): 748–50. http://dx.doi.org/10.1097/00004424-199006000-00027.

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Watt, R. C., E. S. Maslana, M. J. Navabi, and K. C. Mylrea. "ARTIFICIAL NEURAL NETWORKS." Anesthesiology 77, Supplement (1992): A506. http://dx.doi.org/10.1097/00000542-199209001-00506.

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8

Allinson, N. M. "Artificial Neural Networks." Electronics & Communications Engineering Journal 2, no. 6 (1990): 249. http://dx.doi.org/10.1049/ecej:19900051.

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9

Drew, Philip J., and John R. T. Monson. "Artificial neural networks." Surgery 127, no. 1 (2000): 3–11. http://dx.doi.org/10.1067/msy.2000.102173.

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10

McGuire, Tim. "Artificial neural networks." Computer Audit Update 1997, no. 7 (1997): 25–29. http://dx.doi.org/10.1016/s0960-2593(97)84495-3.

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Tesi sul tema "Artificial neural networks"

1

Boychenko, I. V., and G. I. Litvinenko. "Artificial neural networks." Thesis, Вид-во СумДУ, 2009. http://essuir.sumdu.edu.ua/handle/123456789/17044.

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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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3

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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4

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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5

Varoonchotikul, Pichaid. "Flood forecasting using artificial neural networks /." Lisse : Balkema, 2003. http://www.e-streams.com/es0704/es0704_3168.html.

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6

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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Millevik, Daniel, and Michael Wang. "Stock Forecasting Using Artificial Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166455.

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This paper studies the potential of artificial neural networks (ANNs) in stock forecasting. It also investigates how the number of neurons in the network, as well as the distribution of the training data into training, validation and testing sets, affect the accuracy of the network. By using MATLAB and its Neural Network Toolbox tests were carried out with a two-layer feedforward neural network (FFNN). These are carried out by collecting five years of historical data from the Dow Jones Industrial Average (DJIA) stock index, which is then used for training the network. Finally, retraining the n

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Prasad, Jayan Ganesh Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "Financial forecasting using artificial neural networks." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Information Technology and Electrical Engineering, 2008. http://handle.unsw.edu.au/1959.4/38700.

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Despite the extent of a theoretical framework in financial market studies, a vast majority of the traders, investors and computer scientists have relied only on technical and timeseries data for predicting future prices. So far, the forecasting models have rarely incorporated macro-economic and market fundamentals successfully, especially with short-term predictions ranging less than a month. In this investigation on the predictability of certain financial markets, an attempt has been made to incorporate a un-exampled and encompassing set of parameters into an Artificial Neural Network predict

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Ng, Roger K. W. "Rapid prototyping of artificial neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq23440.pdf.

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Hook, Jaroslav. "Are artificial neural networks learning machines?" Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ38651.pdf.

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Libri sul tema "Artificial neural networks"

1

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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3

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

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

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9

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

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Akhmet, Marat, Madina Tleubergenova, Akylbek Zhamanshin, and Zakhira Nugayeva. Artificial Neural Networks. Springer Nature Switzerland, 2025. http://dx.doi.org/10.1007/978-3-031-68966-6.

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Capitoli di libri sul tema "Artificial neural networks"

1

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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2

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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3

Dix, Alan, and Janet Finlay. "Neural Networks." In Artificial Intelligence, 2nd ed. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003082880-8.

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4

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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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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6

Wehenkel, Louis A. "Artificial Neural Networks." In Automatic Learning Techniques in Power Systems. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5451-6_4.

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Czischek, Stefanie. "Artificial Neural Networks." In Springer Theses. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52715-0_3.

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Fortuna, Luigi, Gianguido Rizzotto, Mario Lavorgna, Giuseppe Nunnari, M. Gabriella Xibilia, and Riccardo Caponetto. "Artificial Neural Networks." In Advanced Textbooks in Control and Signal Processing. Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0357-8_4.

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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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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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Atti di convegni sul tema "Artificial neural networks"

1

Gomes, David Emanuel, António Espírito Santo, and José Páscoa. "Artificial Neural Network SoftSensor for Water Distribution Networks." In IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2024. https://doi.org/10.1109/iecon55916.2024.10905827.

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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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Lacrama, Dan L., Loredana Ileana Viscu, and Cornelia Victoria Anghel Drugarin. "Artificial vs. natural neural networks." In 2016 13th Symposium on Neural Networks and Applications (NEUREL). IEEE, 2016. http://dx.doi.org/10.1109/neurel.2016.7800093.

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4

Zheng, Shengjie, Lang Qian, Pingsheng Li, Chenggang He, Xiaoqi Qin, and Xiaojian Li. "An Introductory Review of Spiking Neural Network and Artificial Neural Network: From Biological Intelligence to Artificial Intelligence." In 8th International Conference on Artificial Intelligence (ARIN 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121010.

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Stemming from the rapid development of artificial intelligence, which has gained expansive success in pattern recognition, robotics, and bioinformatics, neuroscience is also gaining tremendous progress. A kind of spiking neural network with biological interpretability is gradually receiving wide attention, and this kind of neural network is also regarded as one of the directions toward general artificial intelligence. This review summarizes the basic properties of artificial neural networks as well as spiking neural networks. Our focus is on the biological background and theoretical basis of s

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Atashgahi, Zahra. "Cost-effective Artificial Neural Networks." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/810.

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Deep neural networks (DNNs) have gained huge attention over the last several years due to their promising results in various tasks. However, due to their large model size and over-parameterization, they are recognized as being computationally demanding. Therefore, deep learning models are not well-suited to applications with limited computational resources and battery life. Current solutions to reduce computation costs mainly focus on inference efficiency while being resource-intensive during training. This Ph.D. research aims to address these challenges by developing cost-effective neural net

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6

Pryor, Connor, Charles Dickens, Eriq Augustine, Alon Albalak, William Yang Wang, and Lise Getoor. "NeuPSL: Neural Probabilistic Soft Logic." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/461.

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In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks. To model the boundary between neural and symbolic representations, we propose a family of energy-based models, NeSy Energy-Based Models, and show that they are general enough to include NeuPSL and many other NeSy approaches. Using this framework, we show how to seamlessly integrate neural and symbolic parameter learning and inference in NeuPSL. Through an extensive empirical evaluatio

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Barua, Susamma. "Optical and systolic implementation of an artificial neural network." In Photonic Neural Networks. SPIE, 1993. http://dx.doi.org/10.1117/12.983197.

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Caulfield, H. John. "Artificial neural/chemical networks." In International Symposium on Optical Science and Technology, edited by Bruno Bosacchi, David B. Fogel, and James C. Bezdek. SPIE, 2001. http://dx.doi.org/10.1117/12.448345.

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Badgero, M. L. "Digitizing artificial neural networks." In Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94). IEEE, 1994. http://dx.doi.org/10.1109/icnn.1994.374850.

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Xie, Xuan, Kristian Kersting, and Daniel Neider. "Neuro-Symbolic Verification of Deep Neural Networks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/503.

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Formal verification has emerged as a powerful approach to ensure the safety and reliability of deep neural networks. However, current verification tools are limited to only a handful of properties that can be expressed as first-order constraints over the inputs and output of a network. While adversarial robustness and fairness fall under this category, many real-world properties (e.g., "an autonomous vehicle has to stop in front of a stop sign") remain outside the scope of existing verification technology. To mitigate this severe practical restriction, we introduce a novel framework for verify

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Rapporti di organizzazioni sul tema "Artificial neural networks"

1

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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2

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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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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Teeter, Corinne. Short Term Plasticity for Artificial Neural Networks. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/2004879.

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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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Gonzalez Pibernat, Gabriel, and Miguel Mascaró Portells. Dynamic structure of single-layer neural networks. Fundación Avanza, 2023. http://dx.doi.org/10.60096/fundacionavanza/2392022.

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This article examines the practical applications of single hidden layer neural networks in machine learning and artificial intelligence. They have been used in diverse fields, such as finance, medicine, and autonomous vehicles, due to their simplicit

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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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Waqas, Muhammad Talha. Synaptic Symmetry: Exploring Similarities in Neural Connections between Human Brain and Artificial Neural Networks. ResearchHub Technologies, Inc., 2024. http://dx.doi.org/10.55277/researchhub.c4dckln9.

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