###### Abstract
They present two different approaches to the fusion of EEG sensor signals with DL models trained and validated on self-designed games (one serious game and one flight simulator with specific scenarios). The Special Issue also represents other areas of the **Machine Learning** field. The paper led by [PERSON] [10] refers to the application field of unsupervised learning in Industry 4.0 (sector C. Manufacturing), in particular, additive manufacturing and large 3D printers. It presents a modification of the classical CURE strategy that scales up hierarchical clustering to large datasets by introducing bootstrap techniques into the basic algorithm and an automatic criterion to detect the number of clusters in the dendrogram. The proposal, named bootstrap-CURE, is applied to identify operation modes of 3D printers by analysing multivariate sensor data. The proposed methodology is scalable and significantly reduces computational costs, and it is being currently used at a leading real 3D printer manufacturer. The work by [PERSON] et al. [11] proposes a new predictive support tool to optimise the number of copies to print when a new book is published (sector G. Trade and retail), using data from the book, from the authors' social networks, and from the author's web mentions. The tool introduces the Combined model of Artificial Intelligence techniques (CAIT) that combines a classifier with a predictive model. First, it applies an XGBoost algorithm to classify the book into one of the possible book market segments. Next, a regressor also based in XGBoost is used to predict the appropriate number of copies to print. The paper [12] led by [PERSON] is centred on the explainability of the output of complex automatic classifiers, a key step to allow users to make a good and informed decision, particularly in medical applications. The paper by [PERSON] et al. proposes a method to generate explanations of classifiers in the form of a minimal set of short rules, using both numerical and qualitative variables. Two different machine learning methods for creating classification rules are analysed and compared in the case of RETIPROGRAM, an intelligent clinical decision support system that computes the personalised risk of developing diabetic retinopathy. Short explanations are obtained consisting of one representative rule and some counter-examples. The issue includes two works on **Perception**: one on computer vision and the other on voice recognition (audio processing). The paper [13] led by [PERSON] contributes to early diagnosis of lung cancer. It proposes an accurate lung nodule detection and segmentation in computed tomography (CT) images. The proposed system combines nodule detection, based on fine-tuned Faster R-CNN to localise the nodules in CT images, with nodule segmentation, to enhance the ability to discriminate between nodule and non-nodule feature representations. The work shows promising experimental results. In the paper \"An Approach for Pronunciation Classification of Classical Arabic Phonemes using Deep Learning\" [14], a group of researchers from Saudi Arabia, led by [PERSON], face the problem of recognition of precise pronunciation of the large number of short vowels in Arabic alphabets, which cannot be dealt with using traditional audio processing techniques, contributing to the sector of P. Education. They present a new classification architecture based on convolutional neural networks. Identifying the vowels correctly is crucial in the Arabic language since a mistake in a short vowel can change the meaning of a complete sentence. The **Integration and Interaction** area has two papers devoted to research on multiagent systems. The paper led by [PERSON] [15] focuses on a sensible topic in social networks: hate messages (sector L. Real estate). Although hate propagators are less than 1% of participants, they create a high amount of hate content (racial, gender, religion, etc.). The goal is to detect that hate content as soon as possible to avoid its propagation.