International Journal of Neutrosophic Science

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2690-6805ISSN (Online) 2692-6148ISSN (Print)

Orthogonal distance and similarity for single-valued neutrosophic fuzzy soft expert environment and its application in decision-making

Faisal Al-Sharqi , Ashraf Al-Quran , Hamiden Abd El- Wahed Khalifa , Haifa Alqahtani , Badria A. Ali Yousif , Rawan A. shlaka , Mona Aladil

A soft expert set(SES) is a concept that combines elements of soft sets and expert systems. It aims to incorporate expert knowledge and uncertainty-handling capabilities into the analysis and decision-making processes. On the other hand, the idea of single neutrosophic sets (SVNSs) and fuzzy sets (FSs) are imported models for handling the uncertainty data. In this work, the authors combine the critical features of FSs and SVNSs under expert systems in one model. Accordingly, this model worked to provide decision-makers with more flexibility in the process of interpreting uncertain information. From a scientific point of view, the process of evaluating this high-performance SVNFSES disappears. Therefore, in this paper, we initiated a new approach known as single-valued neutrosophic fuzzy soft expert sets (SVNFSESs) as a new development in a fuzzy soft computing environment. We investigate some fundamental operations on SVNFSESS along with their basic properties. Also, we investigate AND and OR operations between two SVNFSESS as well as several numerical examples to clarify the above fundamental operations. Finally, we have given an Orthogonal Distance and Similarity for SVNFSESs to construct a new algorithm to demonstrate the method’s effectiveness in handling some real-life applications.

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Vol. 23 Issue. 4 PP. 08-22, (2024)

Secondary K-Range Symmetric Neutrosophic Fuzzy Matrices

M. Anandhkumar , H. Prathab , S. M. Chithra , A. S. Prakaash , A. Bobin

This paper introduces and explores the concept of secondary k-Range Symmetric (RS) Neutrosophic Fuzzy Matrices (NFM) and establishes its properties and relationships with other symmetric and secondary symmetric NFMs. The study defines secondary k-RS NFMs and provides insightful numerical examples to illustrate their characteristics. The paper investigates the interconnections among s-k-RS, s-RS, k-RS, and RS NFMs, discuss on their mutual relations. Additionally, the necessary and sufficient conditions for a given NFM to qualify as a s-k-RS NFM are identified. The research demonstrates that k-symmetry implies k-RS, and vice versa, contributing to a comprehensive understanding between different types of symmetries in NFMs. Graphical representations of RS, column symmetric, and kernel symmetric adjacency and incidence NFMs are presented, unveiling intriguing patterns and relationships. While every adjacency NFM is symmetric, range symmetric, column symmetric, and kernel symmetric, the incidence matrix satisfies only kernel symmetric conditions. The study further establishes that every range symmetric adjacency NFM is a kernel symmetric adjacency NFM, though the converse does not hold in general. The existence of multiple generalized inverses of NFMs in Fn is explored, with additional equivalent conditions for certain g-inverses of s-κ-RS NFMs to retain the s-κ-RS property. We conclude by characterizing the generalized inverses belonging to specific sets {1, 2}, {1, 2, 3}, and {1, 2, 4} of s-k-RS NFMs, providing a comprehensive framework for understanding the structure and properties of secondary k-Range Symmetric Neutrosophic Fuzzy Matrices. This research contributes to the mathematical literature by introducing a novel class of NFMs and establishing their fundamental properties and relationships, presenting new perspectives on matrix theory in the context of neutrosophic fuzzy logic.  

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Vol. 23 Issue. 4 PP. 23-28, (2024)

Golden Jackal Optimization with Neutrosophic Rule-Based Classification System for Enhanced Traffic Sign Detection

Mohammed Assiri

Traffic signs detection is a critical function of automatic driving and assisted driving is a significant part of Cooperative Intelligent Transport Systems (CITS). The drivers can obtain the data attained via automated traffic sign detection to improve the comfort and security of motor vehicle driving and regulate the behaviors of drivers.  Recently, deep learning (DL) has been utilized in the fields of traffic sign detection and achieve better results. But there are two major problems in traffic sign recognition to be immediately resolved. Some false sign is always detected due to the interference caused by bad weather, and illumination variation. Some traffic signs of smaller size are increasingly complex to identify than larger size hence the smaller traffic signs go unnoticed. The objective is to achieve the accuracy and robustness of traffic sign detection for detecting smaller traffic signs in a complex environment. Thus, the study presents a Golden Jackal Optimization with Neutrosophic Rule-Based Classification System (GJO-NRCS) technique for Enhanced Traffic Sign Detection. The GJO-NRCS technique aims to detect the presence of distinct types of traffic signs. In the GJO-NRCS technique, DenseNet201 model is exploited for feature extraction process and the GJO algorithm is used for hyperparameter tuning process. For final recognition of traffic signals, the GJO-NRCS technique applies NRCS technique. The simulation values of the GJO-NRCS method can be examined using benchmark dataset. The experimental results inferred that the GJO-NRCS method reaches high efficiency than other techniques.

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Vol. 23 Issue. 4 PP. 29-40, (2024)

A Multi-Criteria Decision-Making Analysis for COVID-19 in Public Health under Neutrosophic Set: Case Study

Heba R. Abdelhady , Shereen Zaki , Mahmoud M. Ismail , Mohamed Emad , Shimaa Said

This paper proposed a framework for assessing the COVID-19 response in Egypt. COVID-19 plays a vital role in public health, and selecting the best response can decrease the impact of the disease. This study used the type 2 neutrosophic set as a framework for dealing with uncertainty. The assessment of the COVID-19 response has various conflicting criteria, so the concept of multi-criteria decision-making (MCDM) is used to deal with COVID-19 criteria. The MCDM methods are adopted with the type-2neutrosophic set—the typ-2 neutrosophic AHP-COPRAS-VIKOR methodology framework. The AHP is used to compute the criteria weights. The COPRAS and VIKOR methods are used to rank the alternatives. The case study in Egypt is conducted to show the best response to COVID-19. Five main criteria and nineteen sub-criteria are used in this paper. The methodologies employed in this paper aid as examples for future research endeavors, inspiring a continued dialogue on refining and advancing MCDM methodologies in public health disasters.

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Vol. 23 Issue. 4 PP. 41-62, (2024)

Enhanced Academic Stress-Coping Skills Assessment in College Students: A Comparative Study of Neutrosophic Distance Measure and Proposed Cubic Pythagorean Fuzzy Hypersoft TOPSIS Method

E. Prabu , M. Gopala Krishnan , A. Bobin

In multi-criteria decision-making scenarios involving real numbers, interval numbers, and a combination of membership and non-membership grades, accurate decision-making is crucial yet challenging. The integration of diverse grade values into a single value poses a significant challenge for decision-makers. To address this issue, this study introduces the concept of a cubic Pythagorean fuzzy hypersoft set, facilitating information aggregation without ambiguity. The characteristics of correlation coefficients and aggregation operators are emphasized, underscoring their importance in decision-making processes. An algorithm based on correlation coefficients (CC) is proposed for the TOPSIS method, which ranks preferences based on their similarity to the ideal solution, applied here to examine how college students cope with academic stress. Furthermore, the efficiency of the proposed method is demonstrated through a comparative study, wherein the correlation coefficient in the TOPSIS method is contrasted with existing distance measures (DMs). Results indicate the superiority of CC in the TOPSIS method over DMs. In addition to comparing the proposed method with existing distance measures, the efficacy of the proposed approach is further demonstrated through a comparative analysis with established neutrosophic distance measures. This comprehensive evaluation highlights the robustness and versatility of the proposed method in addressing the complexities of multi-criteria decision-making scenarios, particularly in assessing stress management strategies among college students, thus providing valuable contributions to decision-making contexts. This study contributes to enhancing decision-making processes, particularly in evaluating stress management strategies among college students, thereby offering valuable insights for academic contexts.

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Vol. 23 Issue. 4 PP. 63-82, (2024)

The Box and Muller Technique for Generating Neutrosophic Random Variables Follow a Normal Distribution

Maissam Jdid , Florentin Smarandache

The focus of operations research is the existence of a problem that requires making an appropriate decision that helps reduce risk and achieves a good level of performance. Operations research methods depend on formulating realistic issues through mathematical models consisting of a goal function and constraints, and the optimal solution is the ideal decision, despite the multiplicity of these methods. However, we encounter many complex issues that cannot be represented mathematically, or many issues that cannot be studied directly. Here comes the importance of the simulation process in all branches of science, as it depends on applying the study to systems similar to real systems and then projecting this. The results if they fit on the real system. So simulation is the process of building, testing, and running models that simulate complex phenomena or systems using specific mathematical models. The simulation process depends on generating a series of random numbers subject to a regular probability distribution in the field [0, 1], and then converting these random numbers into random variables subject to the distribution law. Probability, according to which the system to be simulated operates, using appropriate techniques for both the probability density function and the cumulative distribution function. Classical studies have provided many techniques that are used during the simulation process, and to keep pace with the great scientific development witnessed by our contemporary world, we found that a new vision must be presented for this. Techniques A vision based on the concepts of neutrosophics, the science founded by the American mathematical philosopher Florentin Smarandache. The year 1995, in which new concepts of probabilities and probability distributions are used, as we presented in previous research some techniques from a neutrosophic perspective, and as an extension of what we presented previously, we present in this research a neutrosophic vision of the Box and Muller technique used to generate random variables that follow a normal distribution.

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Vol. 23 Issue. 4 PP. 83-87, (2024)