The problem of recognition and classification of emotions in speech is one of the most prominent research topics, that has gained popularity, in human-computer interaction in the last decades. Having recognized the feelings or emotions in human conversations might have a deep impact on understanding a human’s physical and psychological situation. This study proposes a novel hybrid architecture based on acoustic and deep features to increase the classification accuracy in the problem of speech emotion recognition. The proposed method consists of feature extraction, feature selection and classification stages. At first, acoustic features such as Root Mean Square energy (RMS), Mel-Frequency Cepstral Coefficients (MFCC) and Zero-crossing Rate are obtained from voice records. Subsequently, spectrogram images of the original sound signals are given as input to the pre-trained deep network architecture, which is VGG16, ResNet18, ResNet50, ResNet101, SqueezeNet and DenseNet201 and deep features are extracted. Thereafter, a hybrid feature vector is created by combining acoustic and deep features. Also, the ReliefF algorithm is used to select more efficient features from the hybrid feature vector. Finally, in order for the completion of the classification task, Support vector machine (SVM) is used. Experiments are made using three popular datasets used in the literature so as to evaluate the effect of various techniques. These datasets are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Berlin (EMO-DB) and Interactive Emotional Dyadic Motion Capture (IEMOCAP). As a consequence, we reach to 79.41%, 90.21% and 85.37% accuracy rates for RAVDESS, EMO-DB, and IEMOCAP datasets, respectively. The Final results obtained in experiments, clearly, show that the proposed technique might be utilized to accomplish the task of speech emotion recognition efficiently. Moreover, when our technique is compared with those of methods used in the context, it is obvious that our method outperforms others in terms of classification accuracy rates.

Music has a great role and importance in human life since it has the ability to trigger or convey feelings. As recognizing music emotions is the subject of many studies conducted in many disciplines like science, psychology, musicology and art, it has attracted the attention of researchers as an up-to-date research topic in recent years. Many researchers extract acoustic features from music and investigate relations between emotional tags corresponding to these features. In recent studies, on the other hand, music types are classified emotionally by using deep learning through music spectrograms that involved both time and frequency domain information. In the present study, a new method is presented for music emotion recognition by employing pre-trained deep learning model with chroma spectrograms extracted from music recordings. The AlexNet architecture is used as the pre-trained network model. The conv5, Fc6, Fc7 and Fc8 layers of the AlexNet model are chosen as the feature extracting layer, and deep visual features are extracted from these layers. The extracted deep features are used to train and test the Support Vector Machines (SVM) and the Softmax classifiers. Besides, deep visual features are extracted from conv5_3, Fc6, Fc7 and Fc8 layers of the VGG-16 deep network model and the same experimental applications are made in order to find out the effective power of pre-trained deep networks in music emotion recognition. Several experiments are conducted on two datasets, and better results are obtained with the proposed method. The best result is obtained from the VGG-16 in the Fc7 layer as 89.2% on our dataset. According to the obtained results, it is observed that the presented method performs better.

The aim of this study is to investigate the effect of health expenditures on economic growth in the period 2000–2019 in 27 European Union (EU) countries. First, the causality relationship between the variables was analyzed using the panel Fourier Toda–Yamamoto Causality test. The findings demonstrate a bidirectional causality relationship between health expenditures and economic growth on a panel basis. Secondly, the effects of health expenditures on economic growth were examined using the Random Forest Method for the panel and then for each country. According to the Random Forest Method, health expenditures positively affected economic growth, but on the country basis, the effect was different. Then, government health expenditures, private health expenditures, and out-of-pocket expenditures were used, and these three variables were ranked in order of importance in terms of their effects on growth using the Random Forest Method. Accordingly, government health expenditures were the most important variable for economic growth. Finally, Support Vector Regression, Gaussian Process Regression, and Decision Tree Regression models were designed for the simulation of the data used in this study, and the performances of the designed models were analyzed.

Diyabet, vücudun yeterli miktarda insülini üretmemesi veya iyi kullanamadığı durumda kan şekerinin normalin üstüne çıkması ile ortaya çıkan bir hastalıktır. Kan şekeri insanların ana enerji kaynağıdır ve bu enerji tüketilen yiyeceklerden gıdalardan gelir. Bu hastalık tedavi edilmez ise ölümcül olabilir. Ancak, erken tanı konulup tedaviye başlandığında tedavisi en olanaklı hastalıklardan biridir. Geleneksel diyabet teşhis süreci zorlu olduğundan, diyabetin klinik ve fiziksel verileri kullanılarak yapay sinir ağı, görüntü işleme ve derin öğrenme gibi sistemler kullanılarak hastalık teşhis edilebilmektedir. Bu araştırmada diyabet teşhisi için derin öğrenmeye dayalı bir model sunulmaktadır. Bu bağlamda Evrişimsel Sinir Ağı (ESA), Uzun Kısa Süreli Bellek (Long-short Term Memory Networks- LSTM) modelinin hibrit kullanımı sınıflandırma için tercih edilmiştir. Ayrıca ESA ve LSTM modelleri deneylerde ayrı ayrı kullanılmıştır. Önerilen modelin performansını değerlendirmek için literatürde yaygın olarak kullanılan Pima Indians Diabetes veri seti kullanılmıştır. En yüksek sınıflandırma başarısı %86,45 olarak ESA+LSTM modelinden elde edilmiştir.