IADES Startup

h1. IADES Publications

h2. "Linguistic Data Summarization: A Systematic Review"

Pérez Pupo, I., Piñero Pérez, P.Y., Bello Pérez, R.E., García Vacacela, R., Villavicencio Bermúdez, N. (2022). Linguistic Data Summarization: A Systematic Review. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_1

*Abstract*

This paper constitutes a systematic review of Linguistic Data Summarization theory and its trends. Authors analyzed the advantages and limitations of different techniques to generate linguistic summaries. In this investigation, authors discuss about the high variability of protoforms used by researches in several domains. This review exposes the strategy of validation of researches reported in bibliography. We identify the approaches in data summarization followed by the main authors. As conclusions, of this investigation, authors identify the following opportunities for improvement in this research area. One opportunity deal with the use of linguistic summaries not only for description, but also for prediction in decision-making problems. Other is about the necessity of new algorithms to facilitate the generation of linguistic summaries and its applications in several domains.

h2. "New Linguistic Data Summarization Approach for Prediction Problems in Project Management Applications"

Pérez Pupo, I., Piñero Pérez, P.Y., Al-subhi, S.H., García Vacacela, R., Martínez Noriega, H.A., Villavicencio Bermúdez, N. (2022). New Linguistic Data Summarization Approach for Prediction Problems in Project Management Applications. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_2

*Abstract*

In the proposal, multilayer networks are built where the layers represent the different elements that make up the linguistic summaries and systems with single-value triangular neutrosophic numbers are combined for the computation of the inference. In the validation, the new inference model was applied in the scenario of making-decision in project management and the “Arithmetic mean of the error in the prediction” was applied as a metric. Comparisons are made by evaluating the inference model with different summary parameters. In addition, the linguistic summaries are combined with the fuzzy cognitive maps and the extension NCM_LDS is generated. The new proposed extension is compared with other map extensions, it is shown that the proposed inference mode-lo reports better results than the rest of the maps in the diagnostic stage, but that it does not have significantly better results than the m-FCM in the decision and prognosis stages. The NCM_Indeterminacy map is shown to be the one with the worst results.

h2. "Linguistic Data Summarization with Multilingual Approach"

Pérez Pupo, I., Piñero Pérez, P.Y., Al-subhi, S.H., Mahdi, G.S.S., Bello Pérez, R.E. (2022). Linguistic Data Summarization with Multilingual Approach. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_3

*Abstract*

The development of information systems increases the volume of data and the need for its processing for decision-making. Algorithms are required that allow the discovery of behavior patterns and their interpretability. In this context, the linguistic summarization of data arises as one of the descriptive knowledge discovery techniques with a promising approach to produce summaries from a database using natural language, where authors such as Yager and Zadeh were pioneers and set guidelines in the development of these techniques. In this work, new algorithms are proposed for the generation of linguistic summaries from data that combine different soft computing techniques such as: rough sets, the learning of probabilistic graphs with controlled natural languages for the generation of linguistic summaries with an approach multilingual. In particular, controlled natural languages are proposed for the Spanish, English, Japanese and Arabic languages. The proposed algorithms are compared with other techniques reported in the bibliography and are subject to expert evaluation.

h2. Project to Improve Offensive Phase Finalization of Futsal Teams by Using Linguistic Data Summarization Techniques

Morales González, G., Sánchez Córdova, B., Mesa Anoceto, M., Piñero Pérez, P.Y., Pérez Pupo, I. (2022). Project to Improve Offensive Phase Finalization of Futsal Teams by Using Linguistic Data Summarization Techniques. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_4

*Abstract*

In this paper authors collect and analyze data from 2018 futsal season of the University of Informatics Sciences team. In order to obtain linguistic summaries, authors apply linguistic data summarization techniques. The linguistic summaries are storage in database and later use for coaches for decision-making on 2019 season. To validate the research, authors analyze the variables involved in the offensive phase finalization in the “Universidad de las Ciencias Informáticas (UCI)” futsal team with respect to the 2018/2019 seasons, from observation and SPSS statistical analysis, significant differences are evident in the variable goal and positive passes, managing to score 33 goals in 2019 and 13 goals in 2018; for this, 85 positive shots were produced averaging 2.57 shots to reach goal. In the plays of static positional strategies there are significant differences in the variables goal and positive shots. Ten more goals are scored in 2019 as every 3.69 positive shot opportunities the team scores a goal. While in 2018 three goals are scored and an average of 7.66 positive shots are used to score a goal. Positional transitions on the move improve in 2019 where eight goals are scored and on average 2.12 shots are produced to score a goal. Counterattacking transitions present a stable behavior; this variable depends on the way of defending to close and counterattack. In free-kicks two goals are scored in 2019, improving in this regard. The free-kick is improved in 2019 since no goals were scored in 2018 and two goals were scored this year

h2. "New Indicators for the Assessment of Linguistic Summaries Considering a Rough Sets Approach"

Pérez Pupo, I., Piñero Pérez, P.Y., Bello Pérez, R.E. (2022). New Indicators for the Assessment of Linguistic Summaries Considering a Rough Sets Approach. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_6

*Abstract*

In this work, different approaches to the evaluation of linguistic summaries are analyzed. In the design of the indicators, elements of rough set theory and the neutrosophic theory are combined. Several of the proposed indicators expand the traditional indicators reported in the bibliography. For comparisons, the authors design a set of 29 test cases that represent different equivalence classes of project management decision-making scenarios. The indicators reported in the bibliography are compared with the new ones proposed in the 29 test cases. For each indicator, the authors present different graphs that allow the indicators to compare their advantages and disadvantages. Finally, the authors present the conclusion of this work and future work.

h2. "Constraints Learning Univariate Estimation of Distribution Algorithm on the Multi-mode Project Scheduling Problem"

Mahdi, G.S.S., Piñero Pérez, P.Y., Madera Quintana, J., Al-subhi, S.H., García Vacacela, R. (2022). Constraints Learning Univariate Estimation of Distribution Algorithm on the Multi-mode Project Scheduling Problem. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_7

*Abstract*

Project Scheduling Problems (PSP) constitute a family of problems that includes different variants, which range from simple task planning without taking into account the resources they consume, to more sophisticated variants that consider several modes of processing of projects tasks, generalization of precedence relationships, multiple projects simultaneously and projects with variable resources. In this sense, various algorithms, both exact and heuristic, have been used to find optimal or quasi-optimal project schedules. This research aims to propose a new Constraints Learning Univariate Estimation of Distribution Algorithm (CL_UMDA) as an extension of the Univariate Marginal Distribution Algorithm (UMDA). The new algorithm incorporates the constraints handling inside the probabilistic model, for the solution of the PSP problem in its multimode variant (MMRCPSP). For this purpose, a group of experiments was developed on five databases of the PSPLib library, comparing the proposed algorithm with others reported in the literature. The experimental results show the superiority of the CL_UMDA performance over other algorithms used in the experimentation.

h2. "New Methods for Feasibility Analysis of Investment Projects in Uncertain Environments"

Peña Abreu, M., Rodríguez Rodríguez, C.R., Piñero Pérez, P.Y., García García, Y. (2022). New Methods for Feasibility Analysis of Investment Projects in Uncertain Environments. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_8

*Abstract*

Project feasibility analysis is a key process in organizations to predict the success of projects in the future. This process becomes complex because it involves multiple criteria, which are evaluated in uncertain environments. This paper presents a model to perform the feasibility analysis of investment projects in uncertain environments using soft computing techniques. The model includes technical, economic, social and commercial criteria and the methods for its calculation. Traditional economic criteria are extended by introducing triangular fuzzy numbers, which provides greater flexibility and certainty in predicting economic feasibility. The 2-tuple linguistic representation model is used in the qualitative evaluation of the criteria and the calculation of the final results. The model output is the feasibility analysis of the projects, integrating the results of all the evaluated criteria without loss of information, which provides greater interpretability for decision making. The case study and experiments conducted demonstrate that the model is applicable in real environments and that its ability to predict feasibility is greater than that of other proposals.

h2. "Sustainability Risk Management for Project-Oriented Organizations"

Sánchez, Y.A., Jane, L.G., Soler, J.A.P., Delgado, F.M. (2022). Sustainability Risk Management for Project-Oriented Organizations. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_9

*Abstract*

Sustainability is used as a reference in the management of organizations, so that, in addition to increasing economic benefits, social and environmental strategies are applied. To this end, approaches and models are used to define the dimensions for measuring sustainability. The increase of Cuban companies makes necessary the implementation of tools that contribute to the sustainability of an organization. This research aims to develop a procedure to contribute to sustainable risk management in project-oriented organizations. The proposed procedure consists of five stages: Prior preparation, Organizational analysis, Risk assessment, Treatment and Follow-up and continuous improvement. It incorporates fundamental characteristics of sustainability, such as multidimensional assessment, process approach and ethical character. The validation of the proposed strategy was carried out using the following methods: expert criteria using the Likert scale, taking into account that it is a useful validation method to check the reliability of a research, obtaining a percentage index higher than 90.77 in all the questions. The case study as a method that allows verifying the applicability of the proposal made possible its application in a real environment, which shows a high level of efficiency and effectiveness of the procedure. 

h2. New Extensions of Fuzzy Cognitive Maps for Sequential Multistage Decision-Making Problems: Application in Project Management

Al-subhi, S.S.H., Pérez Pupo, I., Piñero Pérez, P.Y., Mahdi, G.S.S., Villavicencio Bermúdez, N. (2022). New Extensions of Fuzzy Cognitive Maps for Sequential Multistage Decision-Making Problems: Application in Project Management. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_10

*

Abstract*

From a systematic review on the use of FCMs and their extensions, it is identified that there are shortcomings in the works reported in the consulted bibliography regarding the treatment of indeterminacy and the solution of multistage sequential decision-making problems. In this paper, two new extensions of Fuzzy Cognitive Maps (FCMs) for multistage sequential decision-making problems are proposed. The Multistage Sequential Triangular Neutrosophic Cognitive Map (MSTrNCM) combines neutrosophic theory with computer with words techniques to represent the map’s relationships and the inference process. This extension improves the modeling of indeterminacy and the interpretability of results. The second map, which is called Neutrosophic Cognitive Map based on linguistic Data Summarization (NCM-LDS), uses linguistic summaries to represent the map’s relations and to carry out the inference process. One of the main advantages of this extension is that it facilitates the maps construction and interpretability. Furthermore, the suggested extensions are applied as a decision-making support tool for projects evaluation using a dataset with 1011 projects records. In experimental analysis, the two proposed extensions MSTrNCM and NCM_LDS report better results than the traditional FCM and NCM_Indeterminacy reported in bibliography.

h2. "Artificial Intelligence in Project Management and Making Decisions"

Artificial Intelligence in Project Management and Making Decisions. (2022). Switzerland: Springer International Publishing.

Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer,  https://doi.org/10.1007/978-3-030-97269-1

This book presents new developments and advances in the theory, applications, and design methods of computational intelligence, integrated in various areas of project management and BIM environments. The chapters of the book span different soft computing techniques, such as: linguistic data summarization, fuzzy systems, evolutionary algorithms, estimation distribution algorithms, computing with words, augmented reality, and hybrid intelligence systems. In addition, different applications of the neutrosophic theory are presented for the treatment of uncertainty and indeterminacy in decision-making processes. Several chapters of the book constitute systematic reviews, useful for future investigations in the following topics: linguistic summarization of data, augmented reality, and the development of BIM technologies. It is a particularly interesting book for engineers, researchers, specialists, teachers, and students related to project management and the development of BIM technologies

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