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Öğe An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLPQ(Multidisciplinary Digital Publishing Institute (MDPI), 2022) Macin, Gulay; Taşçı, Burak; Taşçı, İrem; Faust, Oliver; Barua, Prabal Datta; Doğan, Şengül; Tuncer, Türker; Tan, Ru-San; Acharya, U. RajendraMultiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented in the literature. We developed a computationally lightweight machine learning model for MS diagnosis using a novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain MRI images that were prospectively acquired from 72 MS and 59 healthy subjects who attended the Ozal University Medical Faculty in 2021. The dataset was divided into three study subsets: axial images only (n = 1652), sagittal images only (n = 1775), and combined axial and sagittal images (n = 3427) of both MS and healthy classes. All images were resized to 224 × 224. Subsequently, the features were generated with a fixed-size patch-based (exemplar) feature extraction model based on local phase quantization (LPQ) with three-parameter settings. The resulting exemplar multiple parameters LPQ (ExMPLPQ) features were concatenated to form a large final feature vector. The top discriminative features were selected using iterative neighborhood component analysis (INCA). Finally, a k-nearest neighbor (kNN) algorithm, Fine kNN, was deployed to perform binary classification of the brain images into MS vs. healthy classes. The ExMPLPQ-based model attained 98.37%, 97.75%, and 98.22% binary classification accuracy rates for axial, sagittal, and hybrid datasets, respectively, using Fine kNN with 10-fold cross-validation. Furthermore, our model outperformed 19 established pre-trained deep learning models that were trained and tested with the same data. Unlike deep models, the ExMPLPQ-based model is computationally lightweight yet highly accurate. It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients.Öğe A chaotic optimization method based on logistic-sine map for numerical function optimization(Springer, 2020) Fahrettin Burak; Tuncer, Türker; Kocamaz, Adnan FatihMeta-heuristic optimization algorithms have been used to solve mathematically unidentifiable problems. The main purpose of the optimization methods on problem-solving is to choose the best solution in predefined conditions. To increase performance of the optimization methods, chaotic maps for instance Logistic, Singer, Sine, Tent, Chebyshev, Circle have been widely used in the literature. However, hybrid 1D chaotic maps have higher performance than the 1D chaotic maps. The hybrid chaotic maps have not been used in the optimization process. In this article, 1D hybrid chaotic map (logistic-sine map)-based novel swarm optimization method is proposed to achieve higher numerical results than other optimization methods. Logistic-sine map has good statistical result, and this advantage is used directly to calculate global optimum value in this study. The proposed algorithm is a swarm-based optimization algorithm, and the seed value of the logistic-sine map is generated from local best solutions to reach global optimum. In order to test the proposed hybrid chaotic map-based optimization method, widely used numerical benchmark functions are chosen. The proposed chaotic optimization method is also tested on compression spring design problem. Results and comparisons clearly show that the proposed chaotic optimization method is successful.Öğe Lojistik-Singer Harita Tabanlı Yeni Bir Kaotik Sürü Optimizasyon Yöntemi(IEEE (Institute of Electrical and Electronics Engineers), 2019) Demir, Fahrettin Burak; Tuncer, Türker; Kocamaz, Adnan FatihGünlük yaşamda pek çok problem, sonsuz çözüm uzayına sahip olduğu için klasik matematiksel yöntemler kullanılarak çözülememektedir. Bu nedenle, benzer problemlerin çözümünde, sonsuz çözüm uzayını küçülten ve matematiksel tahmin prensibine dayanan meta-sezgisel optimizasyon yöntemlerinin kullanılması önerilmektedir. Meta-sezgisel optimizasyon yöntemlerinin başarımını artırmak amacıyla sayı üreteci ve parametre belirleyici olarak kaotik haritalar kullanılmaktadır. Bu makalede yeni bir kaotik optimizasyon yöntemi geliştirilmiş ve önerilen optimizasyon yönteminde lojistik ve singer harita kullanılmıştır. Önerilen yöntemin performansını test etmek amacıyla literatürde sıkça kullanılan 6 farklı kıyaslama fonksiyonu ve 3 farklı sürü tabanlı optimizasyon yöntemi kullanılmıştır. Önerilen yöntem bütün fonksiyonlar için daha optimum sonuçlar üretmiştir. Ve bu sayede sürü optimizasyon yöntemlerinin lokal çözümlere takılması önlenmeye çalışılmıştır.Öğe New human identification method using Tietze graph-based feature generation(Springer, 2021) Tuncer, Türker; Aydemir, Emrah; Doğan, Şengül; Kobat, Mehmet Ali; Kaya, Muhammed Çağrı; Metin, SerkanElectrocardiogram (ECG) signals have been widely used for disease diagnosis. Besides, the ECG signals can be used for human identification. In this work, a Tietze pattern and neighborhood component analysis (NCA)-based human identification method is proposed. Our model uses two feature generation methods to extract both statistical and textural features. The Tietze graph is considered to create a pattern of the presented local graph structure (LGS). Both statistical and textural feature generations are not enough to present a high-accurate model. Therefore, a multileveled structure must be created. Tunable Q-factor wavelet transform (TQWT) is employed as a decomposer. The generated/extracted features in each level are merged, and the merged features are selected using NCA. The k-nearest neighbors (kNN) classifier is deployed on the chosen features in the classification phase to obtain predicted values. The recommended method was tested on two ECG signal corpora called ECGID and MIT-BIH. The model achieved 99.12% and 99.94% accuracies on the used ECGID and MIT-BIH datasets, respectively.Öğe Shoelace pattern-based speech emotion recognition of the lecturers in distance education: ShoePat23(Elsevier, 2022) Tanko,Dahiru; Doğan, Şengül; Demir, Fahrettin Burak; Baygın, Mehmet; Tuncer, TürkerBackground and objective: We are living in the pandemic age, and many educational institutions have shifted to a distance education system to ensure learning continuity while at the same time curtailing the spread of the Covid-19 virus. Automated speech emotion classification models can be used to measure the lecturer's performance during the lecture. Material and method: In this work, we collected a new lecturer's speech dataset to detect three emotions: positive, neutral, and negative. The dataset is divided into segments with a length of five seconds per segment. Each segment has been utilized as an observation and contains 9541 observations. To automatically classify these emotions, a hand-modeled learning approach is presented. This approach has a comprehensive feature extraction method. In the feature extraction, a shoelace-based local feature generator is introduced, called Shoelace Pattern. The suggested feature extractor generates features at a low level. To further improve the feature generation capability of the Shoelace Pattern, tunable q wavelet transform (TQWT) is used to create sub-bands. Shoelace Pattern generates features from raw speech and sub-bands, and the proposed feature extraction method selects the most suitable feature vectors. The top four feature vectors are selected and merged to obtain the final feature vector. By deploying neighborhood component analysis (NCA), we chose the most informative 512 features, and these features are classified using a support vector machine (SVM) classifier using 10-fold cross-validation. Results: The proposed learning model based on the shoelace pattern (ShoePat23) attained 94.97% and 96.41% classification accuracies on the collected speech databases consecutively. Conclusions: The findings demonstrate the success of the ShoePat23 on speech emotion recognition. Moreover, this model has been used in the distance education system to detect the performance of the lecturers
