arXiv subjects

Dana Turlykozhayeva

Publications and source records attributed to Dana Turlykozhayeva.

4 recordsLinked to original sources

arXiv2025

The Role of Fractal Dimension in Wireless Mesh Network Performance

Wireless mesh networks (WMNs) depend on the spatial distribution of nodes, which directly influences connectivity, routing efficiency, and overall network performance. Conventional models typically assume uniform or random node placement, which inadequately represent the complex, hierarchical spatial patterns observed in practical deployments. In this study, we present a novel algorithm that constructs WMN topologies with tunable fractal dimensions, allowing precise control over spatial self-similarity. By systematically varying the fractal dimension, the algorithm generates network layouts spanning a continuum of spatial complexities, ranging from sparse fragmented clusters to dense, cohesive structures. Through NS-3 simulations, Key performance metrics including throughput, latency, jitter, and packet delivery ratio were evaluated across a range of fractal dimensions. Comparative evaluations against classical random, small-world, scale-free, grid and hierarchical tree networks models reveal that high-dimensional fractal topologies achieve enhanced resilience and throughput under equivalent conditions. These findings demonstrate the potential of fractal geometry as a design paradigm for scalable and efficient WMN architectures.

cs.NI↗

arXiv2025

Luminis Stellarum et Machina: Applications of Machine Learning in Light Curve Analysis

The rapid advancement of observational capabilities in astronomy has led to an exponential growth in the volume of light curve (LC) data, creating both opportunities and challenges for time-domain astronomy. Traditional analytical methods often struggle to fully extract the scientific value of these large and complex datasets. Machine learning (ML) algorithms are increasingly used for LC analysis, enabling classification, prediction, pattern discovery and anomaly detection. However, research in this area remains fragmented, with no comprehensive synthesis of how ML methods address the specific challenges of LC data. Key difficulties include class imbalance, noisy or sparse measurements, effective feature extraction, and limited interpretability of models. This lack of a unified overview makes it difficult for researchers to identify suitable approaches or recognize unresolved problems that require methodological advances. To address this gap, this survey systematically reviews ML techniques applied to LC analysis, outlining their principles and applications in tasks such as exoplanet detection, variable star classification and supernova identification. By clarifying the current state of the field and highlighting open challenges, this work provides guidance for future research and supports the effective integration of ML into astronomical big data studies.

astro-ph.IM↗

arXiv2025

Machine learning-based classification of variable stars using phase-folded light curves

Classifying variable stars is crucial for advancing our understanding of stellar evolution and dynamics. As large-scale surveys generate increasing volumes of light curve data, the demand for automated and reliable classification techniques continues to grow. Traditional methods often rely on manual feature extraction and selection, which can be labor-intensive and less effective for managing extensive datasets. In this study, we present a convolutional neural network (CNN)-based method for classifying variable stars using raw light curve data and their known periods. Our approach eliminates the need for manual feature extraction and preselected preprocessing steps. By applying phase-folding and interpolation to structure the light curves, the model learns variability patterns critical for accurate classification. Trained and evaluated on the All-Sky Automated Survey for Supernovae (ASAS-SN) dataset, our model achieves an average accuracy of 90% and an F1 score of 0.86 across six well-known classes of variable stars. The CNN effectively handles the diverse shapes and sampling cadences of light curves, offering a robust, automated, data-driven solution for classifying variable stars. This automated, data-driven method provides a robust solution for classifying variable stars, enabling the efficient analysis of large datasets from both current and future sky surveys.

astro-ph.SR↗

arXiv2024

Routing Algorithm for Software Defined Network Based on Boxcovering Algorithm

A routing algorithm is the most fundamental problem in complex network communication. In complex networks, the amount of computation increases as the number of nodes increases which reduces routing performance. In this paper, we propose a routing algorithm for software-defined networking (SDN) based on a box-covering (BC) algorithm. It is known that using the BC algorithm it is possible to increase performance in complex SDN. We partition the entire SDN network into subnets using three existing box-covering methods such as MEMB, GC, and CIEA, then we use Dijkstra\textquotesingle s algorithm to find the shortest path between subnets and within each subnet. We compared all box-covering algorithms and found that the GC algorithm has the highest performance for SDN routing.

cs.NI↗