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Abstract

Task-instructions: "How to develop your own intelligent cloud Eidos app»
E.V. Lutsenko
Task-instruction for developing your own intelligent cloud Eidos application
DOI:10.13140/RG.2.2.27946.44488,License:CC BY SA 4.0
No.
The content of the stage of work
1
Reading:http://lc.kubagro.ru/aidos/Presentation_Aidos-online.pdf
Downloadinghere:http://lc.kubagro.ru/aidos/_Aidos-X.htmand install the Eidos system on your computer.
2
We launch the Eidos system, in mode 1.3, install and master the basic laboratory work built into the full installation:LR-3.03. This lab is covered in great detail in many video lessons.
Then we study text mining applications (LR-3.02) spectral ASC-analysis of images (cloud Eidos application No.277):https://disk.yandex.ru/i/WoIb6aF4bTuA0Q.
At will, we study cloud-based Eidos applications, giving priority to newer ones, because they better reflect the capabilities of the current version of the Eidos system and are described according to a more advanced description template.
Links to video classes and works of Prof. E.V. Lutsenko:
at Perm National University:https://bigbluebutton.pstu.ru/b/w3y-2ir-ukd-bqn(2021)https://bigbluebutton.pstu.ru/b/3kc-n8a-gon-tjz(2022)
at Kuban State University and Kuban State Agrarian University:https://disk.yandex.ru/d/knISAD5qzV83Ng?w=1(2020-2022)
- links to the works of Prof. E.V. Lutsenko on various topics in the public domain:http://lc.kubagro.ru/aidos/index.htmandhttp://lc.kubagro.ru/aidos/_Aidos-X.htm
works on ASC-analysis of texts:http://lc.kubagro.ru/aidos/Works_on_ASK-analysis_of_texts.htm;
work on ASC-analysis of images:http://lc.kubagro.ru/aidos/Works_on_ASK-analysis_of_images.htm; work on scenario ASC-analysis:http://lc.kubagro.ru/aidos/Works_on_Scenario_ASC-analysis.htm;
page in Researchgate:https://www.researchgate.net/profile/Eugene-Lutsenko
3
Link:https://www.researchgate.net/profile/Eugene_Lutsenko/publicationswe study the publications of Prof. E.V. Lutsenko with a description of the applications of the Eidos system.
4
We are looking for a topic and initial data for our own intelligent cloud Eidos application: - the topic and content of the workdon't have to be very similarwith the names and content of intelligent applications already available in the Eidos
cloud:http://lc.kubagro.ru/aidos/WebAppls.htm; (this is allowed only if the quality of the solution of the problem and the quality of its description are significantly higher than in an earlier similar topic); - it is recommended to search for the
initial data on the sites: Kaggle and UCI, as well as in search engines on request: "Machine learning datasets»
http://archive.ics.uci.edu/ml/datasets.php
https://www.kaggle.com/competitions(priority to active topics)
https://www.kaggle.com/kernels
as well as links on the page:http://lc.kubagro.ru/aidos/p14.htm(below the table).
You can also use any other source data,not contradictorygenerally acceptedin Russiamoral and ethical standards and the current legislation of the Russian Federation.
Links to the best (according to the author) free online CSV=>XLS (XLSX) converters:
https://online-converting.ru/documents/csv-to-xls/(converts CSV files larger than 100 MB)
https://convertio.co/en/csv-xls/
https://onlineconvertfree.com/en/convert-format/csv-to-xls/
https://document.online-convert.com/en/convert/csv-to-excel
The SCV standard has not settled down, there are many different specific features in CSV files, so sometimes one converter is better, and sometimes another.
Source data file: Inp_data.xls, Inp_data.xlsx should beless than 10 MB, because larger filesare automatically deletedfrom the ftp server of the Eidos system.
Therefore, it is important to know and take into account that the same file in the XLSX standard is usually approximatelytwice smallerin size than XLS.
But it is better to take an even smaller amount of data (not megabytes, but hundreds or even tens of kilobytes), then the duration of the calculations will be more acceptable.
5
We show Prof. E.V. Lutsenko in class or send a link to their source of initial data and the data themselves for the application in the form of an Excel or CSV file in the program interface (API) 2.3.2.2 standard of the Eidos system and an
approximate topic on E-mail to Prof. E.V. Lutsenko:prof.lutsenko@gmail.comfor approval. The statement is possible only if the model turns out to be sufficiently reliable or at least reasonable.
Aftersubject approval can follow the following steps.
6
Describingthe created Eidos application, taking as a sample (i.e. as a description template) the Word file of one of the articles:
1. Lutsenko E.V. Automated system-cognitive analysis of the strength and direction of the influence of the morphological properties of tomatoes on the quantitative, qualitative, financial and economic results of their cultivation and the
degree of determination of these results in unheated greenhouses in the South of Russia / E.V. Lutsenko, R.A. Gish, E.K. Pechurina, S.S. Tsygikalo // Polythematic network electronic scientific journal of the Kuban State Agrarian University
(Scientific journal of KubGAU) [Electronic resource]. - Krasnodar: KubGAU, 2019. - No. 06 (150). pp. 79 129. IDA [article ID]: 1501906015. Access mode:http://ej.kubagro.ru/get.asp?id=7763&t=2, 3.188 c.p.l.
2. Thematic collections of publications on the application of ASC-analysis and the Eidos system in various subject areas:http://lc.kubagro.ru/aidos/_Aidos-X.htm#_Toc99666361
3. Template for describing a scientific study using ASC analysis and the Eidos system(IMRAD standard): Detailed example in word file:https://disk.yandex.ru/i/2sWbEH56egfvIA. TO AVOID TYPICAL ERRORS, IT IS
MANDATORY TO write the name of the application in mode 1.3. In subsection 3.2 of the description, you need to insert a table of initial data (or its fragment), a link to the source of this table on the Internet. In EACH SUB-
SECTION of the description template, starting with the description of the results and then screenshots of the screen forms of the corresponding modes of the Eidos system (Alt + PrScreen) and the tables generated by it, as well as
the text of their interpretation or explanation. The text describing the results of evaluating the reliability of models in mode 3.4 must correspond to the screen form of this mode. If the resulting screen forms are unreadable, then
use the image settings to make them readable. Carefully format the description: images on one page should be the same width.
IMPORTANT!!!Look carefully so that in the final description, if it is dedicated, for example, to processors or video cards, nothing is left about the genome,tomatoes, morphological and biochemical properties, yield, fat content, etc.
7
We show Prof. E.V. Lutsenko in class or send the initial data for the application in the form of an Excel file in the program interface (API) 2.3.2.2 standard of the Eidos system and a description of the application (files:inp_data.xls(x),
readme.doc(x), c:\Aidos-X\_2_3_2_2.arx) to the e-mail of Prof. E.V. Lutsenko:prof.lutsenko@gmail.comfor making a decision and, if it is positive, then for placing the created application and its description in the Eidos cloud, and only the
descriptions in ResearchGate and in the RSCI. There are two main criteria for accepting work: 1) the models I created based on your data match yours; 2) your description matches your data and your models based on it.
eight
The placement of the Eidos application in the cloud for students is carried out personally by Prof. E.V. Lutsenko. The application description can be placed in ResearchGate and in the RSCI only after they have been reviewed by Prof. E.V.
Lutsenko and approved by him. The placement of the application description in ResearchGate and in the RSCI is carried out by the student or co-author. To do this, he must register or already be registered with
ResearchGate:https://www.researchgate.net/, as well as inhttps://elibrary.ru/and system inSCIENCE INDEX, receive a SPIN code and conclude an agreement with the RSCI for an individual to place non-periodical publications in the
RSCI:https://elibrary.ru/projects/contracts/publisher/messages/messages.asp? See here for more details:http://lc.kubagro.ru/ResearchGate.doc.
nine
Evaluation of knowledge, skills and abilities acquired by students in the development of ASC-analysis and the Eidos system
Grade
Development and deployment of the Eidos application in:
The cost of the VTsSKI "Eidos" certificate confirming the
academic achievement in mastering the ASC-analysis and
the "Eidos" system (in rubles at the rate of USD, the
Central Bank of the Russian Federation) (optional*)
Links to sample certificates
ResearchGate
(template
description only)
RSCI (only
description by
template)
Fine
Yes
Yes
100
Recipient's choice template
Good
Yes
Not
fifty
Recipient's choice template
Satisfactorily
Not
Not
25
Recipient's choice template
According to the certification results
Not
Not
***
***
*This offer does not apply to students of those universities where the author works.
ten
If the student has not registered with ResearchGate (this requires a corporate email address from a research institute or university) and RSCI, then descriptions of cloud Eidos applications can be placed in ResearchGate
(https://www.researchgate.net/profile/Eugene_Lutsenko) as preprints with DOI assignment, and then will be placed in the RSCI (https://elibrary.ru/) as publications in an open archive, i.e. will be included in the list of the student's publications
and his portfolio. But for this it will be necessary to include Prof. E.V. Lutsenko as a co-author in the description of the application, because. Only their authors can post materials in these systems.
eleven
Literature: https://www.researchgate.net/profile/Eugene_Lutsenko/publications
12
On-line consultations of Prof. E.V. Lutsenko on all issues related to the creation and deployment of a cloud-based Eidos application:https://www.researchgate.net/profile/Eugene_Lutsenkoor by e-mail:prof.lutsenko@gmail.com
Databases required to describe the cloud-based Eidos application
Class_Sc.dbf
Classification scales
Opis_Sc.dbf
Descriptive scales
Classes.dbf
Classification scales and gradations
Attributes.dbf
Descriptive scales and gradations
EventsKO.dbf
Event base (training or training set)
Databases and output forms according to the significance of descriptive scales and gradations and the degree of determination of classification scales and gradations are formed in modes 3.7.2, 3.7.3, 3.7.4 and 3.7.5 of the Eidos system. In the same
modes, information about the names and location of the output databases is displayed at the end.
Mode 5.12 of the Eidos system converts all dbf files in the folder of the current application into xls filesthat open in MS Excel.
The current application is located along the path: ..\Aidos-X\AID_DATA\A0000001\System\.
In general, after the execution of any mode of the Eidos system, the databases generated by it will be at the beginning of the list of files, if you select sorting by creation time in the file manager.
Preprint
Full-text available
The monograph includes five chapters that describe the theo-retical and mathematical foundations of scenario and spectral au-tomated system-cognitive analysis (ASC-analysis) and provide detailed numerical examples of its application to forecasting in fi-nancial markets and image analysis. Designed for undergraduate, master's and graduate students, as well as teachers and developers in the field of artificial intelligence, all interested in this issue.
Preprint
Full-text available
The monograph includes five chapters, which describe the theoretical and mathematical foundations of scenario and spec-tral automated system-cognitive analysis (ASC-analysis) and provide detailed numerical examples of its application for forecasting in financial markets and image analysis. It is intended for undergraduate, graduate and postgraduate students, as well as teachers and developers in the field of artificial intelligence who are interested in this problem.
Preprint
Full-text available
The article solves the problem of identifying images of the American sign language. Images from the Kaggle portal are used as source data. To solve this problem, we have used the automated system-cognitive analysis (ASC-analysis) and its software tools – the intelligent system called "Eidos". In ASC-analysis, there are several options for image processing: by pixels, contours and spectra. These image processing options differ in the sources of information about the image and have different effectiveness in solving various tasks, depending on their specifics. Since monochrome gesture images are presented on the Kaggle portal, we cannot use spectral ASC-analysis. Therefore, the task can be solved using only pixel and contour ASC-analysis of images. We have also carried out a comparison of the reliability of solving the problem by these methods. The article considers possibilities of studying the object of modeling by studying its model in the ASC-analysis of images. A numerical example and detailed instructions for user actions in the Eidos system are given, which makes it possible to use this work for educational purposes
Preprint
Full-text available
In this paper, the actual problem of reliable quantitative forecasting of fraud risks for credit card transactions is posed, considered and solved. To solve the problem, real data from the Kaggle portal is used. Automated system-cognitive analysis (ASK-analysis) and its software tools are used, which is currently used as an intelligent system "Eidos". The object, subject, problem, hypothesis, purpose, method, research tasks are considered (research problems, a brief justification for choosing a research method, research tasks). The solution of the following research tasks is given. Task-1 Cognitive structuring and formalization of the subject area. Task-2. Synthesis and verification of models (synthesis of statistical and system-cognitive models (multiparametric typing), private knowledge criteria, model verification, model reliability criteria, frequency distributions of true and false positive and negative decisions in various models, comparison of the reliability of positive and negative decisions in the model with the probability of random guessing, assessment of the information power of the model, assessment of the share of the impact of transaction characteristics in the fraud risk determination system, synthesis of a random model based on real, comparison of the frequency distributions of true and false positive and negative solutions in real and random models, comparison of the reliability of positive and negative solutions in real and random models compared with random guessing, comparison of the information power of real and random models and evaluation of the ratio of the useful signal to noise). Task-3. The solution of the forecasting problem (system identification), integral criteria, what is an integral criterion and what is it needed for, the 1st integral criterion "Sum of knowledge", the 2nd integral criterion "Semantic resonance of knowledge", some mathematical properties of integral criteria, the solution of the identification and forecasting problem. Task-4. Solution of the decision-making problem (results of multiparameter class typing). Task-5. Solving the problem of studying the simulated subject area by studying its model (inverted SWOT diagrams of the values of transaction characteristics (semantic potentials), cluster-constructive analysis of classes, cluster-constructive analysis of the values of transaction characteristics, non-local neurons, non-local neural network, 3D-integral cognitive maps, 2D-integral cognitive maps of meaningful comparison of classes, 2D-integral cognitive maps of meaningful comparison of factor values, cognitive functions, the significance of transaction characteristics and their values, the degree of determinism of classes and classification scales). The entire presentation in the paper is based on a detailed numerical example that reveals all the listed aspects of solving the problem.
Preprint
Full-text available
This paper sets, considers and solves the actual problem of reliable forecasting of Google stock prices and their dynamics based on the characteristics of the financial markets. A theoretical and practical solution to this problem is proposed by using scenario automated system-cognitive analysis (scenario ASC-analysis) and its software tools – the intelligent system "Eidos". A detailed numerical example is given, based on the data of the Kaggl portal. As the analysis of the results of the numerical experiment shows, the solution of the tasks proposed and implemented in the Eidos system is quite effective, which allows us to reasonably assert that the goal of the work has been achieved, the problem has been solved. As a result of the work done, 3 statistical and 7 system-cognitive models were created using the Eidos system, in which generalized images of classes based on Google stock prices and their dynamics were formed directly on the basis of empirical data, the influence of financial market characteristics on these classes was studied, and, based on this, the problems of identification and forecasting, classification and research of the simulated subject area by studying its model were solved. You can get acquainted with all the models created in this article by installing the cloud Eidos application No. 295 in the 1.3 mode of the Eidos system. The system itself can be downloaded for free from the website of its author and developer at the link: http://lc.kubagro.ru/aidos/_Aidos-X.htm.
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