https://revistas.ulima.edu.pe/index.php/CIIS/issue/feed Actas del Congreso Internacional de Ingeniería de Sistemas 2024-07-02T11:31:28-05:00 Hernán Nina hninaha@ulima.edu.pe Open Journal Systems <p><strong>ISSN: </strong>2810-806X (En línea)</p> <p align="justify">The International Congress of Systems Engineering (CIIS), organized annually by the Systems Engineering program within the Faculty of Engineering at the University of Lima, serves as a participatory platform with global reach. It focuses on exploring themes related to emerging technologies, facilitating the exchange of knowledge, experiences, and research in fields such as information systems, information technology, software engineering, computer science, and allied disciplines. This publication compiles the contributions from participants of each annual edition.</p> https://revistas.ulima.edu.pe/index.php/CIIS/article/view/7075 Presentación 2024-07-02T10:42:50-05:00 Nadia Katherine Rodríguez Rodríguez portalrevistas@ulima.edu.pe 2024-07-02T00:00:00-05:00 Copyright (c) 2024 https://revistas.ulima.edu.pe/index.php/CIIS/article/view/7076 Más allá de las fronteras: inteligencias artificiales generativas para el avance académico sostenible 2024-07-02T10:42:51-05:00 Mushtaq Bilal mushtaq@sdu.dk <p>Desde que fue lanzado en noviembre del 2022, el chatbot transformador generativo preentrenado (generative pre-trained transformer, en inglés) de Open IA, más conocido como ChatGPT, se ha convertido en una de las aplicaciones de inteligencia artificial más populares del mundo (OpenIA, s. f.). Académicos a lo largo y ancho del mundo han expresado su preocupación acerca de cómo ChatGPT está transformando dramáticamente el panorama pedagógico y de la investigación. En este artículo discuto algunas de las mejores prácticas para usar ChatGPT con propósitos académicos.</p> 2024-07-02T00:00:00-05:00 Copyright (c) 2024 https://revistas.ulima.edu.pe/index.php/CIIS/article/view/7077 Sustainability and Artificial Intelligent-based Systems 2024-07-02T10:42:54-05:00 Nelly Condori-Fernández n.condori.fernandez@usc.es <p>Artificial intelligence (AI) has evolved with advancements such as deep learning, Python, and deep neural networks. These advancements have driven the rise of AI, with developments like XAI, Small Data, and ImageNet. Generative AI is changing the economy and is expected to have a significant impact in areas such as content creation, software development, and marketing. The European Commission proposes AI regulation based on a risk approach, with different levels of risk and requirements according to the AI category. Software sustainability is multidimensional, covering technical, economic, environmental, and social aspects. Explainability and transparency in AI models are crucial to ensure accountability and trust in their use. Integrating AI systems with legacy systems and processes involves technical and economic considerations and can generate benefits such as process optimization but also requires significant investments.</p> 2024-07-02T00:00:00-05:00 Copyright (c) 2024 https://revistas.ulima.edu.pe/index.php/CIIS/article/view/7078 Planning a Systemic Restructuring of the UNCAUS Medical Education Unit 2024-07-02T10:42:59-05:00 Paola Budan pbudan1@gmail.com Patricia Zachman ppz@uncaus.edu.ar Pablo Campestrini pabloemanuelcampestrini@gmail.com Ruben Ernesto Andreu rubenandreu@uncaus.edu.ar Emmanuel Chávez emmaignacio.chavez@gmail.com <p>In this article, we present a project to achieve the systemic restructuring of the Medical Educational Unit, dependent on the National University of Chaco Austral, Argentina. This Medical Unit has a variety of data about its patients, which is currently not automated in a patient’s Medical History. Because of that, it is difficult to obtain information with high added value for decision-making based on these records, as well as the development of tools for training medical students at the University. Using systemic techniques and tools, we planned changes to transform the data into structured knowledge. We are considering innovations through the incorporation of computer tools, such as databases and others based on Artificial Intelligence (AI), with the aim of representation and manipulation of knowledge to create models powerful enough to study real-life situations. Likewise, for the restructuring to be systemic, mechanisms are devised so that current employees accept the proposed changes without feeling underestimated.</p> 2024-07-02T00:00:00-05:00 Copyright (c) 2024 https://revistas.ulima.edu.pe/index.php/CIIS/article/view/7079 Hacia la sostenibilidad energética: una revisión de literatura del desarrollo de software verde 2024-07-02T11:31:28-05:00 Angelo Rodrigo Taco Jiménez angelotacoj@gmail.com <p class="style-group-105-abstract--txt"><span style="font-size: 13.5pt; color: black;">En el ámbito organizacional y empresarial, el creciente consumo de energía eléctrica por parte de los equipos informáticos representa un desafío en términos de costo, así como también de impacto ambiental. Para abordar esta problemática se propone una revisión de literatura para recopilar y examinar los avances más recientes en el campo del&nbsp;<span class="charoverride-2">software</span>&nbsp;verde y su impacto en la eficiencia del consumo de energía. Para llevar a cabo esta revisión de literatura, se adaptó y aplicó la estrategia de búsqueda PICo y se seleccionaron un total de 23 artículos relevantes. En relación a la problemática abordada, se ha desarrollado el concepto de&nbsp;<span class="charoverride-2">green software</span>, el cual busca crear programas eficientes y sostenibles que optimicen el consumo de energía. Se exploran las herramientas y prácticas de diseño de&nbsp;<span class="charoverride-2">software&nbsp;</span>sostenible, técnicas para el desarrollo de sistemas eficientes en mención de consumo energético y enfoques en cómo abordar los problemas del consumo de energía en centros de datos y los servicios basados en la nube.</span></p> 2024-07-02T00:00:00-05:00 Copyright (c) 2024 https://revistas.ulima.edu.pe/index.php/CIIS/article/view/7080 Heuristics in evaluating the usability of mobile applications – concepts and application 2024-07-02T10:43:07-05:00 Izabella Barros izabellabl.iza@gmail.com Filipe de Assis Santos filipeabnersantos@gmail.com Sharon Candini Sharonrose18@hotmail.com Marcos Dias de Paula marcosdias.projetos@gmail.com <p>Usability is an essential factor in the development of products and interfaces, seeking to ensure that users can use a system effectively, efficiently and satisfactorily. The International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC) definition highlights that usability is related to the ability of a product to be used by specific users to achieve specific goals in a given context. There are several heuristics and principles that can be applied to evaluate and improve the usability of interfaces. Among them, the principles of efficiency, learnability, memorability, satisfaction and errors stand out. This article presents, through a bibliographic review methodology, the concepts and applications of usability heuristics, their importance in the evaluation of mobile applications and also presents Nielsen’s heuristics as the most used basis in the development of systems for evaluating the usability of mobile applications.</p> 2024-07-02T00:00:00-05:00 Copyright (c) 2024 https://revistas.ulima.edu.pe/index.php/CIIS/article/view/7081 Exploración de la Identificación del Riesgo de Accidente Cerebrovascular mediante Machine Learning: Una Revisión Sistemática 2024-07-02T10:43:12-05:00 Lelis Raquel Atencia Mondragon 20190175@aloe.ulima.edu.pe Melany Cristina Huarcaya Carbajal 20192902@aloe.ulima.edu.pe Rosario Guzmán Jiménez rguzman@ulima.edu.pe <p class="style-group-105-abstract--txt"><span style="font-size: 13.5pt; color: black;">Este trabajo busca sistematizar los estudios sobre la identificación del riesgo de sufrir un accidente cerebrovascular (ACV) en las bases de datos Web of Science y Scopus y su relación con el&nbsp;<span class="charoverride-1">machine learning</span>. La información se organizó en tres secciones: factores de riesgo del ACV, técnicas de preprocesamiento de datos y técnicas para identificar el riesgo de ACV haciendo énfasis en las características más relevantes. Los principales resultados son los siguientes: los factores de riesgo se dividen en modificables (ambiente de trabajo y contaminación del aire) y no modificables (sexo, hipertensión). Las técnicas de preprocesamiento más utilizadas son SMOTE, estandarización y eliminación/imputación de valores. Las técnicas más usadas para identificar el riesgo de sufrir ACV son&nbsp;<span class="charoverride-1">support vector machine</span>, r<span class="charoverride-1">andom forest</span>,&nbsp;<span class="charoverride-1">logistic regression</span>,&nbsp;<span class="charoverride-1">naive Bayes</span>,&nbsp;<span class="charoverride-1">k-nearest neighbors&nbsp;</span>y&nbsp;<span class="charoverride-1">decision tree</span>.</span></p> 2024-07-02T00:00:00-05:00 Copyright (c) 2024 https://revistas.ulima.edu.pe/index.php/CIIS/article/view/7082 Prediction of voltage stability in smart power grids 2024-07-02T10:43:18-05:00 Víctor Gil-Vera victor.gilve@amigo.edu.co <p>Smart grids are a system of electricity transmission networks that enable efficient use of electricity without affecting the environment. A smart grid is considered stable when it can maintain reliable and consistent operation while effectively managing various factors that can cause outages or imbalances within the power grid, stability is important since the entire transmission process is time-dependent. In this work, Deep Learning is employed to predict stability in this type of network. A balanced and free database of 60,000 observations with consumer and producer information obtained from simulations was used. This work concludes that this technique obtained a high performance (Accuracy = 97.98 %), which allows us to affirm that Deep Learning can be safely considered for this task. The number of epochs significantly influenced the performance of the ANNs, those with more complex architectures presented a better Accuracy.</p> 2024-07-02T00:00:00-05:00 Copyright (c) 2024 https://revistas.ulima.edu.pe/index.php/CIIS/article/view/7083 Control energético de motores de combustión mediante redes neuronales para aplicaciones de software de automoción 2024-07-02T10:43:26-05:00 Marcos Henrique Carvalho Silva marcoshencarsil@gmail.com André Vinícius Oliveira Maggio andremaggio@usp.br Armando Antônio Maria Laganá armandolagana@terra.com.br João Francisco Justo Filho jjusto@lme.usp.br Bruno Silva Pereira bruno6_spp@hotmail.com Demerson Moscardini demersondonoc@hotmail.com <p class="style-group-105-abstract--txt"><span style="font-size: 13.5pt; color: black;">Este artículo se enfoca en la aplicación del control mediante redes neuronales para la generación de energía en un motor de combustión interna. Se desarrolló una arquitectura de red neuronal de dos capas y se la probó utilizando datos de laboratorio obtenidos de un dinamómetro de banco para identificar con precisión los parámetros de la red. Esta se utiliza para establecer una correlación precisa entre la magnitud de las señales de actuación y las variables fundamentales responsables de regular la generación de energía dentro del sistema. El sistema de control implementa una rutina de programación de ganancia para ajustar la ganancia del controlador, lo que disminuye el incremento para valores de error bajos. Se presenta un modelo de generación de energía que permite diseñar un motor virtual, lo cual facilita el desarrollo de estrategias de control precisas. Para garantizar el funcionamiento seguro del motor, se implementa una rutina de seguridad que previene que la acción de control adquiera valores que podrían tener un impacto negativo en la respuesta del vehículo a las instrucciones del conductor. El controlador desarrollado demuestra un bajo error absoluto promedio en condiciones de estado estable y un bajo tiempo promedio de subida y caída durante estados transitorios, asegurando la capacidad de conducción y el buen rendimiento del motor. Para habilitar la aplicación en software, en estructuras como el hardware-in-the-loop y la unidad de control del motor, se implementan sistemas para garantizar la operación en tiempo real.</span></p> 2024-07-02T00:00:00-05:00 Copyright (c) 2024 https://revistas.ulima.edu.pe/index.php/CIIS/article/view/7084 Design of an application that recognizes medicines through a camera by means of Artificial Intelligence “AI-Pills” 2024-07-02T10:43:32-05:00 Carolina Aliaga portalrevistas@ulima.edu.pe Jesús Dominguez portalrevistas@ulima.edu.pe Iris Liña portalrevistas@ulima.edu.pe Javier Acuña portalrevistas@ulima.edu.pe <p>In this project, we will make a proposal for an innovative mobile application capable of satisfying multiple needs of some people. The need to recognize the right pill in real time, just by scanning it with their camera. For this, we start from a very common need in old people and people with poor vision, which makes it difficult for them to find the name of the pill because of the size of the letters. AI-Pills is a revolutionary solution designed to help users, especially the ones with vision problems, to accurately identify their medications. The application uses advanced image recognition technology and a comprehensive drug database to provide reliable information about prescribed drugs.</p> <p>&nbsp;</p> 2024-07-02T00:00:00-05:00 Copyright (c) 2024