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Publicación Decision Making in Artificial Intelligence Training Programs(Springer Nature, 2025-03-25) Yzquierdo-Herrera, Raykenler; Piñero-Pérez, Pedro Yobanis; Pérez-Pupo, Iliana; Alvarado-Acuña, Luis Segundo; García-Vacacela, Roberto; Hernández-Pupo, Luis GabrielThis work addresses the challenge of capacity building in the areas of artificial intelligence and data science. It starts by recognizing the need for new academic programs that consider these subjects as central themes. To develop researchers skilled in topics such as computational intelligence, decision-making in uncertain environments, generative artificial intelligence, and other trends in the development of new AI technologies in society, an ethical approach is required. In the methods section, the proposal addresses the fundamental challenges related to these topics and provides a brief analysis of the state of the art. Additionally, a training strategy is proposed, ranging from short-cycle programs to postgraduate education. The proposal includes a short-cycle program for a Data Science Technician, an Artificial Intelligence Engineering degree, and a master’s degree in Artificial Intelligence. In this way, the training is provided at various levels, accompanied by a strategy for continuous education. In the results analysis section, the proposal was evaluated by a group of specialists in curriculum design, yielding positive results. Finally, the conclusions focus on the fair and ethical development of artificial intelligence.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.