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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
Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry Vetrov • arXiv (Cornell University)
(2015)
Deep neural networks currently demonstrate state-of-the-art performance in several domains. At the same time, models of this class are very demanding in terms of computational resources. In particular, a large amount of memory is required b...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1509.06569
Hecht-Nielsen • International Joint Conference on Neural Networks (IJCNN)
(1989)
The author presents a survey of the basic theory of the backpropagation neural network architecture covering architectural design, performance measurement, function approximation capability, and learning. The survey includes previously know...
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DOI: https://doi.org/10.1109/ijcnn.1989.118638
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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Hojjat Adeli • Computer-Aided Civil and Infrastructure Engineering
(2001)
The first journal article on neural network application in civil/structural engineering was published by in this journal in 1989. This article reviews neural network articles published in archival research journals since then. The emphasis ...
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cc-by-nc-nd
DOI: https://doi.org/10.1111/0885-9507.00219
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
Paul Refenes • Wiley eBooks
(1995)
PART ONE: NEURAL NETWORKS: Introduction Design Considerations Methods for Optimal Network Design Data Modelling Considerations Testing Strategies and Metrics PART TWO: EQUITY APPLICATIONS: Modelling Stock Returns in the Framework of APT: A ...
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Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna et al. • arXiv (Cornell University)
(2013)
Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpr...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1312.6199
• 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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Aarush Saxena • International Journal for Research in Applied Science and Engineering Technology
(2022)
Abstract: The field of machine learning has taken a dramatic twist in re- cent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models can far exceed the per- performance of previous for...
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Open Access
DOI: https://doi.org/10.22214/ijraset.2022.47789
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Cohen, Jonathan et al. • arXiv (Cornell University)
(2014)
We present a library of efficient implementations of deep learning primitives. Deep learning workloads are computationally intensive, and optimizing their kernels is difficult and time-consuming. As parallel architectures evolve, kernels mu...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1410.0759
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
Zeng Yun, Wu Wenxiu, Yu Dankui • Shiyou jixie
(2018)
Under the rapid development of the world’s powder technology, powder materials are used in a wide range of fields such as metallurgy, chemical industry and petroleum. Turbo air classifier is one of the most important mechanical equipment in...
DOAJ
Open Access (DOAJ)
Miaohui Wang, Wuyuan Xie, Jian Xiong, Dayong Wang et al. • IEEE Access
(2019)
In high efficiency video coding (HEVC), transformation and quantization are separately performed to eliminate the perceptual redundancy of visual signals. However, a uniform quantizer can inevitably degrade the compression efficiency of fix...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2019.2917260
XI Runping, XUE Shaohui • Jisuanji gongcheng
(2020)
The performance evaluation of existing moving target tracking algorithms has many drawbacks,such as massive amount of data,redundant tests and insufficient consideration on algorithm performance under multifactor situation.Therefore,this pa...
DOAJ
Open Access (DOAJ)
DOI: 10.19678/j.issn.1000-3428.0054295
Ce Zhang, Wei-Gong Lv, Sheng Sheng, Jin-Yong Wang et al. • Applied Sciences
(2022)
From the perspective of FDR (fault detection rate), which is an indispensable component in reliability modeling, this paper proposes two kinds of reliability models under imperfect debugging. This model is a relatively flexible and unified ...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/app122110736
Jaewook Lee, Jiwon Choi, Duksan Ryu, Suntae Kim • IEEE Access
(2022)
A software defect prediction (SDP) model identifies the defect-prone modules. Setting appropriate parameters in an SDP model is critical because it affects the model performance. In a recent study, parameters were automatically explored usi...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2022.3211978
XU Le, AN Hong, CHEN Junshi, ZHANG Pengfei, WU Zheng • Jisuanji gongcheng
(2022)
The performance of unstructured grid computing on Sunway TaihuLight, a domestic heterogeneous many-core platform, is limited by sparse storage, discrete memory access, and data dependency.To relieve the sparse storage and discrete memory ac...
DOAJ
Open Access (DOAJ)
DOI: 10.19678/j.issn.1000-3428.0065567
DU Xinxin, HU Xiaohui, ZHAO Jianan • Jisuanji gongcheng
(2022)
A Vehicular Ad-Hoc Network(VANET) is a Mobile Ad-Hoc Network(MANET) composed of mobile vehicular nodes.It does not rely on infrastructure to either establish a communication link orrealize communication. Owingto the high mobility of vehicle...
DOAJ
Open Access (DOAJ)
DOI: 10.19678/j.issn.1000-3428.0063131