Due to the COVID-19 pandemic, the tourism sector has been one of the most affected sectors and requires management entities to develop urgent measures to reactivate and achieve digital transformation using emerging disruptive technologies. The objective of this research is to apply machine learning techniques to predict visitors to tourist attractions on the Moche Route in northern Peru, for which a methodology based on four main stages was applied: (1) data collection, (2) model analysis, (3) model development, and (4) model evaluation. Public data from official sources and internet data (TripAdvisor and Google Trends) during the period from January 2011 to May 2022 are used. Four algorithms are evaluated: linear regression, KNN regression, decision tree, and random forest. In conclusion, for both the prediction of national and foreign tourists, the best algorithm is linear regression, and the results allow for taking the necessary actions to achieve the digital transformation to promote the Moche Route and, thus, reactivate tourism and the economy in the north of Peru.

The research defined the objective of building and validating instruments to assess the level of maturity of digital transformation in higher education institutions (HEIs). It was based on a theoretical-methodological model that specified eight dimensions of analysis. The methodology included 7 phases: contextualization of the instruments, initial proposal, iterative review and improvement, content validation, pilot test, reliability analysis and exploratory factor analysis (EFA). The results of the pilot test, conducted with more than 100 participants, showed adequate levels of reliability and validity. The EFA found an adjusted factorial structure of the instruments, with approximate factors of 59.7% for students, 64.8% for teachers, 72.79% for administrators and 81.9% for managers, being considered acceptable and suggesting that they effectively capture the dimensions of analysis. In addition, it was observed that some items originally assigned to specific dimensions were regrouped into conceptually related factors, suggesting the need for adjustments in the initial proposals. Based on the results obtained, these instruments have the potential to be effective tools for identifying digital maturity in HEIs, providing a comprehensive diagnosis to guide strategic decisions and technology investment planning for HEIs. Future studies could be complemented with the perception of graduates and the increase of the sample.

In order to keep accessible, the patient care information recorded by a dental provider, a software architecture must be designed to allow availability among the different providers. In this research, a software architecture based on the Microservices approach is designed to enable the availability of dental medical records. The quality attributes and functional requirements were identified to design the architecture, determining that it should be composed of 4 Microservices, Patient, Dental Medical Record, Odontogram and Dental Service Provider; each microservice implements its database, the secure communication between the microservices and the clients is done through an API Gateway of HTTP resources and an authentication token. To evaluate the software architecture, a prototype was developed in which each component was deployed in containers using the Microsoft Azure App Service. On this prototype load tests were performed to evaluate Availability and Performance determining that up to 21 dental records per second can be available with 100% availability, and if the demand of requests increases the architecture scales automatically.

Currently higher education institutions are generating a lot of data as a result of their internal processes, but the limiting human capacities for the processing and analysis of large volumes of data cause these institutions to make hasty decisions and without real support, so it is necessary to use computational tools such as analytical systems that allow to speed up decision making based on data. The type of study is mixed and applied with a quasi-experimental design. An analytical system based on eight stages was built, which are: user characterization, business requirements, storage, technological infrastructure, feature engineering, computational processing, visualization and decision making. The analytical system was validated with data from the SQL Server management system of the academic services office and fifteen Excel files from the admission office oriented to academic data in a higher educational institution. The use of various algorithms such as support vector machine, random forest, k nearest neighbours, and neural networks, among others, was applied. The evaluation of the results generated by these algorithms allowed us to identify the support vector machine algorithm with 94.21% accuracy.