Examinando por Autor "Alfaro, Miguel"

Mostrando 1 - 6 de 6
  • No hay miniatura disponible
    Ítem
    Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem
    (Hindawi Limited, 2022) Palominos, Pedro; Ortega, Carla; Alfaro, Miguel; Fuertes, Guillermo; Vargas, Manuel; Camargo, Mauricio; Parada, Victor; Gatica, Gustavo
    Due to the difficulty in solving combinatorial optimization problems, it is necessary to improve the performance of the algorithms by improving techniques to deal with complex optimizations. This research addresses the metaheuristics of marriage in honey-bees optimization (MBO) based on the behavior of bees. The current study proposes a technique for solving combinatorial optimization problems within proper computation times. The purpose of this study focuses on the travelling salesperson problem and the application of chaotic methods in important sections of the MBO metaheuristic. Three experiments were conducted to measure the efficiency and quality of the solutions: (1) MBO with chaos to generate initial solutions (MBO2); (2) MBO with chaos in the workers (MBO3); and (3) MBO with chaos to generate initial solutions and the workers (MBO4). The application of chaotic functions in MBO was significantly better at solving the travelling salesperson problem. © 2022 Pedro Palominos et al.
  • Miniatura
    Ítem
    Framework for the Strategic Adoption of Industry 4.0: A Focus on Intelligent Systems
    (MDPI, 2023-10) Serey, Joel; Alfaro, Miguel; Fuertes, Guillermo; Vargas, Manuel; Ternero, Rodrigo; Duran, Claudia; Sabattin, Jorge; Gutiérrez, Sebastián
    Despite growing interest in smart manufacturing, there is little information on how organizations can approach the alignment of strategic processes with Industry 4.0. This study seeks to fill this knowledge gap by developing a framework for the integration of Industry 4.0 techniques and artificial intelligence systems. This framework will serve as a conceptual guide in the digital transformation processes toward Industry 4.0. This study involved a systematic literature review of the important methodological proposals and identification of thematic axes, research topics, strategic objectives, challenges, drivers, technological trends, models, and design architectures. In total, 160 articles were selected (120 were published between 2017 and 2022). The results provide insights into the prospects for strategic alignment in the adoption of Industry 4.0. The conceptualization of the framework shows that Industry 4.0 needs strategic adjustments mainly in seven objectives (business model, change mindset, skills, human resources, service level, ecosystem, interconnection, and absorption capacity) derived from 10 thematic axes and 28 research topics. Understanding the strategic adoption of Industry 4.0 and artificial intelligence is vital for industrial organizations to stay competitive and relevant in a constantly evolving business landscape.
  • Miniatura
    Ítem
    National Health Systems and COVID-19 Death Toll Doubling Time
    (Frontiers Media S.A., 2021-07) Alfaro, Miguel; Muñoz-Godoy, Diego; Vargas, Manuel; Fuertes, Guillermo; Duran, Claudia; Ternero, Rodrigo; Sabattin, Jorge; Gutierrez, Sebastian; Karstegl, Natalia
    Coronavirus disease 2019 (COVID-19) has placed stress on all National Health Systems (NHSs) worldwide. Recent studies on the disease have evaluated different variables, namely, quarantine models, mitigation efforts, damage to mental health, mortality of the population with chronic diseases, diagnosis, use of masks and social distancing, and mortality based on age. This study focused on the four NHSs recognized by the WHO. These systems are as follows: (1) The Beveridge model, (2) the Bismarck model, (3) the National Health Insurance (NHI) model, and (4) the “Out-of-Pocket” model. The study analyzes the response of the health systems to the pandemic by comparing the time in days required to double the number of disease-related deaths. The statistical analysis was limited to 56 countries representing 70% of the global population. Each country was grouped into the health system defined by the WHO. The study compared the median death toll DT, between health systems using Mood's median test method. The results show high variability of the temporal trends in each group; none of the health systems for the three analyzed periods maintain stable interquartile ranges (IQRs). Nevertheless, the results obtained show similar medians between the study groups. The COVID-19 pandemic saturates health systems regardless of their management structures, and the result measured with the time for doubling death rate variable is similar among the four NHSs. © Copyright © 2021 Alfaro, Muñoz-Godoy, Vargas, Fuertes, Duran, Ternero, Sabattin, Gutierrez and Karstegl.
  • No hay miniatura disponible
    Ítem
    Pattern Recognition and Deep Learning Technologies, Enablers of Industry 4.0, and Their Role in Engineering Research
    (MDPI, 2023-02) Serey, Joel; Alfaro, Miguel; Fuertes, Guillermo; Vargas, Manuel; Durán, Claudia; Ternero, Rodrigo; Rivera, Ricardo; Sabattin, Jorge
    The purpose of this study is to summarize the pattern recognition (PR) and deep learning (DL) artificial intelligence methods developed for the management of data in the last six years. The methodology used for the study of documents is a content analysis. For this study, 186 references are considered, from which 120 are selected for the literature review. First, a general introduction to artificial intelligence is presented, in which PR/DL methods are studied and their relevance to data management evaluated. Next, a literature review is provided of the most recent applications of PR/DL, and the capacity of these methods to process large volumes of data is evaluated. The analysis of the literature also reveals the main applications, challenges, approaches, advantages, and disadvantages of using these methods. Moreover, we discuss the main measurement instruments; the methodological contributions by study areas and research domain; and major databases, journals, and countries that contribute to the field of study. Finally, we identify emerging research trends, their limitations, and possible future research paths. © 2023 by the authors.
  • No hay miniatura disponible
    Ítem
    Procedimiento de agrupación de estudiantes según riesgo de abandono para mejorar la gestión estudiantil en educación superior
    (Lundiana, 2022) Hinojosa, Mauricio; Derpich, Iván; Alfaro, Miguel; Ruete, David; Caroca, Alejandro; Gatica, Gustavo
    The complex problem of student dropout represents an opportunity for the application of data mining technology and methods in higher education. The objective of this research is to obtain the profile of students at risk of dropping out and thus generate student management plans that impact on the variables that explain this situation. For this, it is proposed to use a CRISP-DM methodological structure, applying statistical tools and unsupervised machine learning. The cross-sectional analysis was carried out on a universe of freshmen day students at a private Chilean university. The sociodemographic and behavioural variables used were based on attrition theory and expert judgment, and the data were obtained from the historical records available at the Institution. To obtain the variables that most influenced dropout, correlation and principal component analyses were performed. The application of agglomerative hierarchical clustering and rough sets technique produced four profiles of students with their respective association rules and five academic variables that allowed the design of a support system to reduce dropout and promote retention. © 2022 Universidade Federal de Minas Gerais. All rights reserved.
  • Miniatura
    Ítem
    Reverse logistics for solid waste from the construction industry
    (Hindawi Limited, 2021) Vargas, Manuel; Alfaro, Miguel; Karstegl, Natalia; Fuertes, Guillermo; Gracia, María D.; Mar-Ortiz, Julio; Sabattin, Jorge; Duran, Claudia; Leal, Naudy
    This article reviews studies on the application of reverse logistics in solid waste from the construction industry. The main objective is to provide a summary of current knowledge and specific areas for future research. In addition, construction, as an economic sector, is in a continuous search for new tools to improve its processes, so this research provides the current situation of the relationship between reverse logistics and solid waste in the industry. The review methodology was content analysis of scientific literature published between 1997 and 2020, and total of 66 articles were used. 73% correspond to research articles, around 13% are case studies, and 12% are literature reviews. Only one of the articles is a survey. In addition, 52% of the works reviewed correspond to solid waste studies and 49% are related to the construction industry; only one publication does not classify in any. The most used keywords for the identification of published works were reverse logistics and supply chain; both terms are frequently related to the process and general management of solid waste and construction. The least used term was the literature review that shows the low number of articles that provide a summary of the proposed topic. Finally, three materials were chosen for the study because they are the most used in construction: metals, bricks, and concrete. 15% of the articles study all three, 18% study only metals, and 63% study other materials or are related to construction and solid waste in general.