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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Cano Lengua, Miguel Angel
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Design of a chatbot in a mobile application for managing payments and controlling activities in a fast school organization Medina, Gustavo Teves; Cano Lengua, Miguel Angel; Medrano, Hugo Villaverde
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1271-1286

Abstract

The fast school (FS) educational organization, like many contemporary educational institutions, faces challenges in efficient payment management and rigorous control of activities. Technology, particularly through mobile applications, has shown to be a potential solution to these problems, allowing institutions to stay at the forefront and provide optimized services to their educational community. Therefore, this research work focuses on how a chatbot, integrated into a mobile application, can improve payment management and control of activities in the FS educational organization. Through a detailed study on current trends in educational technology, the design and development of a chatbot adapted to the specific needs of the organization is presented. This chatbot not only facilitates payment processes, offering immediate responses and managing transactions, but also allows for more efficient control of academic and extracurricular activities, improving the experience of its users. In conclusion, the integration of chatbots in mobile applications is presented as a viable and promising solution to face and overcome management challenges in modern educational environments, providing adaptive and user-centered tools that enhance the operational efficiency of institutions. This work is developed with the Scrum methodology and presents a security gateway validated by a digital token.
Web system to enhance technical supervision of incidents at the hydrocarbons regulatory institution in Lima–2023 Caceres Sanchez, Cynthia Elvia; Diburga Evangelista, Luis Alfredo; Cano Lengua, Miguel Angel; Rosas Culcos, Fredy Robert
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7701

Abstract

In this article, the implementation of a web system was carried out to improve the process of technical supervision of incidents of a hydrocarbon regulatory company because time was lost in carrying out each process; this research was developed using the SCRUM methodology as it is an agile methodology and adapted to our research. Using the process, events and artifact, it was possible to design the prototypes of the system, architecture, and database. Finally, the implementation was carried out among other important points obtained as results; the average level of optimization of the incident assignment process, derived from the observations, is 91.05% efficiency in assigning incidents to specialists. Regarding the 95% confidence interval for this indicator, it is between 88.98% and 93.11% efficiency, representing two standard deviations with respect to the mean. Regarding the average response time to incidents in all states, obtained from observations, it is 15 days. The 95% confidence interval for this indicator ranges between 14 and 18 days, which represents two standard deviations from the mean. The system is intuitive and not complex. With the implementation of the web system, processes are automated and end user satisfaction is obtained.
Efficiency search: application of nature-inspired algorithms in artificial intelligence forecasting models Neira Villar, José Rolando; Cano Lengua, Miguel Angel
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3528-3541

Abstract

This study reviews how nature-inspired optimization algorithms (NIOAs) have been applied to artificial intelligence-based demand forecasting, using preferred reporting items for systematic reviews and meta-analyses (PRISMA) and clustering analysis to examine 36 selected articles. The findings reveal that NIOAs, particularly genetic algorithms and swarm intelligence methods, including their hybrids, have been frequently applied to long short-term memory (LSTM) and other backpropagation neural network models (BPNN). A key insight is the differentiated application of NIOAs depending on network depth: In shallow networks, they have been effectively used to optimize trainable parameters, whereas in deep networks, their role has focused primarily on hyperparameter optimization due to the prohibitive dimensionality of trainable weights. In all studies, NIOA-optimized models consistently outperform conventional baselines based on backpropagation. However, persistent challenges such as excessive execution times and slow convergence have led to the development of more efficient hybrid strategies and adaptive mechanisms for automated exploration-exploitation control. By mapping explored and unexplored pathways, summarizing key outcomes and techniques, and identifying promising methodologies, this review offers a practical foundation to guide future experiments and implementations involving NIOA-based optimization strategies in neural network models. As a conceptual contribution, it also proposes an innovative use of multispace optimization to address one of the most critical challenges identified: the optimization of trainable parameters in deep neural networks.
Digital maturity assessment models in public administration: a systematic review Mendoza Dionicio, Miguel Abdias; Cano Lengua, Miguel Ángel; Rodríguez, Ciro
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 Caceres Sanchez, Cynthia Elvia Diburga Evangelista, Luis Alfredo Medina, Gustavo Teves Medrano, Hugo Villaverde Mendoza Dionicio, Miguel Abdias Neira Villar, José Rolando Rodriguez, Ciro Rosas Culcos, Fredy Robert