After the COVID-19 disease, monkeypox disease has emerged today and has started to be seen almost everywhere in the world in a short time. Monkeypox causes symptoms such as fever, chills, and headache in people. In addition, rashes are seen on the skin and lumps are formed. Early diagnosis and treatment of monkeypox, which is a contagious disease, are of great importance. An expert interpretation and clinical examination are usually needed to detect monkeypox. This may cause the treatment process to be slow. Furthermore, monkeypox is sometimes confused with warts. This leads to incorrect diagnosis and treatment. Because of these disadvantages, in this study, the DNA sequences of HPV causing warts and MPV causing monkeypox were analyzed and the classification of these sequences was performed with a deep learning algorithm. The study consisted of four stages. In the first stage, DNA sequences of viruses that cause warts and monkeypox were obtained. In the second stage, these sequences were mapped using various DNA-mapping methods. In the third stage, the mapped sequences were classified using a deep learning algorithm. At the last stage, the performances of DNA-mapping methods were compared by calculating accuracy and F1-score. At the end of the study, an average accuracy of 96.08% and an F1-score of 99.83% were obtained. These results showed that these two diseases can be effectively classified according to their DNA sequences.

Emotion can be defined as a voluntary or involuntary reaction to external factors. People express their emotions through actions, such as words, sounds, facial expressions, and body language. However, emotions expressed in such actions are sometimes manipulated by people and real feelings cannot be conveyed clearly. Therefore, understanding and analyzing emotions is essential. Recently, emotion analysis studies based on EEG signals appear to be in the foreground, due to the more reliable data collected. In this study, emotion analysis based on EEG signals was performed and a deep learning model was proposed. The study consists of four stages. In the first stage, EEG data were obtained from the GAMEEMO dataset. In the second stage, EEG signals were transformed with both VMD (variation mode decomposition) and EMD (empirical mode decomposition), and a total of 14 (nine from EMD, five from VMD) IMFs were obtained from each signal. In the third stage, statistical features were obtained from IMFs and maximum value, minimum value, and average values were used for this. In the last stage, both binary-class and multi-class classifications were made. The proposed deep learning model is compared with kNN (k nearest neighbor), SVM (support vector machines), and RF (random forest). At the end of the study, an accuracy of 70.89% in binary-class classification and 90.33% in multi-class classification was obtained with the proposed deep learning method.

Emotion recognition is actively used in brain–computer interface, health care, security, e‐commerce, education and entertainment applications to increase and control human–machine interaction. Therefore, emotions affect people's lives and decision‐making mechanisms throughout their lives. However, the fact that emotions vary from person to person, being an abstract concept and being dependent on internal and external factors makes the studies in this field difficult. In recent years, studies based on electroencephalography (EEG) signals, which perform emotion analysis in a more robust and reliable way, have gained momentum. In this article, emotion analysis based on EEG signals was performed to predict positive and negative emotions. The study consists of four parts. In the first part, EEG signals were obtained from the GAMEEMO data set. In the second stage, the spectral entropy values of the EEG signals of all channels were calculated and these values were classified by the bidirectional long‐short term memory architecture in the third stage. In the last stage, the performance of the deep‐learning architecture was evaluated with accuracy, sensitivity, specificity and receiver operating characteristic (ROC) curve. With the proposed method, an accuracy of 76.91% and a ROC value of 90% were obtained.