Terrorist attacks affect the confidence and security of citizens; it is a violent form of a political struggle that ends in the destruction of order. In the current decade, along with the growth of social networks, terrorist attacks around the world are still ongoing and have had potential growth in recent years. Consequently, it is necessary to identify where the attacks were committed and where is the possible area for an attack. The objective is to provide assertive solutions to these events. As a solution, this research focuses on one of the branches of artificial intelligence (AI), which is the Automatic Learning, also called Machine Learning. The idea is to use AI techniques to visualize and predict possible terrorist attacks using classification models, the decision trees, and the Random Forest. The input would be a database that has a systematic record of worldwide terrorist attacks from 1970 to the last recorded year, which is 2018. As a final result, it is necessary to know the number of terrorist attacks in the world, the most frequent types of attacks and the number of seizures caused by region; furthermore, to be able to predict what kind of terrorist attack will occur and in which areas of the world. Finally, this research aims to help the scientific community use artificial intelligence to provide various types of solutions related to global events.
In the Image Processing Research Laboratory (INTI-Lab) of the Universidad de Ciencias y Humanidades, the permission to use the embedded systems laboratory was obtained. INTI-Lab researchers will use this laboratory to do different research related to the processing of large scale videos, climate predictions, climate change research, physical simulations, among others. This type of projects, demand a high complexity in their processes, carried out in ordinary computers that result in an unfavorable time for the researcher. For this reason, one opted for the implementation of a high-performance cluster architecture that is a set of computers interconnected to a local network. This set of computers tries to give a unique behavior to solve complex problems using parallel computing techniques. The intention is to reduce the time directly proportional to the number of machines, giving a similarity of having a low-cost supercomputer. Different performance tests were performed scaling from 1 to 28 computers to measure time reduction. The results will show if it is feasible to use the architecture in future projects that demand processes of high scientific complexity.
In recent years, augmented reality is playing an important role in the world of mobile technology, since it is a way to facilitate the teaching-learning processes, this easy teachinglearning process generates a great contribution in companies, either creating opportunities or changing the way in which companies approach and interact with their end customers, this can mean a remarkable growth of the organization, that is why in this work an augmented reality prototype has been made using the methodology Scrum at the University of Sciences and Humanities of Lima-Peru, but with a focus on the nursing career. Knowing that the problem is the limited learning that students acquire in the classrooms, for which, we want to make use of augmented reality, so that this improves the form of education that is provided to university students. The result obtained, from developing the case study, was an augmented reality prototype for the improvement of education at the University of Sciences and Humanities of Lima-Peru, which shows a virtual model (it depends on the image shown), able to interact with the user, making it attractive and motivating for the student, this prototype was achieved, using Unity (3D development platform, Vuforia (augmented reality software development kit), Microsoft Visual Studio (integrated development environment), the Scrum Methodology (Scrum Pillars, Product Backlog, Product Backlog Estimation, Speed, Backlog Prioritization and Sprint Planning) and the C# Language (C Sharp).
Education is one of the sectors that were affected by the Covid-19 pandemic that forced citizens to keep us isolated in our homes, however, education had to adapt to circumstances and as a result Online Classes were implemented. in the different educational centers for which it is necessary to know the best online learning system to be able to implement in the institutions. In this work, a work methodology based on Analytic Hierarchy Process (AHP) was applied to be able to compare the different LMS shown in this article. In this case study, three different LMS platforms were evaluated. The results of this study indicated that the best LMS platform to implement in schools was Moodle. Based on these results, educational institutions could apply this LMS to their online teaching modality since it offers better characteristics compared to Chamilo and Google Classroom.
The Image Processing Research Laboratory (INTI-Lab) of the Universidad de Ciencias y Humanidades has several research projects related to computer science needing high computational resources. Some of these projects are associated with climate prediction, molecule modeling, physical simulations, and others these applications generate a significant amount of data, regarding the big data issue, despite having excellent hardware features, the final result is obtained after hours or days of calculation depending on the algorithm complexity. For this reason, it is not possible to present optimal solutions at an ideal time. .In this work, we propose the virtualization and configuration of a high-performance cluster (HPC) known commercially as a "supercomputer" that is composed of several computers connected to a high-speed network to behave like a single computer. The virtualization is used to run a scientific algorithm that will apply performance tests using four virtual computers to demonstrate that the reduction of time is achieved by using more machines and thus be able to be implemented in the laboratories of the institution.
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