International Journal of Neutrosophic Science

Among the current generation researcher, artificial intelligence has played vital role in various fields, including healthcare. One of the key areas where it has shown enormous potential is in cancer detection and treatment. AI and methods of machine learning algorithms have been applied to analyze large datasets, such as genomics, transcriptomic, and imaging data, to identify patterns and relationships that can help in cancer diagnosis and therapy. However, due to the inherent complexity and heterogeneity of tumors in individual patients, building a diagnostic and therapeutic platform that can accurately analyze outputs becomes a challenging task. To address this challenge, researchers have proposed the use of explainable AI frameworks in cancer detection. Explainable AI frameworks aim to provide transparency and comprehensibility to the decision-making process of AI algorithms, ensuring that the predictions or classifications generated by these algorithms can be understood and trusted by healthcare professionals. One popular explainable AI method is SHAP (SHapley Additive explanations). SHAP is a well-known XAI method that provides intuitive and interpretable feature importance [13] for individual predictions. Another explainable AI method is LIME (Local Interpretable Model-agnostic Explanations), which generates posthoc explanations and is suitable for quick and satisfactory explanations. These existing explainable AI methods, however, have limitations in their applicability to cancer detection. Therefore, in this research article, we propose the use of two novel frameworks: Neutrosophic Meta SHAP and Neutrosophic Meta Lime. Neutrosophic Meta SHAP and Neutrosophic Meta Lime are efficient frameworks specifically designed for the analysis and interpretation of AI models in oral cancer detection.

In this paper, we propose a new version of the Gumbel Distribution using a sine technique family. We discuss the key properties of this distribution, such as the probability density function, the cumulative distribution function, the survival function, the hazard function, the cumulative hazard, and the moments. Additionally, we present a method for estimating the distribution's parameters. We then analyze a dataset using the original and generalized distributions, comparing the results and using goodness-of-fit measures to determine which distribution best fits the data. Finally, we provide conclusions based on our findings, with many examples and valid comparisons applied on fuzzy data.

In this study, the theory of the Type-II q-rung neutrosophic interval valued soft set (Type-II q-rung NIVS) is introduced. We also define a few operations based on the Type-II q-rung NIVS set. Type-II q-rung NIVS sets are formed by extending neutrosophic interval valued soft (NIVS) sets and q-rung fuzzy soft sets. Type-II q-rung NIVS sets and their similarity measures. An illustrative example illustrates how they can be used to successfully address uncertainty-related problems.

Characterizations of (∈,∈)-neutrosophic ideals and (q,∈ ∨q)-neutrosophic ideals are provided. Given special sets, so-called neutrosophic ∈-subsets, neutrosophic q-subsets, and neutrosophic (q,∈ ∨q)-subsets, conditions for the neutrosophic ∈-subsets, neutrosophic q-subsets, and neutrosophic (q,∈ ∨q)-subsets to be ideals are discussed.

In this paper, we introduce the neutrosophic vague soft set, a combination of vague and neutrosophic soft sets. With the help of aggregated operations, we discuss neutrosophic vague soft sets. Multi-criteria group decision making can be evaluated effectively using the VIKOR approach. In this approach, the score function is generated by aggregating the VIKOR method to a neutrosophic vague soft approach. With the help of closeness values, alternative solutions are presented as optimal ones. To invest some money into the top five companies on the stock exchange, an investment company intends to purchase shares of the companies. Their investment strategy was to allocate some of their cash in percentages of 30 dollars, 25 dollars, 20 dollars, 15 dollars, and 10 dollars according to the top five ranked companies to minimize this effect.

Dijkstra’s algorithm (DA) is a very popular approach for finding the shortest route (SR) in the shortest route problem (SRP). The SRP becomes a challenging and complex problem in real life scenarios. The Fermatean neutrosophic set is a mathematical model that combines Fermatean sets with neutrosophic sets. It can handle the unclear, ambiguous, inconsistent, confusing, and uncertain information that comes from real-world problems. Decision-makers face difficulty accurately determining the precise membership (MG) and non membership levels due to the lack of appropriate data available. The FNS can handle this problem. In this study, we consider the interval FNS to describe the arc weight of a neutrosophic graph (NG). This SRP is called an interval Fermatean neutrosophic shortest route problem (IFNSRP). A modified DA is presented to solve this IFNSRP in an uncertain environment. The effectiveness of the presented method is illustrated with a numerical instance of a neutrosophic network.

       Poverty is an emerging problem that most economies are facing today. The study is aimed at exploring research conducted on measuring non-monetary poverty via machine learning.  Non-monetary poverty is identified through the following factors: demographics, population, distribution of income, climate, culture, ethnics, and availability of natural and artificial resources. Today, one of the most important aspects of non-monetary poverty measurement is using machine learning for multiple data points other than wealth or income to assess the quality of life of an individual or community. The socioeconomic factors that contribute poverty in emerging nations have also been found using machine learning algorithms. To achieve our goal neutrosophic model and machine learning algorithms were applied. Neutrosophic model used for reviewing the poverty indicators along with ML algorithms.   While exploring the utility of machine learning in our study to measure poverty we will find the answers for the following questions: (1) Why it is important to take into consideration of non-monetary approaches while calculating poverty rate? (2) Which machine learning algorithms were used in poverty measurement? (3) What is the future scope of machine learning applications in poverty prediction? In finding answers for those questions, we have analyzed overall 10 papers which were collected according to exclusion and inclusion criteria and the purpose of the selection according to the content of the paper. During the survey it was found out that machine learning gives sophisticated data for identifying non-monetary reasons of poverty and this survey is first that uses machine learning to non-monetary poverty factors.  

Regression analysis is a widely used tool in several fields. In this paper, we propose a comprehensive, multistep regression model for financial forecasting. The proposed hybrid model combines preprocessing, feature selection, and cross-validation to obtain a powerful approach to forecasting. The extension of the proposed model to neutrosophic sets is discussed. The model is applied to the case study of real estate prices. The results demonstrate the efficacy of the model.

While an Automated Intrusion Response System (AIRS) chooses and initiates a suitable reaction from the pool of response groups based on specific response choice requirements to reduce the intrusion immediately, an Intrusion Detection System (IDS) finds the intrusions and generates alerts. The accurate assessment of the critical weight of all responses chosen and the prioritization of the incursion response set are the biggest hurdles when creating an AIRS. This study suggested a multi-criteria decision-making (MCDM) method for ranking intrusion responses. The TOPSIS method is an MCDM method used to rank the alternatives. The TOPSIS method integrated with the single-valued neutrosophic set (SVNS) to overcome uncertainty. This study used 16 criteria and 10 alternatives to be evaluated by experts and decision-makers. The sensitivity analysis shows the rank of other options under different cases. The criteria weights are changed under 17 cases. The results of sensitivity analysis show the rank of alternatives is stable. The suggested method was compared with other MCDM methods to show its effectiveness and robustness.

We introduce the new type neutrosophic set (NS) problems relevant to multiple attribute decision making (MADM). Pythagorean fuzzy set (PFS) and neutrosophic set (NS) can be extended into new type neutrosophic set. We discusses new type neutrosophic weighted averaging (New type NWA), new type neutrosophic weighted geometric (New type NWG), generalized new type neutrosophic weighted averaging (new type GNWA) and generalized new type neutrosophic weighted geometric (new type GNWG). A number of algebraic properties of new type NSs have been established such as associativity, distributivity and idempotency.

The notions of neutrosophic N-subalgebras and neutrosophic N-ideals of Hilbert algebras are introduced, and several properties are investigated. Conditions for neutrosophic N-structures to be neutrosophic Nsubalgebras and neutrosophic N-ideals of Hilbert algebras are provided. The Cartesian product of neutrosophic N-structures is also supplied. Finally, we also find the property of the homomorphic pre-image of neutrosophic N-subalgebras and neutrosophic N-ideals.

  Information technology security, or Cybersecurity, guards against hostile cyberattacks on computers, mobile devices, servers, electronic systems, and networks. Cybersecurity risks have been a significant concern for any vital digital infrastructure in recent years, and different online cyberattacks are also becoming a significant problem for society. Consequently, it's critical to adopt technology created to provide cybersecurity. However, one should consider the associated hazards while selecting among Cybersecurity systems. We have developed a multi-criteria decision-making (MCDM) approach based on a single-valued neutrosophic set (SVNS). This allows specialists more latitude in assessing the criteria and alternatives using language and overcoming uncertain information. The VIKOR is an MCDM methodology used to rank the other options. The VIKOR method is integrated with the neutrosophic set. There are 18 criteria, and 10 alternatives are used in this study. The sensitivity analysis and comparative analysis are conducted in this study. The sensitivity analysis results show the alternatives' rank is stable under different cases. The comparative analysis compares the suggested method with other MCDM methods. The comparative analysis shows the suggested method was effective compared with other MCDM methods. Machine learning methods predict the type of attack in Cybersecurity. This study uses Three machine learning methods: decision tree, random forest, and support vector machine.

This study presents an analysis of consumer opinions on waste medicine management. The study explores consumers' concerns, preferences, and suggestions regarding correctly disposing unused or expired medications. The analysis shows the key points that emerged from consumer opinions, including environmental impact, public health and safety, accessibility and affordability, education and awareness, pharmaceutical industry responsibility, convenience and ease of disposal, privacy and confidentiality, community engagement, alternatives to disposal, extended producer responsibility, international collaboration, technology solutions, environmental stewardship, and government regulation and support. This study shows the importance of understanding consumer perspectives in developing effective waste medicine management strategies prioritizing environmental sustainability, public health, and consumer satisfaction. We used the multi-criteria decision-making (MCDM) methodology to deal with these criteria. We gathered 15 criteria concerned with waste medicine management. We used the DEMATEL method to show the criteria weights and relationships between criteria. The DEMATEL method is integrated with the single-valued neutrosophic set to deal with uncertain data. The results show the environmental impact has the most significant weight.

The burgeoning proliferation of Artificial Intelligence (AI) technologies has engendered a transformative shift in various industries, and digital marketing is not an exception to this trend. The thrust of this paper is to explore, analyze, and conceptualize the multi-dimensional impact of AI on digital marketing strategies using Neutrosophic set. By employing statistical mechanics and stochastic models, we aim to delineate the underlying mechanisms that facilitate the operational synergy between AI algorithms and marketing frameworks in the light of Neutrosophic analysis. We invoke the concept of AI-Enabled Marketing Efficiency (AIME), which we define as AIME =(ROI{AI} - ROI{Traditional} )/(Time{AI}  ) , to assess the quantitative aspects of this interaction. Our empirical findings suggest that AI integration could enhance marketing campaign effectiveness by approximately 27% (p < 0.05) while reducing human-led execution time by 33%. We further discuss the ethical implications of AI-driven decision-making in digital marketing, such as the potential for reinforcing societal biases and the abuse of personal data. Artificial Intelligence has been an area of extensive research and development, permeating through diverse sectors including healthcare, finance, and now more prevalently, digital marketing. While the application of AI in digital marketing is not a nascent concept, the nuanced interplay between the two remains largely underexplored. We leverage neutrosophic set theory as a powerful analytical tool to investigate the transformative effects of Artificial Intelligence on various digital marketing tactics and strategies.

This paper introduces the concept of multiple attribute decision making (MADM) using q-rung square root interval valued neutrosophic sets (q-rung SRIVNS). The interval valued neutrosophic set (IVNS) and the q-rung square root neutrosophic set (q-rung SRNS) deals with the q-rung SRIVNS. The purpose of this article is to provide an analysis of several aggregating operations. In this article, we discuss a novel idea for the q-rung square root interval valued neutrosophic weighted averaging (q-rung SRIVNWA), q-rung ortho square root interval valued neutrosophic weighted geometric (q-rung SRIVNWG), generalized q-rung SRIVN weighted averaging (q-rung GSRIVNWA) and generalized q-rung SRIVN weighted geometric (q-rung GSRIVNWG). Using Euclidean distances and Hamming distances is illustrated with examples. These sets will be subjected to various algebraic operations in this communication. By doing this, models will be more accurate and will be closed to an integer q. The four most important factors for courier services in India are reliability, turnaround time, payment options, and tracking capabilities. Expert judgments and criteria will determine the most appropriate options. Furthermore, several proposed and current models are compared to demonstrate their reliability and utility. A fascinating and intriguing conclusion can be drawn from the study.