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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Miguel Angel Cano Lengua
Universidad Nacional Mayor de San Marcos
Electrical & Electronics Engineering

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Design of a optimization algorithm for binary classification Miguel Angel Cano Lengua; Erik Alex Papa Quiroz; Marco Antonio Alvarado Cifuentes; Carlos Antonio Alvarado Cifuentes
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1596-1608

Abstract

In the present work, the design of a system to classify data is carried out, using the Scrum methodology. The validation was carried out by expert judgment, having favorable results in terms of different criteria such as; integrity, ease of use, innovation, and scalability. Regarding the development of the functional elements of the system, it was obtained; he developed the architecture of the system, the database, and the prototypes, among other points considered. From the implementation of the system, the equation of a classifying plane in three-dimensional space will be obtained, as well as the number of internal iterations that the algorithm develops, the estimated execution time, and the graph of the plane. This system is based on a recently introduced symmetric cone proximal multiplier algorithm to solve separable optimization problems, this algorithm made an application for classification-related support vector machines.
Deep learning techniques business performance optimization in micro, small, and medium-sized enterprises: systematic review Carlos Roberto Sampedro Guaman; Miguel Angel Cano Lengua; Ciro Rodriguez Rodriguez; Igor Aguilar-Alonso
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9819

Abstract

The application of deep learning is transforming how micro, small, and medium-sized enterprises (MSMEs) operate. By using data-driven insights, these firms overcome traditional analytical limitations and improve decision-making. This study explores factors influencing deep learning adoption in MSMEs, identifies effective strategies, and compares performance between companies that implement these methods and those that do not. The objective is to analyze the impact of deep learning on optimizing the performance of MSMEs. The methodology consisted of a scientific review following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) system and a bibliometric analysis to map international contributions. The results show that techniques such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, transformers, and deep reinforcement learning (DRL) are crucial for marketing strategy prediction, customer experience personalization, and inventory management, leading to better return on investment (ROI), loyalty, and efficiency. Despite the potential benefits, there's still no enough research on how small businesses with limited resources use these methods and deal with issues like poor infrastructure and data access. Deep learning is essential for MSMEs' sustainability and competitiveness, even if there are challenges.
Digital maturity assessment models in public administration: a systematic review Miguel Abdias Mendoza Dionicio; Miguel Ángel Cano Lengua; Ciro Rodríguez
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9817

Abstract

Digital transformation (DT) is accelerating societal change and creating major challenges for public organizations seeking to improve efficiency through digital technologies. However, its measurement remains a conceptual and methodological challenge. This study presents a systematic literature review (SLR), conducted under the PRISMA protocol and PICOC strategy, focusing on digital maturity models applied to public administration (PA) between 2020 and 2024. The review covers both scientific databases and institutional gray literature. Five critical aspects were analyzed: included dimensions, internal structural relationships, empirical validation, predictive capacity, and contextual conditions of applicability. Results reveal a recurrent set of dimensions—technology, processes, data, people, and governance—yet with high heterogeneity in levels and approaches. Only a minority of models incorporate causal structures, and fewer than half have been empirically validated. Predictive capacity is almost absent, except for one Bayesian network model. Institutional factors such as digital leadership, budget, and regulatory frameworks strongly influence applicability. Unlike previous reviews, this study integrates a bibliometric analysis and a critical synthesis of enablers and barriers. It concludes that current models are useful for diagnosis but require improvements in structure, validation, and anticipation, providing an updated reference framework for researchers and policymakers in digital governance.
Co-Authors Carlos Antonio Alvarado Cifuentes Carlos Roberto Sampedro Guaman Ciro Rodríguez Ciro Rodriguez Rodriguez Erik Alex Papa Quiroz Igor Aguilar-Alonso Marco Antonio Alvarado Cifuentes Miguel Abdias Mendoza Dionicio