scholarly journals EVALUATION OF ARTIFICIAL NEURAL NETWORKS EFFECTIVENESS FOR UNFOLDING GAMMA-SPECTRUM OF 137CS

2021 ◽  
Vol 2 ◽  
pp. 44-54
Author(s):  
Aleksander N. Nikitin ◽  
◽  
Egor V. Mischenko ◽  
Olga A. Shurankova ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Radiation Source ◽  
Γ Radiation ◽  
Spectrometry Analysis ◽  
Convolution Neural Networks ◽  
Spectrum Unfolding ◽  
The Common ◽  
Artificial Neural ◽  
Fully Connected

Development of machine learning methods for spectrum processing is one of the most promising ways for gamma- spectrometry automation and accuracy improvement. Effectiveness of fully connected and convolution neural networks for quantitative γ-spectrometry analysis using scintillation detector NaI(Tl) and lead shielding is presented in the article. Semi-synthetic spectrums were used for the models training; the semi-synthetic spectrums are in channels additions of random spectrums measured at a short duration. The analysis shows advantages of artificial neural networks compare to the common analytical method of spectrum unfolding. The mean square error of activity evaluation is 2–4 times lower than the common method if measuring time is equal to 100 s. In highly standardized conditions of measuring, the advantages of convolution neural networks appear with increasing radiation source activity. Validation with sources not used in training of neural networks has shown fully connected and convolution neural networks can have advantages over the standard method when activity of γ-radiation source is relatively high.

A neutron spectrum unfolding computer code based on artificial neural networks

2014 ◽  
Vol 95 ◽  
pp. 428-431 ◽  
Author(s):  
J.M. Ortiz-Rodríguez ◽  
A. Reyes Alfaro ◽  
A. Reyes Haro ◽  
J.M. Cervantes Viramontes ◽  
H.R. Vega-Carrillo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Neutron Spectrum ◽  
Computer Code ◽  
Spectrum Unfolding ◽  
Neutron Spectrum Unfolding ◽  
Artificial Neural

scholarly journals KLASIFIKASI BATIK RIAU DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS (CNN)

2020 ◽  
Vol 9 (1) ◽  
pp. 7-10
Author(s):  
Hendry Fonda
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
18Th Century ◽  
The Public ◽  
Artificial Neural ◽  
The Difference ◽  
Fully Connected

ABSTRACT Riau batik is known since the 18th century and is used by royal kings. Riau Batik is made by using a stamp that is mixed with coloring and then printed on fabric. The fabric used is usually silk. As its development, comparing Javanese  batik with riau batik Riau is very slowly accepted by the public. Convolutional Neural Networks (CNN) is a combination of artificial neural networks and deeplearning methods. CNN consists of one or more convolutional layers, often with a subsampling layer followed by one or more fully connected layers as a standard neural network. In the process, CNN will conduct training and testing of Riau batik so that a collection of batik models that have been classified based on the characteristics that exist in Riau batik can be determined so that images are Riau batik and non-Riau batik. Classification using CNN produces Riau batik and not Riau batik with an accuracy of 65%. Accuracy of 65% is due to basically many of the same motifs between batik and other batik with the difference lies in the color of the absorption in the batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning   ABSTRAK   Batik Riau dikenal sejak abad ke 18 dan digunakan oleh bangsawan raja. Batik Riau dibuat dengan menggunakan cap yang dicampur dengan pewarna kemudian dicetak di kain. Kain yang digunakan biasanya sutra. Seiring perkembangannya, dibandingkan batik Jawa maka batik Riau sangat lambat diterima oleh masyarakat. Convolutional Neural Networks (CNN) merupakan kombinasi dari jaringan syaraf tiruan dan metode deeplearning. CNN terdiri dari satu atau lebih lapisan konvolutional, seringnya dengan suatu lapisan subsampling yang diikuti oleh satu atau lebih lapisan yang terhubung penuh sebagai standar jaringan syaraf. Dalam prosesnya CNN akan melakukan training dan testing terhadap batik Riau sehingga didapat kumpulan model batik yang telah terklasi    fikasi berdasarkan ciri khas yang ada pada batik Riau sehingga dapat ditentukan gambar (image) yang merupakan batik Riau dan yang bukan merupakan batik Riau. Klasifikasi menggunakan CNN menghasilkan batik riau dan bukan batik riau dengan akurasi 65%. Akurasi 65% disebabkan pada dasarnya banyak motif yang sama antara batik riau dengan batik lainnya dengan perbedaan terletak pada warna cerap pada batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning

A Survey of Partially Connected Neural Networks

1997 ◽  
Vol 08 (05n06) ◽  
pp. 535-558 ◽  
Author(s):  
D. Elizondo ◽  
E. Fiesler
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Extensive Survey ◽  
Recall Time ◽  
Biological Reality ◽  
Artificial Neural ◽  
Almost All ◽  
Fully Connected

Almost all artificial neural networks are by default fully connected, which often implies a high redundancy and complexity. Little research has been devoted to the study of partially connected neural networks, despite its potential advantages like reduced training and recall time, improved generalization capabilities, reduced hardware requirements, as well as being a step closer to biological reality. This publication presents an extensive survey of the various kinds of partially connected neural networks, clustered into a clear framework, followed by a detailed comparative discussion.

A neutron spectrum unfolding code based on generalized regression artificial neural networks

2016 ◽  
Vol 117 ◽  
pp. 8-14 ◽  
Author(s):  
Ma. del Rosario Martinez-Blanco ◽  
Gerardo Ornelas-Vargas ◽  
Celina Lizeth Castañeda-Miranda ◽  
Luis Octavio Solís-Sánchez ◽  
Rodrigo Castañeda-Miranada ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Neutron Spectrum ◽  
Spectrum Unfolding ◽  
Neutron Spectrum Unfolding ◽  
Artificial Neural ◽  
Generalized Regression

Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks

2013 ◽  
Author(s):  
J. M. Ortiz-Rodríguez ◽  
A. Reyes Alfaro ◽  
A. Reyes Haro ◽  
L. O. Solís Sánches ◽  
R. Castañeda Miranda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Neutron Spectrum ◽  
Spectrum Unfolding ◽  
Neutron Spectrum Unfolding ◽  
Artificial Neural ◽  
Iterative Procedures

scholarly journals Augmented reality using artificial neural networks –a review

2019 ◽  
Vol 8 (4) ◽  
pp. 603
Author(s):  
Sreekumar Narayanan ◽  
Srinath Doss
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Augmented Reality ◽  
Effective Utilization ◽  
The Past ◽  
Diverse Application ◽  
The Common ◽  
Artificial Neural ◽  
The Military ◽  
The Way

The present paper reviews the areas where Augmented Reality (AR) has been used in Artificial Neural Networks (ANN) (Artificial Neural Networks). The focus on systems based on AR is largely on enhancing technologies in diverse application areas such as; defense, robotics, medical, manufacturing, education, entertainment, assisted driving, maintenance and mobile assistance. However, AR is now finding much usage in ANN. The research considered a review based methodology wherein most studies conducted in the past on AR and ANN were reviewed. AR with ANN has profound applications in various sectors and has been developed in an extended way but still has some distance to go afore industries, the military and the common public will receive it as a accustomed user interface. AR would modernize the way people animate and the way industries endeavor by effective utilization. There is an incredible potential in fields such as construction, art, architecture, repair and manufacturing with mediated reality and well-organized visualization through AR.  

scholarly journals Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yongil Cho ◽  
Jong Soo Kim ◽  
Tae Ho Lim ◽  
Inhye Lee ◽  
Jongbong Choi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Activation Function ◽  
X Rays ◽  
Training Process ◽  
X Ray ◽  
Chest X Ray ◽  
Artificial Neural ◽  
Fully Connected ◽  
Better Than

AbstractThe purpose of this study was to evaluate the diagnostic performance achieved by using fully-connected small artificial neural networks (ANNs) and a simple training process, the Kim-Monte Carlo algorithm, to detect the location of pneumothorax in chest X-rays. A total of 1,000 chest X-ray images with pneumothorax were taken randomly from NIH (the National Institutes of Health) public image database and used as the training and test sets. Each X-ray image with pneumothorax was divided into 49 boxes for pneumothorax localization. For each of the boxes in the chest X-ray images contained in the test set, the area under the receiver operating characteristic (ROC) curve (AUC) was 0.882, and the sensitivity and specificity were 80.6% and 83.0%, respectively. In addition, a common currently used deep-learning method for image recognition, the convolution neural network (CNN), was also applied to the same dataset for comparison purposes. The performance of the fully-connected small ANN was better than that of the CNN. Regarding the diagnostic performances of the CNN with different activation functions, the CNN with a sigmoid activation function for fully-connected hidden nodes was better than the CNN with the rectified linear unit (RELU) activation function. This study showed that our approach can accurately detect the location of pneumothorax in chest X-rays, significantly reduce the time delay incurred when diagnosing urgent diseases such as pneumothorax, and increase the effectiveness of clinical practice and patient care.

scholarly journals Relationship between training database size and structural connectivity in artificial neural networks: Do specific local connections improve prediction accuracy?

2021 ◽  
Author(s):  
Min Sik Park
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Accuracy ◽  
Structural Connectivity ◽  
Large Size ◽  
Artificial Neural ◽  
Neuronal Systems ◽  
Similar Accuracy ◽  
Fully Connected

Abstract Training of artificial neural networks is very expensive, as a large-size database is necessary. Moreover, it is usually difficult to find such large-size training databases. Hence, it will be interesting to design artificial neural networks that can be used for training with a small-size database, while maintaining a similar accuracy for prediction compared to fully connected neural networks. We studied neural networks with partial disconnections, additional bypass connections, and negative activation nodes, which are found in the neuronal systems of the human brain. By combining the fully connected neural network and the above three brain-like elements, we found that the modified neural network showed improved prediction accuracy of 13% compared to the fully connected one despite the small size of the training database. To analyze the improved neural network, the contribution of each node in the hidden layers affecting the total prediction accuracy of the neural networks was studied. We also found important local connections that improve the prediction accuracy, and discussed the design of a neural network with a small-size training database without reduction in prediction accuracy.

scholarly journals Methodology for Knowledge Extraction from Trained Artificial Neural Networks

2018 ◽  
Vol 21 ◽  
pp. 6-14
Author(s):  
Andrey Bondarenko ◽  
Ludmila Aleksejeva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Binary Classification ◽  
Knowledge Extraction ◽  
Classification Problems ◽  
Critical Areas ◽  
Artificial Neural ◽  
Fully Connected

Artificial neural networks are widely spread models that outperform more basic, but explainable machine learning models like classification decision tree. Although their lack of explainability severely limits their area of application. All mission critical areas or law regulated areas (like European GDPR) require model to be explained. Explainability allows model validation for correctness and lack of bias. Thus methods for knowledge extraction from artificial neural networks have gained attention and development efforts. Current paper addresses this problem and describes knowledge extraction methodology which can be applied to classification problems. It is based on previous research and allows knowledge to be extracted from trained fully connected feed-forward artificial neural network, from radial basis function neural network and from hyper-polytope based classifier in the form of binary classification decision tree, elliptical rules and If-Then rules.

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