According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.

Personality is a unique trait that distinguishes an individual. It includes an ensemble of peculiarities on how people think, feel, and behave that affects the interactions and relationships of people. Personality is useful in diverse areas such as marketing, training, education, and human resource management. There are various approaches for personality recognition and different psychological models. Preceding work indicates that linguistic analysis is a promising way to recognize personality. In this work, a proposal for personality recognition relying on the dominance, influence, steadiness, and compliance (DISC) model and statistical methods for language analysis is presented. To build the model, a survey was conducted with 120 participants. The survey consisted in the completion of a personality test and handwritten paragraphs. The study resulted in a dataset that was used to train several machine learning algorithms. It was found that the AdaBoost classifier achieved the best results followed by Random Forest. In both cases a feature selection pre-process with Pearson’s Correlation was conducted. AdaBoost classifier obtained the average scores: accuracy = 0.782, precision = 0.795, recall = 0.782, F-measure = 0.786, receiver operating characteristic (ROC) area = 0.939.

Several developed countries have implemented smart systems for public transport that provide mobility services for citizens. Most of these systems use special infrastructures to determine the location of citizens and public buses. However, the implementation of these systems does not take into account the poor infrastructure of developing countries. In Mexico, Urban Passenger Transport has insufficient transport units to meet the demand of passengers who move throughout the day, and these do not consider hardware infrastructure. Our solution is focused on inexpensive devices that are accessible to citizens, such as mobile phones. In this research work, a smart information system for passengers of urban transport is presented that allows passengers to know the expected arrival times and also to know the availability of seats on the bus that is heading towards their position. The solution was evaluated with real routes, and the results are promising for a pilot project to be put in practice.

Current computer systems are accumulating huge amounts of information in several application domains. The outbreak of COVID-19 has increased rekindled interest in the use of data mining techniques for the analysis of factors that are related to the emergence of an epidemic. Data mining techniques are being used in the analysis and interpretation of information, which helps in the discovery of patterns, planning of isolation policies, and even predicting the speed of proliferation of contagion in a viral disease such as COVID-19. This research provides a comprehensive study of various data mining algorithms that are used in conjunction with epidemiological prediction models. The document considers that there is an opportunity to improve or develop tools that offer an accurate prognosis in the management of viral diseases through the use of data mining tools, based on a comparative study of 35 research papers.

Quantum mechanics as the basis of quantum computing imposes an inherent complexity for practitioners not formed on quantum physics, for understanding such new paradigm of computation and getting involved in the prominent quantum world. Strong technical skills on quantum topics, mathematics and related disciplines are required to attend job positions in the labor market. Furthermore, the multidisciplinary environment where quantum computing is immersed requires soft skills to work in integrative teams. This paper presents a literature review, supported by the systematic mapping methodology, to gather the core knowledge and technical skills, as well as soft skills required to prepare students and practitioners to work on quantum computing. The results allow us to have a wide spectrum of the competences involved, enabling academic institutions and educational bodies to design individual courses or curricula blocks to prepare the workforce.