International Journal of Neutrosophic Science

Document clustering is an integral and important part of text mining.  In case of classical clustering, data item belongs to only one cluster, whereas in Plithogenic approach to fuzzy clustering, data point may fall into more than one cluster. Thus, Plithogenic approach fuzzy clustering leads to wherein each data point is associated with more than one membership function that expresses the degree to which individual data points belong to the cluster. Additionally, his speeches at the UN will be analyzed with a neutrosphic approach. With the help of this approaches, in this study, speeches of the UN sessions attended by Turkish diplomats in the United Nations (UN) will be analyzed to understand the priorities of the country in international politics, the change and continuity in these policies. The texts of the UN sessions attended by Turkish diplomats between 2015 and 2023 were taken as data set. Such a large volume of data was analyzed with the help of text mining. For this purpose, each year's data was analyzed separately using word frequencies and clustering analysis, and then topic modeling was performed for each year's data using the Latent Dirichlet Allocation (LDA) method.

In this study, we defined a symbolic plithogenic complex number's general exponential form. A symbolic plithogenic complex number's general trigonometric form was defined. Theories have been supported by evidence showing how to find the general exponential form's conjugate for symbolic plithogenic complex numbers, division for symbolic plithogenic complex numbers, multiplication for two symbolic plithogenic complex numbers, and inversion for symbolic plithogenic complex numbers.

This paper is dedicated to studying some examples of n-refined neutrosophic groups and their algebraic substructures, where we deal with three different types of them, 3-refined, 4-refined, and 5-refined neutrosophic groups. Also, we present the algebraic structure of many substructures such as 3-refined neutrosophic AH-subgroups and kernels, 4-refined neutrosophic AH-homomorphisms and subgroups, and 5-refined neutrosophic AH-isomorphisms. On the other hand, many related examples will be provided to explain the algebraic concepts and their properties.

Predicting dorsalgia involves a multifaceted approach that encompasses the analysis of demographic, lifestyle, and medical data. Machine learning algorithms and advanced data analytics play a pivotal role in forecasting the risk of developing back pain. Early prediction aids in proactive interventions and personalized healthcare strategies, thereby mitigating the burden of dorsalgia on individuals and healthcare systems. The proposed feature selection is the initial feature set’s most educational elements by evolutionary gravitational search-based feature selection (EGSFS). Specifically, the framework is trained and fine-tuned using spinal geometry parameters, enabling precise identification of individuals at risk of developing dorsalgia. This study presents a novel approach for classification tasks using a Genetic Algorithm (GA)-optimized hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. The GA optimizes the model’s architecture and hyperparameters to enhance its performance. The framework is implemented using Python. In the categorization procedure, the Single Neutrosophic sets aid in capturing ambiguity, which is particularly beneficial when handling dorsalgia disorders that may present with confusing symptoms, thus enhancing the accuracy of classifying various dorsalgia conditions. Experimental results demonstrate that this hybrid approach significantly improves classification accuracy, making it a viable option for several practical applications. Experimental results exhibit remarkable improvements in accuracy and predictive power, underscoring the potential of this innovative approach in advancing preventative and personalized healthcare strategies for back pain management. The experiment was built on the lower back pain symptoms dataset. A comparison is made between the experimental results and previous prediction models like Logistic Regression, Decision Tree Classifier, Random Forest, and Support Vector Machine in terms of accuracy, F1-score, precision, and recall. The accuracy of normal and abnormal data is 99%.

The idea of interval-valued neutrosophic soft sets (IVNSSs) is a new generalization of the neutrosophic soft sets to the neutrosophic sets when the authors combine the critical features of IVNS and soft sets (SSs) 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 highperformance IVNSS disappears. Therefore, in this paper, we initiated a new approach known as possibility interval-valued neutrosophic soft sets (PIVNSSs) as a new development in a fuzzy soft computing environment. We investigate some fundamental operations on PIVNSSs along with their basic properties. Also, we investigate AND and OR operations between two PIVNSSs as well as several numerical examples to clarify the above fundamental operations. Finally, we have given aggregation operators (AO) to construct a new algorithm that is used to demonstrate the effectiveness of the method in handling some real-life applications.

hand the idea of interval-valued neutrosophic soft sets (IVNSSs) is a new generalization of the neutrosophic soft sets to the neutrosophic sets when the authors combine the critical features of IVNS and soft sets (SSs) 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 highperformance IVNSS disappears. Therefore, in this paper, we initiated a new approach known as possibility interval-valued neutrosophic soft sets (PIVNSSs) as a new development in a fuzzy soft computing environment. We investigate some fundamental operations on PIVNSSs along with their basic properties. Also, we investigate AND and OR operations between two PIVNSSs as well as several numerical examples to clarify the above fundamental operations. Finally, we have given similarity measure (SM) between two PIVNSSs to construct a new algorithm that is used to demonstrate the effectiveness of the method in handling some real-life applications.

Soft set(SS) is one of the soft computing techniques that plays an important role in addressing the hiddenness and uncertainty associated with uncertain data. In other hand the idea of interval-valued neutrosophic soft sets (IVNSSs) is a new generalization of the neutrosophic soft sets to the neutrosophic sets when the authors combine the critical features of IVNS and soft sets (SSs) 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 IVNSS disappears. Therefore, in this paper, we initiated a new approach known as possibility interval-valued neutrosophic soft sets (PIVNSSs) as a new development in a fuzzy soft computing environment. We investigate some fundamental operations on PIVNSSs along with their basic properties. Also, we investigate AND and OR operations between two PIVNSSs as well as several numerical examples to clarify the above fundamental operations. Finally, we have given distance measures (DM) between two PIVNSSs to construct a new algorithm that is used to demonstrate the effectiveness of the method in handling some real-life applications.

In this paper, we introduce and study the concept of neutrosophic submodules and neutrosophic primary submodule with the help of the definition of a radical submodule, and we also study the properties of these submodules. Furthermore, homomorphic image and preimage of neutrosophic primary submodule are investigated.

The neutrosophic set has numerous uses in a variety of industries. There are more advantages to use the Bipolar Neutrosophic set than other sets for elucidating a multi-criteria decision-making problem. The bipolar-neutrosophic set addresses both the positive and negative facets of the issue, improving the probability of a successful resolution. By implementing the removal area method, the de-bipolarization of the bipolar neutrosophic number with eleven parameters is formulated. The proposed Neutrosophic number is used to solve the selection of agricultural aircraft using given criteria and the linguistic variables by the TOPSIS approach. A comparative study has been conducted to determine its robustness. Also, the multi-criteria decision-making problem (MCDM) is solved using the TOPSIS approach with MATLAB programming.

In this paper, we redefine the dense neutrosophic set operations and, by using them, we introduce new definition for topological space in neutrosophic topological space and also gives the basic specifications for the new definitions of neutrosophic topological space in neutrosophic topological space, we also obtained some properties that show the relationship between neutrosophic topological space and neutrosophic semi-open sets in the neutrosophic topological space, we also studied image characterization and preimages of neutrosophic    topological  space .