=Paper=
{{Paper
|id=Vol-3826/short30
|storemode=property
|title=Improving a machine learning method for an automated control system (short paper)
|pdfUrl=https://ceur-ws.org/Vol-3826/short30.pdf
|volume=Vol-3826
|authors=Viktoriia Zhebka,Oleksii Ananchenko,Kateryna Osadcha,Serhii Zhebka,Andrii Aronov
|dblpUrl=https://dblp.org/rec/conf/cpits/ZhebkaAOZA24
}}
==Improving a machine learning method for an automated control system (short paper)==
https://ceur-ws.org/Vol-3826/short30.pdf
Improving a machine learning method for an automated
control system ⋆
Viktoriia Zhebka1,†, Oleksii Ananchenko1,†, Kateryna Osadcha2,3,*,†, Serhii Zhebka1,†
and Andrii Aronov1,†
1
State University of Information and Communication Technologies, 7 Solomenskaya str., 03110 Kyiv, Ukraine
2
Norwegian University of Science and Technology, 1 Høgskoleringen, 7034 Trondheim, Norway
3
Bogdan Khmelnitsky Melitopol State Pedagogical University, 59 Naukove mistechko str., 69000 Zaporizhzhia, Ukraine
Abstract
The paper is devoted to the improvement of the automated learning management system by integrating the
metric proximal gradient method. Improving the automated learning management system helps to increase
the efficiency, quality, and safety of the educational process by automating routine tasks and implementing
individualized curricula. The paper discusses the use of machine learning to analyze student performance
and detect suspicious user activity, which increases the transparency and reliability of the system. The use
of the metric proximal gradient method ensures efficient solutions to optimization problems and increases
the adaptability of the model in a dynamic educational environment. Also the paper presents an improved
approach to automated learning management systems through the implementation of an advanced machine
learning method based on the metric proximal gradient algorithm. The research addresses key challenges
in educational process management, including system efficiency, quality assurance, and security
enhancement. The proposed method incorporates a specialized database for comprehensive event logging
and implements clustering and regression algorithms for student performance analysis. The improved
metric proximal gradient algorithm demonstrates effective convergence properties through diagonal step
sizing and non-monotonic linear search strategies. Results indicate that this approach provides enhanced
optimization capabilities for handling complex data structures and adapting to dynamic educational
environments. The implementation shows particular promise in personalizing educational routes,
optimizing curricula, and maintaining system security through automated anomaly detection.
Keywords
machine learning method, automated control system, metric proximal gradient, educational process 1
1. Introduction approach to education, contributing to better learning and
improved academic results [3].
Improving an automated learning management system The transparency and fairness of the educational
(ACS) is an important step towards increasing the process will also benefit from the improvement of the
efficiency, quality, and safety of the educational process. automated learning management system. Automating the
Automating routine administrative tasks, such as creating assessment and recording of student results reduces the risk
timetables, recording attendance, and generating reports, of human error and subjectivity, which helps build trust in
allows you to optimize resource management and reduce the system on the part of students and their parents. The
staff workload. This allows administrators to focus on more transparency of teachers’ and administrators’ actions
strategic tasks, such as improving curricula and managing provided by automated systems increases the level of
the quality of education [1, 2]. responsibility and openness in the educational environment
Improving the quality of the educational process is one of [4].
the key goals of improving the automated control system. The Improving data security is also an important aspect. In
introduction of machine learning technologies allows for more today’s cyber threat environment, it is necessary to
accurate tracking and analysis of student performance, which implement modern authentication and monitoring methods
helps to identify learning problems on time and provide the to protect the personal data of students and teachers [5, 6].
necessary support. Such innovations also allow for the creation This not only reduces the risk of information compromise
of individualized curricula, which provide a more personalized but also ensures compliance with regulatory data protection
requirements. In addition, the use of data for analysis and
CPITS-II 2024: Workshop on Cybersecurity Providing in Information 0000-0003-4051-1190 (V. Zhebka);
|and Telecommunication Systems II, October 26, 2024, Kyiv, Ukraine 0009-0005-3446-5994 (O. Ananchenko);
∗
Corresponding author. 0000-0003-0653-6423 (K. Osadcha);
†
These authors contributed equally. 0009-0007-4620-9888 (S. Zhebka);
viktoria_zhebka@ukr.net (V. Zhebka); 0009-0000-7868-8341 (A. Aronov)
ananchenko.oe@gmail.com (O. Ananchenko); © 2024 Copyright for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0).
katheryna.osadcha@ntnu.no (K. Osadcha);
szhebka@hotmail.com (S. Zhebka);
webx.ghost@gmail.com (A. Aronov)
CEUR
Workshop
ceur-ws.org
ISSN 1613-0073
372
Proceedings
informed decision-making helps the management of depending on the degree of deviation from the norm. With
educational institutions to identify trends, predict results, the help of classification algorithms, such events can be
and develop strategies for further development. automatically filtered and forwarded to the administrator
Improving the ACS also ensures adaptation to modern for further investigation. In critical cases, the system can
educational trends, such as distance learning, integration of automatically block access to the system from a suspicious
digital resources, and the use of interactive platforms [7]. device until the circumstances are clarified.
This allows educational institutions to remain relevant and The system also monitors the devices from which the
competitive in the market of educational services. In user usually accesses the system. If a new device appears
addition, improving the usability of the system contributes that has not been used before, the system can request
to the satisfaction of students, teachers, and administration, confirmation from the user or simply inform the
which is an important factor in creating a positive administrator to avoid unauthorized access.
educational environment [8–15]. Integration of machine learning into ACS systems
Thus, the improvement of the ACS is necessary to significantly increases the level of security and management
ensure an efficient, high-quality, and safe educational efficiency. The introduction of such technologies allows for
process that meets modern requirements and challenges. automated tracking of student progress, as well as timely
This not only improves the management and provision of detection and response to suspicious user activity, which
education but also contributes to the overall development of contributes to the overall reliability and safety of the
educational institutions in a dynamic world [16–19]. educational process.
Therefore, the purpose of the paper sis to improve the It is proposed to use the method of metric proximal
automated learning management system by introducing an gradient as a machine learning method.
advanced machine learning method [20–23]. The algorithm of the metric proximal gradient:
2. Research results 1. Select the starting point 𝑥 ∈ 𝑅 .
2. Update the metric Mn.
The automated control system (ACS) implements a
y n 1 x n M n f x n
1
specialized database to store logs of all events occurring in
the system. The logs contain information about user
authentication, changes in schedules, grades, documents, x n1 proxq ,Mn y n1
and other critical operations. Such a database provides high
1 2
detail and the ability to retrospectively analyze user actions.
arg min q x y n1 x n
An important aspect is the ability to sort logs by various x 2 M
parameters (user, subject, document, etc.), which allows the
administrator to obtain the necessary information for y y
n1 n
3. If 2 so, then the algorithm stops.
analysis and decision-making.
Otherwise, repeat step 2.
Machine learning (ML) in an automated learning
4. The study of the choice of algorithm step size to
management system can be effectively used to analyze
ensure fast convergence revealed that the diagonal
student performance. Using clustering and regression
step size is more effective than the scalar step size.
algorithms, the system can track the dynamics of the
However, it is necessary to develop clear rules for
academic performance of individual students or groups. For
determining the diagonal step size in convex
example, based on historical data on grades, attendance, and
optimization algorithms.
assignments, the system can predict the likelihood of
5. The standard proximal gradient method is
successful completion of a course or identify students who
guaranteed to converge in sufficiently small steps
need additional support.
1 , provided that f is L-smooth. In this case,
In addition, a ‘Decency’ or ‘Integrity’ rating system can
be introduced that automatically assigns ratings to students L
based on their behavior (completing assignments on time, without using the Lipschitz constant, it is possible
attending classes, etc.). Such ratings can serve as an to search over several rows with a return strategy.
additional motivational tool for students. The methods used in this case guarantee
Another important function of ML in an ACS system is convergence.
to detect suspicious user activity. For this purpose, anomaly
6. A non-monotonic linear search on a string allows
detection algorithms are used that can identify deviations
the objective function F(x) to grow between
from a typical user behavioral pattern. For example, if a user
iterations but leads to a possible decrease in its
who normally interacts with the ‘timetable’ and ‘grades’
value. Given the current iteration xn, the initial
tabs suddenly logs in from another device and immediately
metric Mn, and the potential next iteration xn + 1
accesses confidential documents such as ‘R&D’, the system
check whether the following (Mn, xn + 1) criterion is
generates an alarm.
satisfied.
The system can automatically determine the priority
level (e.g., critical, high, medium, low) for each incident,
373
Figure 1: Algorithm of the metric proximal gradient
1 The next step is to perform a return, changing the scale
F x n1 F n x n1 x n n
2
of the metric M with the coefficient 1 .
n
2 M
The next step is to analyze the convergence of the
where K LC 1 is the search parameter, and F k is found algorithm. Assume that the function f is L-smooth and
as follows: M n 0 . The algorithm of the variable metric proximal
F n max F x n , F x n1 ,....., F x
n min K LC , n 1
gradient with diagonal metric will have the following steps:
1. Set the parameters, starting points, and starting
metrics.
2. Calculate and by the formula.
374
Start
Calculate
Set parameters
Set the initial
points
Set the initial
metric
Calculate
Calculate
Calculate
Yes No
Return metric
Let's initialize
Calculate
Yes No
No Stop criterion satisfied?
Yes
Yes
No
Let's get the
results
End
Calculate
Figure 2: Improved metric proximal gradient algorithm
Set the parameters K ls 1, 1, 0 , BB
n
BB c n , y n
starting points x 0 , x1 R n , and starting metric
BB
n
2 , if BB1 BB 2
n n
M S
0 n
n 1 n
Calculate BB
n
and BB by the formula
n
BB1 BB 2 , in other cases
1 2
Initialize Mn according to the formula
375
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