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  Browse All Reviews > Computer Systems Organization (C) > Processor Architectures (C.1) > Other Architecture Styles (C.1.3) > Neural Nets (C.1.3...)  
 
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  1-10 of 28 Reviews about "Neural Nets (C.1.3...)": Date Reviewed
  Detection of crop pests and diseases based on deep convolutional neural network and improved algorithm
Wu J., Li B., Wu Z.  ICMLT 2019 (Proceedings of the 2019 4th International Conference on Machine Learning Technologies, Nanchang, China, Jun 21-23, 2019) 20-27, 2019.  Type: Proceedings

Especially in large monoculture-based agricultural settings, an outbreak of pests or diseases can have a major impact on yield or quality of a crop. Advances in image processing based on convolutional neural network (CNN) architecture ...

Sep 29 2020
  Introduction to deep learning
Charniak E., The MIT Press, Cambridge, MA, 2019. 192 pp.  Type: Book (978-0-262039-51-2)

Deep learning has taken many application domains by storm, specifically those where artificial intelligence (AI) techniques have been struggling without too much success for decades. One of those domains is natural language processing ...

Mar 5 2020
  Grokking deep learning
Trask A., Manning Publications Co., Shelter Island, NY, 2019. 336 pp.  Type: Book (978-1-617293-70-2)

Deep learning is a hot topic in artificial intelligence (AI). It is exciting to see a book that can help readers understand the ideas of deep learning without advanced knowledge of mathematics....

Feb 14 2020
  Deep neural networks classification over encrypted data
Hesamifard E., Takabi H., Ghasemi M.  CODASPY 2019 (Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy, Richardson, TX, Mar 25-27, 2019) 97-108, 2019.  Type: Proceedings

When we speak about a convolutional neural network (CNN) as a more complex deep learning algorithm, there are privacy-preserving issues that could be addressed in any study on the topic. In deep learning, CNNs are used to analyze compl...

Feb 13 2020
  Neural network classifiers using a hardware-based approximate activation function with a hybrid stochastic multiplier
Li B., Qin Y., Yuan B., Lilja D. ACM Journal on Emerging Technologies in Computing Systems 15(1): 1-21, 2019.  Type: Article

Li et al. present a novel approach for optimizing neural network implementations, that is, “a new architecture of stochastic neural networks” with a hidden approximate activation function and a hybrid stochastic mul...

May 1 2019
  A novel multilayer AAA model for integrated applications
Rezakhani A., Shirazi H., Modiri N. Neural Computing and Applications 29(10): 887-901, 2018.  Type: Article

Unidimensional static security policies cannot cater to the needs of a growing enterprise anymore. Local regulations, business processes, operational levels, and threat modeling are the key anchors around which successful organizations...

Mar 22 2019
  Spiking deep convolutional neural networks for energy-efficient object recognition
Cao Y., Chen Y., Khosla D. International Journal of Computer Vision 113(1): 54-66, 2015.  Type: Article

A novel mechanism for converting convolutional neural networks (CNNs) to spiking neural networks (SNNs) to facilitate ready deployment, that is, mapping on spiking hardware architectures, is proposed in this paper....

Sep 28 2015
  Classification of arrhythmia using hybrid networks
Haseena H., Joseph P., Mathew A. Journal of Medical Systems 35(6): 1617-1630, 2011.  Type: Article

A new methodology for medical differential diagnosis purposes, based on the use of fuzzy clustered hybrid networks, is detailed in this paper. The study is focused on discriminating among the diseases of a certain class that includes n...

May 17 2012
  Self-organized two-state membrane potential transitions in a network of realistically modeled cortical neurons
Kang S., Kitano K., Fukai T. Neural Networks 17(3): 307-312, 2004.  Type: Article

Kang, Kitano, and Fukai present us with a computational model of cortical neurons that have recently been reported (as a result of in vitro and in vivo experiments, conducted as early as 1999) to show spontaneous transitions between tw...

Mar 1 2005
  A complete fuzzy decision tree technique
Olaru C., Wehenkel L. Fuzzy Sets and Systems 138(2): 221-254, 2003.  Type: Article

The common use of decision trees (DTs) as a data mining method is discussed in this paper. Olaru and Wehenkel describe a fuzzy DT technique, soft decision tree (SDT), which combines not only tree growth and pruning, but also refitting ...

Jan 7 2004
 
 
 
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