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In face-to-face and online learning, emotions and emotional intelligence have an influence and play an essential role. Learners’ emotions are crucial for e-learning system because they promote or restrain the learning. Many researchers have investigated the impacts of emotions in enhancing and maximizing e-learning outcomes. Several machine learning and deep learning approaches have also been proposed to achieve this goal. All such approaches are suitable for an offline mode, where the data for emotion classification are stored and can be accessed infinitely. However, these offline mode approaches are inappropriate for real-time emotion classification when the data are coming in a continuous stream and data can be seen to the model at once only. We also need real-time responses according to the emotional state. For this, we propose a real-time emotion classification system (RECS)-based Logistic Regression (LR) trained in an online fashion using the Stochastic Gradient Descent (SGD) algorithm. The proposed RECS is capable of classifying emotions in real-time by training the model in an online fashion using an EEG signal stream. To validate the performance of RECS, we have used the DEAP data set, which is the most widely used benchmark data set for emotion classification. The results show that the proposed approach can effectively classify emotions in real-time from the EEG data stream, which achieved a better accuracy and F1-score than other offline and online approaches. The developed real-time emotion classification system is analyzed in an e-learning context scenario., Peer Reviewed, Postprint (published version)

Currently, there is no therapy targeting septic cardiomyopathy (SC), a key contributor to organ dysfunction in sepsis. In this study, we used a machine learning (ML) pipeline to explore transcriptomic, proteomic, and metabolomic data from patients with septic shock, and prospectively collected measurements of high-sensitive cardiac troponin and echocardiography. The purposes of the study were to suggest an exploratory methodology to identify and characterise the multiOMICs profile of (i) myocardial injury in patients with septic shock, and of (ii) cardiac dysfunction in patients with myocardial injury. The study included 27 adult patients admitted for septic shock. Peripheral blood samples for OMICS analysis and measurements of high-sensitive cardiac troponin T (hscTnT) were collected at two time points during the ICU stay. A ML-based study was designed and implemented to untangle the relations among the OMICS domains and the aforesaid biomarkers. The resulting ML pipeline consisted of two main experimental phases: recursive feature selection (FS) assessing the stability of biomarkers, and classification to characterise the multiOMICS profile of the target biomarkers. The application of a ML pipeline to circulate OMICS data in patients with septic shock has the potential to predict the risk of myocardial injury and the risk of cardiac dysfunction., SCOPUS: ar.j, info:eu-repo/semantics/published

Emotions play a crucial role in learning. To improve and optimize electronic learning (e-Learning) outcomes, many researchers have investigated the role of emotions. Also, researchers have come up with many approaches to utilize one or many data modalities to achieve this goal, and they have been successful. But the recent advancements in technology and the internet of things (IoT) devices have brought a new dimension in e-Learning, with many input devices (such as webcams, fit-bands etc.) for interacting with e-Learners. This new dimension brings not only massive amounts of data with volume, variety, and velocity called multimodal data streams but also more challenges of mining those data in real-time. In this work, we have focused on state-of-the-art emotion recognition in e-Learning utilizing the multimodal data streams of learners. Also, we have thoroughly investigated the past research and surveys on emotion recognition methods in e-Learning to find the affecting emotions and their relations with the emotion measurement channels; and we have compared several data-stream classifiers for emotion recognition by utilizing multimodal physiological data streams. Finally, the future research opportunities to be addressed are also discussed, Peer Reviewed, Postprint (author's final draft)

Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities., This work is written on behalf of the Women’s Brain Project (WBP) (www.womensbrainproject.com/), an international organization advocating for women’s brain and mental health through scientific research, debate and public engagement. The authors would like to gratefully acknowledge Maria Teresa Ferretti and Nicoletta Iacobacci (WBP) for the scientific advice and insightful discussions; Roberto Confalonieri (Alpha Health) for reviewing the manuscript; the Bioinfo4Women programme of Barcelona Supercomputing Center (BSC) for the support. This work has been supported by the Spanish Government (SEV 2015–0493) and grant PT17/0009/0001, of the Acción Estratégica en Salud 2013–2016 of the Programa Estatal de Investigación Orientada a los Retos de la Sociedad, funded by the Instituto de Salud Carlos III (ISCIII) and European Regional Development Fund (ERDF). EG has received funding from the Innovative Medicines Initiative 2 (IMI2) Joint Undertaking under grant agreement No 116030 (TransQST), which is supported by the European Union’s Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA)., Peer Reviewed, Postprint (published version)

Featured Application This study creates an intelligent tutoring system in archaeology for helping students in specialized tasks that require analysis of huge amounts of data. The method proposed here implies the application of classification algorithms that must be trained with a complete data set in order to give accurate results. We have tested our method by developing an intelligent tutoring system in the field of zooarchaeology. Abstract This paper presents a tutoring system which uses three different granularities for helping students to classify animals from bone fragments in zooarchaeology. The 3406 bone remains, which have 64 attributes, were obtained from the excavation of the Middle Palaeolithic site of El Salt (Alicante, Spain). The coarse granularity performs a five-class prediction, the medium a twelve-class prediction, and the fine a fifteen-class prediction. In the coarse granularity, the results show that the first 10 most relevant attributes for classification are width, bone, thickness, length, bone fragment, anatomical group, long bone circumference, X, Y, and Z. Based on those results, a user-friendly interface of the tutor has been built in order to train archaeology students to classify new remains using the coarse granularity. A pilot has been performed in the 2019 excavation season in Abric del Pastor (Alicante, Spain), where the automatic tutoring system was used by students to classify 51 new remains. The pilot experience demonstrated the usefulness of the tutoring system both for students when facing their first classification activities and also for seniors since the tutoring system gives them valuable clues for helping in difficult classification problems.