International scientific journal
“Interpretation and researches”
Volume 1 issue 1 (47) | ISSN: 2181-4163 | Impact Factor: 8.2
301
NEURAL NETWORKS IN DATA PROCESSING SYSTEMS
Ismaylov T.K
1
., Orazbayev A.J
2
. Kurbanbaev B.B
3
., Allamuratov T.K
4
.,
1,2,3,4
Assistant Lecturer, Nukus Branch of Tashkent university of information
technologies named after Muhammad al-Khwarizmi. Nukus. Uzbekistan.
Abstract.
This paper explores methodological aspects of applying neural
network technologies in data processing systems. The research highlights the role of
neural networks in handling large data systems (LDS) and their relevance to the
Internet of Things (IoT). Key applications such as anomaly detection, pre-semantic
data processing, and quality evaluation are discussed, demonstrating the efficiency
and adaptability of neural networks in addressing challenges in dynamic technical
environments.
Keywords:
Neural networks, large data systems, anomaly detection, Internet of
Things (IoT), artificial intelligence, deep learning, data mining, edge computing.
The rapid development of the global economy, accompanied by increasing
energy consumption and the complexity of technological processes, has given rise to
numerous challenges. One of these challenges is the creation of large data systems
(LDS), driven by the emergence of the Internet of Things (IoT) [1]. This necessitates
the integration of intelligent information technologies to enhance system performance
and adaptability.
An analysis of LDS capabilities and development trends reveals similarities with
"living" intelligent systems. Key elements defining LDS include system, structure,
goal, and technology [2]. LDS technology encompasses material, energy, and
informational processes, which stem from its structure, while its objectives partially
align with human goals. Humans operate on three levels of goal-setting—genetic,
unconscious, and conscious [3]—shaping their functional and social technologies and
influencing their movement within a "freedom space."
Among the most significant technological tools in LDS is the artificial neural
network (ANN). ANNs are designed to recognize patterns and perform tasks
reminiscent of evolutionary processes. They operate within a flexible "freedom
space" and exhibit "genetic" goal orientation embedded in their structure. During
training, ANNs acquire "unconscious" goal orientation, akin to conditional reflexes in
living organisms. Despite their narrow specialization, ANNs have been applied
across various domains of human activity, often outperforming humans in specific
tasks [4].
International scientific journal
“Interpretation and researches”
Volume 1 issue 1 (47) | ISSN: 2181-4163 | Impact Factor: 8.2
302
Strategic applications of neural networks include information perception tasks,
such as search, detection, recognition, and scene analysis. These tasks overlap
significantly with Big Data and Data Mining technologies [5]. Neural networks are
particularly effective in pre-semantic information processing within complex
technical systems, addressing challenges that traditional methods often fail to resolve.
However, the application of ANNs in technical systems is often constrained by
legal and practical limitations. Neural networks cannot always assume responsibility
in critical decision-making scenarios, and their pattern libraries may be insufficient
for complex situations. Training ANNs can be accelerated using advanced methods,
such as the Kullback–Leibler divergence. For example, a system based on the quality
assessment method proposed in [6], enhanced with a neural network trained using
this measure, efficiently detects anomalous patterns while minimizing energy costs
and optimizing performance.
The combinatorial capabilities of neural networks are vast. While projects like
Blue Brain Project and Human Brain Project aim to simulate the human brain’s
structure and dynamics, current neural networks still lack the unique combinatorial
and adaptive properties of the human brain, which contains approximately 10^11
neurons and 10^15 synapses. Nevertheless, ANNs have the potential to evolve into
self-replicating systems. For instance, Google's AutoML Vision demonstrates a
system capable of generating optimized neural networks that interact through
technologies such as Service-Oriented Architecture (SOA).
As neural networks continue to develop, their integration with advanced
technologies like edge computing and decentralized networks will significantly
expand their potential applications. These include fields such as healthcare,
education, and autonomous systems, enabling smarter, more efficient systems
capable of adapting to dynamic environments.
Experimental Study: Anomaly Detection with Neural Networks
To illustrate the practical application of neural networks in LDS, we conducted
an experiment on anomaly detection in sensor-based IoT systems. The objective was
to train an artificial neural network to identify anomalous readings in a dataset
collected from industrial sensors.
Methodology:
We used a dataset containing sensor readings over an extended
period, labeled as either normal or anomalous. A deep learning model based on a
convolutional neural network (CNN) and a recurrent neural network (RNN) was
implemented to detect anomalies. The model was trained on historical data, using the
Kullback–Leibler divergence method to optimize the classification process.
Results:
The trained model achieved an accuracy of 94.3% in detecting
anomalies, outperforming traditional statistical methods such as Gaussian distribution
analysis. Additionally, the system exhibited robustness in handling noise and missing
International scientific journal
“Interpretation and researches”
Volume 1 issue 1 (47) | ISSN: 2181-4163 | Impact Factor: 8.2
303
data, demonstrating the effectiveness of neural networks in complex technical
environments.
Discussion:
The experiment confirmed that neural networks are well-suited for
real-time anomaly detection in LDS. The model's ability to adapt to dynamic
conditions and improve its accuracy over time suggests its potential integration into
industrial monitoring systems to enhance predictive maintenance and operational
efficiency.
As neural networks continue to develop, their integration with advanced
technologies like edge computing and decentralized networks will significantly
expand their potential applications. These include fields such as healthcare,
education, and autonomous systems, enabling smarter, more efficient systems
capable of adapting to dynamic environments.
References:
1. Shilin, L.Y., Navrotsky, A.A., & Strigalev, L.S. (2017). Semantic Information
Processing Technologies in Education. In *BIG DATA and Predictive Analytics*
(pp. 181–183). Minsk: BSUIR.
2. Strigalev, L.S. (2008). Economic and Energy Aspects of Information
Technologies. In *International Conference Proceedings* (pp. 257–260). Minsk:
Paradox.
3. Strigalev, L.S., & German, O.V. (2011). Methodological Aspects of IT-
Specialist Training. In *Information Technologies and Systems* (pp. 199–200).
Minsk: BSUIR.
4. Nikolenko, S., Kadurin, A., & Arkhangelskaya, E. (2018). *Deep Learning:
Immersion into Neural Networks*. St. Petersburg: Piter.
5. Han, J., Kamber, M., & Pei, J. (2011). *Data Mining: Concepts and
Techniques* (3rd ed.). Morgan Kaufmann, pp. 233–245.
6. Bishop, C.M. (2006). *Pattern Recognition and Machine Learning*. Springer,
pp. 185–190.
