International Journal of Neutrosophic Science

In this study, a thorough methodology is used to present a unique way for improving skin cancer prediction accuracy. The research uses sophisticated preprocessing methods, such as the Frost filter for noise reduction and histogram equalization for contrast enhancement, to boost contrast on dermoscopic pictures from various sources, using an ISIC 2020 dataset. These actions greatly raise the dermoscopic pictures’ overall quality and usefulness for diagnosis. Utilizing labeled data for training, we offer a Fuzzy-based C-means clustering technique based on Neutrosophic Logic during the segmentation phase. In order to overcome ambiguities in skin lesion segmentation, the neutrosophic set—a groundbreaking idea in philosophy—is used. The suggested model enhances the accuracy of segmentation by modifying the neutrosophic set functions. For precise prediction, the approach combines Support Vector Machine (SVM) classification with Histogram of Oriented Gradient (HOG) feature extraction. While SVM, a supervised learning algorithm, diagnoses skin lesions based on the collected features, HOG features capture gradient information. To improve object recognition and classification, the HOG-SVM architecture is made to methodically collect and quantify essential information using dermoscopic pictures. The use of Neutrosophic Fuzzy Logic, which combines the benefits of fuzzy clustering with neutrosophic sets to produce more precise and nuanced predictions, sets the suggested method apart. The integration of different approaches into a holistic solution for skin cancer prediction is what makes the proposed study innovative. Findings and performance analysis show of the HOG-SVM method exhibits an outstanding accuracy of 98.69%, outperforming LR, KNN, and GNB methods. Python software is used to accomplish the suggested approach. This discovery opens up a possible path for better skin cancer diagnosis and advances the rapidly developing fields of dermatology and medical image processing.

To achieve automation of defect detection, the metal surface micro defect detection algorithm YOLO-MD is proposed. From the perspective of object detection, YOLOv5s is selected as the backbone algorithm and the SPD-Conv module is added to reduce feature loss caused by ordinary convolutional downsampling, improve the adaptability of low-resolution images, and improve the accuracy of small object detection. Using the MPDIoU loss function to accelerate model convergence and improve detection accuracy. Considering the small size of the dataset, data augmentation methods were adopted. After model training, mAP50-95 improved by 0.02 compared to YOLOv5, which has high real-time and robustness and can more effectively detect metal surface micro defects.

This paper introduces an innovative multivariate exponential distribution, specifically of Raftery type, characterized by heterogeneous dependence parameters. Various properties of this distribution family are thoroughly investigated, with particular emphasis placed on the copula derived from this model. Notably, this copula is non-exchangeable and demonstrates multiple dependence parameters. Different properties associated with this novel copula, including the examination of estimation parameters, have been thoroughly investigated. The efficacy of the proposed copula is demonstrated through its successful application in modeling a real neutrosophic dataset associated with the New York and American Stock Exchanges.

In this article, First, we study the different orderings for k-idempotent Neutrosophic fuzzy matrices (NFM). With this idea, we also discover some properties for the k- Neutrosophic fuzzy matrices and demonstrate the connection between the generalized inverse and different orderings. We also go through some properties for the T-ordering, T- reverse ordering, minus, and space ordering in k-idempotent Neutrosophic fuzzy matrices using the g-inverses with numerical examples is given. Minus ordering is a partial ordering in the set of all regular fuzzy matrices. We have introduced ordering on k− idempotent fuzzy matrices and developed the theory of fuzzy matrix partial ordering. The minus ordering and k−space ordering are identical for k− idempotent matrices. Next, we introduce and study the concept of k–Idempotent Neutrosophic fuzzy matrix as a generalization of idempotent NFM via permutations. It is shown that a kidempotent NFM reduces to an idempotent NFM if and only if PK = KP. The Conditions for power symmetric NFM to be k-idempotent are derived and some related results are given.

The present study provides the necessary and sufficient criteria for the k-Kernel symmetry (KS) of a Schur complement (SC) in a k-KS Neutrosophic Fuzzy matrices (NFM) and Intuitionistic Fuzzy Matrices (IFM). Equivalent characterizations of KS and k-KS NFM and IFM are presented in this work. We provide a few fundamental examples about KS NFM and IFM. It is demonstrated that while k-symmetric implies k-KS, but the converse need not be true. A few fundamental characteristics of k-KS IFM and NFM are obtained.

The main purpose of this article is to study about the representation symbolic 2-plithogenic matrices by linear transformations between symbolic 2-plithogenic vector spaces, where it proves that every symbolic 2- bplithogenic matrix can be represented uniquely by a linear transformation between symbolic 2-plithogenic vector spaces. Also, this work proves that any linear function between symbolic 2-plithogenic vector spaces must be an AH-linear transformation.

Neutrosophic sets can be used to model uncertain data in real-world applications. To increase the use of complex neutrosophic sets, the space of quaternion numbers is investigated in this work. Analysts in complex contexts can benefit from the knowledge and direction that quaternion neutrosophic sets can offer by modeling complicated systems and capturing the interactions between various factors. Division algebras are used in some applications, such as particular formulations of class field theory, but they are generally far less important than quaternion numbers. Three-dimensional information with imaginary membership, imaginary indeterminacy, and imaginary non-membership functions is represented using quaternion neutrosophic sets. Intriguing quaternion numbers give us useful results when we analyze complicated data. Some basic characteristics of the derived concepts are examined. Novel quaternion-based operations and the analysis of order relations and logic operations are also explored based on neutrosophic set theory. For modeling uncertainty in quaternion-based systems, quaternion neutrosophic sets are helpful. Other fuzzy sets are unable to adequately capture the sophisticated fuzzy information that they can represent, such as uncertainty in both size and direction. The capacity to define fuzzy distance and similarity metrics is one of its intriguing qualities. We also present two quaternion distance measures and evaluate their properties. We use quaternion representations and measurements in a neutrosophic framework for decision-making models, and the results are excellent. Additionally, it shows readers how to construct the connections between traits and alternatives that are used in decision-making issues. An example is provided at the end to help illustrate the suggested strategy and provide additional context. Finally, we employ a different distance metric that is illustrated in the reliability section to validate the developed methodologies. It is possible to address the findings of studies on the application of quaternion neutrosophic sets for addressing various types of uncertainty in optimization problems related to the design and management of complex systems.

The objective of this paper is to present a novel approach for the a-fuzzy standardized area and its fundamental characteristics with basic attributes of fuzzy compact and fuzzy bounded linear functions. Also, it presents some of the basic attributes of the spectral of fuzzy compact linear functions in terms of theorems that clearly draw the elementary properties of these functions and the mathematical relationships between them. 

This research revolves around the development and validation of a tool, driven by Neutrosophic logic, designed to probe turnover propensities in manufacturing entities. The primary objective is to uncover the determinants of turnover in these organizations by assessing employees' intentions to leave. Initially, pilot interviews were conducted to identify turnover factors, and a synthesis of literature and interview insights led to the emergence of key themes. These themes were then utilized to construct a closed-ended questionnaire, which was subsequently employed in surveys. The instrument underwent validation through Exploratory Factor Analysis, confirming the validity of all items. Confirmatory Factor Analysis further established both convergent and discriminant validity, resulting in the exclusion of two items. This unique tool provides empirical researchers with a fresh approach to understanding turnover causes, particularly in the context of non-executive manufacturing personnel. Notably, the focus extends to addressing linguistic barriers by considering workers who may not be proficient in English, emphasizing the need for a scale catering to languages such as Urdu or Hindi.

In the dynamic landscape of healthcare technology, the amalgamation of Internet of Things (IoT) systems and Neutrosophic Data Analytics has heralded a paradigm shift. This study delves deep into this transformative synergy by presenting an innovative IoT-based wearable system design for the recognition of sleep disorders. Our meticulously crafted multilayer cellular system seamlessly integrates IoT devices, data acquisition, cloud computing, and machine learning to unlock a wealth of insights into sleep patterns, their anomalies, and the presence of sleep disorders. Through fair and rigorous experimental comparisons, we unveil the prowess of Long Short-Term Memory (LSTM) within the machine learning realm, showcasing its superior performance over baseline models. The results affirm LSTM's ability to detect sleep disorders with remarkable accuracy, precision, and recall, revolutionizing sleep medicine and healthcare practices. This research, at the crossroads of innovation and healthcare, not only illuminates the path to advanced sleep disorder diagnosis but also heralds a new era of personalized healthcare interventions and remote monitoring solutions. As we navigate the realm of IoT and data-driven healthcare, our findings hold the promise of improving the quality of life for countless individuals, reaffirming the pivotal role of technology in safeguarding one of the most fundamental aspects of human well-being – a peaceful and restorative night's sleep.

A soft expert set is widely used as a tool to solve and model the problems appearing in computer science and operations research. 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 distance measures (DM) between two SVNFSESs to construct a new algorithm that is used to demonstrate the effectiveness of the method in handling some real-life applications.

The beta-Lindley distribution is used in the field of survival analysis to imitate techniques employed with human lifetime data. The neutrosophic beta-Lindley distribution (NBL) is designed to characterize a range of survival statistics with indeterminacies. The established distribution is used, for instance, to describe unknown data that is roughly favorably skewed. The evolved NBL's three main statistical characteristics—the neutrosophic moments, hazard, and survival functions are covered in this article. Additionally, The well-known maximum likelihood estimation method is used to estimate the neutrosophic parameters. To check if the predicted neutrosophic parameters were met, a simulation study was done. Notably, talks of prospective NBL uses in the real world have made use of actual data. Actual data were utilized to show how well the suggested model performed in compared to the current distributions.

The goal of this study is to bring neutrosophic structured element theory into the assessment of the entrepreneurial orientation of online peer-to-peer lending platforms, as well as to simplify the complicated processes of conventional neutrosophic decision making. This study discusses several methods for assessing the entrepreneurial orientation of online P2P lending systems. Two strategies are offered to deal with the triangular single-valued neutrosophic number multiple attribute decision making issues with incomplete definite knowledge on criteria's weights using the neutrosophic structured element approach. In terms of computational efficiency and performance, the recommended techniques produced encouraging results. The proposed methodologies are easy in understanding and computation, have a great lot of practical utility, and provide new concepts for applying neutrosophic structured element theory to neutrosophic MADM issues and other fields.

The foundational concepts of subalgebra, ideal, and homomorphism within the domain of BF-algebra were originally introduced by Andrzej Walendziak [A. Walendziak, On BF-Algebras, Math. Slovaca, 57(2) (2007), 119-128]. In this paper, we introduce innovative concepts related to Neutrosophic BF-Subalgebras and Neutrosophic BF-Ideals derived from the application of Subalgebra, Ideal, and Homomorphism principles to Neutrosophic sets. We explore the outcomes concerning a Neutrosophic BF-Ideal with respect to principle of homomorphism, homomorphic image of a Neutrosophic BF-Ideal satisfying the sup-inf property, and homomorphic pre-images within the context of a Neutrosophic set embedded in BF-algebra. The outcomes of the above study are equally applicable to Neutrosophic BF-Subalgebra Lastly, we delve into the conceptual understanding of a level set of a Neutrosophic BF-Ideal within a BF-algebra. In the future, the above study can be extended to address various types of implicative ideals and filters within BF-algebra. Moreover, these Neutrosophic BF-Subalgebras and Neutrosophic BF-Ideals can be applied to neutrosophic soft sets within the context of BF-algebra, which are used for various decision making techniques.

We investigate the stability of huge, linked subsystems in separate nonlinear dynamical systems. These systems' properties depend on both their dynamics and their link structure. We examine two concepts of stability. The initial one is connection stability, where a complete system is robust in the meaning of Lyapunov given the uncertainty and temporal fluctuations in the linking lengths among systems. The next is the widely accepted idea of asymptotic stability of the entire system, which is predicated on the premise that all linkages are set at the nominal values. We propose graph-based characteristics of two types of a stable for the situation of homogenous subsystems by making linkages to spectrum graph theory, in particular the spectrum of the sign adjacency matrix. We also obtain constraints on the highest amplitude of the sign adjacency matrix of independent relevance via this method.