Neutrosophic Logic is a neonate research field in which every proposition is assessed to have the proportion (percentage) of truth in a sub-set T, the proportion of indeterminacy in a sub-set I, and the proportion of falsity in a sub-set F. Neutrosophic set (NS) is effectively implemented for undetermined data processing and establishes benefits for handling the indeterminacy data. In the academic industries, early performance prediction of students is significant to the academic community so strategic interference might be planned before students attain the final semester. Forecasting the performance of students has turned into a challenging task owing to the rising number of data in educational procedures. The educational data mining (EDM) models are involved in extracting a pattern to explore hidden data from educational information. Currently, Machine learning (ML) and Artificial intelligence (AI) are implemented in numerous domains generally in the field of education to evaluate and analyze several features of educational datasets gathered from many educational institutions. This study develops a Leveraging Generalized Possibility Neutrosophic Soft Set with Feature Selection for Accurate Students’ Academic Performance Prediction Model (GPNSSFS-SAPPM). The intention of the proposed GPNSSFS-SAPPM system relies on improving the prediction model of students’ higher education performance using metaheuristic optimization algorithms. The data pre-processing model is employed at first by applying mean normalization for converting input data into a suitable format. In addition, the golf optimization algorithm (GOA) is exploited for the feature selection process. Followed by, the classification process is done by generalized possibility neutrosophic soft set (GPNSS). At last, the parameter tuning process is performed through henry gas solubility optimization (HGSO) algorithm to improve the classification performance of the GPNSS classifier. A wide-ranging experimentation was performed to prove the performance of the GPNSSFS-SAPPM method. The experimental results specified that the GPNSSFS-SAPPM model underlined advancement over other recent techniques.
Read MoreDoi: https://doi.org/10.54216/IJNS.260302
Vol. 26 Issue. 3 PP. 14-25, (2025)
The idea of neutrosophic set (NS) from a philosophical viewpoint is a generality of the theory of indeterminacy FS (IFS) and fuzzy set (FS). A NS is considered by a falsity, a truth and indeterminacy membership functions and all membership amount is an actual standard or a non-standard sub-set of the non-standard unit interval ]−0, 1+[. E-commerce is successful for the growth of novel business methods and should be constantly improved in the numerous decades. According to the growing E-commerce, supply chain management (SCM) has been strongly affected as we are now previously overcome by achievement in either developed or developing economies. Nowadays, E-commerce in advanced economy characterizes the newest lead of possibility in physical distribution systems and SCM, even if it emerging economy, e-commerce market is even in its infancy however, it is increasing and become integral part of commercial life. This paper presents a Quadripartitioned Neutrosophic Pythagorean Soft Set-Based Prediction Model for Supply Chain Management (QNPSSPM-SCM) model Using Hybrid Optimization Algorithms. The proposed QNPSSPM-SCM technique is for presenting an advanced E-commerce in SCM using advanced optimization techniques. At first, the min-max normalization method has been applied in the data pre-processing stage to convert input data into a beneficial pattern. In addition, the presented QNPSSPM-SCM system executes quadripartitioned neutrosophic Pythagorean soft set (QNPSS) technique for the prediction process. At last, the hybrid grey wolf optimization and teaching-learning-based optimization (GWO‐TLBO) algorithm fine-tunes the hyperparameter values of the QNPSS model optimally and results in better performance of prediction. The experimental validation of the QNPSSPM-SCM method is verified on a benchmark database and the outcomes are determined regarding different measures. The experimental outcome underlined the development of the QNPSSPM-SCM method in prediction process.
Read MoreDoi: https://doi.org/10.54216/IJNS.260301
Vol. 26 Issue. 3 PP. 01-13, (2025)
A complex linear Diophantine fuzzy (CLDF) set extends a linear Diophantine fuzzy set (LDFS) by handling uncertainty with complex-valued membership degrees within a unit disc. In this paper, we combine the notions of LDFS, BCK-algebra, and complex fuzzy set (CFS) to preface and elaborate the innovative concepts of CLDF subalgebras (CLDF − Subs), CLDF ideals (CLDF − Ids), CLDF implicative ideals (CLDF − IIds), and CLDF positive implicative ideals (CLDF − PIIds) in BCK-algebras, and probe their fundamental characteristics. These new notations of certain kinds of algebraic substructures in BCK-algebras serve as a bridge among CLDFS, crisp set, and BCK-algebra and also demonstrate the influence of the CLDFS on a BCK-algebra. Moreover, we examine some illustrative examples and prevalent features of these innovative notions in detail. Finally, characterizations of these intricate fuzzy structures are given, and related results for ideals, implicative ideals, and positive implicative ideals in the view of CLDFSs are obtained.
Read MoreDoi: https://doi.org/10.54216/IJNS.260303
Vol. 26 Issue. 3 PP. 26-48, (2025)
The primary goal of this paper is to study and introduce fuzzy anti-normed linear spaces, as well as, some additional properties concerning these spaces. From this point of view, some theoretical results are obtained; for example, it was proved that the space of all linear and fuzzy bounded operators over fuzzy anti-normed linear spaces is fuzzy complete. Moreover, some additional theoretical results are stated and proved.
Read MoreDoi: https://doi.org/10.54216/IJNS.260304
Vol. 26 Issue. 3 PP. 49-57, (2025)
From a philosophical viewpoint, the theory of neutrosophic set (NS) is a simplification of the concept of Fuzzy Set (FS) and intuitionistic FS (IFS). An NS is illustrated by a truth, an indeterminacy, and a falsity membership functions and every membership degree is an actual standard or a non-standard sub-set of the non-standard unit range of] −0, 1+ [. Customer churn is when clients stop utilizing a company’s service or product. Moreover, it is also named customer retention, which is vastly significant metric as it is much less costly to keep the existing customers than to obtain novel customers. The prediction of churn plays an essential part in customer retention because it forecasts clients who are in danger of leaving the organization. In the banking sector, the customer attrition arises when clients quit utilizing the services and goods provided by the bank for some time. So, customer churn is vital in today’s economic banking industry. This study proposes a Leveraging Bipolar Fuzzy Hypersoft Set with Heuristic Optimization Algorithms-based Customer Retention Prediction (BFHSS-HOACRP) technique in financial sectors. The BFHSS-HOACRP technique applies optimized techniques to predict the customer retention behavior in the industry of bank. Initially, the mean normalization technique is utilized in the data pre-processing stage to prepare raw data into a suitable format for analysis and modeling. For the selection of feature process, the grasshopper optimization algorithm (GOA) method is employed to identify and select the most relevant features from an input data. In addition, the proposed BFHSS-HOACRP technique implements bipolar fuzzy hypersoft set (BFHSS) method for the classification process. Additionally, the spider monkey optimization (SMO)-based hyperparameter selection process is performed to optimize the classification results of BFHSS model. The efficacy of the BFHSS-HOACRP approach is examined under the bank customer churn prediction dataset. The comparison analysis of the BFHSS-HOACRP approach portrayed a superior accuracy value of 95.41% over existing techniques.
Read MoreDoi: https://doi.org/10.54216/IJNS.260305
Vol. 26 Issue. 3 PP. 58-75, (2025)