International Journal of Neutrosophic Science

With the widespread usage of social media in our day-to-day lives, it becomes a platform for persons to express and share their feelings, views, thoughts, and opinions. Recognizing emotions has numerous applications extending from dynamic advertisement to behavior analyses. People express their emotional state in a language that is often complemented by figures of speech and ambiguity, making it problematic even for human beings to understand. Categorizing tweets is a dynamic application of NLP, allowing the scrutiny of topical discussions, user opinions, and trends in real-time. Leveraging techniques such as word embeddings, machine learning, and text preprocessing approaches, tweet classification enables tasks like spam detection, sentiment analysis, and topic modeling. This ability assists companies in understanding client feedback and allows policymakers and researchers to track emerging issues and gauge public opinion on social networking media. This study presents a Two-Person Intuitionistic Neutrosophic Soft Games with Harris Hawks Optimizer (TINSG-HHO) based Tweets Classification on NLP Applications. The purpose of the TINSG-HHO technique is to detect the existence of different kinds of emotions or sentiments in the tweets. The TINSG-HHO technique begins with preprocessing of tweets to convert them into useful format.  Then, FastText embedding represents words as contextual similarities, dense vectors, and capturing semantic nuances. Leveraging the embedding, the Neutrosophic classification model proficiently handles vagueness and uncertainty intrinsic in deceptive content detection tasks. Moreover, the HHO technique enhances the parameter of the Neutrosophic classifier, improving its generalization capabilities and performance. Based on the hunting strategy of Harris's hawks, HHO discovers the parameter range to search for optimum configurations for the classifier. Experimental evaluations carried out on different datasets illustrate the effectiveness of the DCRM in precisely detecting the deceptive content

Sufficient CO2 is indispensable for vegetation, but space and oceanic vehicles, industrial chimneys and land use tons of extreme CO2 and are typically accountable for global warming, climate variations and greenhouse effect. Owing to COVID19, CO2 discharge was in 2020 at its lower level than first ten years. However, the time taken is not known to decrease, increase or change carbon emissions to an endurable point. Precise predicting of carbon production has real consequences for selecting the optimal ways of decreasing carbon emissions. A pressing necessity to control these carbon emissions is needed. The preliminary step is to precisely recognize the milestones and threat levels. Specific thresholds should be mapped that formulate the maximum levels of CO2 namely – the point of no return, risk point, and so on. This article focuses on the development of Multiple Attribute Group Decision-Making with Neutrosophic Environment for Carbon Emission Prediction (MAGDM-NECEP) method on Sustainable Urban Management. The MAGDM-NECEP architecture proficiently manages the multi-criteria nature of emission calculation, while neutrosophic logic accommodates ambiguity and uncertainty in input dataset. Furthermore, GSO enhances model parameters, improving prediction performance. The MAGDM synergy and neutrosophic logic offer strong decision-making abilities, whereas GSO fine-tuned the model parameter for superior outcomes. Empirical analysis establishes the efficiency of the presented technique in precisely predicting carbon emission, providing valuable insight for the environmentalist and policymaker in developing efficient mitigation strategy

Among the important and ancient mathematical concepts are the concepts of grill, the local function, and the knowledge of using grill in conjunction with topological spaces, which have gained wide scope in the natural sciences and elsewhere. The main idea here is to development these concepts in conjunction with neutrosophic crisp sets, and on the other hand to highlight their important and influential properties and the relationships that link them to each other.

This article discusses a new approach to multiple attribute decision-making (MADM) based on sine trigonometric (ST) (l1, l2, l3) neutrosophic  sets (NS). We discuss the concept of ST (l1, l2, l3) neutrosophic weighted averaging (NWA), ST (l1, l2, l3) neutrosophic weighted geometric (NWG), ST (l1, l2, l3) generalized neutrosophic weighted averaging (GNWA) and ST (l1, l2, l3) generalized neutrosophic weighted geometric (GNWG). We presented during our discussion showed an algorithm that used these operators. Extensive Hamming distances are illustrated numerically. Also included in this communication are discussions of idempotency, boundness, commutativity, and monotonicity for ST (l1, l2, l3) neutrosophic sets. By using them, you can find the best option faster, easier, and more conveniently. As a result, ST (l1, l2, l3) and more precise conclusions are more closely related. A comparison is made between some current models and those proposed to demonstrate the dependability and utility of the current models. Furthermore, fascinating findings were revealed in the study.

This article provides a comprehensive view of the evolution and ethical implications of genetics, from its historical origins to modern advancements like the Human Genome Project. It emphasizes the importance of genetic information in disease prediction and understanding human diversity while highlighting the need to address associated ethical and privacy issues. It introduces Fuzzy Cognitive Maps (FCM) and the Dependent OWA (D-OWA) aggregation operator as innovative tools for analyzing the complex landscape of the Right to Genetic Information (RGI) in an international context, identifying key factors such as legal frameworks, technological advancements, access to health, culture, ethics, and commercial interests. The research reveals that while legal framework and technology are predominant, other factors also play significant roles in managing RGI. The application of D-OWA provides additional insights, confirming that conventional centrality assessments adequately reflect the priorities and influence of factors in RGI. It concludes by underlining the need for specific strategies to address these challenges, such as strengthening the legal framework, promoting ethics in genetics, improving public education, and respecting cultural diversity, to protect individual rights while leveraging the benefits of genetics for society.

Legal security in property division is a crucial aspect within the field of civil and inheritance law. This process involves the equitable distribution of assets from an inheritance or marital community among heirs or co-owners, with the aim of guaranteeing their rights in a fair and transparent manner, in accordance with the law. This study focuses on examining various options to deepen understanding of the mechanisms that ensure legal security in this property division, with the goal of identifying best practices. To analyze each alternative in terms of scope and objective, the multicriteria decision-making methods OWA and TOPSIS were employed, using neutrosophic numbers as a modeling tool. The use of these methods highlights the challenges of legal inexperience and conflicts of interest, emphasizing the need for a precise and cautious approach. This analysis promotes the search for equitable solutions in property division, highlighting the importance of fair and transparent legal procedures.

This study investigated the dynamics of interaction between key variables in traffic accident mediation and how the inherent indeterminacy in these interactions influences the outcomes of mediation. Neutrosophic Cognitive Maps and neutrosophic logic were used to model and quantify the centrality and influence of such variables. The results highlighted the importance of negotiation strategies and emotional management as factors of high centrality in the mediation process. Based on these findings, the implementation of targeted training programs and the adoption of advanced analytical tools to improve mediation practices were recommended. The study underscores the need for a multidimensional approach that considers the complexity and uncertainty in mediator training, supporting efficiency and effectiveness in resolving traffic disputes.

In Ambato, the low availability of suppliers represents a significant challenge for business logistics, directly impacting operational efficiency, legal compliance, and competitiveness. This study aims to analyze the legal implications of business logistics in this region and propose strategies. The Neutrosophic Saaty AHP method was used for the development of the study, identifying the predominant challenge as the "low availability of suppliers," which can lead to legal risks related to contract non-compliance and supply chain issues. To address this challenge, the strategy of promoting the formation of strategic alliances and diversifying supply sources was proposed using the Neutrosophic IOWA-VIKOR method. This allows for the evaluation of suppliers' ability to meet demand and company requirements. The study also highlights the need to address challenges, manage indeterminacies, and optimize logistics chain operations in Ambato's businesses. It concludes that the strategy of strategic alliances and supplier diversification is crucial for overcoming the low availability of suppliers in Ambato.

Embarking on the exploration of integrating environmental sustainability principles and neutrosophic fuzzy theory in inventory management, this study aims to effectively tackle shortages. It underscores the vital balance between economic efficiency and ecological responsibility in contemporary inventory management practices. Neutrosophic fuzzy theory emerges as a robust tool for navigating the inherent uncertainties in inventory optimization, offering a versatile framework for modelling intricate problems. Strategies for optimizing resource consumption and minimizing waste generation within inventory management are scrutinized, emphasizing the imperative of harmonizing economic objectives with environmental concerns. Introducing a novel framework that melds neutrosophic fuzzy with environmental metrics, the research aims to optimize inventory management processes while mitigating environmental impacts. Furthermore, it delves into the challenges of managing energy consumption, advocating for innovative approaches to address fluctuating energy prices, data limitations, and evolving regulatory requirements. Neutrosophic sets are introduced for energy consumption analysis and cost evaluation, showcasing their efficacy in managing uncertainty and variability in real-world scenarios. The study concludes with a Python-based analysis of neutrosophic mean in energy consumption, offering insights into central tendencies and uncertainties associated with energy-related costs. Utilizing visualization techniques to enhance comprehension and decision-making in energy management, this research contributes to advancing inventory management practices by integrating environmental sustainability principles and sophisticated mathematical techniques, thereby fostering more resilient and sustainable supply chain operations.

The primary purpose of this report is to present an idea that is considered one of the main ideas in neutrosophic binary topological spaces. We plead them the axioms of neutrosophic binary separation axioms associated in the neutrosophic binary kernel. Also we studied its characteristics and the relationships between these new neutrosophic binary separation axioms and their relationships with some other properties.    

Infallibility is an important factor both in fabrication repertoire replica and in the great demand of products. During a fabrication process, more exemplary products with high reliableness aim for increase in product demand although credit rating too is a prominent business strategy. Integrating the above duo concepts, we explain and explore mathematically a fabricating repertoire replica with partisan layaway stratagem and infallibility effect on the fabrication system wherein the demand of the customers is reliant on the product cost and rate of decay is regarded as constant. In this propounded model, commerce layaway stratagem on the fabricator and the customer is acquainted by considering all the achievable sitch due to permitted credit (layaway) duration. As a consequence, considering all the achievable instances for the fabricator and the customer’s layaway duration, seven non-linear optimization issues for the proposed replica are required.

In this article, we use the notion of neutrosophic local function to introduce a new neutrosophic operator in the context of a neutrosophic topological space equipped with a neutrosophic ideal. Also, we introduce and study some new classes of neutrosophic sets defined in terms of the neutrosophic local function and the new notion of neutrosophic operator given.

This article discusses a new approach to multiple attribute decision-making (MADM) based on (l1, l2, l3) neutrosophic sets (NS). This is an extension of the NS. Neuosophic weighted averaging (NWA), neutrosophic weighted geometrics (NWG), generalized neutrosophic weighted averaging (GNWA), and generalized neutrosophic weighted geometrics (GNWG) are the topics of this article. The flowchart we presented during our discussion showed an algorithm that used these operators. Numerical examples are provided for the extended Euclidean and Hamming distance measures. As part of this communication, we will also elaborate on the properties of neutrosophic sets, such as their idempotency, their boundness, their commutativity, and their monotonicity. They make it quicker, easier, and more convenient to find the best option. Thus, there is a stronger connection between (l1, l2, l3) and more precise conclusions. Some of the current models are compared with those that have been proposed in order to demonstrate their dependability and utility. The study also revealed fascinating and intriguing findings.

Applied statistics has been instrumental in predicting behaviours and future market trends. In the field of financial time series analysis, the incorporation of deep learning (DL) methods and applied statistics has made a significant contribution to the prediction model. Practitioners and researchers can extract complex features and dependencies from past financial data by leveraging neural network structures like long short-term memory (LSTM) and recurrent neural networks (RNNs). These DL approaches advance the development of predictive models prone to forecasting different financial metrics, such as asset returns, stock prices, and market volatility, with outstanding accuracy. With the combination of statistical approaches with DL techniques, researchers can leverage the power of both worlds to make more informed investment decisions and improve forecasting capabilities in volatile and dynamic financial markets. This study develops a new Applied Statistics with Single Valued Neutrosophic Fuzzy Soft Expert Sets (AS-SVNFSES) technique for Financial Time Series Forecasting. The presented AS-SVNFSES technique aims to forecast the input financial time series data. The AS-SVNFSES technique primarily applies data preprocessing using a Z-score normalization approach. For the forecasting of financial data, the AS-SVNFSES technique makes use of the SVNFSES technique. Finally, the parameter tuning of the SVNFSES technique is performed using the chimp optimization algorithm's (ChOA) design. A series of experimentations have illustrated the amended performance of the AS-SVNFSES model. The experimental value inferred that the AS-SVNFSES technique gains improved performance over other models.

The concept of a single-valued neutrosophic graph SVNS-G is recently studied, the bond between neutrosophic graph N-G and neutrosophic topological graph NT-G was my goal in this research, I try to find a relation on the vertices of the SVN-G  to structure  NT-G add some theorems and corollaries.