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Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net)
NAECON 2018 - IEEE National Aerospace and Electronics Conference, 2018Bio-medical image segmentation is one of the promising sectors where nuclei segmentation from high-resolution histopathological images enables extraction of very high-quality features for nuclear morphometrics and other analysis metrics in the field of digital pathology.
Md Zahangir Alom +3 more
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Prediction of software reliability using feedforward and recurrent neural nets
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks, 2003The authors present an adaptive modeling approach based on connectionist networks and demonstrate how both feedforward and recurrent networks and various training regimes can be applied to predict software reliability. They make an empirical comparison between this new approach and five well-known software reliability growth prediction models using ...
N. Karunanithi, D. Whitley
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Energy-Time Tradeoff in Recurrent Neural Nets
2015In this chapter, we deal with the energy complexity of perceptron networks which has been inspired by the fact that the activity of neurons in the brain is quite sparse (with only about 1% of neurons firing). This complexity measure has recently been introduced for feedforward architectures (i.e., threshold circuits).
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Recurrent neural nets as dynamical Boolean systems with application to associative memory
IEEE Transactions on Neural Networks, 1997Discrete-time/discrete-state recurrent neural networks are analyzed from a dynamical Boolean systems point of view in order to devise new analytic and design methods for the class of both single and multilayer recurrent artificial neural networks. With the proposed dynamical Boolean systems analysis, we are able to formulate necessary and sufficient ...
P B, Watta, K, Wang, M H, Hassoun
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GRUU-Net: Integrated convolutional and gated recurrent neural network for cell segmentation
Medical Image Analysis, 2019Cell segmentation in microscopy images is a common and challenging task. In recent years, deep neural networks achieved remarkable improvements in the field of computer vision. The dominant paradigm in segmentation is using convolutional neural networks, less common are recurrent neural networks.
T. Wollmann +5 more
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A recurrent neural net approach to one-step ahead control problems
IEEE Transactions on Systems, Man, and Cybernetics, 1994In this paper, we present a recurrent neural net technique to provide control actions for nonlinear dynamic systems. In most current neural net control approaches, two nets are usually required. One acts as a system emulator, and the other one is a controller network.
P.P.C. Yip, null Yoh-Han Pao
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Learning temporal sequences in recurrent self-organising neural nets
1997The learning of temporal sequences is an extremely important component of human and animal behaviour. As well as the motor control involved in routine behaviour such as walking, running, talking, tool use and so on, humans have an apparently remarkable capacity for learning (and subsequently reproducing) temporal sequences. A new connectionist model of
Garry Briscoe, Terry Caelli
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Schema generation in recurrent neural nets for intercepting a moving target
Biological Cybernetics, 2010The grasping of a moving object requires the development of a motor strategy to anticipate the trajectory of the target and to compute an optimal course of interception. During the performance of perception-action cycles, a preprogrammed prototypical movement trajectory, a motor schema, may highly reduce the control load.
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Forecasting Ozone Pollution using Recurrent Neural Nets and Multiple Quantile Regression
2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), 2019Due to its harmful effects on human health and agriculture, ground-level ozone concentrations are continually monitored nowadays in most places in the world. However, predicting ground-level ozone concentrations is difficult and thus poses a major concern in urban areas worldwide.
Daniel Flores-Vergara +5 more
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A Structure Theory for Identification of Recurrent Neural Nets - Part 1
IFAC Proceedings Volumes, 1998Abstract This paper extends some results on identifiability of recurrent (dynamic) neural nets obtained by F.Albertini and E.Sontag. A topological and geometrical description of the set of identifiable paranleters is given. In addition it is shown that the classes of observationally equivalent non-nlinimal systems are essentially finite unions of ...
Monika Dörfler, Manfred Deistler
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