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Academic Articles & Research Papers
Showing 20 of 1,396 academic articles
Nils J. Nilsson • Cambridge University Press eBooks
(2009)
B eyond pattern recognition of individual alphanumeric characters , whether they be of fixed font or handwritten, lies the problem of understanding strings of characters that form words, sentences, or larger assemblages of text in a “natura...
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DOI: https://doi.org/10.1017/cbo9780511819346.009
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jong‐Seok Lee et al. • Artificial Intelligence Review
(2023)
Abstract Over the last decade, neural networks have reached almost every field of science and become a crucial part of various real world applications. Due to the increasing spread, confidence in neural network predictions has become more a...
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DOI: https://doi.org/10.1007/s10462-023-10562-9
• IGI Global eBooks
(2004)
Neural Network is defined as the ability of a group to solve more problems than its individual members.The idea brings that a group of people can solve problems efficiently and offer greater insight and a better answer than any one individu...
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DOI: https://doi.org/10.4018/978-1-59140-176-6
Abhijit S. Pandya, Robert B. Macy • CRC Press eBooks
(2021)
From the Publisher:
The addition of artificial neural network computing to traditionalpattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical gui...
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DOI: https://doi.org/10.1201/9780138744274
Pooja Jain, Makarand Chintamani Deo • Ships and Offshore Structures
(2005)
The soft computing technique of neural network is being extensively used across all disciplines of ocean engineering, namely, offshore, coastal, and deep-ocean engineering including marine engineering. This paper takes a stock of the resear...
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DOI: https://doi.org/10.1533/saos.2004.0005
Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann • arXiv (Cornell University)
(2018)
Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of hyperbolic geometry is not yet on par with Euclidean geomet...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1805.09112
Osama Moselhi, Tarek Hegazy, Paul Fazio • Journal of Construction Engineering and Management
(1991)
The competitive and risk‐averse nature of the construction industry and its heuristic problem‐solving needs, among other reasons, have contributed to the development of nontraditional decision‐making tools. Research in artificial intelligen...
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DOI: https://doi.org/10.1061/(asce)0733-9364(1991)117:4(606)
Saad Albawi, Tareq Abed Mohammed, Saad Al-Azawi (2017)
The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literatur...
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DOI: https://doi.org/10.1109/icengtechnol.2017.8308186
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner et al. • IEEE Transactions on Neural Networks
(2008)
Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper,...
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Open Access
DOI: https://doi.org/10.1109/tnn.2008.2005605
Dana H. Ballard • National Conference on Artificial Intelligence
(1987)
In the development of large-scale knowledge networks much recent progress has been inspired by connections to neurobiology. An important component of any neural network is an accompanying learning algorithm. Such an algorithm, to be biologi...
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Stamatios V. Kartalopoulos • Institute of Electrical and Electronics Engineers eBooks
(1995)
Understand the fundamentals of the emerging field of fuzzy neural networks, their applications and the most used paradigms with this carefully organized state-of-the-art textbook. Previously tested at a number of noteworthy conference tutor...
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DOI: https://doi.org/10.1109/9780470546826
Brad Warner, Manavendra Misra • The American Statistician
(1996)
Neural networks have received a great deal of attention over the last few years. They are being used in the areas of prediction and classification, areas where regression models and other related statistical techniques have traditionally be...
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DOI: https://doi.org/10.1080/00031305.1996.10473554
Ayan Mukherjee (2018)
As famously quoted by Margaret Heffernan: - “A great deal of creativity is about pattern recognition, and what you need to discern patterns is tons of data. Your mind collects that data by taking note of random details and anomalies easily ...
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Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji et al. • Proceedings of the AAAI Conference on Artificial Intelligence
(2019)
In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for comp...
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Open Access
DOI: https://doi.org/10.1609/aaai.v33i01.33013558
• Choice Reviews Online
(1993)
From the Publisher:
Artificial neural networks can be employed to solve a wide spectrum of problems in optimization, parallel computing, matrix algebra and signal processing. Taking a computational approach, this book explains how ANNs pro...
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DOI: https://doi.org/10.5860/choice.31-2156
Christos Christodoulou, Michael Georgiopoulos • CERN Document Server (European Organization for Nuclear Research)
(2001)
From the Book:
Since the early 1990s, a plethora of electromagnetic problems have been tackled using neural networks, some more successfully than others. Because neural networks and electromagnetics are two different scientific fields, not...
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David E. Rumelhart, Bernard Carlos Widrow, Michael A. Lehr • Communications of the ACM
(1994)
article Free AccessThe basic ideas in neural networks Authors: David E. Rumelhart Stanford Univ., Stanford, CA Stanford Univ., Stanford, CAView Profile , Bernard Widrow Stanford Univ., Stanford, CA Stanford Univ., Stanford, CAView Profile ,...
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Open Access
DOI: https://doi.org/10.1145/175247.175256
Fenglei Fan, Jinjun Xiong, Mengzhou Li, Ge Wang • IEEE Transactions on Radiation and Plasma Medical Sciences
(2021)
Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success recently in many important areas that deal with text, images, videos, graphs, and so on. However, the black-box nature of DNNs has become ...
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Open Access
DOI: https://doi.org/10.1109/trpms.2021.3066428
PMC: 9105427
Simon Arthur Chesterman • International and Comparative Law Quarterly
(2020)
Abstract As artificial intelligence (AI) systems become more sophisticated and play a larger role in society, arguments that they should have some form of legal personality gain credence. The arguments are typically framed in instrumental t...
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DOI: https://doi.org/10.1017/s0020589320000366
LIN Yan, JIN Tingyu, YANG Yuchao • Shanghai Jiaotong Daxue xuebao
(2024)
Aimed at the problem of low efficiency of ship pipeline design, an optimization method of pipeline layout is proposed. An optimization mathematical model is established by comprehensively considering the engineering background of safety, ec...
DOAJ
Open Access (DOAJ)
DOI: 10.16183/j.cnki.jsjtu.2022.508