International Journal of Neutrosophic Science

Financial technology (Fintech) is paramount in driving advanced technologies, economies, society, and several other sectors. Smart Fintech is the new-generation Fintech, primarily stimulated and endowed by compuational technology. Smart Fintech syndicates DSAI and renovates economies and finance for dynamic, smart, customized, automated services and systems, economies and financial companies, and the industry. The strength and development of the country’s economies are assessed by the correct forecasting. Financial crisis prediction (FCP) has the substantial consequence on the economies. Previous studies mainly emphasise statistical, DL, and ML methodologies for predicting the financial well-being of the business. Therefore, this article develops a new Arithmetic Optimization Algorithm with Adaptive Neuro-Fuzzy Interference System (AOA-ANFIS) technique for Predicting Financial Crisis. The presented AOA-ANFIS technique aims to predict the presence of financial crises or not. The model incorporates three major elements: Arithmetic Optimization Algorithm (AOA) for feature selection, Adaptive Neuro-Fuzzy Inference System (ANFIS) as the classification algorithm, and Bat Optimization Algorithm (BOA) for parameter tuning. The AOA feature selection model effectively detects the important attributes from a large proportion of financial indicators, augmenting the model's prediction capability while decreasing computational difficulty. Subsequently, the ANFIS classifier exploits the features selected for capturing the intricate non-linear relations intrinsic in financial data, permitting accurate crisis calculation. Additionally, the BOA parameter tuning model augments the ANFIS model's parameters, ensuring robustness and optimum performance. Experimental outcomes on varied financial databases validate the higher efficiency of the AOA-ANFIS technique over underlying processes, demonstrating its effectiveness in forecasting financial crises with great reliability and precision.

Financial fraud may be regarded as any fraud targeting financial organisations including crypto exchanges, banks, fintech, and lending organizations, or any criminal activity associated with the payment process. Financial fraud detection cites protocol set prepared to circumvent the destruction produced by fraudulent activities happening in financial service suppliers. Ecological financial fraud detection (FD) includes the usage of ethical and sustainable performs within fraud actions recognition from the financial area. In recent times, DL and ML techniques have been used in CCF recognition owing to their ability to construct a robust mechanism to discover fraud businesses. Therefore, this study develops an Optimal Single Valued Neutrosophic Sine Trigonometric Aggregation Operator (O-SVNSTAO) for Accurate Financial Fraud Detection Model. The genetic-inspired particle swarm optimization (GIPSO) feature selection model efficiently discerns the relevant attribute from sophisticated financial databases, improving the model's discriminative power while alleviating dimensionality problems. Consequently, the SVNSTAO classifier leverages the features selected to discern complicated features inherent in fraudulent actions, which facilitates accurate diagnosis. Moreover, the COA parameter tuning mechanism enhances the SVNSTAO model's parameter, which ensures adaptability and optimum performance to varied fraud settings. Empirical analysis of real-time financial datasets demonstrates the superiority of O-SVNSTAO technique over classical methods, underlining its effectiveness in discovering financial fraud with exceptional efficiency and reliability

The article addresses the importance of capacity development among public administrators to ensure the efficient functioning of the state and the provision of quality services to the citizenry. This entails acquiring legal knowledge, ethical principles, and technical skills in areas such as strategic planning, financial management, and policy evaluation. In addressing the evaluation of public administrators' training programs, the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods are employed. The methodology delineates the fundamental concepts and key equations to understand the application of these methods in evaluating public administrators' training programs. The findings underscore the significance of specific competencies in public administrators' training. These competencies, combined with comprehensive training encompassing cognitive, attitudinal, and instrumental dimensions, can equip professionals to confront current governmental challenges effectively. The article emphasizes the relevance of capacity development among public administrators and introduces methods such as AHP and TOPSIS to assess and enhance training programs in this field, aiming to bolster the state's capacity to respond efficiently to societal needs.

The main goal of this work is to define for the first time the concept of the metric space of weak fuzzy complex numbers, where we present a suitable metric defined over the ring of weak fuzzy complex numbers, and we study the open balls, closed balls, and torus generated from its structure. On the other hand, we study the symbolic 2-plithogenic and 3-plithogenic/4-plithogenic metric space and its ability to be generated from classical metrics, with many interesting properties related to its analytical structure. Also, many examples will be illustrated to clarify and explain the novelty of this work.

Weak fuzzy complex numbers are defined as , with  as an extension of real numbers with . This paper is dedicated to studying weak fuzzy complex linear Diophantine equations in two weak fuzzy complex variables, by transforming the weak fuzzy complex Diophantine equation to a classical equivalent Diophantine system and going directly from the solutions of classical system into the desired equation. Algorithms for generating solutions of the previous equation will be presented in terms of theorems with many related examples that clarify the validity of our work.

The objective of this paper is to study for the first time the concept of symbolic 5-plithogenic vector space defined over symbolic 5-plithogenic field. Many results about the algebraic properties of this class of spaces will be obtained, where we define AH-subspaces, AH-linear transformations, and kernels. Also, we study the inner products defined over symbolic 5-plithogenic vector spaces and determine the conditions of orthogonality in these spaces with many interesting examples.

The concept of A-curves is considered as a novel application of real field extensions in solving some algebraic vectorial equations defined by Euclidean norms. In this paper, we present a novel insight through the classification of A-curves by illustrating many new semi-module isomorphisms between the direct product of weak fuzzy complex numbers with itself and the direct product of classical Euclidean vector spaces multiplied by itself. These isomorphisms will give us a full classification of A-curves that are related to weak fuzzy complex ring. Also, we provide many examples to explain the contribution of our work.

The notion of neutrosophic N-deductive systems of Hilbert algebras is introduced, and several properties are investigated. Conditions for neutrosophic N-structures to be neutrosophic N-deductive systems of Hilbert algebras are provided. Relations between neutrosophic N-deductive systems and their level subsets are considered. The Cartesian product of neutrosophic N-structures is also supplied. Finally, we also find the property of the homomorphic pre-image of neutrosophic N-deductive systems.

The main objective of this article is to study the inverse of invertible symbolic 3-plithogenic real square matrices using the concept of adjoints and characteristic polynomials. Also, the symbolic 3-plithogenic version of Cayley-Hamilton theorem was proved and provided enough examples to enhance understanding.

Nowadays, financial integrity within sustainable accounting systems is critical endeavor in ensuring intricate landscape of sustainable finance. Detection of financial fraud within sustainable accounting systems is crucial for upholding environmental, social, and governance (ESG) standards and sustaining the integrity of financial practices. Leveraging advanced AI-driven technologies, these systems can effectively analyze abundance of financial data to detect suspicious patterns and anomalies indicative of fraudulent activities. Incorporating Neutrosophic logic into sustainable accounting systems improves the efficiency of financial fraud detection by accommodating inherent uncertainty in complex financial data. By leveraging this ground-breaking technology, organizations can effectively navigate the complex financial landscape while ensuring the integrity of their accounting practices. Neutrosophic logic facilitates the modelling of contradictory and ambiguous information, enabling more nuanced detection and analysis of fraudulent activities that may remain unnoticed. This study develops an automated financial fraud detection using improved sparrow search algorithm with Interval-Valued Neutrosophic Analytic Hierarchy Process (ISSA-IVNAHP) technique. The ISSA-IVNAHP technique aims to protect financial integrity via the identification of financial frauds in Sustainable Accounting Systems. The ISSA-IVNAHP technique incorporates a two-stage process. Initially, the ISSA-IVNAHP method designs ISSA-based feature subset selection approach for the optimal feature selection. Next, in the second stage, the ISSA-IVNAHP technique uses IVNAHP technique for decision-making process that enables to detection of the presence and absence of financial fraud. The simulation results of the ISSA-IVNAHP technique can be examined on financial fraud database. The experimental values reported that the ISSA-IVNAHP methodology attains maximum effeciency over other models

Heart disease (HD) is considered the main cause of death rate around the world. Multiple systems and biomedical instruments in hospitals take large amounts of medical data. Thus, understanding the data linked with HD is vital to enhance the prediction performance. The timely intervention of HD is the most important factor in preventing patients from additional damage. In recent times, non-invasive medical procedures, including artificial intelligence-based approaches have been used in the healthcare sector. Particularly machine learning (ML) applies various techniques and algorithms that are extensively applied and are especially effective in accurately detecting HDs within short period. However, HD prediction is a challenging task. The largest size of medicinal database has made it a challenge for clinicians to understand the complicated feature relations and make disease predictions. Therefore, this study presents a Neutrosophic Fuzzy SAW with Artificial Intelligence for Sustainable Heart Disease Recognition and Classification (NFSAW-AISHDC) technique in Healthcare Sector. The NFSAW-AISHDC technique mainly focuses on the adoption of neutrosophic fuzzy simple additive weighting (NFSAW) with feature selection process for HD detection. The NFSAW-AISHDC method exploits min-max scalar to scale the input medical data. For feature selection, the NFSAW-AISHDC method uses beluga whale optimization (BWO) algorithm to choose feature subsets. Moreover, the NFSAW-AISHDC technique applies NFSAW approach to the identification of HDs. The performance values of the NFSAW-AISHDC methodology undergoes using benchmark database. The experimental outcome underlined the promising results of the NFSAW-AISHDC method with other models.

Predicting sustainable financial crises and promoting green finance are paramount in fast developing economic landscape. Leveraging advanced AI-driven technologies, such as Neutrosophic logic, enables a nuanced understanding of complex sustainability factors influencing financial markets. By incorporating these advanced technologies, organizations can proactively mitigate and identify risks related to unsustainable practices while fostering investment aligned with environmental, social, and governance (ESG) principles. This proactive stance improves financial resilience and contributes to the transition towards a resilient and more sustainable financial ecosystem. We can navigate future challenges with foresight and responsibility through the synergy of sustainable financial crisis prediction and green finance initiatives, which ensures a prosperous and environmentally conscious financial future for the generation to come. This study develops a new optimal Fuzzy Parameterized Single-Valued Neutrosophic Subset for financial crisis prediction and green finance (OFPSVNS-FCPGF) technique. The OFPSVNS-FCPGF technique intends to recognize the presence of the financial disaster in the sustainable and green finance sector. In the OFPSVNS-FCPGF technique, Z-score normalization is primarily used to measure the economic data into a beneficial layout. For the procedure of prediction, the OFPSVNS-FCPGF approach designs the FPSVNS approach which detects the occurrence of financial crises or not. Furthermore, the parameter tuning of the FPSVNS technique takes place utilizing the grasshopper optimization algorithm (GOA). To illustrate the improved FCP outcomes of the OFPSVNS-FCPGF model, a series of simulations were involved. An wide comparison study specified that the OFPSVNS-FCPGF method gains significant outcomes in the green finance sector.

The tax behavior of taxpayers has gained increasing relevance in the economic and legal context of Ecuador. The tax system plays a fundamental role in the generation of income for the State and the financing of public policies. In this sense, understanding and evaluating how taxpayers comply with their tax obligations becomes crucial to guarantee equity and efficiency in tax collection. In this regard, the objective is to examine the various legal aspects that affect the compliance of tax obligations by taxpayers in the Babahoyo canton. The Neutrosophic Delphi method was used to model the study. As a result, it is seen that the tax authorities should promote educational campaigns in the canton for the knowledge and understanding of their citizens about the current regulations and thus strengthen the tax system for the benefit of the economic and social development of the country.

The objective of this paper is to study the expansion of symbolic 2-plithogenic and symbolic 3-plithogenic real functions in one variable with real series, where many famous expansions will be presented according to the Taylor series applied for symbolic plithogenic functions defined over symbolic plithogenic rings. Also, we provide many related examples to clarify and to explain the expansion method and properties.

We introduce the concept of an interval-valued neutrosophic cubic vague subbisemiring (IVNCVSBS), level sets of IVNCVSBS of a bisemiring. IVNCVSBSs are the new extension of neutrosophic subbisemirings and SBS over bisemirings. Let ℵ be a neutrosophic vague subset in $X$, we show that ℶ is a IVNCVSBS of X if and only if all non-empty level set is a SBS of X. Let ℵ be a IVNCVSBS of a bisemiring X and strongest cubic neutrosophic vague relation of X, we prove that ℵ is a IVNCVSBS of X × X. Let ℵ be any IVNCVSBS of X, prove that pseudo cubic neutrosophic vague coset is a IVNCVSBS of X. Let ℵ1, ℵ2,..., ℵn be the family of IVNCVSBS of X1, X2,..., Xn respectively. The homomorphic image of every IVNCVSBS is an IVNCVSBS. The homomorphic pre-image of every IVNCVSBS is an IVNCVSBS. Examples are provided to strengthen our results.