International Journal of Neutrosophic Science

Journal DOI

https://doi.org/10.54216/IJNS

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2690-6805ISSN (Online) 2692-6148ISSN (Print)

A Robust MCDM Framework for Cloud Service Selection Using Spherical Fermatean Neutrosophic Bonferroni Mean

P. Roopadevi , M. Karpagadevi , Said Broumi , S. Gomathi

This study presents an innovative approach to cloud service provider selection using the spherical Fermatean neutrosophic Bonferroni mean. As organizations increasingly rely on cloud services, selecting the optimal provider becomes critical, necessitating robust multi criteria decision making methods. Traditional approaches often fall short in capturing the diverse perspectives of decision-makers, leading to suboptimal choices. The spherical Fermatean neutrosophic Bonferroni mean addresses this gap by integrating a spherical representation that encompasses membership, non-membership and indeterminacy functions, enhanced by the Bonferroni mean. This structure effectively encapsulates the opinions of all decision makers, offering a comprehensive and balanced perspective. The study evaluates six cloud service providers based on four criteria: cost (nonbeneficiary), performance, security and scalability (beneficiary). Three decision makers with distinct priorities participate in the evaluation, ensuring a thorough assessment. The proposed spherical Fermatean neutrosophic Bonferroni mean method excels in resolving ambiguity and managing risk with greater precision than conventional FNSs, providing a more accurate and effective decision-making framework. A numerical example illustrates the practical application of spherical Fermatean neutrosophic Bonferroni mean, demonstrating its utility in selecting the optimal cloud service provider for an organization.

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Doi: https://doi.org/10.54216/IJNS.240432

Vol. 24 Issue. 4 PP. 420-431, (2024)

Numerical Solutions for Fractional Multi-Group Neutron Diffusion System of Equations

Mohammed Shqair , Iqbal M. Batiha , Mohammed H. E. Abu-Seiā€™leek , Shameseddin Alshorm , Amira Abdelnebi , Iqbal H. Jebril , S. A. Abd El-Azeem

This paper addresses fractional-order versions of multi-group neutron diffusion systems of equations, focusing on two numerical solutions. First, it employs the Laplace transform method to solve the classical version of multi-group neutron diffusion equations. Subsequently, it transforms these equations into their corresponding fractional-order versions using the Caputo differentiator. To handle the resultant fractional-order system, a novel approach is introduced to reduce it from a system of 2α-order to a system of α-order. This converted system is then solved using the so-called Modified Fractional Euler Method (MFEM). As far as we know, this is the first time that such numerical schemes have been used to deal with the systems at hand. The paper covers the multi-group neutron diffusion equations in spherical, cylindrical, and slab reactors, all solved and converted for verification purposes.

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Doi: https://doi.org/10.54216/IJNS.240401

Vol. 24 Issue. 4 PP. 08-38, (2024)

Harnessing Dimensionality Reduction with Neutrosophic Net-RBF Neural Networks for Financial Distress Prediction

Tawfiq Hasanin

Neutrosophy is the study of neutralities and extends the discussion of the truth of opinions. Neutrosophic logic may be employed in any domain, for providing the solution for the ambiguity problems. Several real-time data experience problems such as indeterminacy, incompleteness, and inconsistency. A fuzzy set provides an uncertain solution, and intuitionistic fuzzy set handles incomplete data, but both fail to manage uncertain data. Before bankruptcy, financial distress is the early stage. Bankruptcies caused by financial problems can be seen in the financial statement of the company. The capability to predict financial problems became a crucial area of research since it provides earlier warning for the company. Moreover, predicting financial problems is advantageous for creditors and investors. In this article, we develop a new Dimensionality Reduction with Neutrosophic Net-RBF Neural Networks (DR-NSRBFNN) technique for FCP process. The DR-NSRBFNN technique concentrates on the predictive modelling of financial distress. In the DR-NSRBFNN technique, two major stages are involved. In the preliminary phase, the high dimensionality features can be reduced by the use of arithmetic optimization algorithm (AOA). In the second phase, the DR-NSRBFNN technique applies the NSRBFNN model to predict financial distress. The performance evaluation of the DR-NSRBFNN technique can be examined using distinct aspects. The widespread study stated the improved performance of the DR-NSRBFNN technique compared to other systems

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Doi: https://doi.org/10.54216/IJNS.240402

Vol. 24 Issue. 4 PP. 39-49, (2024)

Enhancing Inventory Management through Advanced Technologies and Mathematical Methods: Utilizing Neutrosophic Fuzzy Logic

C. Balakrishna Moorthy , D. Rajani , A. P. Pushpalatha , S. Ramya , A. Selvaraj , Mohit Tiwari

Optimal inventory management is one of the most critical components for companies to thrive in the competitive market while meeting their customers’ demands, reducing costs, and developing their operations. In this paper, the utilization of different technologies and instruments ranging from the most modern ones to mathematical ones was analyzed to demonstrate how the system can function successfully. It is expected that Neutrosophic fuzzy logic is one of the most complicated approaches that allow for proper uncertainty management, forecasting, and inventory control improvements. Fundamentally, the process could be that much more insightful due to the availability of mathematical modelling and on-the-go support systems. Through the use of dynamic programming with the help of Python tools to process these models, Full optimization under fuzzy demand is possible to achieve. Therefore, one could conclude that companies have many opportunities to develop their operations, reduce costs, and keep their customers happy even in a highly dynamic and uncertain business environment.

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Doi: https://doi.org/10.54216/IJNS.240403

Vol. 24 Issue. 4 PP. 50-58, (2024)

Neutrosophic Delphi for evaluating sustainability models of native and non-native digital media.

Karla Valeria A. Sigcha , Evelyn M. Lema Basantes , Lourdes Y. Cabrera Martinez , Tonguc Cagin

Technological globalization has brought many changes in different fields, one of which is related to the media. In the case of traditional media, they are forced to find new ways to rethink practice, while digital media emerges in a digital context, albeit with limitations. Experience In both cases, sustainability is one of the factors to be rethought. Building on this, the overall objective is to use the Neutrosophic Delphi method to investigate the extent to which native and non-native digital media have durable patterns that allow them to be successful in their communication activities. To achieve this objective, we work with a mixed methodology, that is, qualitative and quantitative approaches: for qualitative, we use interview methods, for quantitative, we use survey methods. The population studied included both native and non-native digital media. Specifically, the survey and interviews were applied to a group of media owners. The article concludes with a series of Neutrosophic reflections on the conditions of media sustainability.

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Doi: https://doi.org/10.54216/IJNS.240404

Vol. 24 Issue. 4 PP. 59-70, (2024)

Leveraging Bat Algorithm with Rough Neutrosophic Soft Set for Enhanced Oral Cancer Detection and Classification

Arwa Darwish Alzughaibi , Ebtesam Al-Mansor

Neutrosophic soft sets (NSS) are highly effective in representing neutral uncertain data. NSS model attracts several authors because it has huge range of applications in several areas such as decision-making, data analysis, smoothness of functions, probability theory, measurement theory, predicting, and operations research. Oral squamous cell carcinoma (OSCC) is the most general tumor around the world and its occurrence is on the increase in several populations. Early diagnosis plays vital role in improving diagnosis, treatment outcomes and survival rates. Although the new developments in understanding molecular mechanisms, late analysis and the implementation of precision medicine for OSCC patients continue to present problems. Early diagnosis and detection can support doctors in offering optimum patient care and effectual treatment. In recent years, the execution of several machine-learning (ML) approaches in cancer analysis has provided valuable insights, facilitating more effective and precise treatment decision-making. Oral Cancer screening can progress with the execution of artificial intelligence (AI) approaches. AI offers support to the oncology region by correctly examining a huge database in many imaging modalities. This article develops a Bat Algorithm with Rough Neutrosophic Soft Set for Oral Cancer Diagnosis (BARNSS-OCD) technique. The main intention of the BARNSS-OCD technique is to exploit deep learning (DL) model for enhanced identification of OC. In the BARNSS-OCD technique, median filtering (MF) is used for image pre-processing and the feature extraction takes place using deep convolutional neural network (DCNN) model. In addition, bat algorithm (BA) is used for the hyperparameter selection of the DCNN model. For OC detection process, the BARNSS-OCD technique applies RNSS model. To exhibit the improved performance of the BARNSS-OCD technique, a sequence of experiments is involved. The simulation outcomes indicate that the BARNSS-OCD technique gains better performance compared to other DL models

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Doi: https://doi.org/10.54216/IJNS.240405

Vol. 24 Issue. 4 PP. 71-81, (2024)