Alvarado-Acuña, Luis Segundo
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Alvarado-Acuña
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Luis Segundo
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lualvar@ucn.cl
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Publicación Method for Revenue Assurance and Financial Fraud Alerting Supported by LDS Techniques(Springer Nature, 2025-03-25) Pérez-Pupo, Iliana; Piñero-Pérez, Pedro Yobanis; Yzquierdo Herrera, Raykenler; Alvarado-Acuña, Luis Segundo; Piñero-Ramírez, Carlos Manuel; Piñero-Ramírez, Pedro E.Revenue assurance and the issuance of Financial Fraud alerts are essential challenges that impact both financial institutions and businesses. This is a complex problem where new challenges and methods consistently arise, necessitating the continuous improvement of detection systems. In this context, the use of various computational intelligence techniques and elements from neutrosophic theory can assist in managing uncertainty and indeterminacy. The methods section includes a brief analysis of the state of the art in artificial intelligence for detecting financial fraud situations. Furthermore, an algorithm is proposed for detecting potential financial fraud situations, supported by data linguistic summarization techniques. These techniques are employed in combination with principles from neutrosophic theory. Subsequently, in the results section, the proposal is validated by comparing the proposed method with a rule-based approach reported in the literature. Additionally, the model is evaluated by subject matter experts, demonstrating the contributions of the proposed model.Publicación Sport Customized Training Plan Assisted by Linguistic Data Summarization(Springer Nature, 2025-03-25) Calderón, Carlos Amador; Pérez-Pupo, Iliana; Yzquierdo-Herrera, Raykenler; Piñero-Pérez, Pedro Yobanis; Palacios-Pulgarón, Rolando; Alvarado-Acuña, Luis SegundoPlanning high-performance sports training involves making decisions under uncertainty. There are no deterministic algorithms that allow handling the complexity of biological systems and individual variability in the construction of plans. For this reason, in this study, artificial intelligence techniques are applied to the construction of personalized training plans. In the methods section, the CACIA model is presented to construct training plans that combine linguistic data summarization techniques with elements of neutrosophic theory. The variables considered by the proposed model were anthropometric indicators, biorhythms, nutrition, and psychological factors. Then, in the results section, the proposal is validated based on an analysis of the performances of athletes at the national championships. The results were compared between the control and experimental groups using non-parametric tests and the SPSS tool. It was found that the CACIA model significantly improved the results of the experimental group compared with the control group. In this study, the use of linguistic summarization of the data allowed the creation of linguistic summaries that were used in the adaptation and improvement of the constructed plans.