Tocto Cano, Esteban

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Tocto Cano, Esteban
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estocan@upeu.edu.pe
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  • Publicacion
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    A Systematic Review of the Application of Maturity Models in Universities
    (2020-10-01)
    Esteban Tocto Cano  
    ;
    Sandro Paz Collado
    ;
    Javier Linkolk López‐Gonzales  
    ;
    Josué Edison Turpo Chaparro  
    A maturity model is a widely used tool in software engineering and has mostly been extended to domains such as education, health, energy, finance, government, and general use. It is valuable for evaluations and continuous improvement of business processes or certain aspects of organizations, as it represents a more organized and systematic way of doing business. In this paper, we only focus on college higher education. For this reason, we present a novel approach that allows detecting some gaps in the existing maturity models for universities, as they are not models that address the dimensions in their entirety. To identify these models and their validities, as well as a classification of models that were identified in universities, we carried out a systematic literature review on 27,289 articles retrieved with respect to maturity models and published in peer-reviewed journals between 2007 and 2020. We found 23 articles that find maturity models applied in universities, through exclusion and inclusion criteria. We then grouped these items into nine categories with specific purposes. We concluded that maturity models used in Universities move towards agility, which is supported by the semantic web.
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    Modelo de Control Operacional basado en el Modelo de Control Estratégico Balanced Scorecard - Operational Control Model based on the model of strategic control Balanced Scorecard
    (2014-06-03)
    Esteban Tocto Cano  
    ;
    Héctor José Ñopo Aguilar
    La presente investigación tiene como propósito identificar similitud de estructura entre un Modelo de Gestión Estratégica Balanced Scoreard (BSC) y el Modelo de Gestión por Procesos; de tal forma que se pueda diseñar una herramienta de control, basada en la estructura identificada, que pueda servir tanto para el Control Estratégico como para el Control Operacional. Se han definido dos modelos, representando dos formas complementarias de analizar la gestión empresarial, se presenta una estructura similar para controlar la estrategia y controlar los procesos, ambos temas muy diferentes.
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    Data-Driven Socioeconomic Segmentation for Residential Energy Planning: A Machine Learning Approach
    (2026-05-05)
    Lucas Camaz Ferreira
    ;
    Felipe Leite Coelho da Silva
    ;
    Josiane da Silva Cordeiro
    ;
    Javier Linkolk López‐Gonzales  
    ;
    Esteban Tocto Cano  
    The Brazilian residential sector is one of the largest consumers of electricity, making residential energy consumption a critical component of national energy systems. Electricity consumption patterns in this sector are closely associated with household appliance ownership and, consequently, with socioeconomic status. For residential energy planning to operate more equitably and efficiently, it is essential that consumption analyses be aligned with the socioeconomic conditions of the population. This study examines the role of socioeconomic variables in residential energy planning through the application of supervised machine learning algorithms within a data-driven socioeconomic segmentation framework. Decision trees, support vector machines, and artificial neural networks were implemented using data from the Brazilian residential sector to evaluate model performance and to determine the extent to which household socioeconomic status can be inferred from variables related to appliance ownership and electricity consumption characteristics. The results showed that household appliances, such as refrigerators, microwave ovens, and air conditioners, exhibited substantial predictive power in relation to socioeconomic status, thus improving the interpretation and understanding of residential energy consumption from a multidimensional perspective. The neural network model achieved the highest predictive performance. By enabling data-driven socioeconomic segmentation based on observable electricity consumption patterns, this approach provides relevant insights for residential energy planning and contributes to more targeted and equitable energy policy design, supporting Sustainable Development Goal 7 on Affordable and Clean Energy and Sustainable Development Goal 10 on Reduced Inequalities.
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    AI-driven optimization in cloud computing: a systematic review of cost, resource management, and security
    (2026-04-30)
    Ronaldy Solano Ito López
    ;
    Romel Gutierrez Oscata
    ;
    Ángel Rosendo Condori-Coaquira
    ;
    Ronny Ivan Gonzales Medina
    ;
    Javier Linkolk López‐Gonzales  
    Cloud computing environments face persistent structural challenges in cost control, dynamic resource allocation, and security risk management, which traditional infrastructure approaches fail to address adequately. This systematic literature review aimed to synthesize empirical evidence on the application of artificial intelligence (AI) and machine learning (ML) models for cost optimisation, resource management, and security enhancement in cloud computing environments. Following the PRISMA 2020 guidelines and the Kitchenham-Charters methodology, a structured search was conducted across IEEE Xplore, Web of Science, ScienceDirect, and the ACM Digital Library, covering the period 2020-2025. From an initial pool of 216 records, 18 primary studies were selected after applying the PICOC framework, predefined inclusion and exclusion criteria, and a dual-reviewer quality assessment process yielding substantial inter-rater agreement (Cohen's κ = 0.86). The synthesized evidence demonstrates that predictive provisioning systems and intelligent load-balancing mechanisms reduce operational costs by up to 85%, metaheuristic algorithms such as the Whale Optimization Algorithm and Particle Swarm Optimization improve energy efficiency by 30%-40% and increase resource utilization by up to 80%, and deep learning-based intrusion detection systems achieve accuracy levels exceeding 92%. These findings confirm that AI constitutes a structural mechanism for strengthening economic efficiency, operational resilience, and the sustainability of cloud infrastructures. However, heterogeneity in simulation environments, limited validation in production-scale deployments, and insufficient coverage of virtual machine migration dynamics represent critical gaps requiring standardized benchmarking frameworks and empirical validation in hybrid and multicloud architectures. A quantitative synthesis (Table 1) reveals that metaheuristic algorithms achieve 30%-40% cost and energy efficiency improvements, while ensemble deep learning approaches attain >97% security threat detection rates.
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    Modelo de Control Operacional basado en el Modelo de Control Estratégico Balanced Scorecard
    (2014-06-03)
    Esteban Tocto Cano  
    ;
    Héctor José Ñopo Aguilar
    La presente investigacion tiene como proposito identificar similitud de estructura entre un Modelo de Gestion Estrategica Balanced Scoreard  (BSC) y el Modelo de Gestion por Procesos; de tal forma que se pueda disenar una herramienta de control, basada en la estructura identificada, que pueda servir tanto para el Control Estrategico como para el Control Operacional. Se han definido dos modelos, representando dos formas complementarias de analizar la gestion empresarial, se presenta una estructura similar para controlar la estrategia y controlar los procesos, ambos temas muy diferentes.
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    A Holistic Maturity Model for Quality Assessment and Innovation in Peruvian Universities
    (2025-01-24)
    Esteban Tocto Cano  
    ;
    Sandro Paz Collado
    ;
    Javier Linkolk López‐Gonzales  
    This study proposes a holistic maturity model to evaluate and optimize the performance of Peruvian universities. It addresses key dimensions such as favorable governance, university talent (including students, faculty, and administrators), substantial resources, and results. It is based on the Design Science Research methodology and the Mettler framework. On the other hand, the Delphi method was selected for its ability to consolidate expert opinion. Aiken’s V coefficient was used to determine content validity, evaluating criteria such as clarity, relevance, and coherence, to ensure the reliability of the instrument. This model defines concrete practices for each maturity level, facilitating the progressive implementation of improvements in different university contexts. It contributes to Education 4.0 through the IT strategic alignment practices of the enabling governance dimension, promoting the implementation of personalized teaching methods and hybrid learning models. Regarding the Society 5.0 approach, the model prioritizes social impact and environmental sustainability through university social responsibility, ensuring that universities contribute to human and technological development. Finally, this proposal will support decision making in university management and educational policies in Peru and in international contexts.
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    Optimización y cuantificación de procesos utilizando BPM - Process Optimization and Quantification using BPM
    (2011-11-01)
    Esteban Tocto Cano  
    Se muestra una propuesta para la Optimización y la Cuantificación de Procesos usando herramientas Business Process Management (BPM) en el ámbito universitario. Se optimizó y automatizó el Proceso de Gestión de Prácticas Pre Profesionales (PGPP) de la Universidad Peruana Unión, filial Tarapoto (UPeU FT). El desarrollo del modelo se realizó en función a entrevistas en el nivel operativo como a nivel de dirección, la optimización del proceso se efectuó utilizando una herramienta para la construcción de modelos BPM. La herramienta cuenta con diferentes utilitarios los cuales permitieron redefinir los procesos, subprocesos, tareas, entregables, roles, responsabilidades. En términos de la medición de efectividad del proceso propuesto se implementó en la solución una serie de Key Performance Indicators (KPI’s) que permitieron la medición de la efectividad de la solución puesta. El enfoque cuantitativo, así la definición y trabajo con los indicadores fueron definidos en función a los diversos principios expuestos en la metodología de BPM.
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    Exploring patterns in intercultural bilingual education in Peru using machine learning
    (2026-06-05)
    Jenny Tarrillo Vásquez
    ;
    Lina Mamani
    ;
    Esteban Tocto Cano  
    ;
    Hasnain Iftikhar
    ;
    Ronny Ivan Gonzales Medina
    Introduction Intercultural Bilingual Education (IBE) in Peru aims to provide culturally and linguistically relevant instruction for Indigenous students; however, its implementation continues to face structural, linguistic, and resource-related challenges. This study identified latent patterns in IBE implementation using machine learning techniques and compared the predictive performance of different classification algorithms. Methods A dataset comprising 84,558 institutional records and 17 variables was analyzed. After data preprocessing, including missing-value imputation, categorical encoding, and dimensionality reduction, multi-class classification models were developed to predict five IBE implementation scenarios. Eight machine learning algorithms were evaluated under different training-test configurations (80/20, 50/50, 25/75, and 10/90). Model performance was assessed using accuracy, precision, recall, weighted F1-score, and five-fold cross-validation. Results Ensemble-based methods consistently outperformed linear and distance-based classifiers. Gradient Boosting achieved the best performance (accuracy ≈ 0.77; weighted F1-score = 0.74), followed by Random Forest and K-Nearest Neighbors. Cross-validation confirmed model robustness, with mean accuracies ranging from 0.766 to 0.774 and low standard deviations (< 0.007). Discussion The findings demonstrate that machine learning models can effectively capture complex and nonlinear relationships in educational systems characterized by linguistic and institutional diversity. The proposed framework offers a scalable and reproducible approach for evidence-based policy development, supporting data-driven strategies to improve equity and quality in intercultural bilingual education.
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    Optimización y cuantificación de procesos utilizando BPM
    (2013-04-29)
    Esteban Tocto Cano  
    Se muestra una propuesta para la Optimización y la Cuantificación de Procesos usando herramientas Business Process Management (BPM) en el ámbito universi- tario. Se optimizó y automatizó el Proceso de Gestión de Prácticas Pre Profesiona- les (PGPP) de la Universidad Peruana Unión, filial Tarapoto (UPeU FT). El desarrollo del modelo se realizó en función a entrevistas en el nivel operativo como a nivel de dirección, la optimización del proceso se efectuó utilizando una herramienta para la construcción de modelos BPM. La herramienta cuenta con diferentes utilitarios los cuales permitieron redefinir los procesos, subprocesos, tareas, entregables, roles, responsabilidades. En términos de la medición de efectividad del proceso propuesto se implementó en la solución una serie de Key Performance Indicators (KPI’s) que permitieron la medición de la efectividad de la solución puesta. El enfo- que cuantitativo, así la definición y trabajo con los indicadores fueron definidos en función a los diversos principios expuestos en la metodología de BPM.
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