Obesity is considered an epidemic that is continuously growing around the world. Heart diseases, diabetes, and bone and joint diseases are some of the diseases that people who are overweight or obese can develop. One of the vital causes of those disorders is poor nutrition education; there is no raising awareness about eating healthy food and practicing physical activities to burn off the excess energy. Therefore, it is necessary to use new technologies to build methods/tools that help people to overcome these avoidable nutrition disorders. For this reason, we implemented a recommendation engine capable of identifying the different levels of overweight and obesity in users and providing dietary strategies to mitigate them. To do so, we defined the Ontology of Dietary Recommendations (ODR) with axioms to model recipes, ingredients, and a set of diets to assist people who suffer from obesity. We validated the defined model by using a real set of individuals who were anonymized. A panel of advisors evaluated each individual record and suggested the most appropriate diets from those included in the ontology. Then, the proposed system was asked to also provide diet recommendations for each individual, which were compared with those proposed by the advisors (ground truth), reaching a mean accuracy of 87%.

Artificial Intelligence in Education is a field of scientific research that has emerged over 3 decades and is particularly interested in the development of AI-based tools to support and understand educational processes. This research aims to present the state of Artificial Intelligence in Education, using a comprehensive literature review method. It comprised 3 main stages: selection, classification and analysis of the literature. This allowed to identify the applications, technologies, challenges and ethical implications of Artificial Intelligence in Education.

In recent years, organizations and Higher Education Institutions have generated large amounts of data through various computer systems. This has marked the transition of an era; now, we are in the era of Big Data where efforts are focused on what to do with the data generated; due to this, Business Intelligence and Analytics, which encompass a set of technologies and strategies that allow analyzing data and providing information to support decision making. Bluefields Indian & Caribbean University (BICU), has expressed interest in recent years in acquiring and implementing tools that allow it to minimize the management time, analysis, extraction and visualization of its academic data. The objective of this project is the development of a Business Intelligence and Analytics based tool to increase the efficiency of decision making at BICU. The development was focused on Bill Inmon’s methodology; in this way, it is guaranteed that the data are organized by topics and entity known by the workers of the institution to ensure a good interpretation of the data, the design of the Data Warehouse, was carried out in a top-down way. Satisfactory results were obtained; however, there are aspects that can be improved, among those considered is to incorporate predictive models to predict whether the academic history prior to college, location of origin, ethnicity or gender influences the academic performance of students.

The Autonomous Regions of the Nicaraguan Caribbean Coast are multiethnic and multicultural, inhabited by the Miskitu, Garifuna, Mestizo, Mayangna, Rama and Creole ethnic groups, each with its customs, traditions and languages. This project aimed to develop a mobile dictionary as a strategy for the rescue and linguistic conservation of the Miskitu and Rama ethnic groups of the Nicaraguan Caribbean Coast, with translation into Spanish and English. The study was applied with a mixed approach of an exploratory nature, based on the use of mobile technology as a strategy for rescue and linguistic conservation through the development of an application, for which the Mobile-D software development methodology was used. The results showed that the proposed mobile dictionary represents a substantial contribution to the rescue and conservation of the Miskitu and Rama languages of the Nicaraguan Caribbean Coast.

Nowadays, small and medium-sized enterprises (SMEs) are forced to make use of technology to gain a foothold in the market, taking advantage of the different tools that are emerging and that, consequently, allow them to remain competitive. Therefore, a predictive model based on Machine Learning is an essential tool, its use generates information that allows making decisions that will positively affect business processes. In this sense, the main objective of the project is to develop a predictive model based on Machine Learning aimed at sales SMEs in Bluefields city, Autonomous Region of the South Caribbean Coast, Nicaragua, which serves as a tool to maximize the profits of these companies. The development methodology adopted in the construction of the predictive model was the "waterfall model", which is the best suited to the context of the project. This methodology suggests a systematic and sequential approach, disciplined and based on analysis, design, testing and maintenance. As a result, a predictive model was obtained with the necessary requirements to remedy the deficiencies of these companies. The model is composed of two modules, the first one is the SGPN's Companion that guarantees the storage, manipulation and security of the data, the second module is the Business Process Management System (BPMS) or prediction model, which allows to carry out the business process studies.

Information and Communication Technologies (ICT) and the Internet are disruptive factors in educational systems; however, the digital divide prevents achieving the desired educational transformation. This research was carried out with the objective of exposing the digital gap in which the educational systems of communities with minimal or no access to these technologies coexist, due to their geographical location. It was conducted in 2 of the 12 municipalities of the South Caribbean Coast Autonomous Region (RACCS) of Nicaragua, the selected municipalities were Bluefields and Pearl Lagoon. The study was descriptive with a qualitative approach; the sample consisted of 180 trainees. The results made it possible to identify the state of the digital gap in the communities of the RACCS and clear indicators to reduce the digital gap of the educational system in these communities, giving clear guidelines of the challenge we face in the educational transformation through the Internet.