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  • Küçük Resim Yok
    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, Fatih
    Survey 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.
  • Küçük Resim
    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, Turker
    Background 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.