The Frechet distribution is a versatile probability distribution that is used within a loose range in many important statistical fields, such as image processing, data analysis, and pattern recognition. It aims to explore and study the estimation of the parameters of the Frechet distribution using the noise-robust least squares method, as in the research paper, and it also has uses. There are many real-world scenarios. It is known that there is a growing challenge in estimating the parameter because of the noisy data. Depending on rigorous simulations and experimental analysis, we provide a novel powerful way to estimate the parameters for the Frechet Distribution Robust Least Squares approach to be flexible. Also, the results approach of this work will be very helpful in estimating the Frechet distribution parameters for diverse statistical applications. Also, we generalize our results to include the generalized neutrosophic case of this distribution dealing with neutrosophic numbers.
Read MoreDoi: https://doi.org/10.54216/IJNS.250301
Vol. 25 Issue. 3 PP. 01-13, (2025)
We employ the Laplace Residual Power Series Method to approximate analytical solutions for differential equations and neutrosophic differential equations with associated parameters, including non-homogeneous equations and fractional formulas in partial differential equations (PDEs). This approach showcases the method's simplicity, effectiveness, and robustness in deriving analytical series solutions for PDEs that involve associated parameters, especially in the context of fractional differential equations. Several practical uses of LRPSM with an emphasis on non-homogeneous and partial differential equations and neutrosophic equations with fractions (PDEs). These applications are significant in a variety of scientific and engineering domains that simulate complicated dynamic system such as anomalous diffusion in physics, viscoelastic material modeling in engineering and signal processing.
Read MoreDoi: https://doi.org/10.54216/IJNS.250302
Vol. 25 Issue. 3 PP. 14-24, (2025)
This paper uses finite difference methods to study the numerical solution for neutrosophic Sine-Gordon system in one dimension. We use the explicit method and Crank-Nicholson method. Also, an effective comparison between the results of the two methods has been made, where we obtain the result that Crank-Nicholson method is more accurate than the explicit method, but the explicit method is easier. We also study the stability analysis for each method by using Fourier (Von-Neumann) method and get that Crank-Nicholson method is unconditionally stable while the Explicit method is stable under the condition 𝑟2≤1𝑐2 and 𝑟2≤1.
Read MoreDoi: https://doi.org/10.54216/IJNS.250303
Vol. 25 Issue. 3 PP. 25-36, (2025)
In this study, we define time-fuzzy soft set (T-FSS) as an extension of fuzzy soft set. We will also define and investigate the features of its main operations (complement, union intersection, ”AND” and ”OR”). Finally, we’ll apply this approach to decision-making difficulties.
Read MoreDoi: https://doi.org/10.54216/IJNS.250304
Vol. 25 Issue. 3 PP. 37-50, (2025)
This study develops a new version of the lognormal distribution, the neutrosophic lognormal distribution (NLND), to address uncertainties commonly exist in reliability studies within the engineering field. The NLND is suitable for analyzing complex data with symmetrical or right-skewed patterns. The paper discusses the mathematical characteristics of the NLND, including concepts of reliability like mean time failure, hazard rate, cumulative failure rate, and reliability function. The model is based on real-life examples from life-test data and uses the maximum likelihood method to determine two key parameters. A simulation experiment was conducted to evaluate the accuracy of the estimated parameters, showing that maximum likelihood estimators can effectively estimate unknown parameters, especially with a large sample size. Finally, a real-world data is used to demonstrate the adequacy of the proposed model in a practical scenario.
Read MoreDoi: https://doi.org/10.54216/IJNS.250305
Vol. 25 Issue. 3 PP. 51-59, ()