Neutrosophic Computing and Machine Learning. ISSN 2574-1101
*Neutrosophic Computing and Machine Learning* (NCML) is an academic journal established for the publication of advanced studies in neutrosophy, neutrosophic sets, neutrosophic logic, neutrosophic probability, neutrosophic statistics, neutrosophic approaches to machine learning, and their applications across various fields. All submitted works should be professional, written in proper English or Spanish, and include a brief review of a problem along with the obtained results. Submissions are checked for plagiarism and self-plagiarism using automated software. For detailed submission guidelines, please refer to the [Information for Authors](https://fs.unm.edu/NCML/). Manuscripts should be formatted according to the provided [MS Word Template](https://fs.unm.edu/NCML/). To submit an article, please email the file to the editors. For printed issues, contact the editors directly. This is an open-access, non-commercial academic journal with no publication fees. The University of New Mexico hosts a website dedicated to neutrosophic theories and applications: [https://fs.unm.edu/neutrosophy.htm](https://fs.unm.edu/neutrosophy.htm). NCML is sponsored by the University of New Mexico, the Neutrosophic Science International Association (NSIA), and its branch, the Asociación Latinoamericana de Ciencia Neutrosóficas. Their respective websites are: - [https://fs.unm.edu/NCML/](https://fs.unm.edu/NCML/) - [https://fs.unm.edu/NCML2/](https://fs.unm.edu/NCML2/) - [https://fs.unm.edu/NCML/Articulos.htm](https://fs.unm.edu/NCML/Articulos.htm) *Publication Ethics and Malpractice Statement* NCML is committed to ensuring publication ethics and the quality of articles. The journal aims to publish only original scientific articles that contribute to the field of neutrosophic theories, their applications, and related topics. All parties involved—authors, editors, and reviewers—are expected to adhere to ethical standards. Our [Publication Ethics and Malpractice Statement](https://fs.unm.edu/NCML/) is based on the [Committee on Publication Ethics (COPE)](https://publicationethics.org/) Best Practice Guidelines for Journal Editors. *Reviewer Report* The review process involves selecting at least three reviewers worldwide. Each reviewer provides a report and recommendation: accept, reject, or accept after minor or major corrections. Reviewers are asked to complete the [Reviewer Report](https://fs.unm.edu/NCML/). *Copyright Transfer Agreement* Authors are required to sign and return the [Copyright Transfer Agreement](https://fs.unm.edu/NCML/). *Open Access Statement* Users are free to read, download, copy, distribute, link to, or print the full texts of the articles in this journal without prior permission from the editors or authors. This aligns with the [Budapest Open Access Initiative](https://www.budapestopenaccessinitiative.org/) definition of open access. All articles, volumes, and other materials on this website are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/), permitting unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. *Download Journal Issues* Issues of Neutrosophic Computing and Machine Learning are accessible in PDF format through the following links: - Archives Visit our [Open-Source Digital Science Library](http://fs.unm.edu/ScienceLibrary.htm). Authors of neutrosophic articles are included in the [Encyclopedia of Neutrosophic Researchers](https://fs.unm.edu/NCML/). *Neutrosophic Science International Association (NSIA)* NSIA has branches in 40 countries worldwide. *Mailing Address* Neutrosophic Computing and Machine Learning University of New Mexico Department of Mathematics and Sciences 705 Gurley Ave. Gallup, NM 87301, U.S.A. Email: smarand@unm.edu *Copyright* © Neutrosophic Computing and Machine Learning. This is an open-access journal distributed under the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), permitting unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. *Archiving and Digital Preservation Policies* We utilize digital preservation services such as Internet Archive (archive.org), a non-profit digital library dedicated to archiving and preserving digital elements of cultural and historical significance
Neutrosophic Computing and Machine Learning (NCML)
Plitogenic and Latin American Indigenous Philosophies: A Comparative Exploration
Plitogenic, a philosophical-mathematical extension of Neutrosophic, models the genesis of new entities from the organic fusion of contradictory, neutral, and non-contradictory antecedents. This article explores how the fundamental mechanisms of plitogeny—multivalued attributes, degrees of membership, and quantified contradiction—resonate with a set of Latin American Indigenous concepts: Yanantin (complementary opposites), Ch'ixi (heterogeneous coexistence), Pachakuti (radical transformation), Ayni (reciprocal exchange), Sumak Kawsay/Buen Vivir (holistic well-being), and Teotl (monistic sacred energy). By mapping each Indigenous notion onto plitogenic sets and illustrating the role of the degree of contradiction, the article demonstrates that plitogeny provides a rigorous yet culturally respectful formalism for capturing the multiplicity, tension, and emergence inherent in these worldviews. This synthesis underscores the potential of plitogeny as a bridge between Indigenous epistemologies and contemporary logico-mathematical theory, opening avenues for interdisciplinary research, decision-support tools, and decolonial analytics.
Plitogenic , Neutrosophic, Yanantin , Ch'ixi , Pachakuti , Ayni , Sumak Kawsay , Buen Vivir , Teotl , Analectics , Philosophy of Liberation , Multivalued Logic , Degree of Contradiction , Indigenous Epistemologies , Latin American Philosophy , Multicriteria Decision Making , Emergent Systems , Decolonial Analytics
Copyright (c) 2025 Neutrosophic Computing and Machine Learning. ISSN 2574-1101 https://creativecommons.org/licenses/by/4.0/
Applications of Logic and Neutrosophic Sets in Medical Diagnosis.
In recent years, with the development of new technologies, medical diagnosis has undergone considerable evolution, allowing for increasingly accurate diagnoses. In this context, logic and neurotrophic sets have become useful tools for making diagnoses with a higher level of accuracy and precision. This review article aims to analyze the different approaches that medical diagnosis uses to provide higher quality service and to appreciate the impact of early disease detection, as well as its influence on improving healthcare systems. Fuzzy logic and neurotrophic sets represent significant advances in the field of medical diagnosis, allowing for greater precision and thus facilitating more effective treatment.
Logic, Neurotrophic Sets, Medical Diagnosis
Application of electronic document management strategies with multi-criteria techniques for medical records of the Santa Rosa Health Center, Ambato.
This study aimed to analyze and prioritize electronic document management strategies by integrating multicriteria methods specifically AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to optimize the management of medical records in a public health context. The specific objectives focused on: (1) identifying the critical criteria that influence electronic document management in the health sector, (2) applying multicriteria techniques to evaluate technological alternatives, and (3) proposing a priority strategy to strengthen the efficiency, traceability, and security of clinical information at the Santa Rosa Health Center. The results obtained through AHP analysis showed that the criteria with the greatest decisional weight were impact on operational efficiency (28,9%) and ease of use and adoption by staff (11.7%), reflecting a clear focus on usability and operational improvement. Subsequently, the application of the TOPSIS model determined that the most favorable alternative was progressive digitization with a hybrid (physical-electronic) archive, with a score of 0.93, followed by the development of a proprietary electronic document management system (0.51). These findings suggest that a gradual, hybrid strategy is the most viable option for public institutions with budgetary constraints and traditional organizational structures. ritical and comparative analysis with previous studies [12], [16] confirms the effectiveness of the multi-criteria approach in technology prioritization processes within the public sector, although it highlights the need to strengthen institutional capacities for the full adoption of digital systems. Furthermore, a convergence is identified with research that underscores the importance of the human component in digital transformation [10], as well as with theories of organizational change management and technological innovation [9]. The implications of this study extend beyond the scope of the analyzed case, providing a replicable methodological basis for strategic decision-making in public health settings. From a practical perspective, the results guide health authorities on the need to combine operational efficiency with financial sustainability and staff acceptance. However, limitations related to sample size and institutional scope are acknowledged, which opens the door for future research integrating socio-technical variables and predictive models based on artificial intelligence. In conclusion, this research contributes to strengthening the theoretical and methodological framework for prioritizing electronic document management strategies in the Ecuadorian context, positioning itself as a significant contribution to digital public management in the knowledge economy.
electronic document management, AHP, TOPSIS, medical records, healthcare sector, digital transformation, Ecuador.
Prioritization of Digital Transformation Projects in the Latacunga GAD: A Hybrid AHP-TOPSIS Model Based on Collective Intelligence.
Digital transformation (DT) in local governments (LGs) is a complex managerial challenge, not only due to technical or financial barriers, but also because of conflicting citizen demands. This study proposes a multi-criteria decision model (MCDA) to prioritize DT projects in the Latacunga Municipal Government. The model is based on the findings of a collective intelligence survey (N=64) conducted on the Pol.is platform, which revealed a polarization of citizens into two groups: "Efficiency Pragmatists" (65%) and "Guardians of Trust" (35%). To resolve this tension, a hybrid method is applied. First, the Analytic Hierarchy Process (AHP) is used to weight four criteria (Impact on Efficiency, Impact on Trust, Implementation Cost, and Social Inclusion), resulting in Trust (45.6%) and Cost (29.3%) being the decisive factors. Second, the TOPSIS technique is used to rank three project alternatives: "Zero Bureaucracy Platform," "Transparency and Cybersecurity Portal," and "Digital Inclusion Plan." The results identify the "Transparency and Cybersecurity Portal" (Ci = 0.814) as the highest priority project. The study concludes that public administrations should prioritize building digital trust (addressing Group B) as a critical enabler before deploying transactional efficiency solutions (demanded by Group A).
Digital Transformation, AHP, TOPSIS, MCDA, Local Government, Latacunga Municipal Government, Policies, Digital Trust, Project Prioritization.
Digital governance model based on multi-criteria methods AHP–TOPSIS: a strategy to optimize administrative efficiency in the Municipal Government of San Pedro de Pelileo.
The digital transformation of local governments demands governance models that integrate technical, administrative, and social criteria to improve institutional efficiency. This study aimed to design a digital governance model based on the multicriteria methods AHP–TOPSIS, focused on optimizing administrative efficiency in the Decentralized Autonomous Government (GAD) of San Pedro de Pelileo. An applied research approach with a quantitative and participatory methodology was implemented and a massive deliberative citizen consultation using artificial intelligence (Pol.is). Six strategic criteria—operational efficiency, transparency, citizen participation, territorial equity, innovation, and sustainability—were weighted through the Analytic Hierarchy Process (AHP) using the judgments of ten institutional experts. Subsequently, digital project alternatives were evaluated using the TOPSIS technique to determine their relative closeness to the ideal solution. The results revealed that operational efficiency (weight = 0.261) was the most significant criterion, while the implementation of an integrated digital document management system achieved the highest score (Ci = 0.865), emerging as the optimal alternative. In conclusion, the AHP–TOPSIS model provides a robust and transparent framework for prioritizing technological projects and strengthening municipal digital governance under principles of efficiency, accountability, and administrative effectiveness.
Digital governance; AHP; TOPSIS; Public administration; Multicriteria decision-making; Administrative efficiency.
Multiple regression to optimize hotel provisioning: a model with a regulatory, sustainable and digital approach.
This study proposes a multivariable predictive model aimed at optimizing secure provisioning in hotel facilities, integrating advanced statistical tools to identify the factors that significantly influence logistical efficiency. Through multiple regression analysis, variables such as average delivery time, regulatory compliance level, proportion of sustainable products, inventory turnover, process digitalization, and hotel occupancy are examined to construct a composite index of operational efficiency. The methodology employed enables validation of the statistical significance of each variable, assessment of multicollinearity, and estimation of the relative impact of each component on the continuous improvement of the logistical system. This approach not only provides technical rigor but also incorporates territorial sensitivity and institutional ethics, reframing logistics as a symbolic process that links service quality, sustainability, and regulatory traceability. The model is presented as a strategic tool for decision-making in complex tourism contexts, where uncertainty, demand variability, and regulatory pressure demand adaptive and scientifically grounded solutions. The proposal bridges the technical and the symbolic, offering an evolutionary reading of hotel provisioning that transcends operational concerns and aligns with a logic of institutional, territorial, and emotional transformation.
Multiple regression, secure provisioning, predictive logistics, operational sustainability, regulatory traceability.
Implementation of a digital citizen platform for the management of municipal procedures in the cantons of the province of Esmeraldas.
The digitization of municipal services is a significant challenge for local governments, especially in areas like the cantons of Esmeraldas province, where large socio-technological gaps exist. The objective of this study was to identify the best technological alternative for implementing a digital platform that optimizes the management of municipal procedures, using a hybrid AHP-TOPSIS approach. A multi-criteria research design was applied, considering three key factors: implementation cost, impact on administrative efficiency, and citizen participation. Three technological alternatives were evaluated. The weights of each factor were calculated using the AHP method, based on expert opinions, which allowed for verification of the consistency of the comparisons. The alternatives were then ranked using the TOPSIS technique to determine which one most closely resembled the ideal solution. The results indicated that the most relevant factor was the “impact on administrative efficiency” (weight 0.682), and the best option was the mobile application with automatic notifications (Ci = 0.73), followed by the web platform (Ci = 0.59). In conclusion, the AHP–TOPSIS model proved to be an effective and clear tool for decision-making in municipal digitization processes, and the results offer valuable guidance for planning technological solutions that improve the efficiency and accessibility of public services.
Digital Government, AHP, TOPSIS, Municipal Management.
Prioritization of Technological Solutions for the Digital Transformation of SERCOP through AHP–TOPSIS
The digital modernization of public procurement is a strategic pillar for strengthening transparency, administrative efficiency, and citizen participation in the public sector. In Ecuador, the National Public Procurement Service (SERCOP) faces the challenge of selecting, from among multiple technological alternatives, the most suitable option to strengthen its digital platform and improve data governance. This study proposes a multi-criteria evaluation model based on the integration of the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods, in order to prioritize strategic criteria—accessibility, data security, efficiency in information management, transparency and accountability, level of technological innovation, and sustainability—and to prioritize digital innovation alternatives. Twelve experts in public management and technological innovation participated, and their assessments established that transparency is the most relevant criterion (40%), followed by technological innovation (22.4%) and efficiency (15.6%). When applying the AHP weights to the TOPSIS model, the “Blockchain Platform for Public Procurement” obtained the highest index of proximity to the ideal solution, positioning itself as the priority alternative for SERCOP. The results demonstrate the relevance of the AHP-TOPSIS approach in complex public decision-making processes and highlight the need to strengthen data governance, digital inclusion, and institutional sustainability.
Public procurement, Technological innovation, Data governance, Transparency, AHP, TOPSIS, Multi-criteria decision-making, Blockchain, Digital transformation, Public management
Medical image processing using neutrosophic ensembles.
Medical image processing is a cornerstone of computer-aided diagnosis, where the inherent uncertainty and ambiguity of biomedical data often limit the performance of conventional analytical methods. Within this context, neutrosophic theory has emerged as a robust mathematical framework for handling imprecise and inconsistent information. This review article provides a comprehensive overview of the state of the art in medical image processing using neutrosophic sets, covering its theoretical foundations, key techniques for preprocessing, segmentation, and feature extraction, and its integration with artificial intelligence and deep learning models. The advantages of these approaches over classical fuzzy and probabilistic methods are analyzed, together with current limitations regarding scalability, parameter optimization, and clinical workflow integration. Finally, the paper outlines future research directions, emphasizing the potential of hybrid and explainable neutrosophic models to enhance the accuracy, interpretability, and reliability of next-generation precision medicine systems.
Neutrosophic; Medical Image Processing; Segmentation; Deep Learning; Uncertainty; Neutrosophic Theory
Innovation in digital public services in the Municipal Government of the Sucre Canton.
This article examines digital public service innovation at the Sucre Municipal Government using a multi-criteria decision model that integrates AHP and TOPSIS. The aim was to prioritize digital initiatives with methodological traceability while aligning with the principles of digital by design, openness, citizen-centeredness and proactivity. Criteria and subcriteria were operationalized, expert judgments were elicited with consistency verification and alternatives were ranked against ideal and anti-ideal solutions. Findings indicate that impact on efficiency received the highest weight, followed by total cost and institutional transparency, with an acceptable consistency ratio. Based on these weights, TOPSIS ranked the One-Stop Digital Window as the top priority, followed by Online Appointments and the Proactive Notifications initiative. The study discusses implications for municipal portfolio governance and for closing local gaps in online services, and proposes an implementation sequence that consolidates processes, strengthens interoperability and builds data capabilities to enable proactivity. Limitations concern the size and composition of the expert panel and the restricted set of criteria, and future research avenues are outlined to test ranking stability and assess realized impacts.
digital government, AHP, TOPSIS, project prioritization, one-stop window, administrative efficiency, public transparency, local government.
The integration of contradictory perspectives in Latin American philosophy: A Multi-Analyst approach.
This study explores the integration of Indigenous philosophies and postcolonial modernist theories within MultiAlism, highlighting the potential for dialogue and synthesis among these diverse philosophical traditions in Latin America. MultiAlism, a concept developed by Florentin Smarandache, is used to bridge the gaps and contradictions between systems, focusing on areas of neutrality such as, in this case, cultural diversity and critiques of power structures. The research underscores the importance of incorporating both ancestral wisdom and modern critiques to foster a deeper understanding and appreciation of Latin America’s rich philosophical heritage. This integrative approach offers practical perspectives for inclusive policymaking and the success of social justice efforts in the region. Extending the application of MultiAlism to other global contexts suggests its broader relevance for addressing contemporary challenges through a flexible and holistic philosophical approach.
MultiAlism, Indigenous philosophies, postcolonial modernist theories, Latin America
Formalizing Contextual Truth Flips: Upside-Down Logic and ItsVariants
In real settings, conclusions can flip: a claim once treated as true becomes false, or a prohibited action later becomes required. We call this phenomenon Upside-Down Logic. Upside-Down Logic provides a formal operator that, given a contextual trigger, reverses the accepted outcome (True→False / False→True) while keeping the underlying information rather than discarding it. We define several concrete variants — defea- sible, belief-based, paraconsistent, dynamic epistemic, and abductive — and show how each realizes controlled reversal through priority flips, entrenchment inversion, model update, or explanation reordering. These log- ics model realistic situations such as building access in emergencies, medication policy after contraindications, travel advisories, market disclosures, and security assessment.
Upside-down logic, defeasible logic, belief-based logic, paraconsistent logic, dynamic epistemic logic, abductive logic
Data analysis to identify operational patterns at a port terminal in Guayaquil: A case study (2024–2025).
Operational congestion at the Port of Guayaquil, caused by the increase in vehicular flow, represents a challenge to logistical efficiency. This study aimed to identify patterns of operational behavior that contribute to improving port traffic management through data analysis techniques. The CRISP-DM model was applied to 29,067 records processed using the K-Means and Expectation Maximization (EM) clustering algorithms in WEKA, in order to segment the data and characterize different operational patterns. K-Means produced 2 groups (45%, 55%) and 3 groups (35%, 40%, 25%) that segment operations at a macro level, whereas EM identified 6 groups, with 2 dominant ones (56%, 25%) and 4 minority groups (5%, 7%, 3%, 4%) classified as critical. The data were integrated into business intelligence dashboards in Google Looker Studio to visualize congestion patterns and support resource planning. It was determined that the EM algorithm better captured the complexity of the data by identifying 6 groups with distinct characteristics. These operational data, transformed into useful analytical information, enabled improvements in logistics and evidence-based decision-making.
Port terminal, Data analysis, Data visualization, Operational patterns, Temporalities, Clustering.
Actor Hypernetworks and Urban Road Hypernetworks withReal-life Applications
Hypergraphs extend classical graphs by allowing hyperedges to connect arbitrary subsets of ver- tices, while superhypergraphs further enrich this structure through iterated powersets that capture hierarchical and self-referential relationships. An actor network links heterogeneous actants—humans, artifacts, texts, and rules—through directed associations, emphasizing relational materiality and performative agency. An urban road network is a directed, weighted graph of intersections and road segments, modeling connectivity, capac- ities, and travel dynamics. In this paper, we extend actor networks and urban road networks by employing HyperGraphs and SuperHyperGraphs. These extensions are expected to provide clearer and more expressive representations of hierarchical structures in real-world actor networks and urban road networks.
Superhypergraph, Hypergraph, Actor Networks, Actor Hypernetworks, Actor Superhypernet- works, Hypernetworks, Superhypernetworks, Urban Road Networks
Credit Risk Analysis of Ecuadorian Financial Institutions: A Descriptive and Predictive Approach Integrated with Machine Learning and Business Intelligence.
Credit risk management is essential for financial stability, especially in economies where credit access fuels growth. This study improves credit risk assessment in Ecuadorian financial institutions by integrating Machine Learning (ML), Explainable Artificial Intelligence (XAI), and Business Intelligence (BI). Using real demographic and financial data, two classification algorithms—Random Forest and XGBoost—were trained to predict loan default probability. Random Forest delivered the best results (ROC-AUC = 0.916; PR-AUC = 0.998), showing high accuracy even with imbalanced data. SHAP values explained the influence of each feature, identifying total exposure, loan maturity, and credit history as the strongest predictors. Interactive dashboards developed in Tableau converted analytical findings into practical insights for decision-makers, allowing early detection of risky clients and faster intervention. This integrated approach enhances transparency, supports proactive management, and strengthens governance in credit portfolios. The framework can be applied by other financial institutions seeking to make data-driven, fair, and efficient credit decisions.
Credit risk; Machine Learning; Explainable AI; SHAP; Business Intelligence; Predictive modeling; Financial institutions; Risk management
Neutrosophic in Artificial Intelligence: Advances and Applications
This special edition of the international scientific journal "Neutrosophic Computing and Machine Learning" (Vol. 41, 2025) explores the advances and applications of Neutrosophic in Artificial Intelligence (AI), presented at the joint conferences organized by the BARNA Management School (Dominican Republic) and the Universidad del Trabajo del Uruguay (UTU) under the title "Thinking in Uncertainty." The conferences brought together international experts, with special participation from the founder of Neutrosophic, Dr. Florentin Smarandache, to discuss the challenges of AI. By adding uncertainty to classical binary logic, Neutrosophi coffers a robust framework for managing incomplete and contradictory information, overcoming the limitations of current AI systems, such as LLM models. The discussions emphasized that the future lies in the symbiosis between AI and human critical thinking, highlighting interdisciplinary and Latin American perspectives (e.g., yanantin) and inviting readers to explore ways to integrate technology and humanity in an ethical and contextual balance.
Neutrosophic, Artificial Intelligence (AI), Uncertainty/Indeterminacy, Critical Thinking, Human-AI Symbiosis
Promoting Reading Comprehension in Fourth Grade from Neutrosophic Psychology on Digital Platforms
This research addresses the difficulties in developing reading comprehension in fourth-grade students, a crucial challenge in basic education that impacts comprehensive learning. This topic gains relevance in a context where digital literacy and communication skills are essential to facing the challenges of the 21st century. Although there are studies on pedagogical strategies for reading, few integrate approaches that consider students' emotional and cognitive uncertainty, leaving a gap in the literature. To address this gap, neutrosophic psychology was used in the design of an interactive blog, analyzing students' emotional and cognitive responses to literary and scientific texts. The findings reveal that this methodology significantly improves comprehension at the literal, inferential, and critical levels by personalizing activities and fostering motivation. The study's contribution lies in offering a theoretical framework that combines neutrosophic psychology and educational technology, along with a practical tool for teachers seeking to innovate in reading instruction. This approach promotes meaningful learning, adaptable to individual needs, and strengthens digital and communicative skills in educational contexts.
Reading Comprehension, Neutrosophic Psychology, Interactive Blog, Elementary Education, Digital Literacy, Motivation, Meaningful Learning.
Neutrosophic Analysis of Disruptive Behaviors in Children Aged 6 to 8 Years and Its Impact on Academic Performance: Application of the Plithogenic Hypothesis Method
This study examines disruptive behaviors in 6- to 8-year-old children at the Abdón Calderón Educational Unit, located in Guayaquil, Guayas, Ecuador, and their influence on academic performance. The relevance of this topic lies in its impact on learning and the school environment, especially in urban contexts such as Guayaquil, where educational challenges are pressing. Although numerous studies have addressed disruptive behaviors, few have integrated uncertainty and the multiple factors that characterize them, leaving a gap in multidimensional analysis. To address this limitation, the plithogenic hypothesis method, based on neutrosophic, was applied, which models indeterminacy and contradictions in behavioral data. The results indicate that behaviors such as impulsivity and inattention in students at the Abdón Calderón Educational Unit negatively affect performance in mathematics and reading. This work contributes to the educational field by proposing an innovative analytical framework that captures the complexity of these interactions, also offering practical intervention strategies for teachers and psychologists in Ecuadorian contexts. Thus, the study enriches educational theory and provides applicable tools to improve academic performance in local schools.
Disruptive Behaviors, Academic Performance, Neutrosophic, Plithogenic Hypothesis, Children, Education, Ecuador.
Analysis of Risk Mitigation Strategies in the Retail Sector. Application of AHP and TOPSIS Methods.
The retail sector faces a complex landscape marked by operational risks that directly impact its sustainability and profitability. Problems such as inventory errors, checkout fraud, and distribution failures generate significant economic losses and jeopardize consumer confidence. In an increasingly competitive market characterized by demanding customers and constant pressure for efficiency, it is essential to identify and select effective and adaptable risk mitigation strategies. Previous literature offers partial approaches, as most studies do not integrate a structured, objective prioritization of alternatives under multiple criteria, which limits informed decision-making. This work addresses this gap through the combined application of the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods, multi-criteria tools that allow for weighing factors such as cost, impact, time, and residual risk. The results reveal that technological solutions, specifically advanced inventory control and report automation, outperform intensive training in terms of loss reduction and operational efficiency. Consequently, the research not only provides a replicable model for other sectors, but also practical guidelines for strengthening retail resilience.
Retail, Operational Risks, AHP, TOPSIS, Multi-criteria, Mitigation, Efficiency.
Fuzzy Tolerance Hypergraphs and Fuzzy ToleranceSuperhypergraphs
Hypergraphs generalize this framework by allowing hyperedges that join more than two vertices [1,2]. Superhypergraphs further enrich the model through iterated powerset constructions, capturing hierarchical and self-referential structures among hyperedges [3]. In this paper, we introduce new classes of graphs, namely the Tolerance SuperHyperGraph, Tolerance HyperGraph, Fuzzy Tolerance Hypergraph, and Fuzzy Tolerance SuperHypergraph, and examine their properties.
uperHyperGraph, HyperGraph, Fuzzy Graph, Fuzzy Hypergraph, Fuzzy SuperHyperGraph, Tolerance SuperHyperGraph, Tolerance HyperGraph
Risk management in agro-commercial retail ventures in Manabí through AHP-TOPSIS
In Ecuador, entrepreneurship has experienced significant growth in recent years, particularly after the COVID-19 pandemic, when society was forced to seek economic alternatives to confront the crisis. Within this context, the feed retail sector in Manabí constitutes a key space, presenting structural weaknesses related to the volatility of corn and other raw material prices, poor inventory management, and changes resulting from the latest tax reforms for the Rimpe regime. This study addresses risk management in a local feed and raw material retail business using a multi-criteria AHP-TOPSIS methodology, the objective of which is to prioritize strategies that strengthen business sustainability. The research considers factors related to business management, entrepreneurial culture, and the Ecuadorian legal framework for microenterprises. Four alternatives were evaluated under five key criteria: cost, effectiveness, ease of implementation, customer satisfaction, and sustainability. The results obtained using the TOPSIS method indicate that inventory digitization is the most successful alternative, with a score of 1.0, above the strategy of advance contracting with suppliers (0.52). These findings confirm that the use of multi-criteria methodologies improves business resilience and provides transferable methodological input to university teaching in areas such as management, economic law, and entrepreneurship, strengthening professional training through decision-support tools.
Risk management, AHP, TOPSIS, entrepreneurship, administration, economic law, business resilience.
Attention To Low Ability Students In The Learning Process: Teacher Professional Performance Challenge
Teacher preparation for the attention to diversity in the classroom is an indispensable element to achieve an inclusive education. Therefore, the goal of this paper was aimed at describing the factors that limit the effectiveness of care to the diversity of low ability students, in Calixto Garcia Municipality of Holguin province. The case study revealed the poor pedagogical management of the areas, laboratories and workshops of educational institutions, inadequacies in the application of psycho-pedagogical strategies for the developer teaching, current approaches and methods of conceiving the educational process. Consequently, the quality of attention to the diversity of low abilities students is conditioned by the preparation and level of professional performance of teachers and it is a concrete expression of inclusive education.
Attention To The Diversity, Teacher Professional Performance, Low Ability Students` Results, Psycho-Pedagogical Strategy.
Iterative MultiFuzzy Set, Iterative MultiNeutrosophic Set,Iterative Multisoft set, and MultiPlithogenic Sets
Concepts such as Fuzzy Sets [1, 2], Neutrosophic Sets [3, 4], and Plithogenic Sets [5] have been extensively studied as effective frameworks for handling uncertainty, with applications spanning various fields. As extensions of Fuzzy Sets and Neutrosophic Sets, MultiFuzzy Sets [6, 7] and MultiNeutrosophic Sets [8, 9] have been introduced in the literature. In this paper, we further extend these frameworks by introducing and defining new structures: Iterative MultiFuzzy Sets, Iterative MultiNeutrosophic Sets, Iterative MultiSoft Sets, Iterative MultiRough Sets, and MultiPlithogenic Sets
Plithogenic Set, MultiPlithogenic Set, Iterative MultiFuzzy Sets, Iterative MultiNeutrosophic Sets, Iterative MultiSoft Set
Reinventing Education: An Experience of Emotional Measurement with Artificial Intelligence and Playful Strategies in the Classroom
This article reports on an educational innovation experience carried out at Unidad Educativa Liceo Cristiano de Cuenca and Unidad Educativa Particular Carlos Crespi II, within the framework of the project “Reinventing Education.” The intervention was conducted by master’s students, who designed and implemented ludic strategies for School Projects, Religion, and Mathematics (logical-mathematical relations), with the aim of energizing learning and observing its emotional effects. An action research design was adopted, and a multimethod assessment was implemented: self-report through the Achievement Emotions Questionnaire (AEQ), administered before and after the sessions, and automatic emotion recognition with Emolive, recording emotions in the phases before, during, and after through a tripod-mounted camera. Data analysis was conducted in Python, integrating data processing and visualization, and triangulating results from both sources. The findings show a consistent increase in positive emotions (motivation, enjoyment, confidence) and a reduction of negative emotions (boredom, frustration, anger) after the intervention. Emolive allowed the observation of in-session dynamics, with a progressive decrease of negative states during the activity and predominance of positive emotions at the end, while the AEQ captured significant changes between the beginning and the end. These results suggest that the combination of ludic strategies with AI-supported emotional measurement contributes to a more favorable socio-emotional climate and supports informed teacher decision-making. Implications for formative assessment and future research lines integrating multimodal measurement and larger samples are discussed.
Academic emotions, facial recognition, Emolive, AEQ, gamification, action research, formative assessment.
Causal combinations for successful implementation of RPA and AI in Human Resources: A qualitative comparative analysis (fsQCA)
This study seeks to identify the combinations of factors that enable successful implementation of Robotic Process Automation (RPA) and Artificial Intelligence (AI) in Human Resources (HR), moving beyond the descriptive approaches that have dominated previous research. A Qualitative Comparative Analysis (fsQCA) was conducted on a set of 10 documented cases, examining four factors: repetitive process, structured approach, full automation, and type of technology (accuracy versus employee experience). The findings reveal the principle of adaptability, showing that multiple pathways can lead to success. Each factor individually proved to be sufficient (consistency = 1.0), with none being strictly indispensable. This suggests that success can be achieved either through the efficient automation of repetitive processes or through strategies focused on employee experience, provided they are grounded in a solid design. The study offers a perspective for analyzing different combinations that provide strategic flexibility to CEOs, integrates findings that appear contradictory in the literature, and demonstrates that the benefits of HR automation go beyond cost reduction, positioning it as an opportunity for the strategic reinvention of the HR function.
Robotic Process Automation, Artificial Intelligence, Human Resources, fsQCA, causal combinations, equifinality.
Integration of Fuzzy Cognitive Maps and AHP-TOPSIS for Prioritizing and Selecting Risk Mitigation Strategies in Retail.
The high complexity and interdependence of operational risks in the retail sector pose a significant challenge to traditional management approaches. This article suggests a way of making decisions when multiple aspects must be considered, such as the likelihood of events occurring and their interdependencies. The goal is to classify risks, while also examining how they are related and what actions can be taken to mitigate them. This is approached using three methods: (1) developing maps that show how factors may change and influence one another; (2) applying a method that helps determine which risks are more important than others based on expert judgments; and (3) employing a method that evaluates different strategies for dealing with risks according to their strengths and weaknesses. The AHP results indicated that the most critical risks for the company were financial loss (29.2%) and reputational damage (29.2%). Financial and reputational risks were identified as the main concerns according to AHP, with a consistency ratio of 5.2%, meaning that the judgments were coherent. FCM showed that some seemingly minor internal issues could trigger several other problems with substantial impact on the company. The TOPSIS ranking demonstrated that the best strategy was to use technology to enhance the supply chain, with a score of 0.75, significantly higher than the other alternatives. These methods support senior management in identifying and addressing the most significant risks, deciding where and how to act more effectively, and making strategic decisions that align with the actual business context.
Fuzzy Cognitive Maps (FCM), Analytic Hierarchy Process (AHP), TOPSIS, Risk Management, Operational Risk, Scenario Simulation, Multi-Criteria Decision Making, Retail Sector.
Causal Modeling and Digital Transformation in Ecuadorian Higher Education through Fuzzy Cognitive Maps
The effective integration of technological innovation in Ecuadorian EIs faces predominantly human barriers, characterized by the perception of risk and complexity and the high teacher workload. This study addressed this challenge using the Fuzzy Cognitive Mapping (FCM) methodology to model and analyze the causal interdependencies that affect teachers' sustained technology use rates. Intervention simulations consistently resulted in near-complete success. Strategically, the most effective policy was determined to be risk mitigation through time availability and technical support. Interventions in these logistical and human factors proved more powerful than providing economic incentives alone, successfully defusing resistance and leading to a positive attitude that drives sustained use. The study concludes that the success of digital transformation in Ecuador depends on the management of human capacity, recommending freeing up teaching loads for experimentation and institutionalizing evidence of impact as critical levers for decision-making.
Fuzzy Cognitive Maps, Technology, Higher Education, Risk Analysis
Comprehensive Risk Analysis for the Self-Evaluation Process of Higher Education Institutions in Ecuador
Institutional self-evaluation in Ecuadorian Higher Education Institutions (HEIs) is a key process for continuous improvement and accreditation. However, it is exposed to operational risks that may compromise the validity and reliability of its results. To address this issue, a hybrid methodology is proposed, combining Fuzzy Cognitive Maps (FCM), the Analytic Hierarchy Process (AHP), and the TOPSIS technique to conduct a comprehensive risk analysis and prioritize mitigation strategies. Through FCM, seven critical factors were identified: governance, data quality, staff training, process documentation, student participation, technological infrastructure, and external auditing. The influence matrix revealed that governance plays a central role, significantly impacting auditing, institutional processes, and infrastructure. Simulations showed high organizational stability, even under specific interventions, indicating a mature structure within the analyzed HEIs. Using AHP, four key evaluation criteria were weighted, with governance emerging as the most relevant (0.5424). Subsequently, TOPSIS was applied to rank three mitigation strategies, with the political-organizational approach being the closest to the ideal solution (0.6794), outperforming technological and training-based alternatives. The results suggest that strengthening institutional structures and internal policies has a greater impact than investing solely in technology. The proposed framework offers a replicable and practical tool to enhance risk management in self-evaluation processes within Ecuadorian HEIs.
Fuzzy Cognitive Maps, AHP, TOPSIS, risk management, institutional self-evaluation, HEIs, quality assurance
Analysis of causal configurations in mathematics academic performance: A study based on equifinality using fsQCA.
In this study, we examine the configurations that lead to good results in the area of ​​Mathematics through Fuzzy Set Qualitative Comparative Analysis (fsQCA), a core competency in which extensive assessments such as ERCE 2019 and PISA-D, as well as the national assessment Ser Estudiante, have revealed significant discrepancies. This method, unlike traditional methods that seek net effects, focuses on individual cases and enables the identification of equifinality, the principle that indicates that various combinations of conditions can lead to a similar result. The analysis of necessary conditions revealed that study time is a necessary condition for good results, with high consistency, placing it as a necessary condition. Among the most relevant results, the combinations with the greatest explanatory power in the sufficiency analysis were satisfaction and study time (consistency = 0.878, coverage = 0.776) and family support and study time (consistency = 0.851, coverage = 0.787), demonstrating that high performance is achieved by combining dedication with socioemotional factors. Furthermore, the combined coverage of the model was 0.625, meaning that these causal configurations account for a large proportion of cases. Ultimately, success in mathematics is not determined by a single cause, but rather by the combination of individual and contextual factors, a result with implications for the design of multifactorial educational interventions.
Mathematics, fsQCA, Educational Assessments, Study Time, Family Support, Academic Performance, Equifinality
Fuzzy Cognitive Maps and Their Application to the Analysis of Financial Risk Indicators in Ecuadorian Credit Unions
The analysis of financial risk factors in credit unions is a challenge due to the complexity of the interactions between variables that affect their stability. In this regard, fuzzy cognitive maps were used as a methodological instrument to represent and understand these causal interactions, thereby facilitating the simulation of intervention scenarios and informing strategic decision-making. The objective of this study was to analyze the dynamics of financial risk factors in savings and credit cooperatives in Ecuador using fuzzy cognitive maps along with the evaluation of their implications through simulated scenarios, The methodology consisted of the construction of a model from eight previously selected financial indicators, the definition of causal relationships with weights on a scale of -1 to +1 and the simulation of three intervention scenarios using the MentalModeler platform. The main results indicate that the model provided an opportunity to clarify the ramifications of non- performing loans, the improvement of provisions and the decrease in non-performing assets on fundamental variables such as equity vulnerability, operational efficiency and provision coverage. These results underscore the ability of fuzzy cognitive maps to predict impacts and reinforce the financial sustainability of cooperatives.
Risk Management, Fuzzy Cognitive Maps, Savings And Credit Cooperatives, Financial Sustainability, Intervention Scenarios.