Big Data Processing Algorithms for Decision-Making System Accuracy Improvement in Telecommunication Systems Using AI
Students Name: Svidrak Ivan Mykolaiovych
Qualification Level: magister
Speciality: Information Communication Networks Design and Administration
Institute: Institute of Telecommunications, Radioelectronics and Electronic Engineering
Mode of Study: full
Academic Year: 2024-2025 н.р.
Language of Defence: ukrainian
Abstract: Modern communication systems, the growing demand for high-speed network services and volumes of transmitted data stimulate the active development of algorithms for processing huge data sets (big data) and their integration with artificial intelligence. This study examines big data processing algorithms to improve the accuracy of decision-making systems in communication networks, focusing on the key characteristics of big data such as volume, velocity, and diversity that define unique requirements for real-time data processing systems. The importance of big data processing in telecommunications lies in the need for effective resource management, prediction and anomaly detection, thereby reducing the risk of network failures and improving customer service. The use of artificial intelligence combined with machine learning can automate complex processes, which can help make companies more competitive. However, despite the numerous advantages, many challenges remain, such as ensuring information security and privacy. The study analyzed modern big data processing methods, such as machine and deep learning, as well as statistical data analysis methods that can efficiently process large amounts of information. In particular, data dimensionality reduction algorithms, classification and clustering, and regression techniques are studied to not only make accurate predictions, but also to extract key data attributes for further processing. The use of data regularization and clustering techniques helps to avoid overtraining the model, thereby increasing the accuracy of the model. Algorithms of clustering, classification, and regression open up new opportunities for data processing in communications and can significantly improve network performance and provide customers with personalized services [1-3]. Artificial intelligence and machine learning allow processing large volumes of data in real time, revealing hidden patterns, which is especially valuable for optimizing network resources and maintaining stable network operation. At the same time, despite the huge potential of artificial intelligence in communications, there are still problems with the quality and completeness of data, which affects the accuracy of the results obtained. Overtraining can lead to conflicting results in real-world settings, so it’s important to find a balance between automated processes and the human factor. Given the rapid development of communication technologies, improved models will be needed in the future to ensure compliance with ethical standards and information security. The experimental part of the study is based on a model of a distributed industrial Internet of Things system, showing how cloud computing and big data analytics can help improve production processes. Providing continuous network monitoring and real-time anomaly detection is critical to maintaining network security in the face of growing workloads. Many machine learning algorithms, such as convolutional neural networks, decision trees, and random forests, have been analyzed in experiments and provide high accuracy for detecting vulnerabilities in IoT networks. The use of built-in models and algorithms not only identifies threats, but also reduces the number of false positives, which is critical for the safety of industrial environments [4-6]. This comprehensive study demonstrates the effectiveness of using big data processing algorithms in communication to improve the accuracy and quality of decision-making. Big data and artificial intelligence offer unique opportunities to automate and optimize network resources, making companies more competitive and improving customer service. At the same time, maintaining high standards of data security and privacy, ensuring data quality, and balancing automation and the human factor remain important aspects. The combination of Industry 5.0 and IoT technologies with artificial intelligence contributes to the innovative development of communication networks and maintains stability amid dynamic changes. The study confirms the enormous potential for the implementation of new methods of data processing and analysis in the field of communications, ensuring the accuracy, efficiency and security of network operations. Study object - Big data processing algorithms in communication systems and their integration with artificial intelligence technologies to improve decision-making systems. Scope of research - Big data processing in the communications sector, with an emphasis on the application of machine learning and artificial intelligence methods to ensure the accuracy, efficiency, and security of telecommunication networks. Goal of research - Development and improvement of big data processing algorithms using artificial intelligence to enhance decision-making accuracy and speed in communication networks. Modern communication systems and the growing demand for high-speed network services drive the development of big data processing algorithms and their integration with AI. This study analyzes big data processing algorithms to improve the accuracy of decisionmaking systems in communication networks. Classification, clustering, and regression algorithms are used for forecasting and anomaly detection, enabling efficient network resource management, reducing the risk of failures, and enhancing customer service quality.