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International Journal of Neutrosophic Science | 2025

Decision Making in the Case of Confirmed Data Neutrosophic Linear Models to Choose the Advertising Medium

In light of the great development witnessed by our contemporary world, it has become necessary to focus on scientific methods and use the quantitative method to reach more accurate decisions, appropriate to the surrounding circumstances and factors. The process of decision-making and choosing the optimal alternative depends on the type and quality of data that describes the issue for which the decision is to be made. Regarding it, in this chapter we present a study of the issue of determining the ideal advertising medium to display a company’s products. This issue is considered one of the issues of decision-making in the case of confirmed data, so we build the appropriate mathematical model and through the optimal solution to it we can make the ideal decision through which the company achieves its goal from the campaign. Informative, we will divide this study into two parts. In the first section, we will develop a general formula for this issue, and the data will be classical values. We will obtain a linear mathematical model. In the second section, we will formulate the issue from the perspective of neutrosophic science, meaning we will take the data as neutrosophic values, obtaining a linear neutrosophic model.

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Maissam Jdid mail -
Florentin Smarandache mail
link https://doi.org/10.54216/IJNS.250310

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new
International Journal of Neutrosophic Science | 2025

Modern Free-Derivative Numerical Optimization of Approximate Algorithms Convergence and Neutrosophic Convergence

The aim of this study is to compare common and previously used numerical algorithms for nonlinear problems under different conditions. This study proposes a parallel implementation of two free derivative optimization methods, Powell's method and Nelder-Mead's method, combined with two restart strategies to achieve a global search. In terms of total time, the Powell method converges faster than the Nelder-Mead method. The final function value obtained by the Powell method is slightly lower. Both are optimization techniques used to find the minimum of an objective function in multidimensional space, without requiring derivatives. Also, we extend our results to apply to some neutrosophic non-linear problems under different neutrosophic-based conditions with many examples that explain the validity of our approach.

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Ahmed Sabah Ahmed Al-Jilawi mail
link https://doi.org/10.54216/IJNS.250311

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new
International Journal of Neutrosophic Science | 2025

On A Subclass of Analytic Functions Specified By Touchard Polynomials

In this investigation, we present a new collection of analytic functions that includes Touchard polynomials. We then aim to calculate the Maclaurin coefficients |π‘Ž2 | and |π‘Ž3 | and address the Fekete-Szegö functional problem within this specific subfamily. Additionally, we demonstrate several new outcomes by specifying the parameters used in our main findings.

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Ala Amourah mail -
𝐎πͺπ₯𝐚𝐑 𝐀π₯ π‘πžπŸπšπ’ mail -
π“πšπ«π’πͺ 𝐀π₯ π‡πšπ°πšπ«π² mail -
π‰πšπ¦πšπ₯ π’πšπ₯𝐚𝐑 mail -
𝐁𝐚𝐬𝐞𝐦 π…π«πšπ¬π’π§ mail
link https://doi.org/10.54216/IJNS.250312

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new
International Journal of Neutrosophic Science | 2025

Quantitative Easing and Its Long-term Effects on U.S. Credit Market Sustainability: A Neutrosophic Science Perspective

The cyclical nature of credit is a pivotal component of the broader business cycle, with credit expansion serving as a crucial mechanism for economic resurgence post-crisis. This paper delves into the ramifications of stringent financial regulations implemented in the wake of the 2007–2008 financial crisis, which notably decelerated the credit expansion phase, culminating in an anomalously extended period of credit contraction within the non-financial private sector. From a Neutrosophic Science perspective, this study posits that the typical progression of the credit cycle was significantly altered due to the heightened requirements under Basel III and the overhaul of the United States financial system. Distinct from prior crises, the post-2007–2008 period witnessed a more languid recuperation in credit activity, with the credit volume to the non-financial private sector yet to attain pre-crisis levels. This article offers a comparative analysis, scrutinizing the temporal dynamics of credit recovery following various crises. Drawing on Minsky’s financial instability hypothesis, Crotty’s theory of endogenous credit standard formation, and the Neutrosophic Science framework, the research investigates the phenomenon termed "credit paralysis." It hypothesizes that banking credit standards are intrinsically linked to macroeconomic variables such as GDP levels, interest rates, and loan volumes. Employing a vector autoregressive model, the study examines the alterations in credit activity vis-à-vis shifts in credit standards and explores the genesis of these standards in relation to macroeconomic indicators. The analysis leads to the conclusion that the augmented credit standards, necessitated by Basel III's implementation in crisis response, disrupted the normal trajectory of the credit cycle. The research culminates in the development of a stylized model of the U.S. credit cycle, which incorporates specific factors from the 2007–2008 crisis, including pre-crisis financial innovations, the subsequent intensification of financial regulations, and the principles of Neutrosophic Science.

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Khayrilla Kurbonov mail
link https://doi.org/10.54216/IJNS.250313

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new
International Journal of Neutrosophic Science | 2025

Several arithmetic operations on neutrosophics type-2 fuzzy sets

In this paper, we explore the theoretical foundations of neutrosophics type-2 fuzzy sets by investigating its algebraic properties, demonstrating how neutrosophics type-2 fuzzy sets can generalize and extend existing operations in Type-1 and traditional Type-2 fuzzy sets. We also provide illustrative examples to clarify the practical applications of these operations, showcasing the potential of neutrosophics type-2 fuzzy sets in areas requiring sophisticated decision-making tools and uncertainty management.

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Brahim Ziane mail -
Soheyb Milles mail
link https://doi.org/10.54216/IJNS.250314

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new
International Journal of Neutrosophic Science | 2025

Time Factor’s Impact On Fuzzy Soft Expert Sets

In this study, I introduce time-fuzzy soft expert set (T-FSES) as an extension of fuzzy soft set. I will also define and investigate the features of its main operations (complement, union intersection, AND and OR). Finally, I’ll apply this approach to decision-making difficulties.

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Ayman.A Hazaymeh mail
link https://doi.org/10.54216/IJNS.250315

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new
Journal of Artificial Intelligence and Metaheuristics | 2024

Ocotillo Optimization Algorithm (OcOA): A Desert-Inspired Metaheuristic for Adaptive Optimization

In this paper, we propose the Ocotillo Optimization Algorithm (OcOA), a novel desert-inspired metaheuristic designed to solve complex optimization problems. Inspired by the adaptive strategies of desert plants, OcOA aims to achieve a balance between exploration and exploitation in high-dimensional and multimodal search spaces. The algorithm dynamically adjusts its behavior based on feedback from prior iterations, optimizing both search breadth and solution refinement. To evaluate its effectiveness, OcOA was tested against several well-known algorithms on a range of benchmark functions, including unimodal and multimodal functions from the CEC 2005 suite such as Sphere, Rosenbrock, Ackley, and Rastrigin. The results demonstrate that OcOA outperforms competing approaches in terms of accuracy, convergence speed, and computational efficiency. Additionally, its adaptability was validated through feature selection tasks, highlighting its robustness in handling both continuous and discrete optimization challenges. This study positions OcOA as a competitive optimization tool for various real-world applications

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El-Sayed M. El-Kenawy mail -
Faris H. Rizk mail -
Ahmed Mohamed Zaki mail -
Mahmoud Elshabrawy Mohamed mail -
Abdelhameed Ibrahim mail -
Abdelaziz A. Abdelhamid mail -
Nima Khodadadi mail -
Ehab M. Almetwally mail -
Marwa M. Eid mail
link https://doi.org/10.54216/JAIM.080104

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new
Fusion: Practice and Applications | 2025

Artificial Intelligence-Enhanced Green Building Design for Environmental Sustainability

Green buildings are those that use sustainable methods of construction to either maintain or improve the local quality of life. Decisions affecting a project's quality, safety, profitability, and timetable are made using Artificial Intelligence (AI) in Green Construction by analyzing data gathered from monitoring the construction site and using predictive analytics. For instance, increased accuracy in weather predictions might lead to more production, less waste, lower costs, and less greenhouse gas emissions. Green building construction is a significant source of carbon dioxide released through the breakdown of carbonates. Researchers have concluded that integrating industrial wastes is crucial in green concrete making due to its benefits, such as reducing the requirement for cement. When planning with concrete, its compressive strength must be considered. Due to their high predictive power, AI algorithms may be used to determine the compressive strength of concrete mixtures. Existing artificial intelligence (AI) models may be evaluated for their modeling process and accuracy to inform the creation of new models that more accurately represent the comprehensive evaluation of setting parameters on model performance and boost accuracy. Potential sources of conflict in this anthropocentric future include climate change and the availability of renewable energy sources. Scientists think there is a connection between the increased emission of greenhouse gases like carbon dioxide (Co2) from the combustion of fossil fuels and the acceleration of climate change and global warming. Research has demonstrated that the building sector is a significant source of atmospheric carbon dioxide (Co2). Construction, building activities, and subpar energy sources have all significantly increased atmospheric CO2. The proposed research set out to measure how well AI in Green Building Construction (AI-GBC) might reduce carbon emissions and utility bills. Artificial intelligence uses SVM and GA to reduce energy use and carbon dioxide emissions. Several statistical metrics, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Root Mean Squared Log Error (RMSLE), are used to evaluate the AI-GBC's precision. Both Machine Learning (ML) models yielded positive results, with prediction accuracies above 95%. Regarding predicting Co_2, GA models were close to the mark, with an R2 of 0.95. Ninety-six percent will complete a performance analysis, and 97% will conduct a k-fold cross-validation analysis. Cross-validation is used to ensure that the findings of the extended modeling technique are accurate and prevent overfitting.

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Saif Mohammed Ali mail -
Omar Al-Boridi mail
link https://doi.org/10.54216/FPA.170215

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new
Fusion: Practice and Applications | 2025

Enhanced EEG Signal Classification Using Machine Learning and Optimization Algorithm

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance while minimizing redundancy. This optimization process improves the performance of the classification model in general. In case of classification, the Support Vector Machine (SVM) and Neural Network (NN) hybrid model is presented. This combines an SVM classifier's capacity to manage functions in high dimensional space, as well as a neural network capacity to learn non-linearly with its feature (pattern learning). The model was trained and tested on an EEG dataset and performed a classification accuracy of 97%, indicating the robustness and efficacy of our method. The results indicate that this improved classifier is able to be used in brain–computer interface systems and neurologic evaluations. The combination of machine learning and optimization techniques has established this paradigm as a highly effective way to pursue further research in EEG signal processing for brain language recognition.

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Mohammed Yousif mail -
Iman Ameer Ahmad mail -
Assef Raad Hmeed mail -
Abdulrahman Abbas Mukhlif mail
link https://doi.org/10.54216/FPA.170216

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new