Background: Melanoma has become more widespread over the past 30 years and early detection is a major factor in reducing mortality rates associated with this type of skin cancer. Therefore, having access to an automatic, reliable system that is able to detect the presence of melanoma via a dermatoscopic image of lesions and/or skin pigmentation can be a very useful tool in the area of medical diagnosis. Methods: Among state-of-the-art methods used for automated or computer assisted medical diagnosis, attention should be drawn to Deep Learning based on Convolutional Neural Networks, wherewith segmentation, classification and detection systems for several diseases have been implemented. The method proposed in this paper involves an initial stage that automatically crops the region of interest within a dermatoscopic image using the Mask and Region-based Convolutional Neural Network technique, and a second stage based on a ResNet152 structure, which classifies lesions as either “benign” or “malignant”. Results: Training, validation and testing of the proposed model was carried out using the database associated to the challenge set out at the 2017 International Symposium on Biomedical Imaging. On the test data set, the proposed model achieves an increase in accuracy and balanced accuracy of 3.66% and 9.96%, respectively, with respect to the best accuracy and the best sensitivity/specificity ratio reported to date for melanoma detection in this challenge. Additionally, unlike previous models, the specificity and sensitivity achieve a high score (greater than 0.8) simultaneously, which indicates that the model is good for accurate discrimination between benign and malignant lesion, not biased towards any of those classes. Conclusions: The results achieved with the proposed model suggest a significant improvement over the results obtained in the state of the art as far as performance of skin lesion classifiers (malignant/benign) is concerned.

In this work, authors address workload computation combining human activity recognition and heart rate measurements to establish a scalable framework for health at work and fitness-related applications. The proposed architecture consists of two wearable sensors: one for motion, and another for heart rate. The system employs machine learning algorithms to determine the activity performed by a user, and takes a concept from ergonomics, the Frimat’s score, to compute the corresponding physical workload from measured heart rate values providing in addition a qualitative description of the workload. A random forest activity classifier is trained and validated with data from nine subjects, achieving an accuracy of 97.5%. Then, tests with 20 subjects show the reliability of the activity classifier, which keeps an accuracy up to 92% during real-time testing. Additionally, a single-subject twenty-day physical workload tracking case study evinces the system capabilities to detect body adaptation to a custom exercise routine. The proposed system enables remote and multi-user workload monitoring, which facilitates the job for experts in ergonomics and workplace health.

The dynamic features of microgrid operation, such as on-grid/off-grid operation mode, the intermittency of distributed generators, and its dynamic topology due to its ability to reconfigure itself, cause misfiring of conventional protection schemes. To solve this issue, adaptive protection schemes that use robust communication systems have been proposed for the protection of microgrids. However, the cost of this solution is significantly high. This paper presented an intelligent fault detection (FD) system for microgrids on the basis of local measurements and machine learning (ML) techniques. This proposed FD system provided a smart level to intelligent electronic devices (IED) installed on the microgrid through the integration of ML models. This allowed each IED to autonomously determine if a fault occurred on the microgrid, eliminating the requirement of robust communication infrastructure between IEDs for microgrid protection. Additionally, the proposed system presented a methodology composed of four stages, which allowed its implementation in any microgrid. In addition, each stage provided important recommendations for the proper use of ML techniques on the protection problem. The proposed FD system was validated on the modified IEEE 13-nodes test feeder. This took into consideration typical features of microgrids such as the load imbalance, reconfiguration, and off-grid/on-grid operation modes. The results demonstrated the flexibility and simplicity of the FD system in determining the best accuracy performance among several ML models. The ease of design’s implementation, formulation of parameters, and promising test results indicated the potential for real-life applications.

A system for the automatic classification of cardiac sounds can be of great help for doctors in the diagnosis of cardiac diseases. Generally speaking, the main stages of such systems are (i) the pre-processing of the heart sound signal, (ii) the segmentation of the cardiac cycles, (iii) feature extraction and (iv) classification. In this paper, we propose methods for each of these stages. The modified empirical wavelet transform (EWT) and the normalized Shannon average energy are used in pre-processing and automatic segmentation to identify the systolic and diastolic intervals in a heart sound recording; then, six power characteristics are extracted (three for the systole and three for the diastole)—the motivation behind using power features is to achieve a low computational cost to facilitate eventual real-time implementations. Finally, different models of machine learning (support vector machine (SVM), k-nearest neighbor (KNN), random forest and multilayer perceptron) are used to determine the classifier with the best performance. The automatic segmentation method was tested with the heart sounds from the Pascal Challenge database. The results indicated an error (computed as the sum of the differences between manual segmentation labels from the database and the segmentation labels obtained by the proposed algorithm) of 843,440.8 for dataset A and 17,074.1 for dataset B, which are better values than those reported with the state-of-the-art methods. For automatic classification, 805 sample recordings from different databases were used. The best accuracy result was 99.26% using the KNN classifier, with a specificity of 100% and a sensitivity of 98.57%. These results compare favorably with similar works using the state-of-the-art methods.

Today reading comprehension is considered an essential skill in modern life, therefore, higher education students require more specific skills to understand, interpret and evaluate texts effectively. Short answer questions (SAQs) are one of the relevant and proper tools for assessing reading comprehension skills. Unlike multiple-choice questions, SAQs allow for the assessment of cognitive abilities such as attention, language, perception, and problem solving. However, the task of SAQs scoring is time-consuming and susceptible to ambiguity. Automatic Short Answer Grading (ASAG) is a new paradigm that could help solve these problems. This experimental analysis aims to implement ASAG using several approaches to sentence embedding based on deep learning with a multilayer perceptron regression layer on the top, trained with a reading comprehension dataset based on aphorisms. For experimental testing, the available dataset is composed of answers given by 199 undergraduate students in Spanish. BERT and Skip-Thought models are tested with different hyperparameters to find the best performance in terms of Pearson correlation coefficient and RMSE against human experts grades. The result of the current study showed that BERT model performed better than other approaches.

Currently, there are many works in the literature focused on the analysis of heart sounds, specifically on the development of intelligent systems for the classification of normal and abnormal heart sounds. However, the available heart sound databases are not yet large enough to train generalized machine learning models. Therefore, there is interest in the development of algorithms capable of generating heart sounds that could augment current databases. In this article, we propose a model based on generative adversary networks (GANs) to generate normal synthetic heart sounds. Additionally, a denoising algorithm is implemented using the empirical wavelet transform (EWT), allowing a decrease in the number of epochs and the computational cost that the GAN model requires. A distortion metric (mel–cepstral distortion) was used to objectively assess the quality of synthetic heart sounds. The proposed method was favorably compared with a mathematical model that is based on the morphology of the phonocardiography (PCG) signal published as the state of the art. Additionally, different heart sound classification models proposed as state-of-the-art were also used to test the performance of such models when the GAN-generated synthetic signals were used as test dataset. In this experiment, good accuracy results were obtained with most of the implemented models, suggesting that the GAN-generated sounds correctly capture the characteristics of natural heart sounds.