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Angel Cano Lengua, Miguel
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Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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A systematic literature review on the use of artificial intelligence for cybercrime rate forecasting Martin Morales Barrenechea, Manuel; Angel Cano Lengua, Miguel
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.9213

Abstract

Cybercrime has a significant impact on the quality of life and economy of individuals, businesses and countries, and the speed of the increase has made it a pressing issue in today's digital age. This systematic review aims to identify the artificial intelligence models recently developed to forecast the rate of cybercrime and to help authorities and police forces define strategies in the fight against cybercrime. The PRISMA methodology was used with 229 articles retrieved from Scopus, IEEE and Web of Science, of which 30 met the eligibility criteria. The results showed that the traditional machine learning methods random forest, support vector machine (SVM) and logistic regression (LR) excel in their use to forecast cybercrimes by achieving more accurate results among the different methods tested. It was concluded that machine learning methods are, so far, effective in forecasting the rate of cybercrime, with accuracy ratios of up to 99.9%. However, the potential for future research lies in creating new forecasting models such as autoregressive integrated moving average long short term memory (ARIMA-LSTM) proposed in this study to improve the performance and accuracy of cybercrime forecasting.
Predicting demand in changing environments: a review on the use of reinforcement learning in forecasting models Rolando Neira Villar, José; Angel Cano Lengua, Miguel
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8848

Abstract

This systematic review, carried out under the PRISMA methodology, aims to identify how reinforcement learning has been used in demand forecasting, distinguishing the problems they are trying to overcome, recognizing the algorithms used, detailing the performance metrics used, recognizing the performance achieved by these models and identifying the business sectors in which it has been developed. Studies from all sectors were considered to expand the search range. A total of 24 articles were qualitatively analyzed, and the main results were that reinforcement learning has been used mainly for the selection or dynamic integration of the best predictors from a base of them to adapt to changing environments; whereas forecasting in volatile and complex environments is the main issue addressed; whereas Q-learning (QL), deep q network (DQN), double deep q network (DDQN), and deep deterministic policy gradient (DDPG) are the most widely used algorithms; and that, finally, the sectors of electric power, thermal energy, transport and telecommunications are the sectors where this type of forecast has been developed. Finally, given that all the models studied lack mechanisms for detecting concept drift, a new use of reinforcement learning for this purpose is proposed.
Co-Authors Martin Morales Barrenechea, Manuel Rolando Neira Villar, José