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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Kartbayev, Amandyk
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Computer Science & IT Electrical & Electronics Engineering

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Optimizing loss functions for improved energy demand prediction in smart power grids Nussipova, Fariza; Rysbekov, Shynggys; Abdiakhmetova, Zukhra; Kartbayev, Amandyk
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3415-3426

Abstract

In this paper, our aim is to improve the accuracy and effectiveness of energy demand forecasting, particularly within modern electricity transmission systems and smart grid technology. To achieve this, we developed a hybrid approach that combines machine learning, representation learning, and other deep learning techniques. This approach is based on extracting essential features, including time-based attributes, identifiable trends, and optimal lags. The outcome of our investigation is the observation that triplet losses demonstrate remarkable accuracy, particularly when employed with a larger margin size and for longer prediction lengths. This finding signifies a substantial improvement in the precision and reliability of energy demand forecasting within modern electricity transmission systems. Our research not only improves predictive modeling in the power grid but also demonstrates the practical use of advanced analytics in addressing renewable energy integration challenges, refining energy demand forecasting for efficient management, system operation, and market analysis.
Advancing network security: a comparative research of machine learning techniques for intrusion detection Rysbekov, Shynggys; Aitbanov, Abylay; Abdiakhmetova, Zukhra; Kartbayev, Amandyk
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2271-2281

Abstract

In the current digital era, the advancement of network-based technologies has brought a surge in security vulnerabilities, necessitating complex and dynamic defense mechanisms. This paper explores the integration of machine learning techniques within intrusion detection systems (IDS) to tackle the intricacies of modern network threats. A detailed comparative analysis of various algorithms, including k-nearest neighbors (KNN), logistic regression, and perceptron neural networks, is conducted to evaluate their efficiency in detecting and classifying different types of network intrusions such as denial of service (DoS), probe, user to root (U2R), and remote to local (R2L). Utilizing the national software laboratory knowledge discovery and data mining (NSL-KDD) dataset, a standard in the field, the study examines the algorithms’ ability to identify complex patterns and anomalies indicative of security breaches. Principal component analysis is utilized to streamline the dataset into 20 principal components for data processing efficiency. Results indicate that the neural network model is particularly effective, demonstrating exceptional performance metrics across accuracy, precision, and recall in both training and testing phases, affirming its reliability and utility in IDS. The potential for hybrid models combining different machine learning (ML) strategies is also discussed, highlighting a path towards more robust and adaptable IDS solutions.
Strategic processor task allocation through game-theoretic modeling in distributed computing environments Telmanov, Merlan; Suchkov, Mikhail; Abdiakhmetova, Zukhra; Kartbayev, Amandyk
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9257

Abstract

This paper explores a game-theoretic model for task allocation in distributed systems, where processors with varying speeds and external load factors are considered strategic players. The goal is to understand the impact of processors' strategic behaviors on workload management and overall system efficiency, focusing on the attainment of a pure strategy Nash Equilibrium (NE). The research rigorously develops a formal mathematical model and validates it through extensive simulations, highlighting how NE ensures stability but may not always yield optimal system performance. The adaptive algorithms for dynamic task allocation are proposed to enhance efficiency in real-time processing environments. Results demonstrate that while NE provides stability, the adoption of optimal cooperative strategies significantly improves operational efficiency and reduces transaction costs. The findings contribute valuable insights into the strategic interactions within computational frameworks, offering guidelines for developing more efficient systems. This study not only advances the theoretical understanding of strategic task allocation but also has practical implications for system design and policy-making in areas such as cloud computing and traffic management.
Co-Authors Abdiakhmetova, Zukhra Aitbanov, Abylay Nussipova, Fariza Rysbekov, Shynggys Suchkov, Mikhail Telmanov, Merlan