Perceptions of Inclusion in Disability Communication with Plithogenic Fuzzy Soft Sets

Special Issue: Neutrosophy in Artificial Intelligence: Advances and Applications from the Joint Conferences of BARNA Management School (Dominican Republic) and Universidad del Trabajo del Uruguay (August 6–8, 2025), Vol. 92, 2025 Sonia Guerra Iglesias, Andrea Eugenia Pacheco Lemus, Vallardo Villegas-Ricauter, Lorena del Carmen Bodero Arizaga, Luis Enrique Silva Adriano. Perceptions of Inclusion in Communication about Disability with plithogenic fuzzy soft sets. University of New Mexico Perceptions of Inclusion in Disability Communication with Plithogenic Fuzzy Soft Sets Sonia Guerra Iglesias1*, Andrea Eugenia Pacheco Lemus2, Vallardo Villegas-Ricauter3, Lorena del Carmen Bodero Arizaga 4, and Luis Enrique Silva Adriano5 1 Bolivarian University of Ecuador, Ecuador. [email protected]c 2 Bolivarian University of Ecuador, Ecuador. [email protected] 3 Bolivarian University of Ecuador, Ecuador. vvvillega[email protected]u.ec 4 Bolivarian University of Ecuador, Ecuador. [email protected] 5 Bolivarian University of Ecuador, Ecuador. [email protected] Abstract. This article explores how scientific communication with disability can foster social inclusion through an awareness of the challenge of conveying ambiguous perceptions across fields. This is a timely topic because inclusive communication facilitates equity and subsequent participation in academic and socio-cultural settings by persons with disabilities; however, the existing body of literature does not take an approach that represents an intrinsic indeterminacy of human perceptions. Absent this intrinsic human quality, research results oversimplify abilities and failures to include persons with disabilities. My approach fulfills this gap by using plitogenic fuzzy soft sets, which is a more generalized mathematical construct to theorize ambiguity of transformations from qualitative and quantitative data. An interdisciplinary experiment was conducted with graduate students and faculty where perceptions were evaluated and subsequently ranked. Results show that plitogenic soft set components generate noncategorical patterns in communication relative to scientific communication with disability inclusion which presents challenges due to complicated nature but also opportunities due to clear understandings of practical ability. Therefore, this study contributes to the literature on scientific communication relative to disability inclusion by not only expanding existing theoretical frameworks of inclusive communication but also providing suggestions for effective construction of inclusive communication strategies. Thus, it supports trained professionals for ethical equitable higher education access, thereby, facilitating social engagement opportunities for persons with disabilities. Keywords: Inclusion, Disability, Scientific Communication, Plithogenic Sets, Interdisciplinarity, Postgraduate, Equity. 1. Introduction Inclusive scientific communication is an essential pillar for promoting social and educational equity for people with disabilities, a topic of growing relevance in the global academic context. In a world where diversity and inclusion are fundamental values, ensuring that the production and dissemination of scientific knowledge are accessible is crucial. This study analyzes how perceptions about inclusion in scientific communication can strengthen the participation of people with disabilities in postgraduate settings. According to Susinos , inclusive research challenges traditional narratives that perpetuate exclusion, advocating for approaches that prioritize the voices of the actors involved [1]. Therefore, this research focuses on exploring these perceptions, highlighting their importance in transforming academic practices. Historically, disability has been conceptualized from medical and social models, evolving towards more integrative paradigms such as the biopsychosocial one. Over time, perceptions about disability Neutrosophy in Artificial Intelligence: Advances and Applications from the Joint Conferences of BARNA Management School (Dominican Republic) and Universidad del Trabajo del Uruguay (August 6–8, 2025), Vol. 92, 2025 Sonia Guerra Iglesias, Andrea Eugenia Pacheco Lemus, Vallardo Villegas-Ricauter, Lorena del Carmen Bodero Arizaga, Luis Enrique Silva Adriano. Perceptions of Inclusion in Communication about Disability with plithogenic fuzzy soft sets. 642 have changed, influenced by social movements and international regulations, such as the Convention on the Rights of Persons with Disabilities [2]. In the academic field, scientific communication has traditionally been exclusive, limiting access to people with disabilities. Currently, the need for accessible formats and interdisciplinary approaches drives a shift towards more equitable communication, as Revuelt points out [3]. The problem lies in the difficulty of capturing the ambiguity and variability in perceptions about inclusion in scientific communication. Previous studies often focus on technical or theoretical aspects, neglecting complex human perspectives. This research addresses this shortcoming by examining how the perceptions of graduate students and faculty reflect barriers to and opportunities for inclusion. As Lawson points out, epistemological barriers limit the integration of inclusive approaches in academia [4]. The central question is: how can ambiguous perceptions about inclusion in scientific communication be modeled to foster equitable practices in disability? The relevance of this problem transcends the academic sphere, impacting the training of professionals committed to social inclusion. Inclusive scientific communication not only improves access to knowledge but also empowers people with disabilities, promoting their active participation in society. Studies such as those by Paz-Maldonado highlight that communication barriers persist in higher education, affecting equity [5]. Therefore, addressing these perceptions from an interdisciplinary perspective is essential to generating significant change. Despite progress, existing literature presents gaps in the analysis of the inherent indeterminacy of human perceptions about inclusion. While some studies, such as those by Gesser , address intersectionality in disability, few integrate tools to model ambiguity [6]. This work proposes an innovative approach, using plithogenic fuzzy soft sets to capture the complexity of perceptions in a postgraduate context, aligning with the need for advanced methodologies pointed out by Canal [7]. Interdisciplinarity is key to understanding inclusive communication in disability. By integrating perspectives from education, psychology, and communication, this study seeks to overcome the limitations of single-disciplinary approaches. Huguet (2014) emphasizes that interdisciplinary teams are essential for designing inclusive interventions, a principle that guides this research [8]. Thus, the perceptions of students and teachers are analyzed to identify patterns that strengthen equitable scientific communication. The problem of inclusion in scholarly communication requires approaches that address its multidimensionality. The research question is formulated as a challenge to model complex perceptions, ensuring that communication practices are accessible and relevant. This study not only seeks to understand these dynamics but also to propose practical strategies that transform graduate education, promoting an inclusive culture. The objectives of this research are to analyze perceptions of inclusion in scientific communication about disability using plithogenic fuzzy soft sets, identify barriers and opportunities in graduate settings, and propose strategies to strengthen equitable communication practices. These objectives, aligned with the research question, seek to contribute to the theory and practice of inclusion, fostering accessible and transformative scientific communication. 2. Preliminaries. 2.1. Perceptions of Inclusion in Communication. Inclusion in scientific communication, especially in the field of disability, constitutes a crucial challenge to ensure equity in the production and dissemination of knowledge. The perceptions of students, faculty, and other stakeholders about how science is communicated largely determine the accessibility and relevance of academic content. This study, by addressing perceptions of inclusion, seeks to unravel the attitudes and beliefs that shape communication practices in graduate settings. The importance of this topic lies in its ability to transform barriers into opportunities, promoting the active participation of people with disabilities in academia. Neutrosophy in Artificial Intelligence: Advances and Applications from the Joint Conferences of BARNA Management School (Dominican Republic) and Universidad del Trabajo del Uruguay (August 6–8, 2025), Vol. 92, 2025 Sonia Guerra Iglesias, Andrea Eugenia Pacheco Lemus, Vallardo Villegas-Ricauter, Lorena del Carmen Bodero Arizaga, Luis Enrique Silva Adriano. Perceptions of Inclusion in Communication about Disability with plithogenic fuzzy soft sets. 643 Historically, scholarly communication has favored standardized formats, often inaccessible to people with disabilities. For example, dense texts or unadapted visual presentations limit access to knowledge. However, advances in inclusive policies, such as those noted by Jones, have driven the development of more equitable communication strategies [9]. However, perceptions about these strategies vary widely, reflecting the complexity of integrating diversity in academic contexts. Understanding these perceptions is essential for designing effective interventions that not only comply with regulations but also respond to the real needs of those involved. A critical aspect of perception analysis is the inherent ambiguity of human opinions. Attitudes toward inclusion are not univocal; they often combine support, resistance, and ignorance, making them difficult to study using traditional methods. In this sense, the use of plithogenic fuzzy soft sets offers an innovative tool for modeling this indeterminacy. By capturing variability in perceptions, this approach allows for the identification of patterns that conventional quantitative or qualitative methods might miss. Thus, the study provides a novel perspective by addressing inclusion from an interdisciplinary framework. The assessment of this approach reveals its potential to transform scholarly communication. Analyzing perceptions reveals specific barriers, such as a lack of accessibility training among faculty, and strengths, such as students' motivation to promote inclusive environments. According to García-Carmona, raising awareness about inclusion in higher education remains a key challenge [10]. This study, by employing plithogenic sets, not only identifies these dynamics but also proposes practical solutions, such as guides for adapting scientific materials, thus strengthening equity in postgraduate training. However, perception analysis faces significant challenges. The subjectivity of opinions and the diversity of cultural and academic contexts complicate the generalization of findings. For example, what is perceived as inclusive in one setting may be insufficient in another. This variability, although problematic, enriches the study by highlighting the need for contextualized approaches. As Oliver points out, perceptions of inclusion are deeply influenced by social and cultural factors, demanding flexible methodologies [11]. Plithogenic ensembles, by modeling this complexity, offer a robust solution for addressing such differences. The relevance of this topic transcends the academic sphere, impacting society as a whole. Inclusive scientific communication empowers people with disabilities, allowing them to actively contribute to knowledge. Furthermore, it fosters a culture of equity in higher education, aligning with the principles of social justice advocated by Smith [12]. By analyzing perceptions, the study not only identifies obstacles but also highlights opportunities to build bridges between academia and communities with disabilities, promoting greater social cohesion. Another valuable aspect is this study's ability to integrate interdisciplinary perspectives. Inclusion in scientific communication requires input from disciplines such as education, social psychology, and communication studies. This convergence, although complex, enriches the analysis by considering multiple dimensions of inclusion. For example, the plithogenic approach allows for modeling how teachers' and students' perceptions interact, revealing patterns that inform pedagogical strategies. As Torres points out, interdisciplinarity is key to addressing complex social problems such as inclusion [13]. The critical appraisal of this study also recognizes its limitations. The application of plithogenic fuzzy soft sets, although innovative, requires a high level of technical expertise, which could limit its adoption in resource-limited contexts. Furthermore, the reliance on subjective data poses challenges for validating the results. However, these limitations do not diminish the study's value; rather, they open avenues for future research exploring complementary methods or more diverse contexts. In terms of practical impact, this analysis offers concrete tools for improving scholarly communication. The insights identified can guide the design of accessible materials, such as publications in adapted formats or inclusive digital platforms. Furthermore, the study contributes to the training of faculty and graduate students, raising awareness about the importance of inclusion. This practical approach, combined with a solid theoretical foundation, positions the study as a benchmark for transforming Neutrosophy in Artificial Intelligence: Advances and Applications from the Joint Conferences of BARNA Management School (Dominican Republic) and Universidad del Trabajo del Uruguay (August 6–8, 2025), Vol. 92, 2025 Sonia Guerra Iglesias, Andrea Eugenia Pacheco Lemus, Vallardo Villegas-Ricauter, Lorena del Carmen Bodero Arizaga, Luis Enrique Silva Adriano. Perceptions of Inclusion in Communication about Disability with plithogenic fuzzy soft sets. 644 communication practices in academia. In conclusion, the analysis of perceptions of inclusion in scientific communication about disability, using plithogenic fuzzy soft sets, represents a significant advance in promoting equity. By addressing the ambiguity and variability of opinions, this study not only enriches inclusion theory but also offers practical solutions for graduate settings. Its interdisciplinary approach and emphasis on social justice make it a valuable contribution to academia and society, fostering truly inclusive scientific communication. 2.2. Soft sets and extensions In this section, we review the main concepts related to soft sets, fuzzy soft sets, intuitionistic fuzzy soft sets, and neutrosophic soft sets. The next subsection contains the elements of plithogenic sets and plithogenic soft sets. Definition 1 ([14]). A smooth set on 𝑈is a pair (𝐹,𝐸), where 𝑈is the initial universal set, 𝐸is the set of parameters and 𝐹is the map of 𝐸to 𝒫(𝑈), which is the power set of 𝑈. So, given a parameter 𝜀∈𝐸, we have 𝐹(𝜀)∈𝒫(𝑈)as the set of 𝜀−approximate elements of (𝐹,𝐸). Definition 2 ([16]). A fuzzy The smooth set on 𝑈is a pair (𝐹,𝐸), where 𝑈is the initial universal set, 𝐸is the set of parameters and 𝐹is the map of 𝐸to ℱ(𝑈), which is the set of fuzzy subsets of 𝑈. Definition 3 ([15]). An intuitionist Diffuse The smooth set on 𝑈is a pair (𝐹,𝐸), where 𝑈is the initial universal set, 𝐸is the parameter set and 𝐹is the map of 𝐸to ℐℱ(𝑈), which is the set of intuitionistic fuzzy subsets of 𝑈. Definition 4 ([15]). A Neutrosophic The smooth set over 𝑈is a pair (𝐹,𝐸), where 𝑈is the initial universal set, 𝐸is the parameter set and 𝐹is the map of 𝐸to 𝒩(𝑈), which is the set of neutrosophic subsets of 𝑈. 2.3. Plithogenic assemblages and soft plithogenic assemblages Yeah𝑈 It is the universe of discourse, then fix 𝑃, which is a non-empty set of elements, and 𝑃⊂𝑈[17, 18]. It further follows that Ais the non-empty set of one-dimensional attributes, such that A = {α1,α2,…,αm}, m ≥ 1. With each, α ∈ Awe have a spectrum of all possible values (or states).𝑆 which may be a discrete finite set S = {s1,s2,…,sl}, 1 ≤ l <∞or a countably infinite set S = {s1,s2,…,s∞}, or an uncountably infinite (continuous) set S = ]a,b[, a < b. ]…[denotes any open, half-open, or closed interval of the set of real numbers or another general set. On the other hand,𝑉⊂𝑆 and 𝑉≠∅is the range of all attributes that experts need for the given application. Then, for each, x∈Pthe values of all attributes in V = {v1,v2,…,vn}, and are defined.n ≥ 1 There is generally a value called the dominant attribute value in V, which is selected by experts based on their criteria as to which is the most important attribute to meet the proposed objective. The item v ∈ Vhas an approval rating. d(x,v)from element x, to set P, for some assumed criteria. The degree of belonging is classified as the diffuse degree of belonging, a diffuse intuitionistic degree of belonging, or a neutrosophic degree of belonging to the plithogenic set. So, we have the value of the attribute accessory degree function as: ∀𝑥∈ 𝑃,𝑑: 𝑃×𝑉→ 𝒫 ([0,1]𝑧)(1) That is, 𝑑(𝑥,𝑣)is a subset of [0,1]z, such that 𝒫([0,1]z)is the power set of [0,1]z, where determines the zmembership type. In particular, z = 1 means a fuzzy degree of membership, z = 2 denotes an intuitionistic fuzzy degree of belonging, yz = 3 is for the neutrosophic degree of belonging. The function c: V × V → [0,1]is the degree of contradiction function of the attribute value Between any two attribute values v1and v2. This satisfies the following axioms: 1. c(v1,v1) = 0, that is, the degree of contradiction between the same attribute values is zero; 2. c(v1,v2) = c(v2,v1), commutativity. Neutrosophy in Artificial Intelligence: Advances and Applications from the Joint Conferences of BARNA Management School (Dominican Republic) and Universidad del Trabajo del Uruguay (August 6–8, 2025), Vol. 92, 2025 Sonia Guerra Iglesias, Andrea Eugenia Pacheco Lemus, Vallardo Villegas-Ricauter, Lorena del Carmen Bodero Arizaga, Luis Enrique Silva Adriano. Perceptions of Inclusion in Communication about Disability with plithogenic fuzzy soft sets. 645 There is a distinction between functions caccording to the value 𝑧. The degree of contradiction function of the fuzzy attribute value is denoted by cF, the degree of contradiction function of the intuitionistic fuzzy attribute value is a function cIF: V × V → [0,1]2, while the degree of contradiction function of the neutrosophic attribute value is defined by cN: V × V → [0,1]3. Generally, these are one-dimensional attribute values and their degree of discrepancy. If you have multidimensional attribute values, these can be broken down into one-dimensional attribute values. The attribute value degree of contradiction function allows for greater precision when performing calculations on some grouping methods and ranking systems. These values are based on expert judgment regarding the specific problem to be solved. If an attribute cannot be determined, the precision of the value degree of contradiction function will be lost, although the entire theory can still be used. Once the above concepts have been defined,(𝑃,𝑎,𝑉,𝑑,𝑐) It is a plithogenic set that meets the following: 1. 𝑃is a set, 𝑎is a one-dimensional or generally multidimensional attribute, 𝑉is the range of the attribute values, 𝑑is the degree of membership of the attribute value of each element 𝑥to the set 𝑃, x ∈ P, for some given criteria. Finally, 𝑑is dF, dIF, or dN, when it is a fuzzy degree of membership, an intuitionistic fuzzy degree of membership or a neutrosophic degree of membership, respectively, of an element x to the plithogenic set P; 2. On the other hand, we define 𝑐as cF, cIFor cN, if it is the fuzzy degree of contradiction, intuitionistic fuzzy degree of contradiction or neutrosophic degree of contradiction between attribute values, respectively. Experts define d(∙,∙)and c(∙,∙)define the domain of specialization in which they operate. The notation used is as follows: 𝑥(𝑑(𝑥,𝑉)), where 𝑑(𝑥,𝑉) = {𝑑(𝑥,𝑣),𝑓𝑜𝑟 𝑎𝑙𝑙 𝑣 ∈ 𝑉},∀𝑥 ∈ 𝑃. To calculate the degree of contradiction of the attribute value, it is performed on each value of the particular attribute and the value of the dominant attribute, called vD. The function of degree of contradiction of attribute value c among attribute values is included in the definition of plithogenic aggregation operators (intersection (AND), union (OR), implication ( ⟹), equivalence ( ⟺), inclusion relation (partial order) and other plithogenic aggregation operators that combine two or more degrees of attribute value acting on t-norm and tconorm . Most plithogenic aggregation operators are linear combinations of the fuzzy t-norm ( ∧F) and the fuzzy t-conorm ( ∨F). Nonlinear combinations can also be defined. Having the calculation of t-norm and t-conorm between the dominant attribute value ( vD) with another attribute value ( v2), and also c(vD,v2)denotes the contradiction between vDand v2, then we can define the following operations: [1 − c(vD,v2)]⋅tnorm(vD,v2) + c(vD,v2)⋅tconorm(vD,v2)(2), Or what is the same: [1 − c(vD,v2)]⋅(vD∧Fv2) + c(vD,v2)⋅(vD∨Fv2)(3), Also, [1 − c(vD,v2)]⋅tconorm(vD,v2) + c(vD,v2)⋅tnorm(vD,v2)(4), EITHER, [1 − c(vD,v2)]⋅(vD∨Fv2) + c(vD,v2)⋅(vD∧Fv2)(5). The plithogenic neutrosophic intersection is defined in equation 6: (a1,a2,a3)∧P(b1,b2,b3) = (a1∧Fb1,1 2[(a2∧Fb2)+(a2∨Fb2)],a3∨Fb3)(6), The Plithogenic Neutrosophic Union is as follows: (a1,a2,a3)∨P(b1,b2,b3) = (a1∨Fb1,1 2[(a2∧Fb2)+(a2∨Fb2)],a3∧Fb3)(7), To define the Plithogenic Neutrosophic Inclusion we have: Since the degrees of contradiction are c(a1,a2) = c(a2,a3) = c(b1,b2) = c(b2,b3) = 0.5, then: a2 ≥ [1 − c(a1,a2)]b2or a2 ≥ (1 − 0.5)b2or a2 ≥ 0.5b2and c(a1,a3) = c(b1,b3) = 1. Neutrosophy in Artificial Intelligence: Advances and Applications from the Joint Conferences of BARNA Management School (Dominican Republic) and Universidad del Trabajo del Uruguay (August 6–8, 2025), Vol. 92, 2025 Sonia Guerra Iglesias, Andrea Eugenia Pacheco Lemus, Vallardo Villegas-Ricauter, Lorena del Carmen Bodero Arizaga, Luis Enrique Silva Adriano. Perceptions of Inclusion in Communication about Disability with plithogenic fuzzy soft sets. 646 When a1≤ b1the converse applies to a3≥ b3, and then (a1,a2,a3)≤P(b1,b2,b3)if and only if a1 ≤ b1and a2 ≥ 0.5b2, a3 ≥ b3. Applications of plithogenic sets and plithogenic logic can be read in [21-22]. Definition 5 ([19, 20]). Let be 𝑈a universe of discourse, 𝒫([0,1]𝑧)is the power z of 𝑈, such that: • z = 0 is the power set of 𝑈, • z = 1 is the fuzzy power set of 𝑈, • z = 2 is the intuitionistic fuzzy power set of 𝑈, • z = 3 is the neutrosophic power set of 𝑈, Sean α1,α2,…,αm, m ≥ 1, 𝑚different attributes, whose attribute values lie in the sets V1,V2,…,Vm, such that Vi∩ Vj=∅if 𝑖≠𝑗, and 𝑖,𝑗∈{1,2,…,𝑚}. Suppose that Vi={𝑣𝑖1,𝑣𝑖2,…,𝑣𝑖𝑛𝑖}and also Υ= V1×V2× …× Vm. D={𝑣𝐷1,𝑣𝐷2,…,𝑣𝐷𝑚}are the dominant attribute elements of Ai, and c𝑖(𝑣𝐷𝑖,𝑣𝑖𝑗)is the attribute contradiction degree function such that: c𝑖:Vi×Vi→[0,1]. We say that the pair (𝐹𝑃𝑧,Υ)is the Plithogenic Smooth Set (PSS) over 𝑈, such that: 𝐹𝑃𝑧: Υ→[0,1]𝐷×𝒫([0,1]𝑧)(8) Definition 6 ([23]). The union of two PSSs (𝐹𝑃𝑧,A)and ( 𝐺𝑃𝑧,B)over 𝑈, denoted by (𝐹𝑃𝑧,A)∨𝑃 𝑧(𝐺𝑃𝑧,B)is the PSS (𝐻𝑃𝑧,Ω), where Ω=A∪Bsuch that ∀𝜀∈Ω, 𝐻𝑃𝑧(𝜀)={𝐹𝑃𝑧(𝜀),𝑖𝑓 𝜀∈𝐴∖ 𝐵 𝐺𝑃𝑧(𝜀),𝑖𝑓 𝜀∈𝐵∖ 𝐴 𝐹𝑃𝑧(𝜀)∨𝑃 𝑧𝐺𝑃𝑧(𝜀),𝑖𝑓 𝜀∈𝐵⋂𝐴 Where ∨𝑃 𝑧is the zplitogenic junction ? Definition 7 ([23]). The intersection of two PSSs (𝐹𝑃𝑧,A)and (𝐺𝑃𝑧,B)on 𝑈, denoted by (𝐹𝑃𝑧,A)∧𝑃 𝑧(𝐺𝑃𝑧,B)is the PSS (𝐻𝑃𝑧,Ω), where Ω=A∩Bsuch that ∀𝜀∈Ω, 𝐻𝑃𝑧(𝜀)={𝐹𝑃𝑧(𝜀),𝑖𝑓 𝜀∈𝐴∖ 𝐵 𝐺𝑃𝑧(𝜀),𝑖𝑓 𝜀∈𝐵∖ 𝐴 𝐹𝑃𝑧(𝜀)∧𝑃 𝑧𝐺𝑃𝑧(𝜀),𝑖𝑓 𝜀∈𝐵⋂𝐴 Where ∨𝑃 𝑧is the zplitogenic intersection ? Definition 8 ([23]). Given (𝐹𝑃𝑧,E)and (𝐺𝑃𝑧,E)are two probability scoring systems (PSS) over (𝑈,𝐸). The similarity between (𝐹𝑃𝑧,E)and (𝐺𝑃𝑧,E)is denoted by 𝒮(𝐹𝑃𝑧,𝐺𝑃𝑧)and is defined by: 𝒮(𝐹𝑃𝑧,𝐺𝑃𝑧)=1 |𝐸|∑𝑀𝑘 |𝐸| 𝑘=1 (9) 𝑀𝑘=1−∑ ∑ |𝐹𝑗(𝑒𝑖𝑘)−𝐺𝑗(𝑒𝑖𝑘)| |𝑒| 𝑖=1 |𝑈| 𝑗=1 ∑ ∑ |𝐹𝑗(𝑒𝑖𝑘)+𝐺𝑗(𝑒𝑖𝑘)| |𝑒| 𝑖=1 |𝑈| 𝑗=1 , for 𝑒∈𝐸. Definition 9 ([23]). Given (𝐹𝑃𝑧,E)and (𝐺𝑃𝑧,E)are two probability scoring systems (PSS) over (𝑈,𝐸). We say that (𝐹𝑃𝑧,E)and (𝐺𝑃𝑧,E)are significantly similar if 𝒮(𝐹𝑃𝑧,𝐺𝑃𝑧)≥1 2. Properties: Given (𝐹𝑃𝑧,E), (𝐺𝑃𝑧,E), and (𝐻𝑃𝑧,E), are three PSSs over (𝑈,𝐸), then: (1) 𝒮(𝐹𝑃𝑧,𝐺𝑃𝑧)=𝒮(𝐺𝑃𝑧,𝐹𝑃𝑧), (2) 0≤𝒮(𝐹𝑃𝑧,𝐺𝑃𝑧)≤1, (3) 𝐹𝑃𝑧=𝐺𝑃𝑧implies 𝒮(𝐹𝑃𝑧,𝐺𝑃𝑧)=1. (4) 𝐹𝑃𝑧⊆𝐺𝑃𝑧⊆𝐻𝑃𝑧implies 𝒮(𝐹𝑃𝑧,𝐻𝑃𝑧)≤𝒮(𝐺𝑃𝑧,𝐻𝑃𝑧). 3. Results. This paper presents a comprehensive study that applies the plithogenic soft sets framework to analyze the relationship between perceptions of inclusion and the formulation of communication strategies about disability. The study uses simulated data from a panel of 10 postgraduate experts to demonstrate how this methodology can model the ambiguity inherent in human perceptions. Model Configuration The analysis is structured according to a precise methodological framework to ensure the rigor of the process. • Universe of Discourse (U): A group of 10 participants (𝑈 = {𝑝₁,𝑝₂,...,𝑝₁₀})composed of graduate students and teachers. Neutrosophy in Artificial Intelligence: Advances and Applications from the Joint Conferences of BARNA Management School (Dominican Republic) and Universidad del Trabajo del Uruguay (August 6–8, 2025), Vol. 92, 2025 Sonia Guerra Iglesias, Andrea Eugenia Pacheco Lemus, Vallardo Villegas-Ricauter, Lorena del Carmen Bodero Arizaga, Luis Enrique Silva Adriano. Perceptions of Inclusion in Communication about Disability with plithogenic fuzzy soft sets. 647 • Parameter Set (E): Four key dimensions for the research are defined: o D1 (e₁): Perception of Format Accessibility. o D2 (e₂): Perception on the use of Inclusive Language. o D3 (e₃): Perception of the Representativeness of people with disabilities. o D4 (e₄): Proposal for Inclusive Communication Strategies. • Evaluation Scale: A 5-point Likert scale is used, which is mapped to neutrosophic values (T, I, F) (True, Indeterminate, False) in order to apply the required plithogenic formulas. • Dominant Vector (e ₐ ): It is defined as the ideal perception,𝑒ₐ = (𝑇𝐴,𝑇𝐴,𝑇𝐴,𝑇𝐴). • Operators: The standard Zadeh operators are used for the t-norm = min(a, b) and the s-norm = 𝑚𝑎𝑥(𝑎,𝑏). Table 1: Mapping of the Likert Scale to Neutrosophic Values Linguistic Value Abbreviation Neutrosophic Value (T, I, F) Totally agree TA (1.00, 0.00, 0.00) OK TO (0.75, 0.25, 0.25) Neither Agree nor Disagree N (0.50, 0.50, 0.50) Disagree NA (0.25, 0.25, 0.75) Totally Disagree TNA (0.00, 0.00, 1.00) Step 1: Collecting and Mapping Simulated Data The median responses of the 10 participants are simulated and converted to their neutrosophic equivalents. Table 2: Simulated Data of Median Responses Participant D1: Accessibility D2: Language D3: Representativeness D4: Strategies p1 TO TA N TO p2 TA TO TO TA p3 N N NA N p4 TO N TO TO p5 NA NA TNA NA p6 TA TA TO TA p7 TO TO TO TO p8 TNA N NA TNA p9 TO TA TA TA p10 N TO N TO Table 3: Data Mapped to Neutrosophic Values Participant D1: N(T,I,F) D2: N(T,I,F) D3: N(T,I,F) D4: N(T,I,F) p1 (0.75, 0.25, 0.25) (1.00, 0.00, 0.00) (0.50, 0.50, 0.50) (0.75, 0.25, 0.25) p2 (1.00, 0.00, 0.00) (0.75, 0.25, 0.25) (0.75, 0.25, 0.25) (1.00, 0.00, 0.00) p3 (0.50, 0.50, 0.50) (0.50, 0.50, 0.50) (0.25, 0.25, 0.75) (0.50, 0.50, 0.50) Neutrosophy in Artificial Intelligence: Advances and Applications from the Joint Conferences of BARNA Management School (Dominican Republic) and Universidad del Trabajo del Uruguay (August 6–8, 2025), Vol. 92, 2025 Sonia Guerra Iglesias, Andrea Eugenia Pacheco Lemus, Vallardo Villegas-Ricauter, Lorena del Carmen Bodero Arizaga, Luis Enrique Silva Adriano. Perceptions of Inclusion in Communication about Disability with plithogenic fuzzy soft sets. 648 Participant D1: N(T,I,F) D2: N(T,I,F) D3: N(T,I,F) D4: N(T,I,F) p4 (0.75, 0.25, 0.25) (0.50, 0.50, 0.50) (0.75, 0.25, 0.25) (0.75, 0.25, 0.25) p5 (0.25, 0.25, 0.75) (0.25, 0.25, 0.75) (0.00, 0.00, 1.00) (0.25, 0.25, 0.75) p6 (1.00, 0.00, 0.00) (1.00, 0.00, 0.00) (0.75, 0.25, 0.25) (1.00, 0.00, 0.00) p7 (0.75, 0.25, 0.25) (0.75, 0.25, 0.25) (0.75, 0.25, 0.25) (0.75, 0.25, 0.25) p8 (0.00, 0.00, 1.00) (0.50, 0.50, 0.50) (0.25, 0.25, 0.75) (0.00, 0.00, 1.00) p9 (0.75, 0.25, 0.25) (1.00, 0.00, 0.00) (1.00, 0.00, 0.00) (1.00, 0.00, 0.00) p10 (0.50, 0.50, 0.50) (0.75, 0.25, 0.25) (0.50, 0.50, 0.50) (0.75, 0.25, 0.25) Step 2: Plithogenic Aggregation of D1, D2 and D3 (Corrected) The value of the first three dimensions is added (𝑁{𝑘1},𝑁{𝑘2},𝑁{𝑘3})into one value 𝑁{𝑎𝑔𝑔,𝑘}using Plithogenic Neutrosophic Intersection (pAND) iteratively. Formula : 𝑁𝑎𝑝𝐴𝑁𝐷 𝑁𝑏= (min(𝑇𝑎,𝑇𝑏),0.5 × (𝐼𝑎+ 𝐼𝑏),max(𝐹𝑎,𝐹𝑏)) Table 4: Added Neutrosophic Values Participant Corrected Added Value𝑵{𝒂𝒈𝒈,𝒌}(𝑻,𝑰,𝑭) p1 (0.500000, 0.312500, 0.500000) p2 (0.750000, 0.187500, 0.250000) p3 (0.250000, 0.375000, 0.750000) p4 (0.500000, 0.312500, 0.500000) p5 (0.000000, 0.125000, 1.000000) p6 (0.750000, 0.125000, 0.250000) p7 (0.750000, 0.250000, 0.250000) p8 (0.000000, 0.250000, 1.000000) p9 (0.750000, 0.062500, 0.250000) p10 (0.500000, 0.437500, 0.500000) Figure 1: Neutrosophic Aggregated Values - Truth, Indeterminacy, and Falsity Components Neutrosophy in Artificial Intelligence: Advances and Applications from the Joint Conferences of BARNA Management School (Dominican Republic) and Universidad del Trabajo del Uruguay (August 6–8, 2025), Vol. 92, 2025 Sonia Guerra Iglesias, Andrea Eugenia Pacheco Lemus, Vallardo Villegas-Ricauter, Lorena del Carmen Bodero Arizaga, Luis Enrique Silva Adriano. Perceptions of Inclusion in Communication about Disability with plithogenic fuzzy soft sets. 649 Step 3: Calculating Individual Similarity s k The cosine similarity ( s k ) between the aggregated value N { agg,k } and the value of the Strategies dimension N {k,4} is calculated for each participant. Formula: 𝑠𝑘=(𝑇{𝑎𝑔𝑔}𝑇4+ 𝐼{𝑎𝑔𝑔}𝐼4+ 𝐹{𝑎𝑔𝑔}𝐹4) (√𝑇{𝑎𝑔𝑔} 2+ 𝐼{𝑎𝑔𝑔} 2+ 𝐹{𝑎𝑔𝑔} 2× √𝑇42+ 𝐼42+ 𝐹42) Table 5: Individual Similarity Results s k k (Participant) s k 1 0.90189354 2 0.92307692 3 0.90726911 4 0.90189354 5 0.93488210 6 0.93704338 7 1.00000000 8 0.97014250 9 0.94573280 10 0.88384660 Figure 2: Individual Similarity Scores ( sk ) Between Aggregated Perceptions and Communication Strategies Step 4: Calculating the Total Similarity S. The overall similarity is calculated as the average of the individual s k values. • Sum of s k : 𝛴 𝑠𝑘= 9.30578049 • Calculation of S: 𝑆 = 9.30578049 10 = 0.930578049 • The total similarity score obtained and verified is 𝑺 ≈ 𝟎.𝟗𝟑𝟎𝟔. A value close to 1 indicates a very strong and positive correlation between the variables. This result demonstrates that the consolidated perceptions about accessibility, language, and representativeness are highly aligned with participants' ability to formulate effective inclusive communication strategies.