scholarly journals Error Detection for Arabic Text Using Neural Sequence Labeling

2020 ◽  
Vol 10 (15) ◽  
pp. 5279
Author(s):  
Nora Madi ◽  
Hend Al-Khalifa
Keyword(s):  
Neural Network ◽  
Error Detection ◽  
English Language ◽  
Network Models ◽  
Network Architectures ◽  
Arabic Text ◽  
Neural Network Models ◽  
Sequence Labeling ◽  
Microsoft Word ◽  
Neural Network Architectures

The English language has, thus far, received the most attention in research concerning automatic grammar error correction and detection. However, these tasks have been less investigated for other languages. In this paper, we present the first experiments using neural network models for the task of error detection for Modern Standard Arabic (MSA) text. We investigate several neural network architectures and report the evaluation results acquired by applying cross-validation on the data. All experiments involve a corpus we created and augmented. The corpus has 494 sentences and 620 sentences after augmentation. Our models achieved a maximum precision of 78.09%, recall of 83.95%, and F0.5 score of 79.62% in the error detection task using SimpleRNN. Using an LSTM, we achieved a maximum precision of 79.21%, recall of 93.8%, and F0.5 score of 79.16%. Finally, the best results were achieved using a BiLSTM with a maximum precision of 80.74%, recall of 85.73%, and F0.5 score of 81.55%. We compared the results of the three models to a baseline, which is a commercially available Arabic grammar checker (Microsoft Word 2007). LSTM, BiLSTM, and SimpleRNN all outperformed the baseline in precision and F0.5. Our work shows preliminary results, demonstrating that neural network architectures for error detection through sequence labeling can successfully be applied to Arabic text.

scholarly journals Neural network models of the tactile system develop first-order units with spatially complex receptive fields

2017 ◽  
Author(s):  
Charlie W. Zhao ◽  
Mark J. Daley ◽  
J. Andrew Pruszynski
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Receptive Fields ◽  
Network Models ◽  
Network Architectures ◽  
Learning Tools ◽  
Neural Network Models ◽  
First Order ◽  
Wide Range ◽  
Tactile System

AbstractFirst-order tactile neurons have spatially complex receptive fields. Here we use machine learning tools to show that such complexity arises for a wide range of training sets and network architectures, and benefits network performance, especially on more difficult tasks and in the presence of noise. Our work suggests that spatially complex receptive fields are normatively good given the biological constraints of the tactile periphery.

Neural Network Models of Perceptual Learning of Angle Discrimination

1996 ◽  
Vol 8 (2) ◽  
pp. 270-299 ◽  
Author(s):  
G. Mato ◽  
H. Sompolinsky
Keyword(s):  
Neural Network ◽  
Perceptual Learning ◽  
Network Models ◽  
Angular Width ◽  
Network Architectures ◽  
Neural Network Models ◽  
Tuning Curves ◽  
Partial Transfer ◽  
Gating Network ◽  
Angle Discrimination

We study neural network models of discriminating between stimuli with two similar angles, using the two-alternative forced choice (2AFC) paradigm. Two network architectures are investigated: a two-layer perceptron network and a gating network. In the two-layer network all hidden units contribute to the decision at all angles, while in the other architecture the gating units select, for each stimulus, the appropriate hidden units that will dominate the decision. We find that both architectures can perform the task reasonably well for all angles. Perceptual learning has been modeled by training the networks to perform the task, using unsupervised Hebb learning algorithms with pairs of stimuli at fixed angles θ and δθ. Perceptual transfer is studied by measuring the performance of the network on stimuli with θ′ ≠ θ. The two-layer perceptron shows a partial transfer for angles that are within a distance a from θ, where a is the angular width of the input tuning curves. The change in performance due to learning is positive for angles close to θ, but for |θ − θ′| ≈ a it is negative, i.e., its performance after training is worse than before. In contrast, negative transfer can be avoided in the gating network by limiting the effects of learning to hidden units that are optimized for angles that are close to the trained angle.

scholarly journals Neural classifiers with limited connectivity and recurrent readouts

2017 ◽  
Author(s):  
Lyudmila Kushnir ◽  
Stefano Fusi
Keyword(s):  
Neural Network ◽  
Long Range ◽  
Network Models ◽  
Classification Performance ◽  
Network Architectures ◽  
Huge Number ◽  
Neural Network Models ◽  
Random Patterns ◽  
Scalable Network ◽  
The Brain

AbstractFor many neural network models in which neurons are trained to classify inputs like perceptrons, the number of inputs that can be classified is limited by the connectivity of each neuron, even when the total number of neurons is very large. This poses the problem of how the biological brain can take advantage of its huge number of neurons given that the connectivity is sparse. One solution is to combine multiple perceptrons together, as in committee machines. The number of classifiable random patterns would then grow linearly with the number of perceptrons, even when each perceptron has limited connectivity. However, the problem is moved to the downstream readout neurons, which would need a number of connections that is as large as the number of perceptrons. Here we propose a different approach in which the readout is implemented by connecting multiple perceptrons in a recurrent attractor neural network. We prove analytically that the number of classifiable random patterns can grow unboundedly with the number of perceptrons, even when the connectivity of each perceptron remains finite. Most importantly, both the recurrent connectivity and the connectivity of downstream readouts also remain finite. Our study shows that feed-forward neural classifiers with numerous long range afferent connections can be replaced by recurrent networks with sparse long range connectivity without sacrificing the classification performance. Our strategy could be used to design more general scalable network architectures with limited connectivity, which resemble more closely the brain neural circuits which are dominated by recurrent connectivity.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

scholarly journals The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

2021 ◽  
Vol 11 (3) ◽  
pp. 908
Author(s):  
Jie Zeng ◽  
Panagiotis G. Asteris ◽  
Anna P. Mamou ◽  
Ahmed Salih Mohammed ◽  
Emmanuil A. Golias ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Models ◽  
Correlation Coefficients ◽  
Network Models ◽  
Small Scale ◽  
Offshore Platforms ◽  
Neural Network Models ◽  
Buried Pipes ◽  
Ensemble Techniques ◽  
Uplift Resistance

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.

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