Decision Making in Artificial Intelligence Training Programs¶
Herrera, R.Y., Pérez, P.Y.P., Pupo, I.P., Acuña, L.A., Vacacela, R.G., Pupo, L.G.H. (2025). Decision Making in Artificial Intelligence Training Programs. In: Piñero Pérez, P.Y., Pérez Pupo, I., Kacprzyk, J., Bello Pérez, R.E. (eds) Computational Intelligence Applied to Decision-Making in Uncertain Environments. Studies in Computational Intelligence, vol 1195. Springer, Cham. https://doi.org/10.1007/978-3-031-83643-5_6¶
Abstract
This 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.
Method for Revenue Assurance and Financial Fraud Alerting Supported by LDS Techniques
Pupo, I.P., Pérez, P.Y.P., Herrera, R.Y., Acuña, L.A., Ramírez, C.M.P., Ramírez, P.E.P. (2025). Method for Revenue Assurance and Financial Fraud Alerting Supported by LDS Techniques. In: Piñero Pérez, P.Y., Pérez Pupo, I., Kacprzyk, J., Bello Pérez, R.E. (eds) Computational Intelligence Applied to Decision-Making in Uncertain Environments. Studies in Computational Intelligence, vol 1195. Springer, Cham. https://doi.org/10.1007/978-3-031-83643-5_3
Abstract
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.
http://cidiia.uce.edu.do/.../Computational_Intelligence...
Characterization of SLM Conversational Systems Models, Overview¶
Mir, Y.O.V., Pupo, I.P., Herrera, R.Y., Pérez, P.Y.P., Acuña, L.A., Pérez, R.B. (2025). Characterization of SLM Conversational Systems Models, Overview. In: Piñero Pérez, P.Y., Pérez Pupo, I., Kacprzyk, J., Bello Pérez, R.E. (eds) Computational Intelligence Applied to Decision-Making in Uncertain Environments. Studies in Computational Intelligence, vol 1195. Springer, Cham. https://doi.org/10.1007/978-3-031-83643-5_1
Abstract
The development of Generative Artificial Intelligence is revolutionizing human–machine interaction models. In this context, large language models (LLMs) have emerged as tools capable of learning complex patterns. However, many of these technologies are highly resource intensive. An alternative to these complex models is the advent of Small Language Models (SLMs). These smaller models process fewer parameters but achieve acceptable performance in their responses, striking a balance between cost and quality. This study characterizes different SLMs to facilitate decision-making in their implementation. In the methods section, a systematic review is conducted, serving as a guide for researchers and professionals seeking to select the most suitable SLM for their specific needs. An analysis of the efficiency of these models contributes to the application of Artificial Intelligence techniques from a sustainability perspective. The results section presents a comparison of various SLMs available on the Ollama platform. The models compared include Qwen2.5, Phi3.5, Mistral-small, Llama3.1, and Gemma2. A comparative analysis evaluates these models based on their efficiency and effectiveness in terms of computational resources and the human effort required to develop task-specific conversational systems. The study demonstrates the feasibility of using these smaller models in various decision-making environments.
Computing with Words to Assess the Perceived Quality of IT Products and Projects¶
Peña Abreu, M., Mejias Cruz, J.C., López Valerio, C., Piñero Pérez, P.Y. (2024). Computing with Words to Assess the Perceived Quality of IT Products and Projects. In: Piñero Pérez, P.Y., Kacprzyk, J., Bello Pérez, R., Pupo, I.P. (eds) Computational Intelligence in Engineering and Project Management. CIIP 2023. Studies in Computational Intelligence, vol 1134. Springer, Cham. https://doi.org/10.1007/978-3-031-50495-2_15
Abstract
This paper proposes a method based on computing with words paradigms to assess the perceived quality of IT services that contribute to decision making. The proposal is divided into four phases. In the first one, the experts and criteria for the evaluation of the perceived quality of the IT services are selected. In the second one, the preferences of the users are collected. In the third one, the evaluation is carried out using the 2-tuple model and in the fourth one, the achieved result is integrated for a more profound analysis during decision making. The experts are selected through the Delphi method. The evaluation criteria are defined using the Focal Group technique and weighted with values between (0, 1) according to their importance. Valuations are expressed in the linguistic domain and aggregation operators are used that avoid the loss of information. As a result, the pair (level of perceived quality, precision) is obtained for each evaluated service. The method was validated with three IT services, in which 24 users evaluated 12 criteria selected by 10 experts. The perceived quality level of service
was excellent with −0.33 of precision, service was very high with −0.16 of precision and reached an excellent value with −0.45 of precision. The proposed method is a favorable solution to assess the perceived quality of IT services in uncertain environments. Its use contributes to the decision making and to the improvement of the IT services evaluated.
https://iajournals.uce.edu.do/index.php/aias/article/view/6
Bello Perez R, Garcia Valdivia Z, Hernandez Cuellar G, Garcia Lorenzo MM, Vazquez Rodriguez R, (2025), “AI in Education: How to Use It? AIAS Artificial Intelligence and Applied Sustainability, Vol. 1, No. 1, pp. 19¶
AI in education: how to use it? how to teach it?¶
Rafael Esteban Bello Pérez
Universidad Central "Marta Abreu" de las Villas
https://orcid.org/0000-0001-5567-2638
Zenaida García Valdivia
Centro de Estudios Informáticos, Universidad Central Marta Abreu de Las Villas
https://orcid.org/0000-0001-8448-1128
Gerardo Hernández Cuellar
Centro de Estudios Informáticos, Universidad Central Marta Abreu de Las Villas
https://orcid.org/0000-0003-1700-9274
María Matilde García Lorenzo
https://orcid.org/0000-0002-1663-5794
Romel Vázquez Rodríguez
https://orcid.org/0000-0003-3313-2459
Keywords: Digital literacy, Inclusive education, Intelligent systems, Personalized learning, Teacher training
Digital Transformation in Project Oriented Organizations, Supported by Intelligence Ecosystems¶
Piñero Pérez, P.Y., Alvarado, L., Pupo, I.P., Ruiz, Y.M., Ramírez, P.E.P. (2024). Digital Transformation in Project Oriented Organizations, Supported by Intelligence Ecosystems. In: Piñero Pérez, P.Y., Kacprzyk, J., Bello Pérez, R., Pupo, I.P. (eds) Computational Intelligence in Engineering and Project Management. CIIP 2023. Studies in Computational Intelligence, vol 1134. Springer, Cham. https://doi.org/10.1007/978-3-031-50495-2_11
Abstract
Digital transformation is a necessary step in the evolution of organizations. Boosting the competitiveness of organizations by improving effectiveness and efficiency in their management. Particularly in project-oriented organizations, digital transformation processes must put the generation of value and internal and external customer satisfaction at the center. These types of organizations have historically worked using traditional methods. However, the development of BIM technologies and the adoption of agile methodologies have changed the way things are done. This work presents a model for the digital transformation and improvement of project-oriented organizations that combine computational intelligence techniques and business architectures. The validation of the proposal is carried out based on a case study in an organization oriented to the development of software projects. The validity of the proposal and the gradual improvement in the organization’s value generation chain are demonstrated.
Vol. 1 No. 1 (2025): Applied Artificial Intelligence: Education, Engineering and Sustainability¶
Articulos
AI in education: how to use it? how to teach it?,¶
- Bello Pérez R, García Valdivia Z, Hernández Cuellar G, García Lorenzo MM, Vázquez Rodríguez R, (2025), “AI in Education: How to Use It? How to Teach It?” AIAS Artificial Intelligence and Applied Sustainability, Vol. 1, No. 1, pp. 19, https://iajournals.uce.edu.do/index.php/aias/article/view/6
Intelligent agent for online teaching of constitutional education, case study¶
Methodology for the development of mobile applications in the desoft matanzas territorial division¶
Explainable artificial intelligence for sustainable development¶
AI-Driven system architecture for enhanced mining project management¶
Proposal for a quality system for a distance learning program in project management.¶
- Pérez Fuentes A., Piñero PérezPY, Alvarado LorcaKJ, Verdecia VicetP,Fustiel ÁlvarezY,(2025). “Proposal for a quality system for a distance learning program in project management”. AIAS Artificial Intelligence and Applied Sustainability, Vol. 1, No. 1., pp. 21, https://iajournals.uce.edu.do/index.php/aias/article/view/10
Framework for digital transformation in logistics management environments assisted by artificial intelligence¶
Ecosystem for Construction of Hybrid Conversational Systems (BRasa)
Mir, Y.O.V., Pupo, I.P., Piñero Pérez, P.Y., Acuña, L.A., Pérez, R.B. (2024). Ecosystem for Construction of Hybrid Conversational Systems (BRasa). In: Piñero Pérez, P.Y., Kacprzyk, J., Bello Pérez, R., Pupo, I.P. (eds) Computational Intelligence in Engineering and Project Management. CIIP 2023. Studies in Computational Intelligence, vol 1134. Springer, Cham. https://doi.org/10.1007/978-3-031-50495-2_8
Abstract
The increasing maturity of artificial intelligence technologies related to conversational systems, such as machine learning algorithms, natural language processing, linguistic data summarization, and natural language generation, is changing the way in which users interact with information systems. In this sense, two trends stand out: the improvement of rule-based smart chatbots and Large Language Models. These two models have different characteristics, with advantages and disadvantages for different scenarios. In this work, the BRasa ecosystem is proposed, which allows the construction of conversational systems that exploit the advantages of the two models. The ecosystem combines different computational intelligence techniques with natural language processing techniques, among which the following stand out: linguistic data summary, fuzzy logic, and neutrosophic theory. As part of the validation of the proposal, using the ecosystem, two conversational systems are built for different environments. The experimental design consists of different tests that demonstrate the applicability and effectiveness of the proposal
Platform as Service for Data Analysis Suppoted by Computational Intelligence Techniques
Ruíz, Y.M., Pérez Pupo, I., Piñero Pérez, P.Y., Acuña, L.A., Vacacela, R.G. (2024). Platform as Service for Data Analysis Suppoted by Computational Intelligence Techniques. In: Piñero Pérez, P.Y., Kacprzyk, J., Bello Pérez, R., Pupo, I.P. (eds) Computational Intelligence in Engineering and Project Management. CIIP 2023. Studies in Computational Intelligence, vol 1134. Springer, Cham. https://doi.org/10.1007/978-3-031-50495-2_7
Abstract
In this work, a cloud-based software architecture for data analysis called BRIntelligent. Its platform allows researchers to perform data analysis by taking advantage of the availability of cloud resources. The proposed architecture constitutes a platform as services that could be extended by groups of researchers based on the development of algorithms and tools for data analysis. With its implementation, it is expected that the end customers of the platform will be able to have access through a web browser to algorithms and data analysis and visualization tools without requiring advanced programming knowledge or installing any specific software. The proposal is structured to consider different architectural views for software development and is made up of three groups of fundamental components. The first component is a data analysis project management system whose functionalities combine computational intelligence techniques and best practices for project management. The second component constitutes a system for the management of algorithm runs, making use of different groups of technological resources. The third component is a set of software assets and services that facilitate data analysis. The proposal has been partially implemented through the customization of the BusinessRedmine software ecosystem using a free software development strategy based on open core.
