International Journal of Neutrosophic Science

  Celiac disease is an autoimmune illness that causes damage to the small intestine and, in some cases, the bones as well. Histological analysis of duodenal biopsies obtained during upper digestive endoscopy is required for a diagnosis. The production of antibodies may be detected by immunological testing by taking a blood sample. Histology takes a long time, and endoscopy is intrusive. This paper used the MCDM method to compute the objective the celiac disease.  In statistical distribution theory, entropy is often employed as a proxy for the uncertainty, unpredictability, or chaos of experimental results. The literature's entropy approaches provide a numeric measure of a random variable's information but struggle to handle data with interval values. The results of an experiment with an unknown outcome are often presented in interval form. The entropy method is used to compute the weights of the criteria. The neutrosophic sets were used to overcome the uncertain information in this study. This paper used six criteria and nine alternatives. The results are shown in this study.  

Chronic obstructive pulmonary disease (COPD), is a debilitating lung condition that may lead to several other serious health problems and even death if left untreated. The ability to diagnose illnesses quickly and affordably is crucial. First and foremost, helping physicians determine how severe COPD cases are is crucial for placing patients in the appropriate institutions. Based on system engineering principles and real-world clinical practice, this article develops a COPD severity evaluation indicator system followed by suggests a neutrosophic distance from the average solution (EDAS) approach to making decisions in a linguistically uncertain setting. The alternatives are ranked by how far they are from the average answer on every factor using the EDAS technique. Distance-based multi-criteria decision-making techniques are analogous to this approach. It expedites the decision-making process by streamlining the computation of distances to an agreed solution. The EDAS method is used to compute the weights of criteria and then rank the alternatives under the neutrosophic model. The neutrosophic set is used in this paper to solve the uncertain information in the process of this evaluation. The EDAS method is applied in various criteria and alternatives and the results are discussed. 

CKD, or chronic kidney failure, is characterized by a gradual decline in kidney operation over time and may be linked to a wide range of medical conditions. Initial detection and therapy are the best tools for combating chronic kidney disease, although they often only delay the development of renal failure. The eGFR-based CKD grading system is useful for risk stratification, patient monitoring, and treatment strategy development. Personalized care and treatment planning will be possible if this research is successful in predicting how soon a CKD individual will need to begin dialysis. The machine learning methods used to predict CKD. But the dataset contains uncertain information, so the neutrosophic set is used to overcome this issue. This paper suggests a framework including the neutrosophic DEMATEL and machine learning method to predict CKD. The neutrosophic DEMATEL method is used to give weights to all variables of the dataset. Then conduct the preprocessing data to eliminate the variables with the least weight. The three machine learning methods used in this paper are Gradient Boosting (GB), Ada Boosting (AB), and Random Forest (RF). The results show the accuracy of the three algorithms. The AB has a 99.166% accuracy, and it is the highest accuracy in this paper followed by the GB has 98.3%, then RF has 92.85%.

Approximately one in eight women will get breast cancer in their lifetime. Because of the risks associated with radiation exposure, various women choose to avoid getting detected with breast cancer. Non-invasive breast cancer detection methods have limitations concerning the safety of radiation exposure and the accuracy with which tumors in the breast are diagnosed. Machine learning methods are commonly used to diagnose breast cancer. This paper applied three different machine learning methods like KNN, Naïve Bayes, and ID3. These methods are applied to the Wisconsin Breast Cancer dataset. In the process of categorization, data with unbalanced classes is problematic because methods are more probable to categorize fresh observations to the majority class since the likelihood of cases forming the plurality class is considerably high. So neutrosophic set is used to overcome the vague and uncertain data. This paper used single-valued neutrosophic numbers to evaluate the criteria. This paper used ROC and accuracy to evaluate the methods. The KNN has a 96.7%, Naïve Bayes has a 95.2%, and ID3 has a 95.3% accuracy.

The Covid-19 epidemic has had devastating effects on the economy and generated many issues for enterprises worldwide. As a result, it is imperative that during periods of pandemic, options be made available to help impacted companies recover and enhance their operations. One industry that has encountered this issue and has had to overcome substantial obstacles is ecotourism centres.  In the first part of this study, we offer several valuable and realistic action strategies for ecotourism centres. Tourism professionals, faculty members, executives, proprietors, and some staff from eco-tourism centres got together for brainstorming meetings to come up with implementation plans. The stated action plans are prioritised based on four factors.  One MCDM approach, known as Multi-Attributive Border Approximation area Comparison (MABAC), uses distance and area-based computing algorithms to systematically represent a complicated choice. In this study, we suggest a bipolar neutrosophic MABAC that incorporates the three possible memberships truth, indeterminate, and false into a single set. Using a bipolar neutrosophic language scale, a panel of specialists was asked to rate the outcome values of requirements and options. The major output of the suggested approach is the distances of options from the Border Approximation Area of bipolar neutrosophic MABAC.

Due to the possible threat, it poses to human health and the ecosystem, healthcare waste (HCW) handling and disposal are of major concern, especially in poor nations. Many countries are now turning to technology innovations as viable waste management solutions. Nevertheless, is still difficult for governments to choose an appropriate technology and an efficient waste management technique for the disposal of HCW. The difficulty of estimating these numbers inaccurately is addressed by using a neutrosophic set. The best alternative to HCW is hard to choose, and the process of doing so may be thought of as a multi-criteria decision-making (MCDM) issue. In this study, we propose a neutrosophic TOPSIS approach for assessing potential HCW alternatives. The weights of the requirements are calculated TOPSIS method. The best HCW alternative is assessed using the suggested approach.

The objective of this paper is to study the algebraic properties of weak fuzzy complex matrices, where many elementary properties will be obtained such as the invertibility, the determinants, and the eigen values and vectors. In addition, a full solution of linear systems of weak fuzzy complex equations will be provided as an effective and easy algorithm. Also, many examples to clarify the validity of our approach.

In this paper, we introduce the concept of neutrosophic hesitant fuzzy UP (BCC)-filters of UP (BCC)-algebras. The characteristic neutrosophic hesitant fuzzy UP (BCC)-filters have also been studied. The relationship between neutrosophic hesitant fuzzy UP (BCC)-filters and their level subsets is provided. The Cartesian product of neutrosophic hesitant fuzzy UP (BCC)-filters is also supplied. Finally, we also find the property of the homomorphic pre-image of neutrosophic hesitant fuzzy UP (BCC)-filters.

The purpose of this article is to study some covering properties in neutrosophic topological spaces via neutrosophic pre-open sets. We define neutrosophic pre-open cover, neutrosophic pre-compactness, neutrosophic countably pre-compactness and neutrosophic pre-Lindel¨ofness and study various properties connecting them. We study some properties involving neutrosophic continuous and neutrosophic pre-continuous functions. We also define neutrosophic pre-base, neutrosophic pre-subbase, neutrosophic pre∗-open function, neutrosophic pre-irresolute function and study some properties. In addition to that, we define and study neutrosophic local pre-compactness.

Mathematical cryptography applies special properties of integers and number theory in encrypting and decrypting messages in our world which is increasingly in demand for information security, especially for social media and multimedia. The objective of this work is to generalize El-Gamal crypto algorithm to be applied by using symbolic 2-plithogenic integers instead of classical integers. Also, we apply the novel algorithm to the encryption and the decryption of square  fuzzy matrices with rational entries, and fuzzy relations which can be represented as fuzzy matrices with rational entries. In addition, many examples with numerical data will be presented.

Symbolic n-plithogenic sets came with many generalizations to classical algebraic structures, with many interesting properties and theorems, where the symbolic 2-plithogenic structures are very similar in their algebraic properties to refined neutrosophic algebraic structures. The main goal of this article is to study the algebraic properties of symbolic 2-plithogenic matrices such as the computing on symbolic 2-plithogenic determinants, symbolic 2-plithogenic special values, and symbolic 2-plithogenic representations by linear functions. In addition, many examples will be presented and discussed in terms of theorems to clarify the validity of the content of this paper.

In this paper, we present and study some concepts in topological space by using two tools in fuzzy theory which namely fuzzy and neutrosophic concepts. n-refined neutrosophic fuzzy topological space have been studied by n-refined neighborhood, n-refined compact set and other notions. Finally, some definitions, examples and new results have been presented in this paper.

We introduced Neutrosophic implicative filters and Neutrosophic positive implicative filters in Lattice implication algebra. We proved some properties and equivalent conditions of both the filters. Finally we proved that “Every Neutrosophic positive implicative filter is a Neutrosophic implicative filter” and “Every Neutrosophic positive implicative filter is a Neutrosophic filter”.

In this paper, we introduce the concept of neutrosophic fuzzy lattice via fuzzy partial ordering. The definition of neutrosophic fuzzy lattice and equipotent are developed with suitable examples. Cartesian product and some equivalent properties of neutrosophic fuzzy lattice are discussed . Also some theorems on neutrosophic fuzzy lattice are developed here. 

One of the achievements of Peruvian higher education is that it has recognized the need for this type of education to be inclusive. That is, certain vulnerable social sectors also can study for a university degree. Within this group are individuals with special abilities, such as the blind, people with motor problems, among others of this type, although people with economic problems and others who are discriminated against for having suffered prison regimes, because of their gender, race, among others can also be included. To make a reality this idea of inclusion, inclusive teaching programs are needed, which also comprise several programs, from which to choose one. The purpose of this paper is to design a technique that allows decision makers to select among several proposed programs the one that is the most suitable for this type of teaching. To this end, we propose a method to evaluate five dimensions of inclusive education, hybridizing the Analytic Hierarchy Process (AHP) technique with plithogenic sets. The Plithogenic AHP method allows us making the most appropriate decision, with the degree of complexity proper of decision making in education, considering the various components that are part of the training of the university students.