This work addresses assessing air quality and noise in urban environments by integrating predictive models and Internet of Things technologies. For this, a model generated heat maps for PM2.5 and noise levels, incorporating traffic data from open sources for precise contextualization. This approach reveals significant correlations between high pollutant/noise concentrations and their proximity to industrial zones and traffic routes. The predictive models, including convolutional neural networks and decision trees, demonstrated high accuracy in predicting pollution and noise levels, with correlation values such as R2 of 0.93 for PM2.5 and 0.90 for noise. These findings highlight the need to address environmental issues in urban planning comprehensively. Furthermore, the study suggests policies based on the quantitative results, such as implementing low-emission zones and promoting green spaces, to improve urban environmental management. This analysis offers a significant contribution to scientific understanding and practical applicability in the planning and management of urban environments, emphasizing the relevance of an integrated and data-driven approach to inform effective policy decisions in urban environmental management.

Currently, private universities, as a result of the pandemic that the world is facing, are going through very delicate moments in several areas, both academic and financial. Academically, there are learning problems and these are directly related to the dropout rate, which brings financial problems. Added to this are the economic problems caused by the pandemic, where the rates of students who want to access a private education have dropped considerably. For this reason, it is necessary for all private universities to have support to improve their student income and avoid cuts in budgets and resources. However, the academic part represents a great effort to fulfill their academic activities, which are the priority, with attention on those interested in pursuing a training programs. To solve these problems, it is important to integrate technologies such as Chatbots, which use artificial intelligence in such a way that tasks such as providing information on an academic courses are addressed by them, reducing the administrative burden and improving the user experience. At the same time, this encourages people to be a part of the college.

Education is one of the sectors that improves the future of societies; unfortunately, the pandemic generated by coronavirus disease 2019 has caused a variety of problems that directly affect learning. Universities have found it necessary to begin a transition towards remote or online educational models. To do so, the only method that guarantees the continuity of classes is using information and communication technologies. The transition in the foreground points to the use of technological platforms that allow interaction and the development of classes through synchronous sessions. In this way, it has been possible to continue developing both administrative and academic activities. However, in effective education, there are factors that create an ideal environment where the generation of knowledge is possible. By moving from traditional educational models to remote models, this environment has been disrupted, significantly affecting student learning. Identifying the factors that influence academic performance has become the priority of universities. This work proposes the use of intelligent techniques that allow the identification of the factors that affect learning and allow effective decision-making that allows improving the educational model.

Currently, universities are going through a critical moment due to the coronavirus disease in 2019. To prevent its spread, countries have declared quarantines and isolation in all sectors of society. This has caused many problems in the learning of students, since, when moving from a face-to-face educational model to a remote model, several academic factors such as psychological, financial, and methodological have been overlooked. To exactly identify the variables and causes that affect learning, in this work a data analysis model using a Hadoop framework is proposed. By processing the data, it is possible to identify and classify students to determine the problems they present in different learning activities. The results are used by an artificial intelligence system that takes student information and converts it into knowledge, evaluates the academic performance problems they present, and determines what type of activity aligns with the students. The artificial intelligence system processes the information and recommends activities that focus on each student’s abilities and needs. The integration of these systems to universities creates an adaptive educational model that responds to the new challenges of society.

Currently, society is going through a health event with devastating results. In their desire to control the 2019 coronavirus disease, large organizations have turned over the execution of their activities to the use of information technology. These tools, adapted to the use of the Internet, have been presented as an effective solution to the measures implemented by the majority of nations where quarantines are generalized. However, the solution given by information technologies has several disadvantages that must be solved. The most important in this regard is with the serious security incidents that exist, where many organizations have been compromised and their data has been exposed. As a solution, this work proposes the design of a guide that allows for the implementation of a computer incident response team on a university campus. Universities are optimal environments for the generation of new technologies; they also serve as the ideal test bed for the generation of security policies and new treatments for incidents in an organization. In addition, with the implementation of the computer incident response team in a university, it is proposed to be part of a response group to any security incident at the national level.

Currently, cybersecurity is a topic of great importance for society. With the increase in the use of technology and the digitization of many activities, the number of cyber threats to which individuals and organizations are exposed has increased. In addition, the COVID-19 pandemic has accelerated the digitization of many processes, further increasing the risk of cyberattacks. One of the main causes of these problems is the lack of cyber security awareness, as many people and organizations do not have a proper understanding of cyber threats and the measures, they must take to protect themselves. As a solution to the lack of cybersecurity knowledge, this work proposes the development of a Capture the Flag platform for learning about cybersecurity. The objective is to provide a tool that allows the education of future professionals in this field and covers the existing demand for this type of specialist. The platform is made up of two sections, one for learning and the other for CTF. The first section allows teachers to contribute to the teaching of their students using challenges. The second section allows one to carry out competitions with effective results when acquiring knowledge and experience. The platform is evaluated using questionnaires and surveys to measure whether the platform fulfills its purpose.