This paper is dedicated to study for the first time the applications of neutrosophic BDF and CDF Newton's methods for finding the numerical solutions of some different problems related to the derivations from first and second order applied on neutrosophic-tabulated functions, where we apply those novel methods on some problems and list the solutions by using the numerical tables. In addition, we provide a theoretical discussion and description of these methods to be applicable on other numerical problems.
Read MoreDoi: https://doi.org/10.54216/IJNS.260401
Vol. 26 Issue. 4 PP. 01-08, (2025)
Starting from semi-explicit perturbed bilinear time varying neutrosophic differential – algebraic equations (PBTVDAs). We develop a method for the stabilization of this controlled bilinear time varying neutrosophic differential – algebraic equations and prove that the controlled perturbed system can be stabilized by putting specific conditions on the proposed control. This method transfers the system to standard canonical form and uses the exponential stability concept. Therefore, the stabilization of this system is achieved finally; we present numerical results for the battery model, which confirm the theoretical results.
Read MoreDoi: https://doi.org/10.54216/IJNS.260402
Vol. 26 Issue. 4 PP. 09-20, (2025)
Let be the direct product of an associative ring . In the work the concepts of Endo Bi-Antiderivation, Jordan Endo Bi-Antiderivation and Quasi Endo Bi-Antiderivation on a ring are introduced, furthermore the relations between these bi-additive mappings are given. As essential point, we searched for appropriate conditions that make equivalence between Jordan Endo Bi-Antiderivation and Quasi Endo Bi-Antiderivation. Also, we prove the same results for the generalized case of neutrosophic rings.
Read MoreDoi: https://doi.org/10.54216/IJNS.260403
Vol. 26 Issue. 4 PP. 21-27, (2025)
This paper proposes a novel smart farming decision-making framework that integrates machine learning (ML) techniques Support Vector Machine (SVM), Fuzzy C-Means (FCM) clustering, with the generalized distance and similarity measures in a linguistic neutrosophic hypersoft set environment. ML processes real-time sensor data to predict weather patterns, while linguistic neutrosophic terms capture uncertainty, indeterminacy, and falsity, allowing for a more precise analysis of imprecise information. Through the application of generalized similarity measures, the framework ranks the cities suitable for farming strategies based on multiple criteria such as temperature, wind speed, and humidity. The use of linguistic neutrosophic terms offer enhanced flexibility in managing weather-related uncertainty compared to existing methods. The outcomes demonstrate that this integrated approach optimizes decision-making under uncertain environmental conditions, enabling more efficient resource management and improving resilience in farming practices. Future research will further explore the inclusion of additional environmental factors and improve similarity measures to increase decision accuracy among broader agricultural contexts. This model also has the potential to be applied to other domains where uncertainty management is crucial, such as climate resilience and environmental sustainability.
Read MoreDoi: https://doi.org/10.54216/IJNS.260404
Vol. 26 Issue. 4 PP. 28-41, (2025)
Multidimensional data cubes are essential components in data warehouses, enabling rich, OLAP-based analysis across dimensions such as time, location, and product category. However, the complexity that supports such analytical flexibility often leads to extreme sparsity—where the majority of cube cells remain empty or only partially filled. This sparsity can hinder the performance of downstream machine learning models, especially when valuable but infrequent patterns are lost during preprocessing. This paper introduces a neutrosophic-based framework for evaluating and managing sparse regions within OLAP cubes. Instead of treating all sparsity as noise, we propose a typology that distinguishes between three forms: semantic sparsity (expected and justifiable absences), non-informative sparsity (regions with little analytical value), and informative sparsity (sparse areas that still carry meaningful insights). Each substructure is modeled using neutrosophic logic, which assigns degrees of truth, indeterminacy, and falsity to reflect its analytical potential. A dedicated Neutrosophic Evaluation Algorithm is developed to classify each region using metrics such as semantic confidence, entropy, and a context-aware informativeness score. These metrics allow for nuanced decisions: preserving informative sparsity, eliminating irrelevant regions, and flagging ambiguous areas for further review. This approach shows how neutrosophic logic can offer a novel and effective way to handle sparsity in OLAP cubes, improving the relevance and robustness of machine learning pipelines trained on multidimensional data.
Read MoreDoi: https://doi.org/10.54216/IJNS.260405
Vol. 26 Issue. 4 PP. 42-49, (2025)
The main purpose of this paper is to define the notion of neutrosophic based normal and regular spaces. This study investigates and open new class and conception of generalization of classical regular and normal spaces. The hereditary and topological properties of neutrosophic based normal and regular spaces have been analyzed and investigated. It also examines neutrosophic topological subspaces, providing insights into their characteristics. Furthermore, the paper investigates neutrosophic regular spaces and demonstrates their hereditary nature, specifically focusing on R_1, R_2, R_3 and R_4. Additionally, we explain some example of a neutrosophic-regular based space X which is a neutrosophic based normal-space but it is not necessary to neutrosophic -〖T〗_1 spaces. Eventually, it is shown that under certain conditions that the images are preserved in neutrosophic based normal and regular spaces.
Read MoreDoi: https://doi.org/10.54216/IJNS.260406
Vol. 26 Issue. 4 PP. 50-56, (2025)
This paper focuses on the stability of Descriptor Predator-Prey economic system and its related neutrosophic system of Holling type-III functional action response with harvested predator under classical real environment and neutrosophic environment. Where the solvability and dimensionless forms have been presented along with the necessary mathematical justifications and proofs with some qualitative properties have been proposed and developed with systematic illustration.
Read MoreDoi: https://doi.org/10.54216/IJNS.260407
Vol. 26 Issue. 4 PP. 57-64, (2025)
A hyperfunction maps each input to a subset of outputs, generalizing classical functions to represent multi-valued or uncertain outcomes. A superhyperfunction extends this idea further by mapping sets (or sets of sets) to higher-order powerset values, thereby capturing complex hierarchical or layered uncertainties. In this paper, we explore the use of hyperfunctions and superhyperfunctions in linear programming. Specifically, we examine the Linear Objective (Profit/Cost) n-SuperHyperfunction and the Linear Utility n-SuperHyperfunction. We hope these concepts will advance both hyperfunction theory and the study of linear programming under uncertainty.
Read MoreDoi: https://doi.org/10.54216/IJNS.260408
Vol. 26 Issue. 4 PP. 65-76, (2025)
This paper presents four new types of continuity in the context of supra-soft topological spaces: supra-soft ω- continuity, supra-soft ω-irresoluteness, supra-soft contra-continuity, and supra-soft contra-ω-continuity. The main contribution is the clear definitions and detailed study of these concepts, which helps us better understand how they work and how they are interconnected. We carefully examine how these new concepts connect among themselves and with analogous concepts in traditional supra-topological spaces. We also demonstrate how these different forms of continuity behave under common mathematical operations, such as composition and restriction. To make everything easier to understand, we introduce several examples that emphasize how these new concepts compare with existing, well-known concepts, giving a better picture of how continuity works in a more generalized topological settings.
Read MoreDoi: https://doi.org/10.54216/IJNS.260409
Vol. 26 Issue. 4 PP. 77-93, (2025)
This study evaluates the influence of technology risks on insurance company performance through Insurtech innovation, focusing on the roles of Data Privacy (DP), Skill Gaps (SG), and Financial Risks (FR) in predicting Insurance Performance (IP). Employing a questionnaire survey approach, the research extended historical empirical studies, capturing demographic profiles and study variables measured on a 5-point Likert scale. A pilot study refined the questionnaire, achieving an 80% response rate, and minor adjustments were made to enhance clarity. The dataset included 243 responses from employees of Jordanian insurance companies, with 37 excluded due to incomplete data. Validity and reliability were assessed using Average Variance Extracted (AVE), Composite Reliability (CR), and Cronbach's Alpha, confirming the robustness of the measurement model. Multicollinearity was tested using correlation, Tolerance, and Variance Inflation Factor (VIF), with no significant issues detected. ANOVA tests were conducted to examine the impact of experience and technology level on DP, SG, FR, and IP, revealing significant differences across groups. A multiple regression model demonstrated that DP and FR positively affect IP, while SG has a negative effect. To further predict IP, the dataset was split into 80-20% and 60-40% training-test sets, and a Multilayer Perceptron (MLP) model was employed. The MLP neural network model, using the Rprop method, highlights the importance of DP, SG, and FR in predicting IP, achieving an accuracy of up to 72%. These findings highlight the importance of addressing technology risks and leveraging Insurtech innovations to enhance insurance company performance, providing valuable insights for industry stakeholders and policymakers.
Read MoreDoi: https://doi.org/10.54216/IJNS.260410
Vol. 26 Issue. 4 PP. 94-112, (2025)
HXDTRU is a multidimensional public key encryption system with sixteen encrypted data vectors at each step. In this work, we propose HXDHS, an improved version of HXDTRU based on hexadecnion algebra with neutrosophic integer coefficients, as well as a new mathematical construction includes three private keys with one public key to enhance the security and robustness of the public-key system. HXDHS is suitable for applications that require concurrent operation from multiple sources.
Read MoreDoi: https://doi.org/10.54216/IJNS.260411
Vol. 26 Issue. 4 PP. 113-121, (2025)
Fuzzy sets and probabilistic methodologies have been integrated with forecasting but do not simultaneously capture the truth, indeterminacy, and falsity—really the crux of Neutrosophic Logic (NL). There is no literature investigating the incorporation of neutrosophic numbers into deep architectures, in particular into Neutrosophic Neural Networks (NNNs) for demand forecasting. This contribution fills the gap with the presentation of a Neutrosophic Neural Network (NNN) model with uncertainty explicitly included, enhancing the reliability and explain ability of demand forecasting. Deep learning-based demand forecasting strategies involving the use of Random Forest regression and XGBoosting algorithms generally do not deal with uncertainty and imprecision related with real-world demand data. The current work introduces a new model Neutrosophic Neural Network (NNN) where Neutrosophic Logic (NL) is integrated into deep learning demand forecasting. A novel neutrosophic activation function and a Neutrosophic Mean Squared Error (NMSE) loss function are proposed study, is implemented with the Random Forest regression and XGBoosting algorithms, and trained using synthetic and real-world demand data. Experimental results establish the better performance of the NNN approach about forecasting accuracy, robustness, and uncertainty handling. The sensitivity analysis also confirms the flexibility of the model with different demand patterns. The work contributes significantly towards neutrosophic deep learning and the possibility of robust and interpretable demand forecasting for supply chain and business intelligence.
Read MoreDoi: https://doi.org/10.54216/IJNS.260412
Vol. 26 Issue. 4 PP. 122-136, (2025)
This work focuses on the estimation reliability function where x and y are two independent Benktander distributions. The greatest likelihood's asymptotic distribution is found. The maximum likelihood estimator, the moment method estimator, and the approximate maximum likelihood estimator of are proposed. We obtain the asymptotic distribution of s maximum likelihood estimate. The confidence interval can be found using the asymptotic distribution.
Read MoreDoi: https://doi.org/10.54216/IJNS.260413
Vol. 26 Issue. 4 PP. 137-142, (2025)
In this work, subsequent expansions of the operators , algebraic sum and geometric product over IVTNFs. The first of is called the shrinking operator and the second, which is an extension of the first, is called - shrink operator. The membership values & the values of non-membership be not completion our for all time possible, although in the branch of IVTNFS, it plays an additional significant character at this time, since the interval valued temporal neutrosophic fuzzy sets provides the best solution for finding the shortest distance in deciding one's career, judgment making and image processing and many more areas. Especially in medical diagnosis, when using this concept, there is a real chance that there will be a non-zero fraction of hesitation at any point in the assessment.
Read MoreDoi: https://doi.org/10.54216/IJNS.260414
Vol. 26 Issue. 4 PP. 143-154, (2025)
This study investigates the finite-time stability (FTS) of the discrete Sel’kov-Schnakenberg reaction-diffusion (SSRD) system, a mathematical model capturing the interplay between local reactions and spatial diffusion. A novel discretization framework based on finite difference methods (FDM) is developed to transform the continuous reaction-diffusion (RD) system into a discrete counterpart, enabling detailed computational analysis. Sufficient conditions for FTS are derived using Lyapunov functions (LF) and eigenvalue-based methods, ensuring precise predictions of the system’s behavior. Numerical simulations validate theoretical findings, demonstrating the proposed methods’ practical applicability to scenarios such as chemical reactions, biological processes, and technological systems. The influence of system parameters, boundary conditions, and initial conditions on the dynamic behavior is systematically analyzed. This study contributes to the broader understanding of RD systems, addressing key challenges in stability analysis and offering a computationally efficient framework with implications for science and engineering.
Read MoreDoi: https://doi.org/10.54216/IJNS.260415
Vol. 26 Issue. 4 PP. 155-166, (2025)
The concentration of linear operators is unpretentious to prove in measurable space but there is few works in weighted space, here we will include characteristics of approximate of unrestrained functions in measured space by lined operators via direct and converse approximation theorems. In addition, the relationship between modulus of softness and K- functional where, we proven are together tools equivalence.
Read MoreDoi: https://doi.org/10.54216/IJNS.260416
Vol. 26 Issue. 4 PP. 167-173, (2025)
The primary goal of the article is to examine the data s shape and crack higher-order graph structures in cell complex topology. Further simplical complex-based kernel estimation methods are explored and discussed.
Read MoreDoi: https://doi.org/10.54216/IJNS.260417
Vol. 26 Issue. 4 PP. 174-183, (2025)
The sophisticated statistical methods known as Bayesian EWMA and DEWMA control charts are intended to track process performance and identify changes in data over time. They improve the capacity to monitor minute changes in the process by combining conventional smoothing methods with Bayesian inference. By integrating the idea of neutrosophic approaches into Bayesian EWMA and DEWMA models, the suggested approach seeks to address and get beyond this restriction. In this study, neutrosophic approaches are utilized to provide the manufacturing process with two tolerance limits instead of a set value for upper and lower control limits, particularly when all observations are uncertain, imprecise, or fuzzy. By combining the Exponential, Inverse Rayleigh, and Weibull distributions, five symmetric loss functions are examined while taking uniform prior into account. Additionally, for mean, variance, and control limits of the proposed work have been derived. Simulation studies were conducted and compared with previous work as well as all projected works. This study significantly advances the subject of control chart technique, especially when it comes to managing hard, vast, and complicated information.
Read MoreDoi: https://doi.org/10.54216/IJNS.260418
Vol. 26 Issue. 4 PP. 184-203, (2025)
Many of the problems that we face in our lives and daily work are how to directly and accurately select candidates or categories from multiple sets of candidates (categories). The ranking and selection approach is a modern and direct method for selecting categories easily, which is associated with a probability of correct selection. In this paper, we employ the neutrosophic Bayes procedure for decision to select multinomial population. Select a mid-range category for multiple categories and employ neutrosophic logic to define a modern Bayesian procedure that incorporates parameters with some indeterminacy and has a prior distribution, which we call the neutrosophic prior distribution.
Read MoreDoi: https://doi.org/10.54216/IJNS.260420
Vol. 26 Issue. 4 PP. 219-225, (2025)
In this paper, we introduce and investigate new generalized subclasses of neutrosophic n-fold symmetric bi-univalent functions defined in the open unit disk U . These subclasses are characterized via four neutrosophic multi-parameters κ, ρ, γ, and β, which provide a flexible framework to capture the truth, indeterminacy, and falsity components inherent in geometric and analytic behaviors. Within this neutrosophic setting, we derive upper bounds for the initial coefficients |dn+1| and |d2n+1|, and establish generalized Fekete–Szeg˝o inequalities for the considered classes. The results obtained extend and unify several existing results in classical and neutrosophic bi-univalent function theory. Examples and corollaries are presented to demonstrate the sharpness and applicability of the results.
Read MoreDoi: https://doi.org/10.54216/IJNS.260419
Vol. 26 Issue. 4 PP. 204-218, (2025)
Transportation optimization remains a critical challenge in international businesses, particularly given the inherent uncertainties of supply chain networks. This paper proposes a novel machine learning-based model for solving multi-objective, multi-item solid transportation problems that fundamentally advances beyond existing fuzzy and neutrosophic approaches. Our key innovation lies in the synergistic integration of neutrosophic Z-numbers (NZNs) with adaptive machine learning techniques, creating a framework that simultaneously captures value vagueness, information reliability, and dynamic uncertainty patterns capabilities absent in conventional fuzzy transportation models. Unlike traditional fuzzy methods that treat all uncertainty uniformly, our NZN representation provides a three-dimensional structure incorporating truth, indeterminacy, and falsity measures, each with associated reliability metrics. This enriched uncertainty modeling enables three ground breaking advancements over existing approaches: (1) a neural scoring system that autonomously learns optimal NZN comparison functions from historical decision patterns, overcoming the limitations of static aggregation operators in fuzzy systems; (2) LSTM networks that jointly forecast demand values and their reliability evolution under uncertainty; and (3) reinforcement learning optimizers that dynamically balance economic efficiency with information quality in routing decisions. Computational experiments demonstrate superior performance compared to six established baseline methods, including traditional fuzzy, intuitionistic fuzzy, neutrosophic, and pure machine learning approaches. Our hybrid framework achieves a 23.4% reduction in transportation costs and 35.4% improvement in uncertainty handling compared to conventional fuzzy transportation models, with statistically significant improvements (p < 0.001) across all evaluation metrics. By coupling the theoretical rigor of neutrosophic mathematics with the adaptive power of machine learning, this study provides businesses with a transformative decision-support system for transportation planning under real-world uncertainty conditions.
Read MoreDoi: https://doi.org/10.54216/IJNS.260421
Vol. 26 Issue. 4 PP. 226-253, (2025)
This study aims to apply Neutrosophic Theory in analyzing monolingual and bilingual lexical entries as an approach capable of accurately representing semantic ambiguity, phonological values, and developmental values. This is because lexical meaning is a vital component of the semantic system, responsible for conveying and clarifying meaning. However, despite its importance, it is insufficient for fully conveying meaning. Lexical entries lack crucial values, especially the recognition of probable meanings. The network of semantic relationships in any dictionary addresses meaning in a binary way. In a language that relies heavily on metaphor or derivation, like Arabic, dictionaries tailored to the Arabic language fail to provide probable meanings for words such as (eye - heart - hand), whose contextual and metaphorical meanings sometimes do not align with the body-part indication but include other potential meanings. This study is based on the hypothesis that the linguistic dictionary in general and Arabic in particular, still require an approach that allows observing the meanings across three dimensions: truth (T), indeterminacy (I), and falsehood or negation (F). By integrating phonological, semantic, and evolutionary analysis within a neutrosophical framework, a more comprehensive lexical model can be developed that captures the interaction between language, usage, context, and history. This research adopted a mixed descriptive–analytical method, combining qualitative linguistic analysis with quantitative Neutrosophic modeling.
Read MoreDoi: https://doi.org/10.54216/IJNS.260422
Vol. 26 Issue. 4 PP. 254-261, (2025)
The goal of the study is to provide the context, substantiation and formation of a strategic model for development of an innovative educational environment in higher education based on application network interaction principles. The study adopts a holistic systems theoretical approach that integrates systemic, institutional and network theories within a neutrosophy based decision-making model to deal with uncertainties and indeterminacy involved in innovation management at HEI level. The study is based on data collected from different universities and institutions with different profiles in terms of innovation potential. The results lead to a strategic model of networked scientific and innovative activity, including mechanisms for knowledge exchange, technology transfer, and collaborating with industry and government. The model together enabling universities’ effectiveness in producing, disseminating and applying new knowledge proposes three levels of interacting channels. This study is new in merging neutrosophic logic with network interaction theory to develop a flexible decision-making model for strategic development of higher education sector. The paper offers policy consolidators, university heads and academic consultants with practical tips aimed at improving innovation-management as well as educational quality, deepening the synergies between education-sciences-business worlds at Universities.
Read MoreDoi: https://doi.org/10.54216/IJNS.260423
Vol. 26 Issue. 4 PP. 262-268, (2025)
This paper responds to the problem of establishing criteria priority for microcredential implementation in Latin American universities, a developing topic with great momentum and the need to professionalize traditional learning models. Amid rapid digital and labor developments, microcredentials emerge as an efficient way of certifying targeted skills and fostering adaptability to market demand. Still, implementation in higher education lacks a clear pathway of systematic substantiation. The state-of-the-art demonstrates that few mixed-method studies have attempted to prioritize institutional, pedagogical, and technological aspects of this endeavor. This paper applies the Analytic Hierarchy Process (AHP) to a criterion for criteria relative assessment as a method for qualitative and quantitative study. This approach assesses relative importance between seemingly equal criteria—digital infrastructure, teacher training, curriculum relevance, and external validity, for example—for better implementation within higher education systems. Results assess teacher training and platform interoperability as the two most important criteria for successful microcredential implementation. This study is relevant theoretically for multicriteria approaches to the assessment of learning flexibility and practically speaking, supports university administrative decisions for more adaptable, equity-driven and sustainable learning options.
Read MoreDoi: https://doi.org/10.54216/IJNS.260424
Vol. 26 Issue. 4 PP. 269-283, (2025)
In an increasingly multicultural world, workforces become more diverse, and the challenge of internal communications exacerbates. What one group deems clear communication can easily be reinterpreted, countered or found invalid by another group valuing different cultural mores, norms, and expectations. As these issues grow, not only does organizational coherence suffer, but also strategic impact potential fails as globalized realities emerge. There becomes a need for a model that successfully implements the nuance and indeterminacy of such communicative interactions. While models of intercultural communications exist, they often operate on a binary method of understanding that fails to acknowledge the simultaneous presence of varying levels of truth, indeterminacy, and untruths. This is where neutrosophic plitogenic logic intervenes as the advanced form through which these properties can be modeled to suggest cognitive/emotional/symbolic determination as a single potentialized system of assessment. Thus, the challenge emerges to neutralize indeterminacy by fluidly responding to the communicative elements relative to what is present at any given moment over time. Neutrosophic Plitogenic Logic emerges as a viable interdisciplinary approach to understanding internal communication by theoretical and practical means - using epistemology through organizational studies fields and management feasibility - as it successfully presents the shifting and multivalent form of such a communicative process within increasingly multicultural dynamics when existing reconciled methods fail. This contribution is theoretical - as it creates a tool for fields of study to manage structural ambiguity - and practical - for management purposes - as it fosters a model for inclusion in resilient, contextually viable messaging design.
Read MoreDoi: https://doi.org/10.54216/IJNS.260425
Vol. 26 Issue. 4 PP. 284-297, (2025)
This work presents a neutrosophic stance detection model to bridge computational assessment and logic of indeterminacy in artificially intelligent (AI)-mediated learning and its outcomes. Utilizing the BART-large-MNLI model, a causal assessment was made of five hypotheses stemming from AI-supported learning between teacher-student relationships. These stances were then transformed into refined neutrosophic values (truth (T), partial support (P_S), indeterminacy (I), partial opposition (P_O) and falsity (F)). Ultimately, findings suggest that partial support is the most prevalent stance applied to any of the hypotheses, revealing that AI is, largely, a boon to education. However, this valence is tempered by indeterminacy among axes as well as stance magnitude. The largest partial support in rank order came from personalized education and access to AI tutors, while the most importance was given to opposition of relying on AI as support and replacement AI learning. Such findings confirm neutrosophic stance analysis and causal graph modeling as increasingly successful for applying measurable patterns to epistemically ambiguous fields. The neutrosophic causal graph integrates the above findings with a visualization of proposed dynamics between each vertex based on both quantitative patterns and epistemic uncertainty trends. The current research holds implications for educational theory, policy and instructional design integrity in 21st century learning. Uncertainty became a tangible concept; instead of devaluing AI in the classroom, it must be present as an enhancing supplemental tool, never replacement, for ethical considerations and equitable access. The potential for neutrosophic to transform apparent truths that are at times contradictory is confirmed through the human-machine interactive learning process, with subsequent suggestions for future research into AI-mediated education's causal relationships and decision-making potential.
Read MoreDoi: https://doi.org/10.54216/IJNS.260426
Vol. 26 Issue. 4 PP. 298-308, (2025)
This paper presents the Fractional Maxwell-Weibull Copula (FMWC) distribution to deal with the heavy tails, extended memory, and nonlinear dependence of price returns of Bitcoins, as the existing financial models face limitations in this aspect. The FMWC provides a flexible model that allows incorporating fractional Weibull distributions to capture persistent autocorrelation, Maxwell components to model significant price changes, and a Student-t copula to capture multivariate dependencies to discuss the volatile returns of Bitcoin. The FMWC was applied to historical Bitcoin data between January 2020 and May 2025 and showed better results than other models, such as Weibull, GARCH-t, and Maxwell-Log Logistic, with an MAE of 0.034374, RMSE of 0.0335, and log-likelihood of 4200.0. Its risk measures (VaR 95% = -0.07983, CVaR 95% = -0.10882) improve tail risk estimation, which is important in risk measurement and portfolio management. Robustness tests also validate its performance over periods and proper handling of outliers. Nevertheless, the FMWC is an excellent tool, despite its computational complexity issues, and can be used by investors, traders, and regulators. Further studies on the computational efficiencies and applications to other cryptocurrencies are required to increase their application in dynamic financial markets.
Read MoreDoi: https://doi.org/10.54216/IJNS.260427
Vol. 26 Issue. 4 PP. 309-326, (2025)
This paper introduces and systematically studies new classes of mappings and set-theoretic structures in the context of neutrosophic soft topological structures. In particular, this study introduces and examines neutrosophic soft semi closed and semi open sets, generalized semi-mappings, and semi-continuous generalized mappings, highlighting their interrelationships and key topological properties The neutrosophic soft generalized semi-closure and semi-interior operators are also formulated, and their principal algebraic and topological characteristics are derived. These developments generalize and unify several existing notions in classical, fuzzy, and neutrosophic soft topologies. Unlike previous studies, this work provides a comprehensive mapping-based approach that clarifies how generalized semi-properties behave under neutrosophic soft transformations. The findings not only extend the theoretical foundations of NST but also open potential directions for modeling and analyzing uncertainty in advanced topological systems.
Read MoreDoi: https://doi.org/10.54216/IJNS.260428
Vol. 26 Issue. 4 PP. 327-347, (2025)