1,701 research outputs found
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Quantum Hopfield neural network
Quantum computing allows for the potential of significant advancements in
both the speed and the capacity of widely used machine learning techniques.
Here we employ quantum algorithms for the Hopfield network, which can be used
for pattern recognition, reconstruction, and optimization as a realization of a
content-addressable memory system. We show that an exponentially large network
can be stored in a polynomial number of quantum bits by encoding the network
into the amplitudes of quantum states. By introducing a classical technique for
operating the Hopfield network, we can leverage quantum algorithms to obtain a
quantum computational complexity that is logarithmic in the dimension of the
data. We also present an application of our method as a genetic sequence
recognizer.Comment: 13 pages, 3 figures, final versio
Industrial process monitoring by means of recurrent neural networks and Self Organizing Maps
Industrial manufacturing plants often suffer from reliability problems during their day-to-day operations which
have the potential for causing a great impact on the effectiveness and performance of the overall process and the
sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this
impact by extracting knowledge regarding the internal dynamics of the process and advice any process deviations
before it affects the productive process. In this paper, a novel industrial condition monitoring approach based on the
combination of Self Organizing Maps for operating point codification and Recurrent Neural Networks for critical signal
modeling is proposed. The combination of both methods presents a strong synergy, the information of the operating
condition given by the interpretation of the maps helps the model to improve generalization, one of the drawbacks of
recurrent networks, while assuring high accuracy and precision rates. Finally, the complete methodology, in terms of
performance and effectiveness is validated experimentally with real data from a copper rod industrial plant.Postprint (published version
Review of Neural Network Algorithms
The artificial neural network is the core tool of machine learning to realize intelligence. It has shown its advantages in the fields of sound, image, sound, picture, and so on. Since entering the 21st century, the progress of science and technology and people\u27s pursuit of artificial intelligence have introduced the research of artificial neural networks into an upsurge. Firstly, this paper introduces the application background and development process of the artificial neural network in order to clarify the research context of neural networks. Five branches and related applications of single-layer perceptron, linear neural network, BP neural network, Hopfield neural network, and depth neural network are analyzed in detail. The analysis shows that the development trend of the artificial neural network is developing towards a more general, flexible, and intelligent direction. Finally, the future development of the artificial neural network in training mode, learning mode, function expansion, and technology combination has prospected
Neural networks in geophysical applications
Neural networks are increasingly popular in geophysics.
Because they are universal approximators, these
tools can approximate any continuous function with an
arbitrary precision. Hence, they may yield important
contributions to finding solutions to a variety of geophysical applications.
However, knowledge of many methods and techniques
recently developed to increase the performance
and to facilitate the use of neural networks does not seem
to be widespread in the geophysical community. Therefore,
the power of these tools has not yet been explored to
their full extent. In this paper, techniques are described
for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size
and architecture
Searching for collective behavior in a network of real neurons
Maximum entropy models are the least structured probability distributions
that exactly reproduce a chosen set of statistics measured in an interacting
network. Here we use this principle to construct probabilistic models which
describe the correlated spiking activity of populations of up to 120 neurons in
the salamander retina as it responds to natural movies. Already in groups as
small as 10 neurons, interactions between spikes can no longer be regarded as
small perturbations in an otherwise independent system; for 40 or more neurons
pairwise interactions need to be supplemented by a global interaction that
controls the distribution of synchrony in the population. Here we show that
such "K-pairwise" models--being systematic extensions of the previously used
pairwise Ising models--provide an excellent account of the data. We explore the
properties of the neural vocabulary by: 1) estimating its entropy, which
constrains the population's capacity to represent visual information; 2)
classifying activity patterns into a small set of metastable collective modes;
3) showing that the neural codeword ensembles are extremely inhomogenous; 4)
demonstrating that the state of individual neurons is highly predictable from
the rest of the population, allowing the capacity for error correction.Comment: 24 pages, 19 figure
Recommended from our members
Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization.
The key operation in stochastic neural networks, which have become the state-of-the-art approach for solving problems in machine learning, information theory, and statistics, is a stochastic dot-product. While there have been many demonstrations of dot-product circuits and, separately, of stochastic neurons, the efficient hardware implementation combining both functionalities is still missing. Here we report compact, fast, energy-efficient, and scalable stochastic dot-product circuits based on either passively integrated metal-oxide memristors or embedded floating-gate memories. The circuit's high performance is due to mixed-signal implementation, while the efficient stochastic operation is achieved by utilizing circuit's noise, intrinsic and/or extrinsic to the memory cell array. The dynamic scaling of weights, enabled by analog memory devices, allows for efficient realization of different annealing approaches to improve functionality. The proposed approach is experimentally verified for two representative applications, namely by implementing neural network for solving a four-node graph-partitioning problem, and a Boltzmann machine with 10-input and 8-hidden neurons
- …