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¶
https://www.facebook.com/permalink.php?story_fbid=pfbid0ZoTEECZ8vuG14Aj4g6FAWgGYdk9dysPMX96AKTQiCnqLNZAo15Gde7FUJZH5mEksl&id=615707334281763
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.
Call for paper (Issue 4, 2025) Convocatoria para la presentación de artículos (Número 4, 2025) Inteligencia Artificial aplicaciones en la Calidad de Vida , la Salud y el Deporte¶
Journal AIAS "Artificial Intelligence and Applied Sustainability" (AIAS) ISSN: 3060-9844¶
Revista AIAS "Inteligencia Artificial y Sostenibilidad Aplicada" (AIAS) revista científica arbitrada de acceso abierto.¶
Call for paper (Issue 4, 2025) Artificial Intelligence Applications in Quality of Life, Health, and Sports
Convocatoria para la presentación de artículos (Número 4, 2025) Inteligencia Artificial aplicaciones en la Calidad de Vida , la Salud y el Deporte
- All interested parties are invited to submit papers related to the topic of Decision Making in Engineering and Project Management, emphasizing the use of Artificial Intelligence and other emerging technologies.
- Se convoca a todos los interesados a publicar trabajos relacionados con la temática de Toma de decisiones en ingeniería y gestión de proyectos, potenciando el uso de la Inteligencia Artificial y otras tecnologías emergentes.
https://iajournals.uce.edu.do/index.php/aias/announcement/view/4
Call for paper (Issue 3, 2025) Decision-Making in Project Management, Control, and Monitoring Assisted by Intelligent Tools¶
Journal AIAS "Artificial Intelligence and Applied Sustainability" (AIAS) ISSN: 3060-9844¶
Revista AIAS "Inteligencia Artificial y Sostenibilidad Aplicada" (AIAS) revista científica arbitrada de acceso abierto.¶
*Convocatoria a presentar trabajos (Call for paper) (Issue 3, 2025) Decision-Making in Project Management, Control, and Monitoring Assisted by Intelligent Tools
- All interested parties are invited to submit papers related to the topic of Decision Making in Engineering and Project Management, emphasizing the use of Artificial Intelligence and other emerging technologies.
- Se convoca a todos los interesados a publicar trabajos relacionados con la temática de Toma de decisiones en ingeniería y gestión de proyectos, potenciando el uso de la Inteligencia Artificial y otras tecnologías emergentes.
https://iajournals.uce.edu.do/index.php/aias/announcement/view/3
Journal AIAS "Artificial Intelligence and Applied Sustainability" (AIAS) is an open-access, peer-reviewed scientific journal.¶
ISSN: 3060-9844
Revista AIAS "Inteligencia Artificial y Sostenibilidad Aplicada" (AIAS) es una revista científica de acceso abierto y arbitrada.¶
(Call for Papers) Convocatoria a presentar trabajos
Call for paper (Issue 2, 2025) Decision-making supported by soft computing techniques*¶
- All interested parties are also invited to submit papers related to the application of soft computing techniques to aid decision-making in different areas of human development.
- Se convoca a todos los interesados a publicar trabajos relacionados con la aplicación de técnicas de soft computing para la ayuda a la toma de decisiones en diferentes áreas del desarrollo humanos.
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.
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
