On the Nature of Solutions of Discrete Time Lyapunov Equations

Mohammed Noori; Emad Farhood

doi:https://doi.org/10.54216/PMTCS.040203

International Journal of Neutrosophic Science | 2025

Boosting Road Damage Detection via DEMATEL with Bipolar Neutrsophic Dombi for Intelligent Smart City Infrastructure

In decision-making, NS permits the representation of information with three membership functions: indeterminacy (I), false (F), and truth (T). All components in an NS have indeterminacy, non-, and membership degrees that are autonomous and vary from (0-1). This generates NS particularly appropriate in composite decision-making situations where information is incomplete, ambiguous, or contradictory, which allows strong and more complex solutions and analysis. Detecting road damage accurately and quickly enables the capability of road maintenance agencies to generate timely maintenance to road surfaces, retain optimum road conditions, enhance the safety of transportation, and reduce transportation charges. Research on road damage detection using AI models achieved more attention at present, particularly in smart cities. This paper develops a Boosting Road Damage Detection using DEMATEL with Bipolar Neutrosophic Dombi and Siberian Tiger Optimization (BRDD-DBNDSTO) algorithm. The presented BRDD-DBNDSTO technique is mainly intended to improve the accuracy and reliability of road damage classification for intelligent smart city infrastructure. To accomplish this, the BRDD-DBNDSTO technique employs adaptive bilateral filtering (ABF) using image preprocessing to effectively enhance image quality by reducing noise. Then, the SqueezeNet method was used to create a collection of feature vectors. For the classification and detection of road damage, the DEMATEL with bipolar neutrosophic Dombi model is exploited. At last, the Siberian tiger optimization (STO) algorithm is used to adjust the parameters related to the classifier model. To guarantee the improved performance of the BRDD-DBNDSTO method, an extensive experimental study was carried out and the gained outcomes illustrate the improvement of the BRDD-DBNDSTO model across the existing techniques.

groups

Imène Issaoui mail -

Afef Selmi mail

link https://doi.org/10.54216/IJNS.250318

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

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

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

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Maksim Kuznetsov mail -

Irina Kosorukova mail -

Veronika Denisovich mail -

Elena Klochko mail -

Alexey Dengaev mail

link https://doi.org/10.54216/IJNS.250319

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

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

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

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Z.A. Latipov mail -

K.A. Naminova mail -

I.S. Abdullayev mail -

A.E. Ilyin mail -

R.A. Shichiyakh mail -

E. Laxmi Lydia mail

link https://doi.org/10.54216/IJNS.250320

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

Pythagorean Neutrosophic Bonferroni Mean Based Machine Learning Model for Data Analytics and Sentiment Classification of Product Reviews

To handle incomplete and indeterminate data, neutrosophic logic/set/probability was recognized. The neutrosophic falsehood, truth, and indeterminacy modules show symmetry as the truth and the falsehood appear the similar and perform in a symmetrical method with esteem to the indeterminacy module which aids as a line of the symmetry. Soft set is a general mathematical device to deal with uncertainty. Sentiment analysis (SA) is the foremost task of natural language processing (NLP), where judgments, opinions, thoughts, or attitudes toward an exact subject are removed. Web is a rich foundation of information and unstructured covering numerous text documents with reviews and opinions. The detection of sentiment will be useful for governments, discrete business groups, and decision-makers. With this motivation, this study develops a Data Analytics Framework for Sentiment Classification Using Pythagorean Neutrosophic Bonferroni Mean (DAFSC-PNBM) technique on Product Reviews. The presented DAFSC-PNBM technique mainly aims to determine the nature of sentiments based on product reviews. Primarily, data preprocessing is performed to increase the product review qualities. For the word embedding process, word2vec model is used. Besides, the DAFSC-PNBM model uses the Pythagorean Neutrosophic Bonferroni Mean (PNBM) technique for classification. To enhance the SA performance of the PNBM model, the grey wolf optimizer (GWO) model has been applied as a hyperparameter tune process. The experimentation outcome analysis of the DAFSC-PNBM method occurs and the outcomes are investigated under several features. The experimental study indicated the improvement of the DAFSC-PNBM method across the modern techniques

groups

Donia Badawood mail

link https://doi.org/10.54216/IJNS.250321

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

A new generalization of interval-valued Q-neutrosophic soft matrix and its applications

Decision-making theory is an effective way to help the decision-maker take the right path to solve a problem. Among the applications of this theory is the medical field, i.e. allowing the decision maker (doctor) to analyze patient data and judge the result of this analysis as to whether the patient is infected or not. In this path and to enrich this theory with more flexible mathematical methods, we present in this work a more flexible expanded method for a previous concept called Interval-valued Q-neutrosophic soft matrix (IV-Q-NSM) as a new generalization of previous mathematical tools. These tools deal with the two-dimensional uncertainty issues that exist in many areas of life. Next, some ordinary algebraic properties and matrix operations have also been studied. After that, we present a new methodology for the decision-making (DM) selection problems in medical diagnoses.

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Yousef Al-Qudah mail -

Abdulqader O. Hamadameen mail -

Nadia Abdalla Kh mail -

Faisal Al-Sharqi mail

link https://doi.org/10.54216/IJNS.250322

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

Journal of Neutrosophic and Fuzzy Systems | 2024

On The 3-Cyclic Refined Neutrosophic Real Roots of Unity and Their Algebraic Classification

The objective of this paper is to find all formulas that describe the 3-cyclic refined neutrosophic real solutions of the equation 𝑋𝑛=1 which are called 3-cyclic refined real roots of unity. Also, we classify the algebraic group represented by these solutions as a direct product of some familiar finite abelian groups. On the other hand, we illustrate many examples to clarify the validity of our work.

groups

Agnes Osagie mail

link https://doi.org/10.54216/JNFS.090105

Volume & Issue

Vol. Volume 9 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Solving of First Order Initial Value Problem Using Fuzzy Kamal Transform in Neutrosophic Environment

This manuscript presents a novel approach for solving first-order initial value problems by leveraging the Fuzzy Kamal Transform within a Neutrosophic framework. By integrating fuzzy logic with Neutrosophic set theory, the method adeptly addresses uncertainties inherent in differential equations. The efficacy of this method is demonstrated through the exposition of various illustrative examples.

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Azal J. Mera mail -

Huda A. Hadi mail -

Sahar M. Jabbar mail

link https://doi.org/10.54216/IJNS.250323

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

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

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.

groups

Saif Salam mail -

Khaldoun Al-Zoubi mail

link https://doi.org/10.54216/IJNS.250324

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

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

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.

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Intisar A.M. Al Sayed mail -

Bourair Al-Attar mail -

Lateef Abd Zaid Qudr mail -

Azmi Shawkat Abdulbaqi mail -

Jamal Fadhil Tawfeq mail -

Ravi Sekhar mail -

Pritesh Shah mail -

Marshiana Devaerakkam mail

link https://doi.org/10.54216/IJNS.250325

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

Pure Mathematics for Theoretical Computer Science | 2024

On the Nature of Solutions of Discrete Time Lyapunov Equations

This paper provides a method to solve the discrete time Lyapunov equation. Identified and discussed. If the equation takes the following form: D (λy+μz) = λDy+ μDz , 𝑦,z∈ y; λ ,μ ∈𝐹 . If ∃ a constant e∈∞ &ni; ||Dy|| ≤ e ||y||, y ∀Y. and D is bounded, then D is called a linear operator equation. In particular, (Lyapunov and Sylvester operator equations) are very important in differential equations, integral equations and many other branches of mathematics. The study of solutions and of the above equestion We also discussed operator equations and special kinds of operators and studied some elementary operators. These operators are generalizations of operators τ𝐴𝐷:𝐷(𝐻)→𝐷(𝐻) τ𝐴𝐷:𝜏𝐴𝐷(𝑦)=𝐴𝑦−𝑦𝐷, 𝑦∈𝐷(𝐻)

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Mohammed Noori Joudah mail -

Emad Farhood Muhi mail

link https://doi.org/10.54216/PMTCS.040203

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new