Optimal selection of neural network architecture for CAD using simulated annealing

2002 ◽  
Author(s):  
M.N. Gurcan ◽  
B. Sahiner ◽  
H.-P. Chan ◽  
L. Hadjiiski ◽  
N. Petrick
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Network Architecture ◽  
Optimal Selection ◽  
Neural Network Architecture ◽  
Selection Of

Features of the Development of a Neural Network to Automate the Recognition of Steel Defects

2020 ◽  
Vol 11 (6) ◽  
pp. 330-334
Author(s):  
R. A. Karelova ◽  
◽  
E. E. Ignatov ◽  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Cross Entropy ◽  
Neural Network Architecture ◽  
Development Environment ◽  
Steel Sheets ◽  
Python Language ◽  
The Neural Network ◽  
Metal Sheets ◽  
Selection Of

The article presents an embodiment of an artificial neural network for recognizing defects in images of steel sheets. Several stages of solving the problem are described: the choice of a development environment, a programming language, and libraries necessary for the implementation; features of data analysis, graphing, histograms, finding dependencies; the selection of a suitable neural network, the choice of neural network architecture, the selection of an algorithm for assessing quality and accuracy; neural network spelling; training and checking accuracy and quality, checking for overfitting (retraining). As development tools, Python language, PyTorch library, Jupyter development environment, convolutional neural network architecture — Unet are proposed. Features of the analysis of input images of steel sheets, features of the implementation of the neural network itself are described. The function of binary cross entropy was chosen as a criterion for assessing accuracy, since it seeks to bring the distribution of the network forecast to the target, fine not only for erroneous predictions, but also for uncertain ones. For additional evaluation, the DICE method was also used. The accuracy of the resulting model is 84 %. The proposed solution can become part of a hardware-software system for automating the recognition of defects on metal sheets.

scholarly journals Selection of neural network architecture and data augmentation procedures for predicting the course of cardiovascular diseases

2021 ◽  
Vol 2094 (3) ◽  
pp. 032037
Author(s):  
M G Dorrer ◽  
S E Golovenkin ◽  
S Yu Nikulina ◽  
Yu V Orlova ◽  
E Yu Pelipeckaya ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cardiovascular Diseases ◽  
Network Architecture ◽  
Data Augmentation ◽  
Case Histories ◽  
Neural Network Architecture ◽  
Original Dataset ◽  
Augmentation Procedure ◽  
Selection Of

Abstract The article solves the problem of creating models for predicting the course and complications of cardiovascular diseases. Artificial neural networks based on the Keras library are used. The original dataset includes 1700 case histories. In addition, the dataset augmentation procedure was used. As a result, the overall accuracy exceeded 84%. Furthermore, optimizing the network architecture and dataset has increased the overall accuracy by 17% and precision by 7%.

Application of stochastic real-valued reinforcement neural networks to batch production rescheduling

1997 ◽  
Vol 211 (8) ◽  
pp. 591-603 ◽  
Author(s):  
M I Heywood ◽  
M-C Chan ◽  
C R Chatwin
Keyword(s):  
Neural Network ◽  
Cross Section ◽  
Network Architecture ◽  
Hybrid Network ◽  
Neural Network Architecture ◽  
Batch Production ◽  
Worst Case ◽  
Hybrid Neural Network ◽  
Definition Of ◽  
Selection Of

This paper details the design and application of a hybrid neural network architecture for the rescheduling problem of batch manufacture. Design issues include the selection of an appropriate neural network paradigm, specification of the network architecture and support for multistep prediction. Application issues include decoupling the network dimension from that of the problem and the definition of suitable rescheduling operators. The ensuing hybrid network is tested against heuristics previously identified as typically representing estimates for best and worst case performance within a cross-section of batch rescheduling problems.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

Sign in / Sign up

Export Citation Format

Share Document