A flexible graphical user interface for embedding heterogeneous neural network simulators

1996 ◽  
Vol 39 (3) ◽  
pp. 367-374 ◽  
Author(s):  
R. Drossu ◽  
Z. Obradovic ◽  
J. Fletcher
Keyword(s):  
Neural Network ◽  
User Interface ◽  
Graphical User Interface ◽  
Network Simulators

scholarly journals Cell-Free DNA Pattern Identification and its Classification using Convolution Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 5362-5364
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
User Interface ◽  
Graphical User Interface ◽  
Probabilistic Method ◽  
Convolution Neural Network ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Free Dna ◽  
Dna Pattern

Circulating cell DNA (cfDNA) design identification assumes a cardinal job in fetal drug, transplantation and oncology. Be that as it may, it has additionally demonstrated to be a biomarker for different maladies. There are numerous order strategies by which the acknowledgment and arrangement should be possible. So as to have a superior time unpredictability and improve the precision further, this strategy targets distinguishing and arranging the general DNA examples and ailments related with them utilizing cfDNA Images in a Convolution Neural Network. A probabilistic method is used for cfDNA image feature extraction, fragmentation and interpretation. Graphical User Interface is the platform where this method is employed since it uses visual indicators in place of text-based interface. The aftereffects of this test demonstrate that the Convolution Neural Network calculation can perceive cfDNA successions accurately and successfully with no dubiety. Prepared with examples, the CNN can effectively characterize the picture surrendered to coordinated and unparalleled examples with numerical highlights.

scholarly journals Polo: an open-source graphical user interface for crystallization screening

2021 ◽  
Vol 54 (2) ◽  
Author(s):  
Ethan T. Holleman ◽  
Erica Duguid ◽  
Lisa J. Keefe ◽  
Sarah E. J. Bowman
Keyword(s):  
Neural Network ◽  
Crystal Growth ◽  
User Interface ◽  
High Throughput ◽  
Graphical User Interface ◽  
Graphical Interface ◽  
Commercial Use ◽  
Crystallization Experiments ◽  
Crystallization Conditions ◽  
General Public License

Polo is a Python-based graphical user interface designed to streamline viewing and analysis of images to monitor crystal growth, with a specific target to enable users of the High-Throughput Crystallization Screening Center at Hauptman-Woodward Medical Research Institute (HWI) to efficiently inspect their crystallization experiments. Polo aims to increase efficiency, reducing time spent manually reviewing crystallization images, and to improve the potential of identifying positive crystallization conditions. Polo provides a streamlined one-click graphical interface for the Machine Recognition of Crystallization Outcomes (MARCO) convolutional neural network for automated image classification, as well as powerful tools to view and score crystallization images, to compare crystallization conditions, and to facilitate collaborative review of crystallization screening results. Crystallization images need not have been captured at HWI to utilize Polo's basic functionality. Polo is free to use and modify for both academic and commercial use under the terms of the copyleft GNU General Public License v3.0.

Application of Neural Network in Predicting Forging Hardness

2020 ◽  
Vol 305 ◽  
pp. 169-177
Author(s):  
Yuan Ping Luh ◽  
Chang Hung Tu ◽  
Huang Li Wang ◽  
Chien Jung Chiu
Keyword(s):  
Neural Network ◽  
Heat Treatment ◽  
User Interface ◽  
Graphical User Interface ◽  
Cold Forging ◽  
High Quality ◽  
Simple Program ◽  
Experimental Parameters ◽  
Neck Diameter ◽  
The Relationship

This study used cold forging to produce car components with the desired hardness without using postprocessing methods such as heat treatment, surface blasting, and polishing. Material diameter, mold neck diameter, and hardness of the material after annealing were used as parameters, and nine sets of experimental parameters were obtained using the Taguchi method. Under the premise of high-quality forging, this study determined the optimal hardness and the relationship between formation parameters and forging hardness. By inputting the hardness data obtained from the Taguchi L9 orthogonal array into a Matlab-implemented neural network, this study determined a hardness formula. Finally, using the inbuilt graphical user interface software of Matlab, a simple program was written that can be used to predict hardness and that could extensively reduce the costs and time associated with forging development.

scholarly journals Prototyping neural networks to evaluate the risk of adverse cardiovascular outcomes in the population

2021 ◽  
Vol 6 (4) ◽  
pp. 67-81
Author(s):  
L. A. Bogdanov ◽  
E. A. Komossky ◽  
V. V. Voronkova ◽  
D. E. Tolstosheev ◽  
G. V. Martsenyuk ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Risk Factors ◽  
Neural Networks ◽  
User Interface ◽  
Arterial Hypertension ◽  
Graphical User Interface ◽  
Cross Validation ◽  
Cardiovascular Outcomes ◽  
Artery Disease

Aim. To develop a neural network basis for the design of artificial intelligence software to predict adverse cardiovascular outcomes in the population.Materials and Methods. Neural networks were designed using the database of 1,525 participants of PURE (Prospective Urban Rural Epidemiology Study), an international, multi-center, prospective study investigating disease risk factors in the urban and rural areas. As this study is still ongoing, we analysed only baseline data, therefore switching prognosis and diagnosis task. Because of its leading prevalence among other cardiovascular diseases, arterial hypertension was selected as an adverse outcome. Neural networks were designed employing STATISTICA Automated Neural Networks (SANN) software, manually selected, cross-validated, and transferred to the original graphical user interface software.Results. Input risk factors were gender, age, place of residence, concomitant diseases (i.e., coronary artery disease, chronic heart failure, diabetes mellitus, chronic obstructive pulmonary disease, and asthma), active or passive smoking, regular use of medications, family history of arterial hypertension, coronary artery disease or stroke, heart rate, body mass index, fasting blood glucose and cholesterol, high- and low-density lipoprotein cholesterol, and serum creatinine levels. Our neural networks showed a moderate efficacy in the virtual diagnostics of arterial hypertension (84.5%, or 1,289 successfully predicted outcomes out of 1,525, area under the ROC curve = 0.88), with almost equal sensitivity (83.6%) and specificity (85.3%), and were successfully integrated into graphical user interface that is necessary for the development of the commercial prognostication software. Cross-validation of this neural network on bootstrapped samples of virtual patients demonstrated sensitivity of 82.7 – 84.7%, specificity of 84.5 – 87.3%, and area under the ROC curve of 0.88 – 0.89.Conclusion. The artificial intelligence prognostication software to predict adverse cardiovascular outcomes in the population can be developed by a combination of automated neural network generation and analysis followed by manual selection, cross-validation, and integration into graphical user interface.

scholarly journals AUTOMATIC IDENTIFICATION OF MINERALS IN IMAGES OF POLISHED SECTIONS

2021 ◽  
Vol XLIV-2/W1-2021 ◽  
pp. 113-118
Author(s):  
A. V. Khvostikov ◽  
D. M. Korshunov ◽  
A. S. Krylov ◽  
M. A. Boguslavskiy
Keyword(s):  
Neural Network ◽  
User Interface ◽  
Graphical User Interface ◽  
Software Tool ◽  
Semantic Segmentation ◽  
Automatic Analysis ◽  
Automatic Identification ◽  
Polished Section ◽  
High Quality ◽  
Mineral Associations

Abstract. Automatic identification of minerals in images of polished section is highly demanded in exploratory geology as it can provide a significant reduction in time spent in the study of ores and eliminate the factor of misdiagnosis of minerals. The development of algorithms for automatic analysis of images of polished sections makes it possible to create of a universal tool for comparing ores from different deposits, which is also much in demand. The main contribution of this paper can be summed up in three parts: i) creation of LumenStone dataset (https://imaging.cs.msu.ru/en/research/geology/lumenstone) which unites high-quality geological images of different mineral associations and provides pixel-level semantic segmentation masks, ii) development of CNN-based neural network for automatic identification of minerals in images of polished sections, iii) implementation of software tool with graphical user interface that can be used by expert geologists to perform an automatic analysis of polished sections images.

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