The accurate detection of emotions has significant implications in healthcare, psychology, and human–computer interaction. Integrating personality information into emotion recognition can enhance its utility in various applications. The present study introduces a novel deep learning approach to emotion recognition, which utilizes electroencephalography (EEG) signals and the Big Five personality traits. The study recruited 60 participants and recorded their EEG data while they viewed unique sequence stimuli designed to effectively capture the dynamic nature of human emotions and personality traits. A pre-trained convolutional neural network (CNN) was used to extract emotion-related features from the raw EEG data. Additionally, a long short-term memory (LSTM) network was used to extract features related to the Big Five personality traits. The network was able to accurately predict personality traits from EEG data. The extracted features were subsequently used in a novel network to predict emotional states within the arousal and valence dimensions. The experimental results showed that the proposed classifier outperformed common classifiers, with a high accuracy of 93.97%. The findings suggest that incorporating personality traits as features in the designed network, for emotion recognition, leads to higher accuracy, highlighting the significance of examining these traits in the analysis of emotions.

In this paper, we propose a new approach to zone identification which is based on considering features with high semantic richness. Out of the scenarios of selecting features for identifying a zone based on classifying the sentences in a text, we came to notice that in the scenario where specialized names belonging to a text’s domain and mode of the verbs together with reduced versions of conventional features, including history, are taken into account, an accuracy rate of 61% (resp. 81%) is obtained which is higher than that belonging to Liakata’s (resp. Fisas’s) approach. Also, to have a genuine comparison, both Liakata’s and Fisas’s corpora are used in our experiments. Such accuracy is obtained at the place where less computational cost for extracting the features was decreased. In order to improve the accuracy of zone identification, a decision-level fusion process based on combining the results of separate classifiers, was considered. With regard to this, two fusion techniques of ‘majority voting’ and ‘average of probabilities’ were used. Experimentations show the fact that ensemble of ‘Logistic Regression’, ‘Support Vector Machine’ and ‘Neural Network’ as classifiers yields the best performance. Also ‘majority voting’ was shown to perform a bit better than ‘average of probabilities’.

In this paper, we design a fuzzy system for image retrieval to reduce the semantic gap in the content-based image retrieval systems. Our main contribution is three-fold: (1) designing a fuzzy modeling approach to model the expert human behavior in the image retrieval task, (2) a fuzzy system for semantic-based image retrieval, and (3) a training algorithm for creating the fuzzy rules. The proposed solution not only is a novel idea in the semantic-based image retrieval field, but has enough potential in learning semantics from the user and making a powerful approach to improve the performance of CBIR systems, as our experiments on a set of 2000 images supports our claim.