Computational Intelligence Applied to Decision-Making in Uncertain Environments (2025) Springer¶

• Pérez, P. Y. P., Pupo, I. P., Kacprzyk, J., & Pérez, R. E. B. (Eds.). (2025). Computational Intelligence Applied to Decision-Making in Uncertain Environments. Springer Nature Switzerland

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.

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.

Ecosystem IADESCom for Conversational System Construction¶

• Pupo, I.P., Pérez, P.Y.P., Mir, Y.O.V., Herrera, R.Y., Acuña, L.A., Ramírez, P.E.P. (2025). Ecosystem IADESCom for Conversational System Construction. 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_4

Abstract

Conversational systems are rapidly changing and modifying the ways in which humans interact with machines. A basic classification divides these systems into two fundamental groups: classical systems based on NLU techniques and systems based on LLM models. In this context, the number of application scenarios for these systems and their impact on business efficiency is increasing. In this sense, opportunities for applications in reservation management, information retrieval and the execution of actions in information systems are identified. In this sense, it was identified that the construction of the training bases for these systems is costly in time and effort. In the methods section of this work, the IADESCom software ecosystem is presented. This ecosystem is made up of a set of COTS components that allow the application of conversational systems in different scenarios. In the results section, the different components are validated and the applicability of the ecosystem is demonstrated. Finally, the conclusions and future work are presented.

A Efficient Model for Startups Creation with Low Risk and Uncertainty, Study Cases in IADES¶

• Pérez, P.Y.P., Pupo, I.P., Ramírez, P.E.P., Herrera, R.Y., Acuña, L.A., Ramírez, C.M.P. (2025). A Efficient Model for Startups Creation with Low Risk and Uncertainty, Study Cases in IADES. 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_5

Abstract

Small and medium-sized enterprises (SMEs) and startups are an essential part of the economy in many countries, having a significant economic and social impact. In this context, recent advances in Artificial Intelligence (AI) have fostered the creation of numerous companies supported by these technologies. However, the creation of such enterprises involves high risks, with 50% of initiatives failing. Multiple factors, coupled with a high degree of uncertainty in decision-making, contribute to this phenomenon. The methods section presents a model for building AI-supported startups. The proposed method facilitates the processes of implementation and governance during its application. This model can be generalized through the IADESPro platform. In the results analysis section, the authors validate the proposal using socio-economic indicators and word computing techniques. Furthermore, the results of applying the model in the IADES Commercial Society, a company dedicated to the development of new AI technologies for Sustainable Development, are presented. The IADES company adopted the proposed model and focused on applying AI in the fields of Sports, Sustainable Development, and Project Management. The application results are demonstrated.

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.

Platform for Project Management IADESPro, Supported by Artificial Intelligence¶

• Sago, Y.Á., Pérez, P.Y.P., Pupo, I.P., Herrera, R.Y., Acuña, L.A., Pupo, L.G.H. (2025). Platform for Project Management IADESPro, Supported by Artificial Intelligence. 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_7

Abstract

This work addresses the challenges of managing science and innovation projects, with a particular focus on the issues faced by the international science and innovation funding and project management office of CITMA. As part of the study, a state-of-the-art review is conducted to examine trends in project management. A critical analysis based on the literature is then presented. In the methods section, a proposed platform for project management, called IADESPro, is introduced. The proposed platform is based on agile management methods and performance domains, incorporating best practices from PMBOK and ISO standards. The platform is supported by artificial intelligence techniques to aid decision-making. The proposed platform focuses on value generation, covering the various performance domains of project management. In the results section, the implementation of the platform is evaluated in the context of managing research and innovation projects. A comparison is made between the proposed platform and others reported in the literature, with a critical analysis of their advantages and disadvantages. The feasibility of the proposal is demonstrated, along with its potential for decision-making in environments characterized by uncertainty.

Model for the Creation and Decision-Making in Project Management Offices (PMO)¶

• Ruiz, D.F., Acuña, L.A., Rojas, B.H., Arabia, J.H., Pérez, P.Y.P., Herrera, R.Y. (2025). Model for the Creation and Decision-Making in Project Management Offices (PMO). 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_8

Abstract

During the evolution of project management, various authors have proposed theories regarding the roles and structures of Project Management Offices (PMOs). In literature, a wide variety of PMOs can be identified. Each PMO has essentially been tailored to the organization that supports it. However, the high rate of project and PMO failures suggests that it is possible to generalize the best practices. The first section of the methods chapter introduces various concepts related to project management and PMOs. The second section presents a model for the creation and decision-making processes within PMOs. The core of the proposal focuses on decision-making methods for PMOs and how these methods evolve with the introduction of artificial intelligence. Additionally, different PMO contexts and best practices for the scenarios analyzed are discussed. The study also examines various roles within PMOs and their relationship to project management maturity levels. In the results analysis, the proposal is validated by subject matter experts using computational techniques based on linguistic approximations and the Delphi method.

Sport Customized Training Plan Assisted by Linguistic Data Summarization¶

• Calderón, C.A., Pupo, I.P., Herrera, R.Y., Pérez, P.Y.P., Pulgarón, R.P., Acuña, L.A. (2025). Sport Customized Training Plan Assisted by Linguistic Data Summarization. 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_9

Abstract

Planning 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.

Sports Talent by Combining Computing with Word and Neutrosophic Theory¶

• Pupo, I.P., Pulgarón, R.P., Acuña, L.A., Calderón, C., Herrera, R.Y. (2025). Sports Talent by Combining Computing with Word and Neutrosophic Theory. 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_11

Abstract

The selection of sports talents is a problem when many variables intervene. These variables are represented by mixed data, where symbolic and numerical data are present. Many coaches still rely on empirical methods to identify talents, which can lead to subjective and biased decisions. On the other hand, the veracity of the input data to the selection process can be affected by subjective factors that increase uncertainty in decision-making. This situation is conducive to applying soft computing techniques to solve this problem. In the methods section of the work, an algorithm is proposed for the selection of sports talent that combines word computing techniques with the neutrosophic theory. Then, in the results section, the proposal is validated by comparing the proposed method against other selection methods. The results are compared using non-parametric tests and the SPSS tool. It is shown that the proposed model reports better results than traditional methods.

Measurement of Perceived Quality in Conversational Systems (Chatbots)¶

• Pupo, L.G.H., Herrera, R.Y., Acuña, L.A., Pérez, P.Y.P., Pupo, I.P., Pérez, R.B. (2025). Measurement of Perceived Quality in Conversational Systems (Chatbots). 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_12

Abstract

The increase in the number of conversational systems (chatbots) applied to different scenarios in society is notable. However, the development of metrics and evaluation methods for chatbots still remains an open line of research. The aim is to create evaluation methods that are less and less invasive and that do not reduce the participation of users or other human agents. In this work, in the methods section, a systematic review is carried out on different evaluation methods for chatbots. Then, in the same section, new metrics for the evaluation of conversations with chatbots inspired by the neutrosophic theory are presented. In the results section, the validation of the proposed model is carried out. The applicability of the model is evaluated and the proposal is subjected to expert triangulation methods. In the work, it is possible to demonstrate that the application of neutrosophic logic can contribute to achieving a more natural response from chatbots. This effect can be useful to mitigate the presence of false responses or hallucinations.

Framework for Strategic Planning and Assisted by Artificial Intelligence¶

• Acuña, L.A., Pérez, P.Y.P., Herrera, R.Y., Ramírez, C.M.P., López, F.J., Vacacela, R.G. (2025). Framework for Strategic Planning and Assisted by Artificial Intelligence. 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_14

Abstract

Strategic forecasting continues to be one of the fundamental elements in decision making. This branch of management sciences, like other branches of knowledge, is being influenced by artificial intelligence and other emerging technologies. In this work, in the first section of the methods section, a brief study is made of the state of the art of trends in strategic planning and the points of contact with artificial intelligence. The opportunities for improvement of existing strategic forecasting techniques with respect to the treatment of uncertainty are analyzed. In the second section, a proposal is made for a framewrok for strategic planning assisted by artificial intelligence techniques. This model allows aiding decision making while maintaining an adequate management of information uncertainty. Then, in the results analysis session, the proposals made are validated. Statistical techniques and expert and data triangulation methods are used. As conclusions, the validity of the proposal and its power for decision making under uncertainty is demonstrated. Future lines of work are also presented.