Crowdfunding platforms are important for startups, since they offer diverse financing options, market validation, and promotional opportunities through an investor community. These platforms provide detailed company information, aiding informed investment decisions within a regulated and secure environment. Machine learning (ML) techniques are important in analyzing large data sets, detecting anomalies and fraud, and enhancing decision-making and business strategies. A systematic review employed PRISMA guidelines, which studied how ML improves fraud detection on digital crowdfunding platforms. The analysis includes English-language studies from peer-reviewed journals published between 2018 and 2023 to analyze the pre- and post-COVID-19 pandemic. The findings indicate that ML techniques such as Random Forest, Support Vector Machine, and Artificial Neural Networks significantly enhance the predictive accuracy and utility of tax planning for startups considering equity crowdfunding. The United States, Germany, Canada, Italy, and Turkey do not present statistically significant differences at the 95% confidence level, standing out for their notable academic visibility. Florida Atlantic and Cornell Universities, Springer and John Wiley & Sons Ltd. publishing houses, and the Journal of Business Ethics and Management Science magazines present the highest citations without statistical differences at the 95% confidence level.

Education has become a key component of any society since it is the means by which humanity functions and governs itself. It allows individuals to appropriately integrate into a given community. For this reason, new ways of interaction between students and educational contents are emerging in order to improve the quality of education. In this context, devices such as computers, smartphones, or electronic tablets represent new ways of accessing educational resources which do not limit students to their usage merely inside the classroom since these devices are available anywhere. Nowadays, television has become one of these technological tools able to support the teaching–learning process through documentary films or movies, among others. However, two main issues appear. First, some of these educational contents are not those needed by a professor since information is restricted, and second, the development of TV-based applications requires an integrative approach involving the support of several specialists in education who provide the guidelines needed to build high-quality contents, as well as application designers and developers who are able to deliver the educational applications demanded by students. This work presents a system called AthenaTV to generate android-based educational applications for TV. AthenaTV takes into account the 10-foot design scheme used by Google to develop interfaces based on interface design patterns established in Google TV, and it is based on the android development guidelines and HTML5 standard.

The oil and gas industries are crucial to global economies, influencing geopolitics, driving technological advancements, employing millions, and impacting financial markets. The complexity and the volume of data generated by these industries demonstrate the need for efficient information management, where effective contract audits play a key role in ensuring market stability, transparency, fair revenue distribution, corruption mitigation, and enhancing industry integrity to attract investors. This study employs bibliometric analysis to explore the application of machine learning (ML) in detecting anomalous contracts within the oil and gas industry. This analysis identifies key research and challenges, laying the groundwork for further computational ML advancements. The PRISMA guidelines identify ML’s role from 2018 to 2023, including post-COVID-19. Principal component analysis (PCA) evaluates the bibliometric contributions of different countries and institutions. China, Indonesia, Egypt, Saudi Arabia, the University of Antwerp Operations Research Group, and the University of Pittsburgh emerge as significant contributors. These findings underscore ML’s pivotal role in fraud detection, risk mitigation, and cost savings, concluding that anomalous contract detection remains open to newer ML techniques and ongoing research.

Diverse movement patterns may be identified when we study a set of moving entities. One of these patterns is known as a V-formation for it is shaped like the letter V. Informally, a set of entities shows a V-formation if the entities are located on one of their two characteristic lines. These lines meet in a position where there is just one entity considered the leader of the formation. Another movement pattern is known as a circular formation for it is shaped like a circle. Informally, circular formations present a set of entities grouped around a center in which the distance from these entities to the center is less than a given threshold. In this paper we present a model to identify V-formations and circular formations with outliers. An outlier is an entity which is part of a formation but is away from it. We also present a model to identify doughnut formations, which are an extension of circular formations. We present formal rules for our models and an algorithm for detecting outliers. The model was validated with NetLogo, a programming and modeling environment for the simulation of natural and social phenomena.