Sinirsel stil aktarımı günümüzde hem akademik hemde endüstriyel alanda en fazla çalışılan konulardan biridir. Yapılan çalışmalarda kalite ve performans artırımı en çok amaçlanan hedeflerdendir. Bu çalışmada da farklı ESA modellerinin sinirsel stil aktarımında performansları incelenmiştir. VGG16, VGG19 ve ResNet50 modelleri kullanarak derin öznitelikler elde edilmiştir. Bu öznitelikler sayesinde içerik görüntüsünün içeriğini, stil görüntüsünün de stilini alıp yeni bir hedef görüntü oluşturulmuştur. Adam, RMSprop ve SGD optimizasyon algoritmaları kullanılmıştır. Yapılan sinirsel sinir aktarımı çalışmalarında, görsel açıdan en iyi performans VGG19 ağ modelinden SGD optimizasyon algoritması kullanılarak elde edilmiştir. Zaman açısından en hızlı sinirsel stil aktarımı ResNet50 evrişimsel sinir ağı modelinde SGD optimizasyon algoritması kullanılarak elde edilmiştir.

Facial expressions, which are important social communication tools in our daily life, provide important information about the mental state of people. Research is being done to obtain this information accurately. The importance of these researchs in the field of human-computer interaction is increasing. Many methods have been used for the recognition of universal facial expressions such as neutral, happiness, surprise, sadness, anger, disgust, and fear by intelligent systems with high accuracy. Emotion recognition is an example of difficult classification due to factors such as ambient light, age, race, gender, and facial position. In this article, a 3-stage system is proposed for emotion detection from facial images. In the first stage, the CNN-based network is trained with the Fer+ dataset. The Binary Particle Swarm Optimization algorithm is applied for feature selection to the feature vector in the fully connected layer of the CNN network trained in the second stage. Selected features are classified by Support Vector Machine. The performance of the proposed system has been tested with the Fer+ dataset. As a result of the test, 85.74% accuracy was measured. The results show that the combination of BPSO and SVM contributes to the classification accuracy and speed of the FER+ dataset.

Electroencephalogram (EEG) is a diagnostic method that provides information about the functioning of the brain. EEG can be used to diagnose the abnormally functioning part of the brain by monitoring electrical activities. An automated system has been proposed to create a computer‐based expert opinion needed in the detection of epilepsy and to capture a more objective view. To this end, the approximation and detail coefficients of EEG signals are calculated by using the wavelet packet transform (WPT). The coefficients were subjected to feature extraction using dispersion entropy and line length methods. The extracted feature vector has been applied as input to the support vector machine (SVM) and k‐nearest neighbour (KNN) classifiers. The proposed method was tested using the public EEG seizure dataset created by the University of Bonn. In this study, the dataset was evaluated in two different ways as binary cases and multiclass cases. Evaluated classification accuracy was 100% for binary classification with SVM. For multiclass classification evaluated accuracy was 99.85% with KNN. The proposed method was compared with other methods in the literature using the same dataset. The comparison results provide the superiority of the proposed method.