Yazar "Sengur, Abdulkadir" seçeneğine göre listele

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  • Küçük Resim Yok
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    A Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE
    (2022) ARI, Berna; Alçin, Ömer Faruk; Sengur, Abdulkadir
    The method is of great importance in systems that include machine learning and classification steps. As a result, academics are constantly working to improve the process. However, the data pertaining to the methodology's performance is equally as valuable as the methodology's creation. While the data is utilized to show the result of the modeling process, it is critical to consider the proper labeling of the data, the technique of acquisition, and the volume. Obtaining data in certain sectors, particularly medical fields, can be costly and time consuming. Thus, data augmenting via classical and synthetic methods has recently gained popularity. Our study uses synthetic data augmentation since it is newer, more efficient, and produces the desired effect. Our study's goal is to classify a data collection of lung sounds into four groups using data augmenting. Obtaining and standardizing the wavelet scatter transformation of each cycle of lung sounds, splitting the transformed data into test and training, augmenting and classifying the training data. In the augmenting stage, we utilized ELM-AE, then ELM-W-AE, with six wavelet functions (Gaussian, Morlet, Mexican, Shannon, Meyer, Ggw) added. The SVM and EBT classifiers improved performance by 4% and 3% in ELM-W-AE compared to the original structure.
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    Accurate detection of autism using Douglas-Peucker algorithm, sparse coding based feature mapping and convolutional neural network techniques with EEG signals
    (Elsevier B.V. All, 2022) Arı, Berna; Sobahi, Nebras; Alçin, Ömer Faruk; Sengur, Abdulkadir; Acharya, U.Rajendra
    Autism Spectrum Disorders (ASD) is a collection of complicated neurological disorders that first show in early childhood. Electroencephalogram (EEG) signals are widely used to record the electrical activities of the brain. Manual screening is prone to human errors, tedious, and time-consuming. Hence, a novel automated method involving the Douglas-Peucker (DP) algorithm, sparse coding-based feature mapping approach, and deep convolutional neural networks (CNNs) is employed to detect ASD using EEG recordings. Initially, the DP algorithm is used for each channel to reduce the number of samples without degradation of the EEG signal. Then, the EEG rhythms are extracted by using the wavelet transform. The EEG rhythms are coded by using the sparse representation. The matching pursuit algorithm is used for sparse coding of the EEG rhythms. The sparse coded rhythms are segmented into 8 bits length and then converted to decimal numbers. An image is formed by concatenating the histograms of the decimated rhythm signals. Extreme learning machines (ELM)-based autoencoders (AE) are employed at a data augmentation step. After data augmentation, the ASD and healthy EEG signals are classified using pre-trained deep CNN models. Our proposed method yielded an accuracy of 98.88%, the sensitivity of 100% and specificity of 96.4%, and the F1-score of 99.19% in the detection of ASD automatically. Our developed model is ready to be tested with more EEG signals before its clinical application.
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    Deep Learning Forecasting Model for Market Demand of Electric Vehicles
    (Mdpi, 2024) Simsek, Ahmed Ihsan; Koc, Erdinc; Tasdemir, Beste Desticioglu; Aksoz, Ahmet; Turkoglu, Muammer; Sengur, Abdulkadir
    The increasing demand for electric vehicles (EVs) requires accurate forecasting to support strategic decisions by manufacturers, policymakers, investors, and infrastructure developers. As EV adoption accelerates due to environmental concerns and technological advances, understanding and predicting this demand becomes critical. In light of these considerations, this study presents an innovative methodology for forecasting EV demand. This model, called EVs-PredNet, is developed using deep learning methods such as LSTM (Long Short-Term Memory) and CNNs (Convolutional Neural Networks). The model comprises convolutional, activation function, max pooling, LSTM, and dense layers. Experimental research has investigated four different categories of electric vehicles: battery electric vehicles (BEV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and all electric vehicles (ALL). Performance measures were calculated after conducting experimental studies to assess the model's ability to predict electric vehicle demand. When the performance measures (mean absolute error, root mean square error, mean squared error, R-Squared) of EVs-PredNet and machine learning regression methods are compared, the proposed model is more effective than the other forecasting methods. The experimental results demonstrate the effectiveness of the proposed approach in forecasting the electric vehicle demand. This model is considered to have significant application potential in assessing the adoption and demand of electric vehicles. This study aims to improve the reliability of forecasting future demand in the electric vehicle market and to develop relevant approaches.
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    Grafik Tablet Kullanılarak Makine Öğrenmesi Yardımı ile El Yazısından Cinsiyet Tespiti
    (Fırat University, 2020) Arı, Berna; Ucuz, İlknur; Arı, Ali; Özdemir, Filiz; Sengur, Abdulkadir
    Günlük hayatın bir rutini olan el yazısı; ruh hali, kişilik özellikleri ve var olan bazı hastalıklar hakkında ipuçları vermektedir. Bunun yanında adli tıp, tıp ve arkeoloji gibi birçok disiplin el yazısını kendi alanlarına yönelik çalışmalarda sıkça kullanmaktadırlar. Örneğin adli tıp; bazı vakaların aydınlatılmasında el yazısından yaş aralığı ve hangi elin kullanıldığı gibi bilgilere ulaşabilmektedir. Bu çalışmada, el yazısından cinsiyet tespiti yapan bir sistem önerilmiştir. Önerilen sistem el yazısından, el yazısını karakterize edecek bir dizi öznitelik çıkarıp bu öznitelikleri makine öğrenmesi teknikleri kullanarak cinsiyete göre sınıflandırmıştır. El yazılarının kaydedildiği tabletle hem kalemin tablete temas halindeki durumunda hem de harfler ve kelimeler arası geçişte kalemin havada izlediği eğri hareketlerinden öznitelik çıkarılmıştır. Bu öznitelikler sırası ile kalem hızı, ivmesi, yazarken oluşan sarsıntı hareketleri, eğim açısı, yazıdaki kavislenmeler, kalemin havada kalma oranı, kalemin yaptığı basınç değeri ve kalemin yükseklik açısıdır. Sınıflandırıcı olarak da Karar Ağaçları (KA), Naive Bayes (NB), Destek Vektör Makineler (DVM) ve k-en Yakın Komşu (k-EYK) yaklaşımları kullanılmıştır. Deneysel çalışmalarda kullanılan veri setinde toplam 410 örnek mevcut olup, deneysel çalışmaların başarımları doğruluk kriteri ile değerlendirilmiştir. Elde edilen sonuçlara göre en iyi başarımın DVM ile elde edildiği ve doğruluk değerinin de %85,1 olduğu görülmüştür.
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    Novel multi-cube extreme learning machine with sparse attention and ridge regression technique for robust machine learning
    (Elsevier, 2026) Zirekgur, Merve; Karakaya, Baris; Sengur, Abdulkadir; Acharya, U. Rajendra
    This study introduces a learning framework designed to enhance the representational capacity and stability of single-layer feed-forward networks (SLFN) when modelling nonlinear and high-dimensional data. To this end, the proposed multi-cube unit with sparse attention and ridge regularisation (MCU-SAR) method integrates three complementary components: (i) a multi-cube unit (MCU) architecture that explicitly encodes higher-order feature interactions, (ii) a sparse attention mechanism that suppresses low-informative multiplicative terms, and (iii) a ridge-regularised extreme learning machine (ELM) output layer to improve generalisation. The proposed model is evaluated on 25 publicly available datasets, including 17 classification and 8 regression tasks, and benchmarked against 15 baseline methods comprising gradient-based optimisation techniques, support vector machines (SVM), and various ELM-based approaches. Performance comparisons are conducted using the Friedman test. MCU-SAR demonstrates consistently strong performance, ranking first on the majority of the 25 benchmark datasets and achieving competitive accuracy in classification as well as low error levels in regression tasks, with all results supported by statistically significant p-values. These results demonstrate that the proposed framework provides a scalable, generalisable, and computationally efficient solution for both classification and regression problems, offering robust performance on engineering-oriented real-world datasets.
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    Wavelet ELM-AE Based Data Augmentation and Deep Learning for Efficient Emotion Recognition Using EEG Recordings
    (Ieee-Inst Electrical Electronics Engineers Inc, 2022) Ari, Berna; Siddique, Kamran; Alcin, Omer Faruk; Aslan, Muzaffer; Sengur, Abdulkadir; Mehmood, Raja Majid
    Emotion perception is critical for behavior prediction. There are many ways to capture emotional states by observing the body and copying actions. Physiological markers such as electroencephalography (EEG) have gained popularity, as facial emotions may not always adequately convey true emotion. This study has two main aims. The first is to measure four emotion categories using deep learning architectures and EEG data. The second purpose is to increase the number of samples in the dataset. To this end, a novel data augmentation approach namely the Extreme Learning Machine Wavelet Auto Encoder (ELM-W-AE) is proposed for data augmentation. The proposed data augmentation approach is both simple and faster than the other synthetic data augmentation approaches. For deep architectures, large datasets are important for performance. For this reason, data multiplexing approaches with classical and synthetic methods have become popular recently. The proposed synthetic data augmentation is the ELM-W-AE because of its efficiency and detail reproduction. The ELM-AE structure uses wavelet activation functions such as Gaussian, groove gap waveguide (GGW), Mexican, Meyer, Morlet, and Shannon. Deep convolutional architectures classify EEG signals as images. EEG waves are scalograms using Continuous Wavelet Transform (CWT). The ResNet18 architecture recognizes emotions. The proposed technique uses GAMEEMO data collected during gameplay. Each of these states is represented in the GAMEEMO data collection. The visual data set created from the signal was divided into two groups 70% training and 30% testing. ResNet18 has been fine-tuned with augmented photos, training images only. It achieved 99.6% classification accuracy in tests. The proposed method is compared with the other approaches on the same dataset, and an approximately 22% performance improvement is achieved.