Approbation of the shearlet transformation method for visualization of pathological changes in the lungs on CT images for diagnosing COVID-19

2021 ◽  
Vol 3 ◽  
Author(s):  
А.S. Kents ◽  
◽  
Y.A. Hamad ◽  
K.V. Simonov
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Experimental Studies ◽  
Decomposition Methods ◽  
Transformation Method ◽  
Ct Images ◽  
Pathological Changes ◽  
Color Coding ◽  
Remote Diagnostics ◽  
Selection Of

Radiation diagnostics is a rapidly developing field of medicine which actively includes such concepts as artificial intelligence, computer vision and new methods of medical imaging. Given the urgency of the problem of the appearance of Covid-19 a methodology for processing, analyzing and interpreting CT images is proposed for the effective detection, texture analysis and visualization of pathological changes in the lungs with Covid-19. In the format of advances in AI and computer vision in diagnostics, combined in a new direction – radiomics which is based on the selection of a set of quantitative parameters of the pathology under study with the most accurate values of indicators (markers). Depending on the purpose of the medical research, the extracted features (markers) will differ. An analysis of textural features was carried out based on spectral decomposition methods (wavelet and shеarlet transform of images) with their contrasting with color coding. This approach makes it possible to more accurately assess the quantitative characteristics of the identified changes. As a result of experimental studies a presentation was formed for a medical specialist, followed by a final X-ray diagnostic conclusion. The study was carried out within the framework of the grant «Methods of artificial intelligence and computer vision to improve the accuracy of remote diagnostics of respiratory diseases in the northern group of regions of the Krasnoyarsk Territory» with financial support from the Krasnoyarsk Regional Fund for the Support of Scientific and Scientific and Technical Activities.

Methods for interpreting pathological changes in the lun gs using computer tomography for diagnosing COVID-19

2021 ◽  
Vol 1 ◽  
pp. 14-23
Author(s):  
Konstantin Simonov ◽  
◽  
Anzhelika Kents ◽  
Yousif Hamad ◽  
Alexey Kruglyakov
Keyword(s):  
Geometric Analysis ◽  
Threshold Value ◽  
Clinical State ◽  
Pathological Changes ◽  
Color Coding ◽  
Remote Diagnostics ◽  
Algorithmic Support ◽  
Diagnostic Report ◽  
Combination Of Methods ◽  
Selection Of

Сomputed tomography of the lungs has been the most common diagnostic procedure aimed at detection of the pathological changes associated with COVID-19. The study is aimed at the use of the developed algorithmic support in combination with texture (geometric) analysis to highlight a number of indicators characterizing the clinical state of the object of interest. Processing is aimed at the solution of a number of diagnostic tasks: highlighting and contrasting the objects of interest, taking into account the color coding. Further, an assessment is performed according to the appropriate criteria in order to find out the nature of the changes and increase both the visualization of pathological changes and the accuracy of the X-ray diagnostic report. For these purposes, it is proposed to use preprocessing algorithms for a series of images in dynamics. Segmentation of the lungs and areas of possible pathology are performed using wavelet transform and Otsu threshold value. Delta-maps and maps obtained using Shearlet transform with contrasting color coding are used as a means of visualization and selection of features (markers). The analysis of the experimental and clinical material carried out in the work shows the effectiveness of the proposed combination of methods for studying of the variability of the internal geometric features (markers) of the object of interest in the CT images. The study was carried out within the framework of the grant «Methods of artificial intelligence and computer vision to improve the accuracy of remote diagnostics of respiratory diseases in the northern group of regions of the Krasnoyarsk Territory» with financial support from the Krasnoyarsk Regional Fund for the Support of Scientific and Scientific and Technical Activities.

scholarly journals Shelf Auditing Based on Image Classification Using Semi-Supervised Deep Learning to Increase On-Shelf Availability in Grocery Stores

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 327
Author(s):  
Ramiz Yilmazer ◽  
Derya Birant
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Experimental Studies ◽  
Grocery Stores ◽  
Software Application ◽  
Explainable Artificial Intelligence ◽  
Key Factor ◽  
First Time

Providing high on-shelf availability (OSA) is a key factor to increase profits in grocery stores. Recently, there has been growing interest in computer vision approaches to monitor OSA. However, the largest and well-known computer vision datasets do not provide annotation for store products, and therefore, a huge effort is needed to manually label products on images. To tackle the annotation problem, this paper proposes a new method that combines two concepts “semi-supervised learning” and “on-shelf availability” (SOSA) for the first time. Moreover, it is the first time that “You Only Look Once” (YOLOv4) deep learning architecture is used to monitor OSA. Furthermore, this paper provides the first demonstration of explainable artificial intelligence (XAI) on OSA. It presents a new software application, called SOSA XAI, with its capabilities and advantages. In the experimental studies, the effectiveness of the proposed SOSA method was verified on image datasets, with different ratios of labeled samples varying from 20% to 80%. The experimental results show that the proposed approach outperforms the existing approaches (RetinaNet and YOLOv3) in terms of accuracy.

scholarly journals METHODS AND MODELS FOR TEXTURE ANALYSIS OF LUNG PATHOLOGICAL CHANGES BASED ON COMPUTED TOMOGRAPHY FOR COVID-19 DIAGNOSIS

2021 ◽  
Vol XLIV-2/W1-2021 ◽  
pp. 99-105
Author(s):  
A. S. Kents ◽  
Y. A. Hamad ◽  
K. V. Simonov ◽  
A. G. Zotin
Keyword(s):  
Computed Tomography ◽  
Geometric Analysis ◽  
Threshold Value ◽  
Clinical State ◽  
Pathological Changes ◽  
Color Coding ◽  
Algorithmic Support ◽  
Diagnostic Report ◽  
Combination Of Methods ◽  
Selection Of

Abstract. In recent years computed tomography of the lungs has been the most common diagnostic procedure aimed at detection of the pathological changes associated with COVID-19. The study is aimed at the use of the developed algorithmic support in combination with texture (geometric) analysis to highlight a number of indicators characterizing the clinical state of the object of interest. Processing is aimed at the solution of a number of diagnostic tasks such as highlighting and contrasting the objects of interest, taking into account the color coding. Further, an assessment is performed according to the appropriate criteria in order to find out the nature of the changes and increase both the visualization of pathological changes and the accuracy of the X-ray diagnostic report. For these purposes, it is proposed to use preprocessing algorithms for a series of images in dynamics. Segmentation of the lungs and areas of possible pathology are performed using wavelet transform and Otsu threshold value. Delta-maps and maps obtained using Shearlet transform with contrasting color coding are used as a means of visualization and selection of features (markers). The analysis of the experimental and clinical material carried out in the work shows the effectiveness of the proposed combination of methods for studying of the variability of the internal geometric features (markers) of the object of interest in the images.

EFFECT OF KAGOCEL PREPARATION ON THE GENERATIVE FUNCTION OF EXPERIMENTAL ANIMALS AT ITS ADMINISTRATION IN THE INFANTILE PERIOD OF THEIR DEVELOPMENT

2017 ◽  
pp. 42-49
Author(s):  
T. A. Borovskaya ◽  
M. E. Poluektova ◽  
A. V. Vychuzhanina ◽  
V. A. Mashanova ◽  
Yu. A. Shchemerova
Keyword(s):  
Toxic Effect ◽  
Reproductive System ◽  
Safety Profile ◽  
Therapeutic Dose ◽  
Antiviral Drug ◽  
Experimental Studies ◽  
Reproductive Age ◽  
Pathological Changes ◽  
Pediatric Practice ◽  
Experimental Animals

In experimental studies on rats (males, females) at their infantile stage starting from 10 days, a potential delayed toxic effect of the antiviral drug Kagocel on the reproductive system was studied. The drug was administered for 12 days in a therapeutic dose and at a dose 10-fold higher than the therapeutic one. Reproductive safety was estimated after animals reached the reproductive age (2.5 months). It was found out that the drug, when administered in both doses, does not decrease the fertility of animals, does not induce morphological and pathological changes in the sex glands, and does not have toxic effect on the offspring. Obtained data characterize Kagocel as a preparation with a wide reproductive safety profile and show that it can be used in pediatric practice for infants.

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

2020 ◽  
Vol 96 (3s) ◽  
pp. 585-588
Author(s):  
С.Е. Фролова ◽  
Е.С. Янакова
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computer Vision ◽  
High Performance ◽  
Systems On Chip ◽  
High Performance Systems ◽  
On Chip ◽  
Network Technologies ◽  
Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.

scholarly journals Functional Method of Designing Self-Compacting Concrete

Materials ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 267
Author(s):  
Tomasz Rudnicki
Keyword(s):  
Coarse Aggregate ◽  
Cement Mortar ◽  
Experimental Studies ◽  
Functional Method ◽  
Structural Viscosity ◽  
Self Compacting Concrete ◽  
Theoretical Assumptions ◽  
Concrete Mix ◽  
Selection Of ◽  
Double Coating

The article presents a new functional method of designing self-compacting concrete (SCC). The assumptions of the functional method of designing self-compacting concrete were based on the double coating assumption (i.e., it was assumed that the grains of coarse aggregate were coated with a layer of cement mortar, whereas the grains of sand with cement paste). The proposed method is composed of four stages, each of which is responsible for the selection of a different component of the concrete mix. The proposed designing procedure takes into consideration such a selection of the mineral skeleton in terms of the volumetric saturation of the mineral skeleton, which prevents the blocking of aggregate grains, and the designed liquid phase demonstrated high structural viscosity and low yield stress. The performed experimental studies, the simulation of the elaborated mathematical model fully allowed for the verification of the theoretical assumptions that are the basis for the development of the method of designing self-compacting concrete.

Simulation of public art communication in colleges based on smart cloud platform and artificial intelligence algorithm

2020 ◽  
pp. 1-11
Author(s):  
Zhang Yingying
Keyword(s):  
Artificial Intelligence ◽  
Public Art ◽  
Optimal Path ◽  
Colleges And Universities ◽  
Cloud Platform ◽  
Current Transmission ◽  
Artificial Intelligence Algorithm ◽  
Offset Coefficient ◽  
Network Status ◽  
Selection Of

Public art communication in colleges and universities needs to be launched with the support of artificial intelligence systems. According to the current situation of public art communication in colleges and universities, this paper builds a smart cloud platform for public art communication in colleges and universities with the support of artificial intelligence algorithms. Moreover, this paper introduces the bandwidth offset coefficient to judge the change of network throughput, introduces the slice download rate difference to first judge the consistency change trend of bandwidth, and then further proposes the calculation method of bandwidth prediction value by situation. In addition, this paper proposes a flexible transmission mechanism based on smart collaborative networks. Through in-depth perception of network status and component behavior, this mechanism implements the selection of the optimal path in the network according to the current network status and user service requirements to complete the transmission of service resources. If the current transmission path fails, the mechanism should ensure the continuity and reliability of the service. The research results show that the system constructed in this paper has good performance and can be applied to practice.

scholarly journals Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3389
Author(s):  
Marcin Kamiński ◽  
Krzysztof Szabat
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Dynamic Properties ◽  
Experimental Studies ◽  
Drive System ◽  
Particle Swarm Optimizer ◽  
The Neural Network ◽  
Control Concept ◽  
Constant Coefficients ◽  
Selection Of

This paper presents issues related to the adaptive control of the drive system with an elastic clutch connecting the main motor and the load machine. Firstly, the problems and the main algorithms often implemented for the mentioned object are analyzed. Then, the control concept based on the RNN (recurrent neural network) for the drive system with the flexible coupling is thoroughly described. For this purpose, an adaptive model inspired by the Elman model is selected, which is related to internal feedback in the neural network. The indicated feature improves the processing of dynamic signals. During the design process, for the selection of constant coefficients of the controller, the PSO (particle swarm optimizer) is applied. Moreover, in order to obtain better dynamic properties and improve work in real conditions, one model based on the ADALINE (adaptive linear neuron) is introduced into the structure. Details of the algorithm used for the weights’ adaptation are presented (including stability analysis) to perform the shaft torque signal filtering. The effectiveness of the proposed approach is examined through simulation and experimental studies.

Constraints on the accuracy of messenger RNA movement

1985 ◽  
Vol 18 (4) ◽  
pp. 423-450 ◽  
Author(s):  
C. G. Kurland ◽  
Måns Ehrenberg
Keyword(s):  
Messenger Rna ◽  
Experimental Studies ◽  
Physical Problem ◽  
Translational Accuracy ◽  
Trna Species ◽  
Selection Of

SUMMARYTheoretical as well as experimental studies of translational accuracy have most often been concerned with the selection of aminoacyl-tRNA by codon-programmed ribosomes. The selection of the successive codons on the mRNA has received much less attention, probably because it represents both conceptually and experimentally, a much more demanding physical problem. Nevertheless, it would seem that errors in the selection of the codon are potentially much more destructive than errors in selection of aminoacyl-tRNA species. This can be appreciated from the following.

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