Integrated Decision Making to Determine the Optimal Order Quantity for Raw Materials Using Genetic Evolutionary Algorithms

Ahmed Jabbar

doi:https://doi.org/10.54216/PMTCS.030204

International Journal of Neutrosophic Science | 2024

Robust Financial Market Share Prediction using Intuitionistic Possibility Fermatean Neutrosophic Soft Set

An addition of soft set theory, Neutrosophic soft set theory offers a versatile framework for handling indeterminacy and uncertainty in data. Using this theory for the prediction of market share includes representing market data in a neutrosophic soft-set format, where elements pose truth, indeterminacy, and false degrees. The predictive model is constructed to estimate future market shares with consideration for ambiguity and uncertainty by analyzing previous market factors and trends affecting market dynamics within these frameworks. The stock market prediction pattern is interpreted as a significant action and it is more beneficial. Therefore, stock prices will result in substantial profits from sound taking choices. Thus, stock market forecasting is a main task for investors to spend their money to create maximum profit due to the noisy and stagnant data. Stock market prediction uses learning tools and mathematical strategies. Therefore, this manuscript offers the design of Financial Market Share Prediction using the Intuitionistic Possibility Fermatean Neutrosophic Soft Set (FMSP-IPFNS) technique. In the FMSP-IPFNS technique, a three-stage approach is followed. Firstly, the data normalization process is executed using a min-max scalar approach. Secondly, the prediction process can be carried out using the IPFNS approach. Thirdly, the parameter adjustment of the IPFNS approach takes place using the grasshopper optimization algorithm (GOA). To validate the performance of the FMSP-IPFNS system, a sequence of experimentations were tested. The obtained values demonstrate the promising results of the FMSP-IPFNS system compared to other models

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Mohammed Basheri mail -

Mahmoud Ragab mail

link https://doi.org/10.54216/IJNS.240310

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

COPRAS Neutrosophic Approach with Big Data Analytics for Enhancing Multi-Dimensional Customer Churn Prediction on Corporate Performance Assessment

A neutrosophic set (NS) is a new computing technology that accesses ambiguous data through three memberships. A soft expert set (SES) is based on the concept of a “soft set” with an expert system. Now, this technique has been applied in different domains namely measurement theory, intelligent systems, game theory, probability theory, cybernetics, etc. Customer Churn prediction implies identifying which consumers are expected to cancel a subscription to a service or leave a service. It is a crucial forecast for several businesses because obtaining new users frequently costs more than holding existing ones. The Churn prediction modeling methods try to understand the accurate customer attributes and behaviors that signal the risk and timing of customers leaving. This manuscript offers the design of an AI-based Multi-Dimensional Customer Churn Prediction for Corporate Performance Assessment (AIMD-CCPCPA) technique. The AIMD-CCPCPA technique mainly aims to detect the presence of customer churns and non-churns. It involves a two-stage process. At the initial stage, the AIMD-CCPCPA technique exploits the COPRAS Neutrosophic Method for prediction purposes. Secondly, the AIMD-CCPCPA technique involves parameter selection using a butterfly optimization algorithm (BOA). The experimental analysis of the AIMD-CCPCPA model is examined using a benchmark dataset. The acquired outcomes stated the supremacy of the AIMD-CCPCPA technique equated to other models

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Adam Mohamed Omer mail

link https://doi.org/10.54216/IJNS.240311

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

Trademark Empowerment using Optimal Neutrosophic Topological Vector Space for Maximizing Customer Attraction

Neutrosophic set is introduced as a generalization of intuitionistic fuzzy set, where any elements x ∈ X we have membership (T), non-membership (F), and indeterminacy (I)degrees. Neurosophic vague topological spaces are presented in various notations like neurosophic vague compactness and continuity. Trademarks are the essential components of intellectual property that allow owner to earn profit based on their name. In this industry, retailers typically use feedback channels like customer care service, website review complaints and suggestions boxes to gain user reviews on service satisfaction. But, there is a gap between these techniques. Customers are not fulfilled with them due to lack of trust in management, a lack of flexibility and slow responsiveness. This has prompted examination of the effect of customer feedback channels (CFCs) on client satisfaction and the necessity to develop a new CFC using artificial intelligence (AI). Thus, this study designs a Trademark Empowerment using Optimal Neutrosophic Topological Vector Space (TE-ONTVS) technique for Maximizing Customer Attraction. The intention of the TE-ONTVS technique lies in the prediction of customer behaviour and attraction. To accomplish this, the TE-ONTVS technique undergoes data scaling using Z-score normalization. In addition, the TE-ONTVS technique uses NTVS approach for the identification of customer behaviour and attraction. Lastly, whale optimization algorithm (WOA) is applied for optimal parameter tuning of the NTVS algorithm. A series of experiments were involved to demonstrate the enhanced outcomes of the TE-ONTVS algorithm. The obtained results stated that the TE-ONTVS technique reaches optimal performance over other models

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Alsadig Ahmed mail

link https://doi.org/10.54216/IJNS.240312

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

Statistical Optimization of Industrial Processes for Sustainable Growth using Neutrosophic Maddala Distribution

The family of neutrosophic distributions has received considerable attention from the scientific community, due to the flexible parametric form of its probability density function, in modeling many physical phenomena with imprecise information. In this study, we consider a generalization of Singh Maddala distribution for handling fuzzy data sets. This study presents a new research endeavor: quantifying the lifespan of manufacturing enterprises using the Neutrosophic Singh Maddala Distribution (NSMD). This work significantly enhances the theoretical foundations by providing novel formulations for the moments and mode of the NSMD distribution. In addition, it expands the study beyond the traditional Maddala model by examining conventional statistical models. For estimating the unknown parameters, the maximum likelihood estimation has been used in neutrosophic framework. Characterizations are obtained in terms of neutrosophic measures. The assessment of model performance, carried out using the goodness of fit criterion, highlights the superiority of NSMD compared to other models. In the application section, a real data on carbon emission is provided for usefulness of the proposed model.

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Fuad S. Al-Duais mail

link https://doi.org/10.54216/IJNS.240313

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

Anti homomorphism and homomorphism of bipolar valued multi Fuzzy HX-subgroups and it’s normal

In this article, homomorphism and anti-homomorphism bipolar valued multi normal fuzzy HX subgroup of a HX group is studied, discussed and their properties are introduced. The theorems presented in this article are considered a generalization of what has been publishedthrough the concept of fuzzy sets. As the concept of fuzzy sets was introduced by Zadeh in 1965, after that this concept, we expanded into more than one types, intuitionistic fuzzy set, bipolar fuzzy sets, tripolar fuzzy set, and bipolar valued multi fuzzy set are one of this.

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Mourad Oqla Massa’deh mail -

Ahlam omar Fallatah mail

link https://doi.org/10.54216/IJNS.240314

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

Details open_in_new

Pure Mathematics for Theoretical Computer Science | 2024

Using Major Pathway and Compound Analysis Methods To Identify Factors Affecting Diabetes

Legal analysis is one of the important methods to study the interrelationships between two types of variables. an important use of this analysis is to reduce the data. Many studies use this analysis as a way to study the interrelationships between two types of variables. There have been no empirical studies of the use of legal analysis as a method. From my point of view, this study aims to shed light on how to use legal analysis as a means of factor analysis, and to show how to apply it in this field by dealing with a practical problem in the active field. The applied problem includes the study of the factorial analysis, the method of the main compounds, the method of path analysis, and the compatibility between them on two types of data, represented by identifying the factors associated with diabetes, and then identifying the variables that affect the rise in the measurement of sugar two hours after eating. impact according to priority and importance.

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Zainab Sami Yaseen mail

link https://doi.org/10.54216/PMTCS.030105

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

Pure Mathematics for Theoretical Computer Science | 2024

Using Nonparametric Methods to Estimate Monitoring Maps Six Sigma of Vegetable Oil Production

Statistics are considered the backbone of the strategies of the quality control system, because of their important role in the use of tools, theories and analysis in these strategies. in the six sigma strategies (DMAIC) and (DMADV), each step is not without statistical methods. The study relied on the application of the statistical nonparametric methods and quantitative tools used in the Six Sigma strategy to apply the quality control performance of the research sample to improve the required quality by knowing the production derivatives and the reasons for the slowdown in the production process (Al-Moatasem Factory for Vegetable Oils), the fat line with its three sections.

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Sanaa Mohammed Naeem mail

link https://doi.org/10.54216/PMTCS.030201

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

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Pure Mathematics for Theoretical Computer Science | 2023

Linear Codes over Finite Fields Based On Greedy Algorithms and Their Applications

In this paper we prove that for any ordered basis  of a vector space there is a basis  for which the greedy code generated using the B-ordering is linear with respect to , where B2 is derived from  by a lower triangular matrix P; . In Addition we prove a similar result for self-orthogonal greedy codes.

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Rama Asad Nadweh mail

link https://doi.org/10.54216/PMTCS.030202

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

Pure Mathematics for Theoretical Computer Science | 2024

Finding The HK Completions of Some Sequence Spaces

In this paper, we prove the following result: If the space  (the finite sequences) is equipped with the norm which is naturally induced by a positive definite Hermitian and diagonally blockwise constructed matrix, then an HK completion exists. Also, we investigate our result through many illustrated examples.

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Husain Alhayek mail

link https://doi.org/10.54216/PMTCS.030203

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

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Pure Mathematics for Theoretical Computer Science | 2024

Integrated Decision Making to Determine the Optimal Order Quantity for Raw Materials Using Genetic Evolutionary Algorithms

Decision Making in economic organizations, especially the productivity, is one of the problems that researchers are interested in. It is important for the organizations that seek global competition and the integration of their decision will lead to coordination of decisions between the departments of these organizations to determine optimal order quantity and establish a correct inventory policy to warrant production with least total costs (costs of transportation, holding and purchasing), these is the objective of any company to achieve an adequate and enough inventory level to meet the future needs. In this paper, integrated decision making with three stages, first stage is forecasting with demands to final products using time series, second stage determine required quantities for raw material to manufacture these final products, and formulated a mathematical model reduces the total cost in third stage, when the transportation and purchase are variable costs with order quantity, while in just-in-time model or economic order quantity model are a fixed demand and purchase cost. The aim of this paper is integrated decision making to reduce total cost of required raw materials for the manufacturing processes and without and determine the optimal order quantity using genetic algorithms. This study was applied in Wasit company for cotton products in the textile factory, the results shown Holt-Winter method is the best method to forecasting because has least mean absolute error and the percentage of purchasing cost 73%, Transportation cost 8%, and holding cost 19%. The percentage of purchasing cost of cotton is biggest value, more 99% of purchasing cost.

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Ahmed Jabbar Hamood mail

link https://doi.org/10.54216/PMTCS.030204

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new