scholarly journals APPLICATION OF NEURAL NETWORKS IN MEDICAL DIAGNOSTICS

2021 ◽  
pp. 40-51
Author(s):  
V. A. Sapozhkov ◽  
O. N. Budadin ◽  
A. S. Churilova ◽  
B. F. Falkov ◽  
Zh. Yu. Sapozhkova
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Clinical Laboratory ◽  
Medical Diagnostics ◽  
Automation System ◽  
Laboratory Examination ◽  
Artificial Neural ◽  
Personalized Approach ◽  
Diagnostic Outcomes

This article discusses the possibilities of application of artificial neural networks to solve problems of increasing the diagnostic outcomes in clinical laboratory examination. High diagnostic sensitivity (96 %) and diagnostic accuracy (89.5 %) of the results were shown on a large amount of cellular material digitized by artificial intelligence microscopy automation system like the Vision Cyto Pap. The high resolution and sharpness of digital slides, the mode of viewing objects (cells) in the gallery, quick access to the results of preclassification, all of these factors together allow to reduce turnearound time in more than 2.5 times reducing disadvantages of the microscopy.<br>Application of artificial neural networks does not substitute a doctor’s skills. The role in validation of reports eligible only for cytopathologist. This concept indicates a carefully approach for staff working with a microscope, respectful attitude to them professional skills, and highlights a personalized approach to patients.

Real-Time Recoloring Ishihara Plates Using Artificial Neural Networks for Helping Colorblind People

2020 ◽  
pp. 138-156
Author(s):  
Martín Montes Rivera ◽  
Alejandro Padilla ◽  
Juana Canul-Reich ◽  
Julio Ponce
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Input Data ◽  
Traffic Signals ◽  
Educational Materials ◽  
Ishihara Test ◽  
Artificial Neural ◽  
Intelligence Technique

Vision sense is achieved using cells called rods (luminosity) and cones (color). Color perception is required when interacting with educational materials, industrial environments, traffic signals, among others, but colorblind people have difficulties perceiving colors. There are different tests for colorblindness like Ishihara plates test, which have numbers with colors that are confused with colorblindness. Advances in computer sciences produced digital assistants for colorblindness, but there are possibilities to improve them using artificial intelligence because its techniques have exhibited great results when classifying parameters. This chapter proposes the use of artificial neural networks, an artificial intelligence technique, for learning the colors that colorblind people cannot distinguish well by using as input data the Ishihara plates and recoloring the image by increasing its brightness. Results are tested with a real colorblind people who successfully pass the Ishihara test.

scholarly journals Application of ANN in Milling Process: A Review

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Salah Al-Zubaidi ◽  
Jaharah A. Ghani ◽  
Che Hassan Che Haron
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nonlinear Problems ◽  
Milling Process ◽  
Machining Processes ◽  
Traditional Methods ◽  
Artificial Neural

In recent years the trends were towards modeling of machining using artificial intelligence. ANN is considered one of the important methods of artificial intelligence in the modeling of nonlinear problems like machining processes. Artificial neural networks show good capability in prediction and optimization of machining processes compared with traditional methods. In view of the importance of artificial neural networks in machining, this paper is an attempt to review the previous studies and investigations on the application of artificial neural networks in the milling process for the last decade.

scholarly journals Artificial Intelligence to Analyze the Cortical Thickness Through Age

2021 ◽  
Vol 4 ◽  
Author(s):  
Sergio Ledesma ◽  
Mario-Alberto Ibarra-Manzano ◽  
Dora-Luz Almanza-Ojeda ◽  
Pascal Fallavollita ◽  
Jason Steffener
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Left Hemisphere ◽  
Cortical Thickness ◽  
Right Hemisphere ◽  
Unseen Data ◽  
Artificial Neural ◽  
The Right ◽  
The Brain

In this study, Artificial Intelligence was used to analyze a dataset containing the cortical thickness from 1,100 healthy individuals. This dataset had the cortical thickness from 31 regions in the left hemisphere of the brain as well as from 31 regions in the right hemisphere. Then, 62 artificial neural networks were trained and validated to estimate the number of neurons in the hidden layer. These neural networks were used to create a model for the cortical thickness through age for each region in the brain. Using the artificial neural networks and kernels with seven points, numerical differentiation was used to compute the derivative of the cortical thickness with respect to age. The derivative was computed to estimate the cortical thickness speed. Finally, color bands were created for each region in the brain to identify a positive derivative, that is, a part of life with an increase in cortical thickness. Likewise, the color bands were used to identify a negative derivative, that is, a lifetime period with a cortical thickness reduction. Regions of the brain with similar derivatives were organized and displayed in clusters. Computer simulations showed that some regions exhibit abrupt changes in cortical thickness at specific periods of life. The simulations also illustrated that some regions in the left hemisphere do not follow the pattern of the same region in the right hemisphere. Finally, it was concluded that each region in the brain must be dynamically modeled. One advantage of using artificial neural networks is that they can learn and model non-linear and complex relationships. Also, artificial neural networks are immune to noise in the samples and can handle unseen data. That is, the models based on artificial neural networks can predict the behavior of samples that were not used for training. Furthermore, several studies have shown that artificial neural networks are capable of deriving information from imprecise data. Because of these advantages, the results obtained in this study by the artificial neural networks provide valuable information to analyze and model the cortical thickness.

scholarly journals The safety assessment of lifting equipment using artificial intelligence methods

2020 ◽  
Vol 224 ◽  
pp. 02018
Author(s):  
A Lyapin
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Safety Assessment ◽  
Data Sources ◽  
Artificial Intelligence Methods ◽  
Artificial Neural ◽  
Lifting Equipment

The article is devoted to the problem of using artificial neural networks to assess the risk of developing emergencies during the operation of lifting crane equipment. The data sources are telemetric measurements from microcontroller load limiters, as well as data from technical and daily inspections of equipment condition, in the last case the data may be fuzzy.

scholarly journals SIMULATION OF SPREADING FOREST FIRE UNDER NONSTATIONARITY AND UNCERTAINTY BY MEANS OF ARTIFICIAL INTELLIGENCE AND DEEP MACHINE LEARNING

2019 ◽  
pp. 97-107
Author(s):  
Tatiana Sergeevna Stankevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Forest Fire ◽  
Noise Removal ◽  
Fire Dynamics ◽  
Operational Forecast ◽  
Forecasting Models ◽  
Artificial Neural

The article describes the results of increasing the efficiency of operational forecast of the forest fire dynamics under nonstationarity and uncertainty through the fire dynamics modeling based on artificial intelligence and deep machine learning. To achieve the goal there were used following methods: system analysis method, theory of neural networks, deep machine learning method, method of operational forecasting of the forest fire dynamics, method of filtering images (modified median filter), MoSCoW method, and ER-method. In the course of study there have been developed forest fire forecasting models (models of treetop and ground fires) using artificial neural networks. The developed models solve the recognition and forecasting problems in order to determine the dynamics of forest fires in successive images and generating images with a forecast of fire spread. There has been given the general logical scheme of the proposed forest fire forecasting models involving five stages: stage 1 - data input; stage 2 - preprocessing of input data (format check; size check; noise removal); stage 3 - object recognition using Convolutional Neural Networks (recognition of fire data; recognition of data on environmental factors; recognition of data on the nature of forest plantations); stage 4 - development of forest fire forecasting; stage 5 - output of the generated image with the operational forecast. To build and train artificial neural networks, a visual forest fire dynamics database was proposed to use. The developed forest fire forecasting models are based on a tree of artificial neural networks in the form of an acyclic graph and identify dependencies between the dynamics of a forest fire and the characteristics of the external and internal environment.

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