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Öğe Automated speech emotion polarization for a distance education system based on orbital local binary pattern and an appropriate sub-band selection technique(Springer, 2023) Tanko, Dahiru; Demir, Fahrettin Burak; Dogan, Sengul; Sahin, Sakir Engin; Tuncer, TurkerThe distance education system was widely adopted during the Covid-19 pandemic by many institutions of learning. To measure the effectiveness of this system, it is essential to evaluate the performance of the lecturers. To this end, an automated speech emotion recognition model is a solution. This research aims to develop an accurate speech emotion recognition model that will check the lecturers/instructors' emotional state during lecture presentations. A new speech emotion dataset is collected, and an automated speech emotion recognition (SER) model is proposed to achieve this aim. The presented SER model contains three main phases, which are (i) feature extraction using multi-level discrete wavelet transform (DWT) and one-dimensional orbital local binary pattern (1D-OLBP), (ii) feature selection using neighborhood component analysis (NCA), (iii) classification using support vector machine (SVM) with ten-fold cross-validation. The proposed 1D-OLBP and NCA-based model is tested on the collected dataset, containing three emotional states with 7101 sound segments. The presented 1D-OLBP and NCA-based technique achieved a 93.40% classification accuracy using the proposed model on the new dataset. Moreover, the proposed architecture has been tested on the three publicly available speech emotion recognition datasets to highlight the general classification ability of this self-organized model. We reached over 70% classification accuracies for all three public datasets, and these results demonstrated the success of this model.Öğe Logistics-Singer Map-Based A New Chaotic Sara Optimization Method(Ieee, 2019) Demir, Fahrettin Burak; Tuncer, Turker; Kocamaz, Adnan FatihMany problems in daily life cannot be solved by using classical mathematical methods for having an infinite solution space. Therefore, it is recommended to use meta heuristic optimization methods that reduce the infinite solution space and based on the mathematical prediction principle in solving similar problems. In order to increase the performance of meta-heuristic optimization methods, number generator and chaotic maps are used. In this article, a new method of chaotic optimization has been developed and logistic and singer maps are used in the proposed optimization method. In order to test the performance of the proposed method, 6 different benchmarking functions and 3 different swarm-based optimization methods were used. The proposed method has produced more optimum results for all functions. In this way, it has been tried to prevent the integration of swarm optimization methods into local solutions.Öğe Lojistik-Gauss Harita Tabanlı Yeni Bir Kaotik Sürü Optimizasyon Yöntemi(Ali KARCI, 2019) Demir, Fahrettin Burak; Tuncer, Türker; Kocamaz, Adnan FatihGerçek hayattaki bazı problemler, klasik matematiksel yöntemler kullanarak çözülememektedir. Bu sebeple, bu problemlerin çözümünde genellikle meta-sezgisel optimizasyon yöntemleri kullanılmaktadır. Meta-sezgisel optimizasyon yöntemlerinin başarı düzeyini artırmak için kullanılan yöntemlerden birisi de kaotik haritalardır. Bu makalede yeni bir hibrit kaotik harita önerilmiş ve önerilen hibrit kaotik harita kullanılarak yeni bir kaotik optimizasyon yöntemi geliştirilmiştir. Önerilen hibrit kaotik harita, lojistik ve gauss haritalarını kullanmaktadır. Optimizasyon aşamasında parçacıkların değerlerini güncellemek için lojistik-gauss haritası kullanılmıştır. Önerilen optimizasyon yöntemi de lojistik-gauss tabanlı kaotik sürü algoritması olarak adlandırılmıştır. Yeni optimizasyon yönteminin performansını test etmek için ise literatürde sıklıkla kullanılan 6 sayısal kıyaslama fonksiyonu tercih edilmiştir. Ve elde edilen değerler yine literatürde yer alan 3 farklı sürü tabanlı optimizasyon yöntemiyle karşılaştırılmıştır. Önerilen yöntem hemen bütün fonksiyonlar için daha optimum sonuçlar üretmiştir. Bu sayede sürü optimizasyon yönteminin, tuzaklı lokal optimum değerlerden uzak durması sağlanmaya çalışılmıştır.
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 A survival classification method for hepatocellular carcinoma patients with chaotic Darcy optimization method based feature selection(Elsevier, 2020) Demir, Fahrettin Burak; Tuncer, Turker; Kocamaz, Adnan Fatih; Ertam, FatihSurvey is one of the crucial data retrieval methods in the literature. However, surveys often contain missing data and redundant features. Therefore, missing feature completion and feature selection have been widely used for knowledge extraction from surveys. We have a hypothesis to solve these two problems. To implement our hypothesis, a classification method is presented. Our proposed method consists of missing feature completion with a statistical moment (average) and feature selection using a novel swarm optimization method. Firstly, an average based supervised feature completion method is applied to Hepatocellular Carcinoma survey (HCC). The used HCC survey consists of 49 features. To select meaningful features, a chaotic Darcy optimization based feature selection method is presented and this method selects 31 most discriminative features of the completed HCC dataset. 0.9879 accuracy rate was obtained by using the proposed chaotic Darcy optimization-based HCC survival classification method.Öğe Uzaktan eğitimde öğretim elemanlarının ayakkabı bağı desenine dayalı konuşma duygu tanıma: ShoePat23(Elsevier Ltd, 2022) Tanko, Dahiru; Doğan, Şengül; Demir, Fahrettin Burak; Baygın, Mehmet; Şahin, Şakir Engin; Tuncer, TurkerBackground 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.
