Evaluating raw waveforms with deep learning frameworks for speech emotion recognition

Speech emotion recognition is a challenging task in speech processing field. For this reason, feature extraction process has a crucial importance to demonstrate and process the speech signals. In this work, we represent a model, which feeds raw audio files directly into the deep neural networks without any feature extraction stage for the recognition of emotions utilizing six different data sets, EMO-DB, RAVDESS, TESS, CREMA, SAVEE, and TESS+RAVDESS. To demonstrate the contribution of proposed model, the performance of traditional feature extraction techniques namely, mel-scale spectogram, mel-frequency cepstral coefficients, are blended with machine learning algorithms, ensemble learning methods, deep and hybrid deep learning techniques. Support vector machine, decision tree, naive Bayes, random forests models are evaluated as machine learning algorithms while majority voting and stacking methods are assessed as ensemble learning techniques. Moreover, convolutional neural networks, long short-term memory networks, and hybrid CNN- LSTM model are evaluated as deep learning techniques and compared with machine learning and ensemble learning methods. To demonstrate the effectiveness of proposed model, the comparison with state-of-the-art studies are carried out. Based on the experiment results, CNN model excels existent approaches with 95.86% of accuracy for TESS+RAVDESS data set using raw audio files, thence determining the new state-of-the-art. The proposed model performs 90.34% of accuracy for EMO-DB with CNN model, 90.42% of accuracy for RAVDESS with CNN model, 99.48% of accuracy for TESS with LSTM model, 69.72% of accuracy for CREMA with CNN model, 85.76% of accuracy for SAVEE with CNN model in speaker-independent audio categorization problems.Comment: 14 pages, 6 Figures, 8 Table

Higher-order smoothing: a novel semantic smoothing method for text classification

It is known that latent semantic indexing (LSI) takes advantage of implicit higher-order (or latent) structure in the association of terms and documents. Higher-order relations in LSI capture "latent semantics". These findings have inspired a novel Bayesian framework for classification named Higher-Order Naive Bayes (HONB), which was introduced previously, that can explicitly make use of these higher-order relations. In this paper, we present a novel semantic smoothing method named Higher-Order Smoothing (HOS) for the Naive Bayes algorithm. HOS is built on a similar graph based data representation of the HONB which allows semantics in higher-order paths to be exploited. We take the concept one step further in HOS and exploit the relationships between instances of different classes. As a result, we move beyond not only instance boundaries, but also class boundaries to exploit the latent information in higher-order paths. This approach improves the parameter estimation when dealing with insufficient labeled data. Results of our extensive experiments demonstrate the value of HOS on several benchmark datasets

Heart Disease Detection using Vision-Based Transformer Models from ECG Images

Heart disease, also known as cardiovascular disease, is a prevalent and critical medical condition characterized by the impairment of the heart and blood vessels, leading to various complications such as coronary artery disease, heart failure, and myocardial infarction. The timely and accurate detection of heart disease is of paramount importance in clinical practice. Early identification of individuals at risk enables proactive interventions, preventive measures, and personalized treatment strategies to mitigate the progression of the disease and reduce adverse outcomes. In recent years, the field of heart disease detection has witnessed notable advancements due to the integration of sophisticated technologies and computational approaches. These include machine learning algorithms, data mining techniques, and predictive modeling frameworks that leverage vast amounts of clinical and physiological data to improve diagnostic accuracy and risk stratification. In this work, we propose to detect heart disease from ECG images using cutting-edge technologies, namely vision transformer models. These models are Google-Vit, Microsoft-Beit, and Swin-Tiny. To the best of our knowledge, this is the initial endeavor concentrating on the detection of heart diseases through image-based ECG data by employing cuttingedge technologies namely, transformer models. To demonstrate the contribution of the proposed framework, the performance of vision transformer models are compared with state-of-the-art studies. Experiment results show that the proposed framework exhibits remarkable classification results

Metinsel veri madenciliği için anlamsal yarı-eğitimli algoritmaların geliştirilmesi

Ganiz, Murat Can (Dogus Author) -- Zeynep Hilal, Kilimci (Dogus Author)Metinsel veri madenciliği büyük miktarlardaki metinsel verilerden faydalı bilgilerin çıkarılması veya bunların otomatik olarak organize edilmesini içerir. Büyük miktarlarda metinsel belgenin otomatik olarak organize edilmesinde metin sınıflandırma algoritmaları önemli bir rol oynar. Bu alanda kullanılan sınıflandırma algoritmaları “eğitimli” (supervised), kümeleme algoritmaları ise “eğitimsiz” (unsupervised) olarak adlandırılırlar. Bunların ortasında yer alan “yarı-eğitimli” (semisupervised) algoritmalar ise etiketli verinin yanı sıra bol miktarda bulunan etiketsiz veriden faydalanarak sınıflandırma başarımını arttırabilirler. Metinsel veri madenciliği algoritmalarında geleneksel olarak kelime sepeti (bag-of-words) olarak tabir edilen model kullanılmaktadır. Kelime sepeti modeli metinde geçen kelimeleri bulundukları yerden ve birbirinden bağımsız olarak değerlendirir. Ayrıca geleneksel algoritmalardaki bir başka varsayım ise metinlerin birbirinden bağımsız ve eşit olarak dağıldıklarıdır. Sonuç olarak bu yaklaşım tarzı kelimelerin ve metinlerin birbirleri arasındaki anlamsal ilişkileri göz ardı etmektedir. Metinsel veri madenciliği alanında son yıllarda özellikle kelimeler arasındaki anlamsal ilişkilerden faydalanan çalışmalara ilgi artmaktadır. Anlamsal bilginin kullanılması geleneksel makine öğrenmesi algoritmalarının başarımını özellikle eldeki verinin az, seyrek veya gürültülü olduğu durumlarda arttırmaktadır. Gerçek hayat uygulamalarında algoritmaların eğitim için kullanacağı veri genellikle sınırlı ve gürültülüdür. Bu yüzden anlamsal bilgiyi kullanabilen algoritmalar gerçek hayat problemlerinde büyük yarar sağlama potansiyeline sahiptir. Bu projede, ilk aşamada eğitimli metinsel veri madenciliği için anlamsal algoritmalar geliştirdik. Bu anlamsal algoritmalar metin sınıflandırma ve özellik seçimi alanlarında performans artışı sağlamaktadır. Projenin ikinci aşamasında ise bu yöntemlerden yola çıkarak etiketli ve etiketsiz verileri kullanan yarı-eğitimli metin sınıflandırma algoritmaları geliştirme faaliyetleri yürüttük. Proje süresince 5 yüksek lisans tezi tamamlanmış, 1 Doktora tezi tez savunma aşamasına gelmiş, 2 adet SCI dergi makalesi yayınlanmış, 8 adet bildiri ulusal ve uluslararası konferanslar ve sempozyumlarda sunulmuş ve yayınlanmıştır. Hazırlanan 2 adet dergi makalesi ise dergilere gönderilmiş ve değerlendirme aşamasındadır. Projenin son aşamasındaki bulgularımızı içeren 1 adet konferans bildirisi 2 adet dergi makalesi de hazırlık aşamasındadır. Ayrıca proje ile ilgili olarak üniversite çıkışlı bir girişim şirketi (spin-off) kurulmuştur.Textual data mining is the process of extracting useful knowledge from large amount of textual data. In this field, classification algorithms are called supervised and clustering algorithms are called unsupervised algorithms. Between these there are semi supervised algorithms which can improve the accuracy of the classification by making use of the unlabeled data. Traditionally, bag-of-words model is being used in textual data mining algorithms. Bag-of-words model assumes that words independent from each other and their positions in the text. Furthermore, traditional algorithms assume that texts are independent and identically distributed. As a result this approach ignores the semantic relationship between words and between texts. There has been a recent interest in works that make use of the semantic relationships especially between the words. Use of semantic knowledge increase the performance of the systems especially when there are few, sparse and noisy data. In fact, there are very sparse and noisy data in real world settings. As a result, algorithms that can make use of the semantic knowledge have a great potential to increase the performance. In this project, in the first phase, we developed semantic algorithms and methods for supervised classification. These semantic algorithms provide performance improvements on text classification and feature selection. On the second phase of the project we have pursued development activities for semi-supervised classification algorithms that make use of labeled and unlabeled data, based on the methods developed in the first phase. During the project, 5 master’s thesis is completed, the PhD student is advanced to the dissertation defense stage, two articles are published on SCI indexed journals, 8 proceedings are presented in national and international conferences. Two journal articles are sent and 1 conference proceeding and two journal articles are in preparation, which include the findings of the last phase of the project. Furthermore, a spin-off technology company is founded related to the project.TÜBİTA

Financial sentiment analysis with Deep Ensemble Models (DEMs) for stock market prediction

Kilimci, Zeynep Hilal/0000-0003-1497-305XThe stock market forecasting is popular research topic for analysts. In this study, it is proposed to estimate direction of Bist100 index by financial sentiment analysis. To our knowledge, this is the first study in literature using Twitter for forecasting stock market direction and doing this with deep ensemble models. The contributions of study are fourfold: First, feature set is enriched semantically to eliminate size limitation problem in Twitter. In first stage, meaningful features that express dataset are selected by means of information gain and ant colony optimization. Next, features are enriched in meaning, context, syntax using document representation models such as Avg(Word2vec), Avg(Glove), Avg(Word2vec)+Avg(Glove), TF-IDF+Avg(Word2vec), TF-IDF+Avg(Glove). Secondly, it is proposed to improve system performance performing classification with multiple learning algorithms. Instead of traditional classification algorithms, a deep ensemble model (DTM) is constructed blending deep learning architectures such as Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory Networks. Third, majority voting and stacking methods are used to obtain final decision of deep ensemble model. Fourthly, Turkish and English Twitter datasets are employed to demonstrate that proposed approach improves classification performance. Consequently, experimental results show that proposed model is significantly superior to previous studies when compared with literature studies

N-gram pattern recognition using multivariate-Bernoulli model with smoothing methods for text classification

Kilimci, Zeynep Hilal (Dogus Author) -- Akyokuş, Selim (Dogus Author) -- Conference full title: 24th Signal Processing and Communication Application Conference, SIU 2016; Zonguldak; Turkey; 16 May 2016 through 19 May 2016.Bu yayında esasen metin sınıflandırma alanında n-gram modeller üzerine odaklandık. N-gram modellerin sınıflandırma başarısı üzerindeki etkisini ölçmek için Naïve Bayes sınıflandırıcıya çeşitli yumuşatma yöntemlerini uyguladık. Naïve Bayes sınıflandırıcısı, metin sınıflandırmada genel olarak Bernoulli ve multinomial olmak üzere iki temel model üzerine inşa edilir. Araştırmacılar, metin sınıflandırma ve benzer alanlarda genellikle multinomial model ve Laplace yumuşatma metodunu birlikte kullanırlar. Bu çalışmanın amacı ise Naïve Bayes sınıflandırma başarısını her iki model için analiz edip n-gram modellerini farklı bir açıdan kullanarak göstermektir. İki model arasındaki çeşitli örüntüleri bulmak için deneylerimizi geniş bir Türkçe veri kümesi üzerinde yürüttük. Deney sonuçları, Bernoulli modelin uygun bir yumuşatma yöntemiyle kullanıldığında n-gram modellerin çoğunda daha iyi bir sonuç verebildiğini gösterdi.In this paper, we mainly study on n-gram models on text classification domain. In order to measure impact of n-gram models on the classification performance, we carry out Naïve Bayes classifier with various smoothing methods. Naïve Bayes classifier has generally used two main event models for text classification which are Bernoulli and multinomial models. Researchers usually address multinomial model and Laplace smoothing on text classification and similar domains. The objective of this study is to demonstrate the classification performance of event models of Naïve Bayes by analyzing both event models with different smoothing methods and using n-gram models from a different perspective. In order to find various patterns between two event models, we carry on experiments a large Turkish dataset. Experiment results indicate that Bernoulli event model with an appropriate smoothing method can outperform on most of the n-gram models

The Analysis of text categorization represented with word embeddings using homogeneous classifiers

Kilimci, Zeynep Hilal (Dogus Author) -- Conference full title: IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019; Sofia; Bulgaria; 3 July 2019 through 5 July 2019.Text data mining is the process of extracting and analyzing valuable information from text. A text data mining process generally consists of lexical and syntax analysis of input text data, the removal of non-informative linguistic features and the representation of text data in appropriate formats, and eventually analysis and interpretation of the output. Text categorization, text clustering, sentiment analysis, and document summarization are some of the important applications of text mining. In this study, we analyze and compare the performance of text categorization by using different single classifiers, an ensemble of classifiers, a neural probabilistic representation model called word2vec on English texts. The neural probabilistic based model namely, word2vec, enables the representation of terms of a text in a new and smaller space with word embedding vectors instead of using original terms. After the representation of text data in new feature space, the training procedure is carried out with the well-known classification algorithms, namely multivariate Bernoulli naïve Bayes, support vector machines and decision trees and an ensemble algorithm such as bagging, random subspace and random forest. A wide range of comparative experiments are conducted on English texts to analyze the effectiveness of word embeddings on text classification. The evaluation of experimental results demonstrates that an ensemble of algorithms models with word embeddings performs better than other classification algorithms that uses traditional methods on English texts

The Evaluation of Word Embedding Models and Deep Learning Algorithms for Turkish Text Classification

4th International Conference on Computer Science and Engineering (UBMK) -- SEP 11-15, 2019 -- Samsun, TURKEYThe use of word embedding models and deep learning algorithms are currently the most common and popular trends to enhance the overall performance of a text classification/categorization system. Word embedding models are vectors that provide a mapping of words with similar meaning to own a similar representation which is learned from a corpus. Deep learning algorithms successful produce more successful results in many areas of their applications when they are compared to the conventional machine learning algorithms. In this study, three different word embedding models Word2Vec, Glove, and FastText are employed fur word representation. Instead of using conventional classification algorithms, three different deep learning architectures Recurrent Neural Networks (RNN), Long Short Term Memory Networks (LSTM) and Convolutional Neural Networks (CNN) are used for classification task by performing experiments on collections of different Turkish documents. Experimental results show that the usage of deep learning algorithms together with word embedding models advances the performance of text classification systems.IEEE, IEEE Turkey Sec

The Impact of Enhanced Space Forests with Homogeneous Classifier Ensembles

In this paper, we propose to advance the classification success of classifier ensembles by investigating the contribution of enhanced space forests. For this purpose, this study especially is focused on enhanced feature spaces by implementing the most popular feature selection techniques, namely information gain, and chi-square. After performing these methods on the original feature space, training phase is evaluated with all the original and the modified versions of most significant features, which are acquired by applying difference operator to the original features and the selected features with feature selection methods. That is, the new training dataset is constructed by combining the original features and the new ones. Then, the training is done with the well-known classification algorithm namely, decision tree using the enhanced feature space. Finally, three types of ensemble algorithms namely, bagging, random subspace, and random forest are carried out. A wide range of comparative experiments are conducted on publicly available and widely-used 36 datasets from the UCI machine learning repository to observe the impact of the enhanced space forests with classifier ensembles. Experiment results demonstrate that the proposed enhanced space forests perform better classification accuracy than the state of the art studies. Approximately, 1% - 3% improvement of the classification success is an indicator that our proposed technique is efficient