International Journal of Neutrosophic Science

This study reviews a comprehensive mathematical framework known as neutrosophic soft sets, which combines neutrosophic theory with the soft set theory. Also, we review neutrosophic fractional order functions. For decision making, this framework effectively conveys ambiguity and uncertainty. The developments in soft set theory and neutrosophic set theory are thoroughly examined in this article. We review the advancements of both theories in general. We examine the qualities, applications, and theoretical underpinnings of both theories. We study the combination of neutrosophic soft set theory and logic. The study talks about important new developments and techniques that make neutrosophic soft suites better at solving difficult real-world problems that aren’t always clear. To promote the advancement of the discipline, we also provide a comprehensive overview of the theories derived from literature methodologies, and propose potential avenues for future research. This review serves as an important resource for researchers and practitioners wishing to utilize neutrophil suites in their work. It provides a deeper understanding of the potential effects and applications. This review also addresses a discussion on fractional order neutrosophic sets (FONS). The fractional order component offers an additional degree of freedom, enhancing the adaptability of neutrosophic sets for many applications.

Kottakkaran Sooppy Nisar

Muhammad Farman

Harish Garg

Mahmoud Abdel-Aty

Soft Computing with Neutrosophic and fractional order frameworks: A state-of-the-Art review

软计算与中智学和分数阶框架：一项最新综述

Neutrosophy is the neutralities study and prolongs the discussion of the truth of opinions. Neutrosophic logic might be used in all sectors, to provide the solution for the indeterminate challenges. Some real-time data experience issues like inconsistency, incompleteness, and indeterminacy. A fuzzy set (FS) offers an uncertain solution, and an intuitionistic fuzzy set (IFS) processes partial data, but both fail to handle uncertain data. Financial fraud, believed as a deceptive strategy to gain financial assistance, has recently become a common threat in organizations and companies. Traditional methods namely manual inspections and verifications are costly, time-consuming, and imprecise to identify such fraudulent actions. With the development of artificial intelligence (AI), machine learning (ML)-based algorithms are applied logically to identify fraud transactions by investigating a larger amount of financial data. Therefore, the study offers an Optimizing Financial Fraud Detection using Bayesian Optimization and Variable Selection with Neutrosophic Vague Soft Set (OFFDBO-VSNVS) Algorithm. The OFFDBO-VSNVS model presents an optimized framework for fraud detection by integrating advanced variable selection techniques and classification models. Initially, the OFFDBO-VSNVS technique applies the Z-score data normalization technique to transform input data into a compatible layout. Next, the grey wolf optimizer (GWO)--based feature selection to effectively reduce dimensionality and highlight the most relevant features. For the classification and detection of financial fraud, the neutrosophic vague soft set (NVS) model can be employed. Eventually, the Bayesian optimization (BO) model adjusts the hyperparameter values of the NVS algorithm optimally and outcomes in greater classification performance. The stimulated outcome study of the OFFDBO-VSNVS model occurs and the outcomes are examined in terms of changing features. The experimental study represented the superiority of the OFFDBO-VSNVS method across the existing state-of-the-art methods

E. E.

K.A. Naminova

I.S. Abdullayev

A.E. Ilyin

R.A. Shichiyakh

E. Laxmi Lydia

Optimizing Financial Fraud Detection: Understandings from Variable Selection with Neutrosophic Vague Soft Set

优化金融欺诈检测：从含中智模糊软集的变量选择中获得的理解

Neutrosophic set (NS) is particularly appropriate in applications where data is incomplete, unclear, or inconsistent, which offers an effectual means for analyzing and exhibiting complex mechanisms. An NS is a mathematical technique to manage uncertainty, indeterminacy, and imprecision. It enlarges classical sets, IF sets, and fuzzy sets by presenting three degrees such as indeterminacy (I), false (F), and truth (T). Financial technology (Fintech) plays an essential part in advancing modern society, technology, economies, and various fields. Financial crisis prediction (FCP) plays a crucial role in shaping economic outcomes. Past research has predominantly focused on using deep learning (DL), machine learning (ML), and statistical methods to forecast the financial stability of business. In this manuscript, we focus on the development of Effective Data Classification using Interval Neutrosophic Covering Rough Sets based on Neighborhoods and Multi-Strategy Improved Butterfly Optimization (EDCINCRS-MSIBO) Algorithm for FinTech Applications. It contains distinct kinds of stages such as data normalization, feature selection, classification, and parameter tuning. In the EDCINCRS-MSIBO technique, a min-max normalization-based data pre-processing model to scale the raw data into a uniform format. For feature subset selection, the whale optimizer algorithm (WOA) is employed to reduce the dimensionality and improve model efficiency by selecting the most relevant features. In addition, the EDCINCRS-MSIBO technique takes place interval neutrosophic covering rough sets (INCRS) classifier is utilized for detection and classification of a financial crisis. Finally, a multi-strategy improved butterfly optimization algorithm (MSIBOA) is exploited for the optimum parameter adjustment of the INCRS model. To confirm the better predictive solution of the EDCINCRS-MSIBO model, a wide range of simulations are executed on the two benchmark databases. The comparative result analysis displays the encouraging outcomes of the EDCINCRS-MSIBO method on the existing techniques

Maksim Maksim

Irina Kosorukova

Veronika Denisovich

Elena Klochko

Alexey Dengaev

Effective Data Classification using Interval Neutrosophic Covering Rough Sets based on Neighborhoods for FinTech Applications

基于邻域的区间中智覆盖粗糙集在金融科技应用中的有效数据分类

Let R be a G-graded ring and M be a G-graded R-module. The graded second spectrum of M, denoted by Specs G(M), is the set of all graded second submodules of M. In this paper, we define a topology on Specs G(M) which is analogous to that for SpecG(R), and investigate several topological properties of this topology.

Khaldoun Khaldoun

Khaldoun Al Al-Zoubi

The Zariski topology on the graded second spectrum of a graded module

分次模的分次第二谱上的Zariski拓扑

Wireless Body Area Networks (WBANs) play a pivotal role in modern healthcare by enabling continuous monitoring of physiological data through sensors placed on or around the human body. Despite their significant benefits, WBANs face challenges such as data uncertainty, complex decision-making processes, and dynamic network conditions. These challenges can lead to inaccuracies and inefficiencies in health monitoring and diagnostics. The paper's main aim is to incorporate neutrosophic theory into Wireless Body Area Networks to provide enhancements in decision-making. In modern healthcare, the use of WBANs for monitoring physiological data by sensors, which are attached to or around the human body, can be continuous. Despite huge advantages, the main challenges that WBANs face are the uncertainties in data, complex decision-making processes, and dynamic network conditions, making health monitoring and diagnostics inaccurate and inefficient. In this paper, authors propose a robust framework to map sensor data into the neutrosophic domain and apply neutrosophic logic for enhanced accuracy and reliability of decision-making. In this paper, a Neutrosophic Decision-Making Algorithm is proposed, and its performance is compared with other decision-making techniques in terms of accuracy, response time, energy efficiency, and reliability. Experimental results show major improvements of around 95.3% in accuracy and a reduction of up to 25% in response time and energy consumption. Results underline the potential of neutrosophic theory for revolutionizing decision-making processes within WBANs to ensure more reliable and efficient health monitoring. This approach enables not only operational life but also improves patient outcome, avoiding a wrong diagnosis, during long-term health monitoring applications using WBAN devices.

Ravi Ravi

Bourair Al Al-Attar

Lateef Abd Zaid Qudr

Azmi Shawkat Abdulbaqi

Jamal Fadhil Tawfeq

Ravi Sekhar

Pritesh Shah

Marshiana Devaerakkam

Integrating Neutrosophic Theory for Improved Decision-Making in Wireless Body Area Networks: Enhancing Accuracy and Efficiency in Health Monitoring

整合中智理论以提升无线体域网决策能力：增强健康监测的准确性与效率

In this study, we introduce fixed point theorems related to integral type contractions, framed within the advanced context of neutrosophic fuzzy metric spaces. Additionally, we derive multiple fixed point results that are relevant to this particular setting.

Anwar Anwar

Ayman A. Hazaymeh

Neutrosophic fuzzy metric spaces and fixed points results with integral contraction type

中智模糊度量空间与积分收缩型不动点结果

The study of geometric properties within the subclass of analytic functions has garnered significant attention in recent years due to its complex and intricate interplay between geometric function theory and complex analysis. This area of study provides deep insights into both mathematical theory and its practical applications. The exploration of these properties is not only of theoretical interest but also offers valuable implications for various applications in mathematical and engineering disciplines. In particular, this paper focuses on a detailed examination of the inclusion, neighborhood, and partial sums properties within a broad and general subclass of analytic functions. This class of functions is defined through a generalized multiplier transformation operator, which adds a layer of complexity to their analysis. By investigating these specific properties, this study aims to validate and build upon many existing findings documented in the literature, offering new perspectives and contributing to a deeper understanding of the field.

Mohamed Mohamed

Maisarah Haji Mohd

Feras Yousef

Shamani Supramaniam

Investigating Inclusion, Neighborhood, and Partial Sums Properties for a General Subclass of Analytic Functions

探究一般解析函数子类中的包含性、邻域性和部分和性质

Multi-criteria decision-making is essential for resolving issues in the real world. The choice of cloud computing services on the basis of service quality is solved in this paper using bipolar-neutrosophic circumstances. The removal area approach is used to carry out the de-bipolarization technique. The results have been compared with other methods found in the research to determine which method provides better cloud service.

A. A.

A. Rajkumar

N. Jose Parvin Praveena

Broumi said

Entropy Measure on Selection of Cloud Computing using Bipolar Neutrosophic Environment Utilizing Topsis Method

利用TOPSIS方法在双极中智环境下选择云计算的熵度量

The notions of neutrosophic IUP-subalgebras, neutrosophic IUP-ideals, neutrosophic IUP-filters, and neutrosophic strong IUP-ideals of IUP-algebras are introduced, and their basic properties are investigated. Conditions for neutrosophic sets to be neutrosophic IUP-subalgebras, neutrosophic IUP-ideals, neutrosophic IUPfilters, and neutrosophic strong IUP-ideals of IUP-algebras are provided. Relations between neutrosophic IUP-subalgebras (resp., neutrosophic IUP-ideals, neutrosophic IUP-filters, neutrosophic strong IUP-ideals) and their level subsets are considered.

ISSN	2690-6805, 2692-6148
h-index	23

Publication Information	Publisher: American Scientific Publishing Group (ASPG)，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2020，Proportion of original research papers: ，Self Citation Rate:， Gold OA Rate:

International Journal of Neutrosophic Science

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Soft Computing with Neutrosophic and fractional order frameworks: A state-of-the-Art review

Optimizing Financial Fraud Detection: Understandings from Variable Selection with Neutrosophic Vague Soft Set

Effective Data Classification using Interval Neutrosophic Covering Rough Sets based on Neighborhoods for FinTech Applications

The Zariski topology on the graded second spectrum of a graded module

Integrating Neutrosophic Theory for Improved Decision-Making in Wireless Body Area Networks: Enhancing Accuracy and Efficiency in Health Monitoring

Soft Computing with Neutrosophic and fractional order frameworks: A state-of-the-Art review

Neutrosophic fuzzy metric spaces and fixed points results with integral contraction type

Investigating Inclusion, Neighborhood, and Partial Sums Properties for a General Subclass of Analytic Functions

Entropy Measure on Selection of Cloud Computing using Bipolar Neutrosophic Environment Utilizing Topsis Method

Neutrosophic sets in IUP-algebras: a new exploration