International Journal of Neutrosophic Science

The theory of neutrosophic hypersoft set (NHSS) is an appropriate extension of the neutrosophic soft set to precisely measure the uncertainty, anxiety, and deficiencies in decision-making and is a parameterized family that handles sub-attributes of the parameters. In contrast to recent studies, NHSS could accommodate more uncertainty, which is the essential procedures to describe fuzzy data in the decision-making method. Hedge funds are financial funds, finance institutions that increase funds from stockholders and accomplish them. Usually, they try to make certain predictions and work with the time sequence dataset. A hedge fund is heterogeneous in its investment strategies and invests in a different resource class with various return features. Furthermore, hedge fund strategy is idiosyncratic and proprietary to the hedge fund manager, and the correct skills of fund managers are not visible to the stockholders. These reasons, united, make hedge fund selection a complex task for the stockholders. Different techniques have been analyzed to select the portfolio of hedge funds for investment. Machine-learning (ML) models employed used for performing individual hedge fund selection within hedge fund style classifications and forecasting hedge fund returns. Therefore, this study designs a new Single Valued Neutrosophic Hypersoft Set VIKOR Model for Hedge Fund Return Prediction (SVNHSS-HFRP) technique. The presented SVNHSS-HFRP technique aims to forecast the hedge fund returns proficiently. In the SVNHSS-HFRP technique, two stages of operations are involved. At the initial stage, the SVNHSS-HFRP technique, the SVNHSS is used for forecasting the hedge funds. Next, in the second stage, the moth flame optimization (MFO) system is applied to optimally choose the parameter values of the SVNHSS model. The performance validation of the SVNHSS-HFRP model is verified on a benchmark dataset. The experimental values highlighted that the SVNHSS-HFRP technique reaches better performance than existing techniques

Single valued neutrosophic number is a special case of single valued neutrosophic set and are of importance for neutrosophic multi-attribute decision making problem. A single valued neutrosophic number seems to define an ill-known quantity as a generalization of intuitionistic number. Applied linguistics in the context of Natural Language Processing (NLP) comprises the practical applications of linguistic approaches for addressing real time language processing issues. Social media become indispensable components in many people’s lives and have been growing rapidly. In the meantime, social networking media have become a widespread source of identity deception. Several social media identity deception cases have appeared presently. The research was performed to detect and prevent deception. Identifying deceptive content in natural language is significant to combat misrepresentation. Leveraging forward-thinking NLP methods, our model contextual cues analyze linguistic patterns, and semantic inconsistencies to flag possibly deceptive contents. By assimilating complex procedures for parameter optimization, feature extraction, and classification, the NLP focused on precisely recognizing deceptive content through different digital platforms, which contributes to the preservation of data integrity and the promotion of digital literacy. This study presents a Single Valued Trapezoidal Neutrosophic Number with Natural Language Processing for Deceptive Content Recognition (STVNNLP-DCR) technique on Social Media. The presented technique includes four important elements: preprocessing, GloVe word embedding, STVN classification, and Chicken Swarm Optimization (CSO) for parameter tuning. The preprocessing stage includes tokenization and text normalization, preparing text information for succeeding analysis. Then, GloVe word embedding represents the word in a continuous vector space, which captures contextual relationships and semantic similarities. The STVN classifier deploys the embedding to discern deceptive patterns within the text, leveraging its capability to effectively manage high-dimensional and sparse datasets. Moreover, the CSO technique enhances the hyperparameter of the STVN classifier, improving its generalization capabilities and performance. Empirical analysis implemented on varied datasets validates the efficacy of the presented technique in precisely recognizing deceptive content. Comparative studies with advanced approaches demonstrate high efficiency. The presented technique shows robustness against different forms of deceptive content, such as clickbait, misinformation, and propaganda

Within the domain of complex systems, inherent uncertainties, and ambiguities that traditional models frequently find difficult to handle pose a constant challenge to decision-making. To dramatically improve decision-making frameworks, this study presents a novel methodology called "ODESMAN," which synergistically integrates fuzzy logic with neutrosophic sets. Neutrosophic sets, on the other hand, allow one to express the degrees of truth, untruth, and indeterminacy as shifts rather than fixed points. Therefore, their use is more elegant than the existing methods offered. The implementation of fuzzy logic into such sets may provide a high level of effectiveness in managing uncertainty, which can be predicted and quantified. For example, the model allows accounting for uncertainty in the system inputs and processes up to 20%, the variability of truth values 10-50%, and the overall uncertainty 15-30%. The application of the model in practice, specifically in the emergency response, and the supply chain system permitted achieving a 40% increase in flexibility capacity and a 25% improvement in decision-making approaches compared to the traditional frameworks. Therefore, the practical strength and broad utility of the model can be proved, which validates its efficiency and allows broad implementation of this complex theoretical framework into the existing systems.

we are introducing the model-type operators over Interval-Valued Fuzzy Neutrosophic Sets with time moments [IVFNS] and learn a few of their properties with numerical examples to demonstrate the defined operations and operators. Also introduce various distance measures over the extension of interval neutrosophic sets as well as apply the introduced measures in ecological management in this direct to decide the type of corrosion disturbing some towns for valuable management to be taken, using this normalized distance measures. The extensions of neutrosophic connection values and non-connection values be not used for all time probable positive to our fulfillment, but this concept IVTNFS part has more significant responsibility at this point since the time progress with IVN-fuzzy sets provide the accurate solution in factual situations similarly, conclusion making, career deciding and so on. This is the main reason for taking in the extensions of neutrosophic sets.

One of the most popular and legally recognized behavioral biometrics is the individual's signature, which is used for verification and identification in many different industries, including business, law, and finance. The purpose of the signature verification method is to distinguish genuine from forged signatures, a task complicated by cultural and personal variances. Analysis, comparison, and evaluation of handwriting features are performed in forensic handwriting analysis to establish whether or not the writing was produced by a known writer. In contrast to other languages, Arabic makes use of diacritics, ligatures, and overlaps that are unique to it. Due to the absence of dynamic information in the writing of Arabic signatures, it will be more difficult to attain greater verification accuracy. On the other hand, the characteristics of Arabic signatures are not very clear and are subject to a great deal of variation (features’ uncertainty). To address this issue, the suggested work offers a novel method of verifying offline Arabic signatures that employs two layers of verification, as opposed to the one level employed by prior attempts or the many classifiers based on statistical learning theory. A static set of signature features is used for layer one verification. The output of a neutrosophic logic module is used for layer two verification, with the accuracy depending on the signature characteristics used in the training dataset and on three membership functions that are unique to each signer based on the degree of truthiness, indeterminacy, and falsity of the signature features. The three memberships of the neutrosophic set are more expressive for decision-making than those of the fuzzy sets. The purpose of the developed model is to account for several kinds of uncertainty in describing Arabic signatures, including ambiguity, inconsistency, redundancy, and incompleteness. The experimental results show that the verification system works as intended and can successfully reduce the FAR and FRR.

Green Human Resource Management (GHRM) state a determination of the association using crossing points of employees to stimulate environment performance activity, increase the employee awareness and sustainable activities, consequently, increasing the employee awareness towards environmental challenges.  The hotel industry is developing quickly in emerging nations owing to an upsurge in the tourism business; but, conversely, the hotel industry is mainly growing the problem of the environment. As a result, owing to the enormous amount of conservation problems that hotel business has faced, there is a growing potency to pay an accurate response to environmental problems and performing sustainable industry performance like the adoption of GHRM practice provides a win-win situation for its stakeholders and the organization. Accordingly, it indicates the requirement to scrutinize how GHRM performs will augment the environment in the hotel business. This manuscript models the design of GHRM using Pythagorean Neutrosophic Bonferroni Mean (GHRM-PNBM) approach. The presented GHRM-PNBM method objectives are to evaluate the limitation of hotel GRHM. Moreover, the presented technique constructs an expert system analysis technique for assessing the performance of hotel GHRM. Adaptive optimization of hotel GHRM assessment can be done using the PNBM technique, and the parameter selection method can be done using Quasi-Oppositional-Teaching-Learning-Based Optimization (QTLBO) method. The empirical analysis reports that the performance calculation of hotel GHRM has good confidence level and high accuracy

Cybersecurity has become a primary concern as the financial sectors generally handle increasing cyber-attacks and an increasing danger of financial crime. Recently, ransomware attacks have intensified, affecting enterprises, and crucial infrastructure worldwide. Ransomware employs sophisticated encryption techniques to encrypt data on the targeted device, then requests payment for decrypting the data. Artificial intelligence (AI) approaches involving ML were progressively employed in the domain of cybersecurity and significantly subsidized to preventing and detecting variety of threats. On the other hand, the several researchers that employed ML to identify ransomware are still constrained by the accuracy of models, the complication of malware, the high false-positive rate, and the lack of setting up the appropriate analysis environment. Therefore, there is a need to design efficient ransomware detection based on ML algorithms. This work introduces a modified Single Value Neutrosophic Fuzzy Soft Expert Set (M-SVNFSES) technique for cyberattack detection. The main purpose of the M-SVNFSES system is to detect and recognize the existence of cyberattacks in the financial sectors. In the M-SVNFSES technique, min-max normalization is used as an initial pre-processing stage. For the identification of cyberattacks in the financial sectors, the M-SVNFSES technique uses the SVNFSES model. To enhance its performance, the M-SVNFSES technique makes use of a bat optimization algorithm (BOA). The performance of the M-SVNFSES methodology was extensively studied using financial datasets. The experimental outcomes depicted that the M-SVNFSES method reaches optimal detection performance in attack detection process

This study aims to propose a new kind of set, which we refer to as Pentapartitioned neutrosophic binary set. Additionally, we prove some of its basic properties.

This study explores the dynamics of job placement and gender inequality at the Universidad Peruana Los Andes in Huancayo, Peru, with a focus on the application of neutrosophic methods. Recognizing the nuanced differences in professional opportunities for men and women, we employ the Smarandachean theory of neutrosophic social structures to examine these disparities. During 2021-2022, we conducted surveys among university graduates, utilizing the 2-tuple linguistic neutrosophic model to measure their satisfaction levels. This approach, grounded in neutrosophy, allows for a more precise capture of the participants' thoughts and feelings by effectively incorporating the inherent indeterminacies of social phenomena. The use of these neutrosophic tools provides a deeper understanding of the complex interplay between job placement and gender in professional settings.

This paper examines the disparities in job opportunities and social prosperity based on gender within Peruvian universities, particularly focusing on the Universidad Peruana Los Andes during 2021-2022. Utilizing Neutrosophic Social Structures and the Neutrosophic 2-tuples Technique, we statistically analyze the entrenched biases that categorize careers by gender, contributing to power imbalances and unequal employment rates between men and women. By modeling student data through intervals or neutrosophic numbers as per Smarandache's theory, we address the unique engagement of each student with their academic environment. Neutrosophic contingency tables are employed to present this data, and a neutrosophic chi-square test is applied to examine the correlation between students' gender and their major fields of study, which include Administrative and Accounting Sciences, Health Sciences, Law and Political Sciences, Engineering Sciences, and Pedagogical Sciences. This neutrosophic approach allows for a nuanced understanding of the indeterminate and complex nature of social phenomena, providing a clearer insight into gender parity in academic professional development.

This paper introduces a new Neutrosophic Adaptive Neuro-Fuzzy Inference System paired with Explainable Artificial Intelligence to classify oral cancer from clinical photos. The ANFIS model’s interpretability and accuracy have been enhanced in resolving challenging medical images by deploying Neutrosophic logic on a 1000-image dataset to solve the word indeterminacy. A combination of Neutrosophic sets addresses ambiguity, enabling an adaptive neuro-fuzzy network to learn from data to accurately classify oral cancer. This exhibits the benefits of fuzzy logic and neural networks in action. The parameters of this model have been changed meticulously to increase sensitivity, specificity, and accuracy toward diagnostic readiness. These results reflect a substantive enhancement in the model’s ability to distinguish between benign and malignant lesions by delivering accurate and understandable diagnostic decisions existence for clinical adoption. AI medical diagnostic confidence increases the understanding of how the model makes decisions. The ideal objective is to develop a strong, dependable, and easy-to-understand tool to diagnose cancer early. The experimentation on this model can be improved as it may lead to real-time testing, more data for the testing dataset, and using how many types of cancer the model can be applied.

Lifetime analyses comprise the techniques dealing with observations obtained from the occurrence of a specified event(s). In most of the situations dealing with lifetime observations, some units are recorded as censored observations. Dealing with censored observations makes these techniques unique. Countless standard statistical tools are available for inference based on censored lifetime observations. These classical techniques consider lifetime observations as precise numbers and ignore the uncertainty of single observations. Whereas in practical applications it is not possible to measure life times as precise numbers, they are always more or less nonprecise. The imprecision in measurements can be covered by neutrosophic set. Fuzzy estimators for life time distributions potentially use neutrosophic system to model and analyze the inherent uncertainties and neutalities present in the data and the parameter estimates. This study aimed to obtain estimators for the Weibull parameters and two exponential parameters based on the up-to-date fuzzy number approach, a special case for neutrosophic set. The suggested estimators incorporate fuzziness in addition to random variation, which makes these estimators more realistic. The same techniques need to be extended to fuzzy and neutrosophic sets.

Fuzzy concepts have been widely used to treat imprecision in many fields of natural and social sciences. In most of the natural science fields such as applied mathematics, physics, chemistry, and engineering, triangular and trapezoidal fuzzy numbers are commonly used and arithmetic operations on those numbers are studied in detail. On the other hand, in engineering and social science fields such as sociology and psychology, while treating the uncertainties, these numbers are not applicable and fuzzy numbers with more parameters and clear definitions of their arithmetic operations are needed. In order to fill this gap in the literature, in this study we propose the generalized pentagonal fuzzy numbers, and we define fuzzy arithmetic operations based on both extension and the function principle.

The notion of a neutrosophic Abelian subgroup of a group is introduced. The characterizations of a neutrosophic Abelian subgroup are investigated. We show that the homomorphic preimage of a neutrosophic Abelian subgroup of a group is a neutrosophic Abelian subgroup, and the onto homomorphic image of a neutrosophic Abelian subgroup of a group is a neutrosophic Abelian subgroup.

In this paper an idea of neutrosophic convex structures (briefly, NC-structures) is given and some of their properties are explored. Also, NC-sets, neutrosophic concave sets and neutrosophic convex hull are defined and their properties are investigated. Moreover, the notions of NC-derived operator and NC-base are studied and their relationship to NC-structures are established.