8,631 research outputs found
Automatic Design of Artificial Neural Networks for Gamma-Ray Detection
The goal of this work is to investigate the possibility of improving current
gamma/hadron discrimination based on their shower patterns recorded on the
ground. To this end we propose the use of Convolutional Neural Networks (CNNs)
for their ability to distinguish patterns based on automatically designed
features. In order to promote the creation of CNNs that properly uncover the
hidden patterns in the data, and at same time avoid the burden of hand-crafting
the topology and learning hyper-parameters we resort to NeuroEvolution; in
particular we use Fast-DENSER++, a variant of Deep Evolutionary Network
Structured Representation. The results show that the best CNN generated by
Fast-DENSER++ improves by a factor of 2 when compared with the results reported
by classic statistical approaches. Additionally, we experiment ensembling the
10 best generated CNNs, one from each of the evolutionary runs; the ensemble
leads to an improvement by a factor of 2.3. These results show that it is
possible to improve the gamma/hadron discrimination based on CNNs that are
automatically generated and are trained with instances of the ground impact
patterns.info:eu-repo/semantics/publishedVersio
Automatic detection of welding defects using the convolutional neural network
Quality control of welded joints is an important step before commissioning of various types of metal structures. The main obstacles to the commissioning of such facilities are the areas where the welded joint deviates from acceptable defective standards. The defects of welded joints include non-welded, foreign inclusions, cracks, pores, etc. The article describes an approach to the detection of the main types of defects of welded joints using a combination of convolutional neural networks and support vector machine methods. Convolutional neural networks are used for primary classification. The support vector machine is used to accurately define defect boundaries. As a preprocessing in our work, we use the methods of morphological filtration. A series of experiments confirms the high efficiency of the proposed method in comparison with pure CNN method for detecting defects
A design of Convolutional Neural Network model for the Diagnosis of the COVID-19
With the spread of COVID-19 around the globe over the past year, the usage of
artificial intelligence (AI) algorithms and image processing methods to analyze
the X-ray images of patients' chest with COVID-19 has become essential. The
COVID-19 virus recognition in the lung area of a patient is one of the basic
and essential needs of clicical centers and hospitals. Most research in this
field has been devoted to papers on the basis of deep learning methods
utilizing CNNs (Convolutional Neural Network), which mainly deal with the
screening of sick and healthy people.In this study, a new structure of a
19-layer CNN has been recommended for accurately recognition of the COVID-19
from the X-ray pictures of chest. The offered CNN is developed to serve as a
precise diagnosis system for a three class (viral pneumonia, Normal, COVID) and
a four classclassification (Lung opacity, Normal, COVID-19, and pneumonia). A
comparison is conducted among the outcomes of the offered procedure and some
popular pretrained networks, including Inception, Alexnet, ResNet50,
Squeezenet, and VGG19 and based on Specificity, Accuracy, Precision,
Sensitivity, Confusion Matrix, and F1-score. The experimental results of the
offered CNN method specify its dominance over the existing published
procedures. This method can be a useful tool for clinicians in deciding
properly about COVID-19
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