Sürdürülebilir Kalkınma Amaçları
Yazarlar
Yayınlar
- MakaleAçık Erişim2025Scopus SJRQ1Deep 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.
AtıfScopus6WoS1OpenAlex6
İncele - MakaleAçık Erişim2024Scopus SJRQ1A new hybrid approach for grapevine leaves recognition based on ESRGAN data augmentation and GASVM feature selection
Doğan, Gürkan; Imak, Andaç; Ergen, Burhan; Sengur, Abdulkadir
Grapevine leaf is a commodity that is collected only once a year and has a high return on investment due to its export. However, only certain types of grapevine leaves are consumed. Therefore, it is extremely important to distinguish the types of grapevine leaves. In particular, performing this process automatically on industrial machines will reduce human errors, workload, and thus cost. In this study, a new hybrid approach based on a convolutional neural network is proposed that can automatically distinguish the types of grapevine leaves. In the proposed approach, firstly, the overfitting of network models is prevented by applying data augmentation techniques. Second, new synthetic images were created with the ESRGAN technique to obtain detailed texture information. Third, the top blocks of the MobileNetV2 and VGG19 CNN models were replaced with the newly designed top block, effectively extracting features with the data. Fourthly, the GASVM algorithm was adapted and used to create a subset of the features to eliminate the ineffective and unimportant ones from the obtained features. Finally, SVM classification was performed with the feature subset consisting of 314 features, and approximately 2% higher accuracy and MCC score were obtained compared to the approaches in the literature.
AtıfScopus8WoS6OpenAlex9İncele - MakaleAçık Erişim2023A new approach based on convolutional neural network and feature selection for recognizing vehicle types
Doğan, Gürkan; Ergen, Burhan
The number of vehicles used in traffic life has reached enormous dimensions today. The increase in the number of vehicles day by day causes some traffic problems along with it; such as traffic congestion, accidents, pollution, and safety. To overcome all these problems, convolutional neural networks (CNN) methods are one of the trend methods used in recent years due to their success. In this study, a new approach is proposed to use this power of CNN in low-power devices. First of all, MobileNetv1, MobileNetv2, and NASNetMobile models were optimized to increase accuracy performance. Then, an approach is proposed in which these optimized mobile CNN approaches are used only as feature extractors, and methods such as combining, selecting, and classifying the obtained features are used together. As a result of the classification made with this approach, the classification accuracy has increased by approximately 5%.
AtıfScopus10OpenAlex12İncele - MakaleAçık Erişim2013Scopus SJRQ1Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines
Eristi, Huseyin; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup
In this paper, a new optimal feature selection based power quality event recognition system is proposed for the classification of power quality events. While Apriori algorithm is capable of processing categorical data, an effective feature vector, which represents distinctive features of digital power quality event data, has been obtained by means of the proposed k-means based Apriori algorithm feature selection approach. The proposed k-means based Apriori algorithm feature selection approach is presented with a power quality event recognition system. In the power quality event recognition system, normalization and segmentation processes have been applied to three-phase event voltage signals. Using 9-level multiresolution analysis, wavelet transform coefficients of the event signals have been obtained. By applying nine different feature extraction processes to these coefficients, a 90 dimensional feature vector belonging to three-phase event voltage signals has been extracted. Optimal feature vector has been obtained by applying the k-means based Apriori algorithm feature selection approach to the obtained feature vector, which has been applied as the last step to the input of the least squares support vector machine classifier and recognition performance results have been obtained. Real power quality event data have been used to evaluate the performance of the proposed feature selection approach and power quality event recognition system. According to the results, the proposed k-means based Apriori algorithm feature selection approach and power quality event recognition system are efficient, reliable and applicable and classify three-phase event types with a high degree of accuracy. (C) 2013 Elsevier Ltd. All rights reserved.
AtıfWoS98OpenAlex131İncele - MakaleYalnız künye2012Scopus SJRQ3The Feature Selection based Power Quality Event Classification using Wavelet Transform and Logistic Model Tree
Eristi, Huseyin; Demir, Yakup
This paper presents a new power quality event classification technique using wavelet transform and logistic model tree. The proposed method uses the samples of three cycle duration of three line voltage of power quality events. The features of these samples are obtained by using the wavelet transform and a few different feature extraction techniques. The sequential forward selection method based a feature selection process is done to ensure good classification accuracy by selecting 20 better features from all 90 features generated from the wavelet transform coefficients. The obtained features are used to train a single logistic model tree. The feasibility of the proposed algorithm has been tested using real life power quality events. The result indicates that the feature selection based proposed method reliably classifies all types of power quality events with high accuracy.
AtıfWoS7İncele