Currently, healthcare is critical environment in our society, which attracts attention to malicious activities and has caused an important number of damaging attacks. In parallel, the recent advancements in technologies, computing systems, and wireless communications are changing healthcare environment by adding different improvements and complexity to it. This article reviews the current state of the literature and provides a holistic view of cybersecurity in healthcare. With this purpose in mind, the article enumerates the main stakeholders and architecture implemented in the healthcare environment, as well as the main security issues (threats, attacks, etc.) produced in healthcare. In this context, this work maps the threats collected with a widely used knowledge-based framework, MITRE ATT&CK, building a contribution not seen so far. This article also enumerates the security mechanisms created to protect healthcare, identifying the principal research lines addressed in the literature, and listing the available public security-focused datasets used in machine-learning to provide security in the medical domain. To conclude, the research challenges that need to be addressed for future research works in this area are presented.

Financial news plays a significant role with regard to predicting the behaviour of financial markets. However, the exponential growth of financial news on the Web has led to a need for new technologies that automatically collect and categorise large volumes of information in a fast and easy manner. Sentiment analysis, or opinion mining, is the field of study that analyses people’s opinions, moods and evaluations using written text on Web platforms. In recent research, a substantial effort has been made to develop sophisticated methods with which to classify sentiments in the financial domain. However, there is a lack of approaches that analyse the positive or negative orientation of each aspect contained in a document. In this respect, we propose a new sentiment analysis method for feature and news polarity classification. The method presented is based on an ontology-driven approach that makes it possible to semantically describe relations between concepts in the financial news domain. The polarity of the features in each document is also calculated by taking into account the words from around the linguistic expression of the feature. These words are obtained by using the ‘N_GRAM After’, ‘N_GRAM Before’, ‘N_GRAM Around’ and ‘All_Phrase’ methods. The effectiveness of our method has been proved by carrying out a set of experiments on a corpus of 1000 financial news items. Our proposal obtained encouraging results with an accuracy of 66.7% and an F-measure of 64.9% for feature polarity classification and an accuracy of 89.8% and an F-measure of 89.7% for news polarity classification. The experimental results additionally show that the N_GRAM Around method provides the best average results.

The online payment for products or for the access to payment-based services can be made by means of a range of (mobile) electronic payment systems – (M)EPS. Both the industrial sector and research community, mainly World Wide Web Consortium (W3C), are working on facilitating these payment methods on Web and supporting the multiple users on how they can select the suitable (M)EPS. However, to the best of our knowledge, there were no thorough studies considering consumer’s preferences when they support multiple (M)EPS. To address this issue, we have performed a survey on an international participants (n=272) aiming to (i) developed a theoretical model to determine their preferences when they are supporting more than one (M)EPS, (ii) find the most valuable option according to them and (iii) determine the surrounding conditions that support their decision to use a specific (M)EPS. The theoretical framework of this study was based on the Technology Acceptance Model (TAM). According to our statistical analysis (Chi-square test), consumers that can pay using different (M)EPS during their online payment transaction, have a preferred payment system based on its security, fees, usefulness, and ease of use as well as on their favorite Web browser for these transactions. Factor analysis was also performed to identify factors that much influence the (M)EPS. Results revealed that the factors influencing online payment preferences differ from those involved in traditional payment methods. Our findings allowed, therefore, providing practical suggestions for supporting payment processes with Web browsers and the W3C payment Application Program Interface (API).

Electronic (mobile) payment systems are an important aspect of e-commerce. However, few reviews highlight the most significant findings and challenges. In this article, we have prepared a bibliometric analysis to provide a statistical overview of previously published research papers on electronic (mobile) payment systems and user preferences, with a particular emphasis on their diverse techniques and analyses, as well as comprehensive and reliable directions to reveal evolutionary nuances and highlighting emerging areas in this specific research. This study reviewed 177 scientific papers published between 2001 and November 2021 in the Web of Science (WoS) database on the subjects of electronic payment systems (EPS) and mobile payment systems (MPS), payment interface (PI), technology acceptance model (TAM), payment frameworks (PF), and user preferences (UP). The amount of studies using the abovementioned topics (EPS, MPS, TAM, PF, and UP) increases annually. Spain, China, and Malaysia are the three main countries that actively participate, and their international academic partnership is relatively close. We have compiled a list of the most relevant publications, prolific authors, institutions, and leading research topics. The articles were used to evaluate bibliometric indicators, analyze research activity, investigate the subject’s evolution, and identify the most interconnected themes. The findings provide a comprehensive overview of existing thematic studies, with a notable rising trend showing the potential for future research in the field. The study provides a guideline for further research.