Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2026Scopus SJRQ3/r/munzur/892
  Analysis of Visible Light Communication Performance in LOS Propagation Model with PSO-Cosh-Based Filter

  Fethiye Merve, Kavak Imak; Fatmanur, Atar; Duygu, Kaya; Turgay, Kaya

  Due to the increasing data transfer in wireless communication technologies, Visible Light Communication (VLC), one of the branches of wireless communication technology, is also developing within this framework. VLC holds an advantage over other communication branches in terms of energy efficiency, high bandwidth, and reliability. In this context, research on VLC-based Light Fidelity (Li-Fi) technology has gained momentum in recent years. In systems built with this Li-Fi technology, data is transmitted through an LED bulb, which is used for illumination. In this study, to analyze the performance of systems where data transmission is provided by VLC, the capacity to improve the shape of the transmitted signal is investigated using systems designed with different filtering techniques under LOS channel conditions. The main objective of this study is to demonstrate the improving effect of the proposed PSO-Cosh-based model on the shape of the transmitted signal compared with traditional filtering methods. In this way, in addition to IIR digital filtering techniques, the analysis of the filter coefficients obtained by optimizing the Cosh window function with Particle Swarm Optimization (PSO) demonstrates the originality of the study.

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• MakaleAçık Erişim2025Scopus SJRQ1/r/munzur/1097
  Deep learning and optimization-based feature selection for fetal health classification using CTG data

  Kaya, Turgay; Kaya, Duygu; Atar, Fatmanur

  This study introduces a DL and metaheuristic optimization-based framework for fetal health assessment using cardiotocography (CTG) signals to mitigate maternal and neonatal mortality. One-dimensional CTG signals were transformed into 2D representations, and deep feature extraction was performed using AlexNet. Feature vectors FC6, FC7, and their combination were subjected to optimization via Whale Optimization Algorithm (WOA + DL) and War Strategy Optimization (WSO + DL), utilizing updated fitness functions tailored for feature selection. Experimental results with SVM classifiers demonstrated superior performance with FC6 (89.98 %) and WSO + DL (90.17 %). FC6 exhibited strong discriminative capacity, while FC7 contained semantically richer features. The concatenated FC6 + FC7 vector increased feature diversity. WSO + DL achieved optimal balance across classification accuracy, feature subset size, convergence rate, and overall performance metrics. The integration of DL and metaheuristic algorithms effectively isolated informative feature subsets, improving training efficiency, minimizing redundant/noisy data, reducing overfitting risk, and enhancing classification accuracy. Optimization method selection proved critical to overall model performance.

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  AtıfScopus6·WoS1·OpenAlex6

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