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Rodriguez, Ciro
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Electrical & Electronics Engineering

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Autoregressive integrated moving average-long short-term memory optimized hybrid model for cybercrime forecasting Martin Morales-Barrenechea, Manuel; Rodriguez, Ciro; David Cancho-Rodriguez, Ernesto; Richard Huamantingo Navarro, Ricardo
Bulletin of Electrical Engineering and Informatics 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/eei.v14i5.9769

Abstract

Cybercrime represents a growing global threat with adverse impacts on citizen security, the digital economy, and quality of life. In this context, an optimized hybrid model was developed that combines autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) for the monthly forecast of cybercrime complaints, applying the cross industry standard process for data mining (CRISP-DM) methodology and applying Python based data science techniques. The model combines the capabilities of the ARIMA statistical approach to capture linear components with the power of LSTM neural networks to address nonlinear temporal relationships. The architecture was trained on a set of 60,378 official records of complaints registered by the National Police of Peru between 2018 and 2023, achieving a mean absolute percentage error (MAPE) of 10.73%, which represents a significant improvement over the singular ARIMA and LSTM predictive models. Compared to previous studies in crime, health, and agriculture, this approach showed a greater ability to generalize over complex time series. It is concluded that the application of the proposed model is a relevant contribution for the police and other security agencies to anticipate crime trends and design preventive and effective strategies to combating cybercrime.
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 Cano Lengua, Miguel Angel David Cancho-Rodriguez, Ernesto Martin Morales-Barrenechea, Manuel Mendoza Dionicio, Miguel Abdias Richard Huamantingo Navarro, Ricardo