In recent years data science has been applied in a variety of real-life applications such as human-computer interaction applications, computer gaming, mobile services, and emotion evaluation. Among the wide range of applications, speech emotion recognition (SER) is also an emerging and challenging research topic. For SER, recent studies used handcrafted features that provide the best results but failed to provide accuracy while applied in complex scenarios. Later, deep learning techniques were used for SER that automatically detect features from speech signals. Deep learning-based SER techniques overcome the issues of accuracy, yet there are still significant gaps in the reported methods. Studies using lightweight CNN failed to learn optimal features from composite acoustic signals. This study proposed a novel SER model to overcome the limitations mentioned earlier in this study. We focused on Arabic vocal emotions in particular because they received relatively little attention in research. The proposed model performs data augmentation before feature extraction. The 273 derived features were fed as input to the transformer model for emotion recognition. This model is applied to four datasets named BAVED, EMO-DB, SAVEE, and EMOVO. The experimental findings demonstrated the robust performance of the proposed model compared to existing techniques. The proposed SER model achieved 95.2%, 93.4%, 85.1%, and 91.7% accuracy on BAVED, EMO-DB, SAVEE, and EMOVO datasets respectively. The highest accuracy was obtained using BAVED dataset, indicating that the proposed model is well suited to Arabic vocal emotions.

Digital media has a massive presence in the modern world, and it significantly impacts kids’ intellectual, cognitive, ethical, and social development. It is nearly impossible to isolate kids from digital media. Therefore, adult content on mobile applications should be avoided by children. Although mobile operating systems provide parental controls, handling such rules is impossible for illiterate people. Consequently, kids may download and use adults’ mobile applications. Mobile applications for adults often publish age group information to distinguish user segments that can be used to automate the downloading process. Sustainable Development Goal (SDG) #4 emphasizes inclusivity and equitability in terms of quality of education and the facilitation of conditions for the promotion of lifelong learning for everyone. The current study can be counted as being in line with SDG#4, as it proposes a machine-learning-based approach to the prediction of the suitability of mobile applications for kids. The approach first leverages natural language processing (NLP) techniques to preprocess user reviews of mobile applications. Second, it performs feature engineering based on the given bag of words (BOW), e.g., abusive words, and constructs a feature vector for each mobile app. Finally, it trains and tests a machine learning algorithm on the given feature vectors. To evaluate the proposed approach, we leverage the 10-fold cross-validation technique. The results of the 10-fold cross-validation indicate that the proposed solution is significant. The average results of the exploited metrics (precision, recall, and F1-score) are 92.76%, 99.33%, and 95.93%, respectively.