International Journal of Neutrosophic Science

Indeterminacy is common in practical decision-making circumstances. So the mathematical models of decision making represent the situations in a better way if the parameters are considered as neutrosophic.  This article studies a general framework of multi-objective neutrosophic linear fractional programming problem (MONLFPP) and proposes unique approach. The issue's parameters are thought of as triangular neutrosophic numbers. The problem is converted into an equal crisp multi-objective linear programming problem (MOLPP) with the help of variable transformation technique and a ranking function.  Fuzzy goal programming is used to solve the MOLPP that has been obtained. Finally, the usefulness of the proposed technique is established using two mathematical models.

In this research, we introduce the Interval Valued Temporal Neutrosophic Fuzzy Sets (IVTNFS) and some of its basic operations. Also, examine some of their properties. The Neutrosophic Fuzzy Sets of membership and non-membership values are not always possible up to our satisfaction, but the IVTNFS part has a more important role here, because the time movement with an interval in NFS gave the best solution to making a decision, deciding their careers in our real-life situation.

This article presents the concepts of neutrosophic INK-subalgebras and INK-ideals of INK-algebras. We also studied neutrosophic INK-subalgebras and INK-ideals that depend on homomorphisms and anti-homomorphisms.

In this paper, a new type of integral transform with complex power parameters called the "Mayan transform" has been introduced. The definitions of the integral transform and its inverse are given in the classical case and the corresponding neutrosophic case. The classical analytical properties of the Mayan integral transforms are proven; the application of it to some essential functions and neutrosophic functions is introduced and proven. Also, its application to the  neutrosophic derivatives (first, second, and third) then the generalization of the application on the Mayan integral transform on the neutrosophic derivatives are given. The worthiness of the introduced integral transform on the actual application is also proven by applying the integral transform in solving two problems: the response of an Undamped forced mechanical oscillator and the response of an undamped forced electrical oscillator.

This article considers a stochastic multi-objective linear programming problem in a neutrosophic framework. The right-hand sides of the constraints are random variables associated with their means and variances, and the single-valued trapezoidal neutrosophic numbers are included in the coefficients of the objective functions to investigate the problem. The problem is successfully converted into the related deterministic programming model based on the formulation of the scoring function and several distributions, such as the Uniform, Exponential, and Gama distributions. Applying fuzzy goal programming, the deterministic problem is solved. An example is then solved to demonstrate the solution methodology.

The goal of this research is to investigate fuzzy multiobjective dynamic programming issues with fuzzy parameters in the objective functions and single valued trapezoidal neutrosophic numbers in the left hand side of the constraints. Piecewise quadratic fuzzy numbers characterize these fuzzy parameters. In addition, applying the score function of the neutrosophic numbers to convert the constraints parameters into its crisp .  Some basic notions in the problem under the pareto optimal solution concept is redefined and analyzed to study the stability of the problem. Furthermore, a technique is presented for obtaining a subset of the parametric space that has the same pareto optimal solution. For a better understanding and comprehension of the suggested concept, a numerical example is provided.

 In this paper, a bi-level chance constrained programming problem is considered when the coefficients of the objective function is presented as neutrosophic numbers and the right- hand side of the constraints is normal variables and the constraints have a joint probability distribution. While the probability problem and applying the score and accurate functions the problem is converted into an equivalent deterministic non- linear programming problem, a fuzzy programming approach is applied by defining membership function. A linear membership function is used for obtaining optimal compromise solution. A numerical example is given to illustrate the proposed methodology.

In this article, we used approximate methods to obtain Bayes method estimations for the shape and scale parameters of the generalized exponential distribution, as three approximation methods were employed: Lindley approximation and neutrosophic Lindley approximation, Gibbs sampling and neutrosophic Gibbs sampling, the most important samples based on the gamma informative prior under the squared error loss function. Through different simulation experiments a comparison was made between those estimators of these three approximate methods, from the simulation results we found a relative preference for the important sampling method over the other two methods. The results of simulation experiments were also confirmed by applying these approximate methods to real data representing the operating times of one of the machines of the publishing, printing, and translation house in Baghdad. On the other hand, we apply the same method to the neutrosophic exponential distribution, and the results will be compared to the classical case.

The complement of the highest result of multiplication of two neutrosophic graphs is determined in this study. In the complement of the maximum product of a neutrosophic graph, the degree of a vertex is investigated. The complement of the maximum product of two normal neutrosophic graphs has several results that are presented and proven. Finally, we have offered a neutrosophic graph application for locating an online streaming service using normalized Hamming distance.

In this paper, the concept of bipolar Pythagorean fuzzy neutrosophic sets (BPNSs) will be introduced which represents an innovative synthesis, incorporating bipolarity, Pythagorean fuzzy sets, and neutrosophic sets to provide a comprehensive solution to the multifaceted issues encountered in decision-making challenges. BPNS is proposed to enhance the expressiveness and flexibility of the model by incorporating both positive and negative preferences that can lead to more accurate and realistic modeling of complex decision scenarios. The essential idea of BPNSs is defined and some definitions for instance complement, intersection, and union are described. The basic operation, the derivation of its properties, and the numerical example of the method are integrated to execute the proposed model.

Several higher education institutions recommended active learning many decades ago. In many fields, artificial intelligence (AI) has turned out to be a dominant aspect of people's lives. One of the fields that welcome AI is education. AI can play a crucial role in active learning, which is a teaching method that encourages students to take an active role in their learning process. Active learning can involve a wide range of activities, such as problem-solving, group discussions, and self-reflection. This quantitative research paper aims to explore students' engagement and perceptions of patterns of embracing AI tools and their relationship to student learning outcomes. A diverse sample of 355 students from a highly reputable university in the United Arab Emirates participated in the research study (UAE). Descriptive Analysis and Correlation Coefficient were used to achieve the paper's objectives. Findings uncovered students’ high engagement level in classroom activities, and they perceived AI tools as useful and effortless, which promotes their engagement and attention inside classrooms. Furthermore, the results demonstrated the existence of a positive association between students' perceptions of embracing AI and student engagement in the learning environment. However, students’ learning outcomes have a non-remarkable association with student engagement and their perception of AI patterns. The study's findings suggest embracing and promoting AI applications in classrooms to keep students engaged. This study's use of Neutrosophic sets offers a fresh way to approach the problem of ambiguity when evaluating students' opinions and performance in relation to AI technologies. Neutrosophic sets, a mathematical framework designed to manage uncertainty, provide a sophisticated way to understand how students' complicated interactions with AI operate. This integration promises a more comprehensive and flexible educational paradigm and is a trailblazing method of negotiating the complexities of active learning in higher education.

In a power plant, the fuel choice directly impacts the efficiency, cost, and ecological impact of generating electricity. For power plants to produce electricity effectively and affordably to fulfill the needs of consumers in homes, companies, and communities, they need a fuel supply that is constant, dependable, and inexpensive. In this study, we used the concept of multi-criteria decision-making (MCDM) to deal with the various criteria of fuel alternatives. We used the EDAS method as an MCDM methodology to rank the fuel alternatives and select the best one. The EDAS method is employed with the interval-valued neutrosophic sets (IVNSs) to deal with the uncertainty information in the evaluation process. We compute the weights of the criteria of thermodynamic parameters. We used ten thermodynamic parameters such as temperature, mass, energy, etc. Then, the principal results show that temperature is the best criterion, and the work interaction is the worst criterion in all criteria. The EDAS method ranked twenty alternatives. The results show that alternative 20 are the best and alternative 14 is the worst of all alternatives. We employed the sensitivity analysis to show the rank of alternatives under ten cases. The results show the 20 alternative is the best in all cases. The results are stable.    

Full exploitation of offshore wind resources still needs to be completed despite their significant potential to reduce the impacts of climate change via the production of renewable power. Planning strategies that include wind resources, safety, economic, social, and government impacts are essential for advancing offshore wind generation projects. This study aims to evaluate the criteria for wind power plants and select the best turbine. This process has various conflict criteria, so the multi-criteria decision-making (MCDM) methodology deals with multiple criteria. The ARAS method is an MCDM method used to rank the alternatives. The ARAS method uses the single-valued neutrosophic set to deal with uncertain information. We gathered eleven criteria and fifteen alternatives. The results show the turbine resource is the best and the economic criterion is the worst. The sensitivity analysis is conducted to ensure the proposed model's results and show the strength of the proposed method. The results show the proposed model is suitable for selecting the best wind power plant.

Traditional approaches to recognizing hazards and evaluating their risks have several shortcomings, including data confusion and unpredictability, an inability to accurately reflect human thought processes, a failure to give weight to factors, the use of established data and tables, and the influence of the evaluator on the final risk evaluation outcomes. Thus, refining current techniques and creating new ways with more precision and sensitivity is essential. We proposed a framework for risk assessment of firefighting. Firefighting has various criteria, so the concept of multi-criteria decision-making (MCDM) deals with these criteria, such as life safety, resource allocation, incident duration, weather conditions, access, etc. We collected ten risk criteria and 25 alternatives. The proposed framework has two main stages. First, we apply the average method to ten risk criteria to show the weights and the importance of the criteria. Then, in the second stage, we used the grey rational analysis (GRA) method to assess the firefighting risks. The GRA method is an MCDM methodology used to rank the alternatives. The GRA method is integrated with the triangular neutrosophic sets (TNSs) to deal with vague and uncertain information. Then, the principal results show that life safety is the highest weight, and the incident duration is the lowest. The outcome of the GRA method shows that risk 25 is the highest and risk 17 is the lowest. We applied the sensitivity analysis to show the stability of the results. We offer the model is adequate, and the results are stable. 

In this work, the possibility neutrosophic bipolar soft sets interact with the possibility bipolar fuzzy soft sets, as well as complementation, union, intersection, AND, and OR. This paper extends the concept of bipolar neutrosophic soft sets to the possibility neutrosophic bipolar soft sets. Our main goal was to demonstrate De Morgan’s law, associate law and distributive law, which are all the laws related to the possibility neutrosophic bipolar soft sets. Also, we present an algorithm that uses a soft set model to solve the decision-making problem primarily in order to simplify the process.