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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    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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    Implementing the SDGs in Higher Education: A Systematic Review
    (2024-09-05)
    Jackson Edgardo Perez Carpio
    ;
    Javier Linkolk López‐Gonzales  
    ;
    Esteban Tocto Cano  
    ;
    Josué Edison Turpo Chaparro  
    Sustainable development is essential for the well-being of society, as it enabling future generations to meet their own needs. Therefore, it is crucial to work on the 17 Sustainable Development Goals (SDGs) to eradicate poverty, protect the planet and ensure peace and prosperity. Universities must integrate actions related to the SDGs and communicate their results in research that promotes sustainability and sustainable development. A methodology including terms related to academic institutions, such as “universi*”, “college”, “the 17 goals”, “17 SDGs” and “sustainable development goals” was used to conduct a systematic literature search using the Boolean operators AND and OR to perform a systematic literature search. The resulting search string was: ((universi*) OR (college)) AND ((“the 17 goals”) OR (“17 SDGs”)) AND (“sustainable development goals”). The goal was to conduct a systematic literature review, that included articles on the implementation of the SDGs from 2015 to 2023, categorized them into three groups. Articles whose titles were not related to the scope of this research were excluded. It was concluded that research on SDG implementation in universities is limited, which can be attributed to the pandemic, lack of demand, and insufficient implementation of international policies regarding SDG compliance. Received: 16 March 2024 / Accepted: 15 August 2024 / Published: 05 September 2024
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    Teaching vocation as a key dimension of teacher education in Peru: evidence from the CVOC-D scale
    (2026-03-26)
    Edwin Michel Vera-Mendoza
    ;
    Ronny Ivan Gonzales Medina
    ;
    David Leandro Orrego Granados
    ;
    Esteban Tocto Cano  
    ;
    Javier Linkolk López‐Gonzales  
    This study aimed to examine the psychometric properties of the Teaching Vocation Scale (CVOC-D), designed to assess teaching vocation within the Peruvian educational context, where no specific instruments currently exist. Using an instrumental design and quantitative approach, data were collected from 319 teachers. Reliability and validity analyses indicated satisfactory results, with an overall reliability of ω = 0.869 and acceptable coefficients for the three identified dimensions: vocational awareness (0.805), satisfaction with teaching (0.807), and commitment to education (0.642). An exploratory factor analysis explained 42.6% of the variance, while confirmatory factor analysis demonstrated a moderately adequate model fit (χ 2 = 5123.53, p < 0.001; CFI = 0.824; TLI = 0.796; RMSEA = 0.097). Although three items were eliminated, the final version of the scale retained strong psychometric properties. The findings confirmed that the CVOC-D is a valid and reliable instrument for measuring teaching vocation, aligned with the cultural and professional characteristics of Peruvian teachers. This tool represents a significant contribution to psychological assessment in education, providing a robust basis for future research and practical applications in teacher training and professional development.
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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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    Machine Learning Models for Predicting Professional Disqualification in Peruvian Association Members
    (2026-04-30)
    Manuel Pretel Pretel
    ;
    Yeny Chávez Llempén
    ;
    Abel Angel Sullon Macalupu
    ;
    Paulo Canas Rodrigues
    ;
    Javier Linkolk López‐Gonzales  
    The disqualification of licensed professionals for non-payment of their monthly fees constitutes a significant operational risk to the financial sustainability of professional associations. This problem highlights the need for predictive tools that can anticipate the risk of disqualification and protect institutional stability. The main objective of this study was to develop a supervised machine learning model for estimating the risk of disqualification among registered professionals based on historical and contextual variables. An empirical, applied, and quantitative study was conducted by analyzing more than 5.7 million financial records corresponding to 27,964 registered professionals. Multiple supervised classification algorithms, including ensemble models such as CatBoost and XGBoost, were evaluated using stratified cross-validation and class-balancing techniques to address the substantial imbalance in the data. The results indicated that CatBoost performed best (F1-score = 57.96%; AUC = 0.72), whereas XGBoost showed greater stability across cross-validation folds. In conclusion, the model developed supports the timely identification of members at high-risk of disqualification, enabling the implementation of early warning systems and proactive institutional financial management strategies.
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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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    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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