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This study suggests a novel statistical distribution known as the neutrosophic loggamma distribution (NLGD) for analyzing the interval value data. The proposed distribution is derived from the transformation method, utilizing the approach of neutrosophic logic. The statistical characteristics of the proposed are studied within a neutrosophic framework. Basic statistical properties such as moments of origin, mode, mean deviation, and other related functions that are commonly employed in statistical applications are derived. In addition to the basic properties of the model, the estimation approach of maximum likelihood is also studied. The derived estimators of the proposed model are further assessed by the simulation method. Comparative research using real-world examples shows that NLGD does a better job of modelling complicated industrial growth and production measures than standard distribution. Application of the proposed model improves our understanding of the theoretical component and our ability to forecast outcomes in statistical applications. This, in turn, leads to improved decision-making and operational efficiency in many industrial sectors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper introduces a new statistical distribution called the Neutrosophic Extended Rayleigh Distribution (NERD), which is specifically developed to handle uncertainty commonly found in industrial applications. We conduct a comprehensive examination of the statistical characteristics of NERD, including important measures such as the quantile function, moments, moment generating function, mean deviation, skewness, kurtosis, reliability measures, uncertainty measures, distributions of order statistics, and L-moments. Parameter estimation is conducted by maximum-likelihood estimation within a neutrosophic framework, guaranteeing resilient inference in practical situations. Through the application of NERD to actual industrial datasets, we evaluate its adaptability and efficiency in simulating industrial processes. A real case study of Al-Kharj region demonstrates the higher performance of NERD. This research highlights the capacity of NERD to greatly improve productivity and efficiency in several industrial sectors. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are powerful, low-pressure weather systems attributed to heavy rainfall and strong winds, and have often resulted in extensive damage to coastal regions. TC intensity prediction, an essential aspect of meteorological forecasting, includes evaluating the strength of the storm to facilitate disaster preparedness and alleviate possible risks. Classical approaches for the prediction of TC intensity rely on different oceanic and atmospheric parameters, but the incorporation of artificial intelligence (AI) approaches, especially those leveraging image data, provides positive breakthroughs in efficiency and accuracy. By harnessing AI techniques like deep learning architectures and convolutional neural networks (CNNs), meteorologists could analyze radar data, satellite imagery, and other visual inputs to distinguish complicated patterns indicative of intensity changes and TC development. This combination of weather science and AI-driven image analysis enables more timely and precise predictions and improves our understanding of TC dynamics, eventually fortifying protection against the impacts of formidable storms. This article introduces Neutrosophic TOPSIS with Artificial Intelligence Driven Tropical Cyclone Intensity Estimation (NTOPSIS-TCIE) technique for Weather Prediction. The presented NTOPSIS-TCIE technique determines the intensities of the TC which in turn helps to forecast weather. In the NTOPSIS-TCIE technique, median filtering (MF) approach is used to remove the noise in the images. In addition, the features are extracted using deep convolutional neural network (CNN) model. To enhance the performance of the CNN model, Harris Hawks Optimization (HHO) algorithm is applied. Finally, the NTOPSIS model is employed for the prediction of TC intensities. The performance of the NTOPSIS-TCIE technique can be studied using TC image dataset and the results signify its promising results over other models. [ABSTRACT FROM AUTHOR]

In the framework of generalized linear models (GLM), this paper explores the design and applicability of partial residual (PRES), augmented partial residual (APRES), and conditional expectation and residuals (CERES) plots for visualizing an outlier's diagnostics as a function of selected variables. Here, a geometric regression as a GLM is thoroughly described. Additionally, plots for PRES, APRES, and CERES have been built. Due to how the response variable and the associated link function interact with various covariates, the effectiveness of these plots for creating an appealing visual impression may vary. On the cervical cancer data, specific methodologies are used to identify trends for effective modelling. When compared to other approaches, the power of the tests for various plots demonstrates that PRES, CERES (L) and CERES (K) have the greatest endurance for the outlier's diagnostics. On the basis of the power of residual plots, the use is recommended for outlier diagnostics in presence of conventional tests. [ABSTRACT FROM AUTHOR]

In the proposed manuscript, we present a novel mathematical model for analysing the persistence of corruption in human communities, based on fractal-fractional concepts and the Mittag-Leffler kernel law. Corruption is considered analogous to a disease that can spread and influence others who are free from corruption. Our model evaluates the equilibrium points of corruption and tests their stability using the corruption reproduction number. We also apply the fixed point theory concept to check for the existence and uniqueness of a solution, in the context of a fractional fractal operator. Solution stability is verified using the perturbed Ulam Hyers technique, and an approximate solution is obtained through the use of Lagrangian polynomials. To test the validity of our model, we simulate all compartments at different fractional orders and time durations, providing additional insights into the dynamics of corruption beyond natural orders. [ABSTRACT FROM AUTHOR]

Environmental science and pollution research has benefits around the globe. Human activity produces more garbage throughout the day as the world's population and lifestyles rise. Choosing a garbage disposal site (GDS) is crucial to effective disposal. In illuminated of the advancements in society, decision-makers concede a significant challenge for assessing an appropriate location for a garbage disposal site. This research used a multi-attribute decision-making (MADM) approach based on q-rung orthopair hesitant fuzzy rough (q-ROHFR) Einstein aggregation information for evaluating GDS selection schemes and providing decision-making (DM) support to select a suitable waste disposal site. In this study, first, q-ROHFR Einstein average aggregation operators are integrated. Some intriguing characteristics of the suggested operators, such as monotonicity, idempotence and boundedness were also explored. Then, a MADM technique was established using the novel concept of q-ROHFR aggregation operators under Einstein t-norm and t-conorm. In order to help the decision makers (DMs) make a final choice, this technique aims to rank and choose an alternative from a collection of feasible alternatives, as well as to propose a solution based on the ranking of alternatives for a problem with conflicting criteria. The model's adaptability and validity are then demonstrated by an analysis and solution of a numerical issue involving garbage disposal plant site selection. We performed a the sensitivity analysis of the proposed aggregation operators to determine the outcomes of the decision-making procedure. To highlight the potential of our new method, we performeda comparison study using the novel extended TOPSIS and VIKOR schemes based on q-ROHFR information. Furthermore, we compared the results with those existing in the literature. The findings demonstrate that this methodology has a larger range of information representation, more flexibility in the assessment environment, and improved consistency in evaluation results. [ABSTRACT FROM AUTHOR]

This work is devoted to establish a numerical scheme for system of fractional-order differential equations (FODEs) with order 1 < ≤ 2. The scheme is established by using Bernstein polynomials (BPs). Based on the said materials, some operational matrices are formed. With the help of obtained operational matrices, the considered system is reduced to some algebraic system of equations. On using MATLAB-16, the system is then solved to get the required numerical solution for the proposed system. Several examples are treated with the help of the proposed method for numerical solutions. Further, error analysis is also recorded for different fractional orders and various scale levels. The mentioned results are displayed graphically. Comparison with exact solution at traditional order derivative is also given. It should be kept in mind that the proposed method does not require any kind of discretization or collocation. Also, there is no external parameter which controls the method. Due to these features, the proposed method is powerful and efficient for different classes of FODEs to compute their numerical solutions. The efficiency of the proposed method can be enhanced by increasing the scale level. [ABSTRACT FROM AUTHOR]

Product reliability is a crucial component of the industrial production process. Several statistical process control techniques have been successfully employed in industrial manufacturing processes to observe changes in reliability-related quality variables. These methods, however, are only applicable to single-stage processes. In reality, manufacturing processes consist of several stages, and the quality variable of the previous stages influences the quality of the present stage. This interdependence between the stages of a multistage process is an important characteristic that must be taken into account in process monitoring. In addition, sometimes datasets contain outliers and consequently, the analysis produces biased results. This study discusses the issue of monitoring reliability data with outliers. To this end, a proportional hazard model has been assumed to model the relationship between the significant quality variables of a two-stage dependent manufacturing process. Robust regression technique known as the M-estimation has been implemented to lessen the effect of outliers present in the dataset corresponding to reliability-related quality characteristics in the second stage of the process assuming Nadarajah and Haghighi distribution. The three monitoring approaches, namely, one lower-sided cumulative sum and two one-sided exponentially weighted moving average control charts have been designed to effectively monitor the two-stage dependent process. Using Monte Carlo simulations, the efficiency of the suggested monitoring schemes has been examined. Finally, two real-world examples of the proposed control approaches are provided in the study. [ABSTRACT FROM AUTHOR]

The logistic distribution is widely used to model various types of applied data. The modified logistic distribution under neutrosophic statistics is introduced in this work. The neutrosophic logistic distribution (NLD) and its engineering applications are mainly emphasized. An appealing characteristic of the suggested NLD is that it is useful to many widely utilized survival assessment metrics, including the reliability function, hazard function, and survival function. Applications of some mathematical and statistical properties of the suggested model are discussed. Numerical investigations on simulated data are used to validate the theoretical findings experimentally. From an application point of view, it is inferred that the proposed distribution fits data with imprecise, hazy, and fuzzy information better than the usual model. In addition, the maximum likelihood (ML) technique for the proposed model is discussed under the neutrosophic inference framework. Eventually, some illustrative examples related to system reliability are provided to clarify further the implementation of the neutrosophic probabilistic model in real-world problems. [ABSTRACT FROM AUTHOR]

Hesitancy is an imperative part of belief system. In order to counter the hesitancy in neutrosophic cubic set (NCS), the notion of neutrosophic cubic hesitant fuzzy set (NCHFS) is presented. NCHFS couple NCS with hesitant fuzzy set (HFS). Operational laws in NCHFS are developed with examples. To meet the challenges of decision making problems, neutrosophic cubic hesitant fuzzy geometric (NCHFG) aggregation operators, neutrosophic cubic hesitant fuzzy Einstein geometric (NCHFEG) aggregation operators, neutrosophic cubic hesitant fuzzy hybrid geometric (NCHFHEG) aggregation operators are developed in the current study. At the end a multi expert decision making (MEDM) process is proposed and furnished upon numerical data of a company as applications. [ABSTRACT FROM AUTHOR]

In this paper, we developed linear exponential (LINEX) loss function by merging weights to produce weighted linear exponential (WLINEX) loss function. Then we utilized WLINEX to derive scale parameter and reliability function of the Weibull distribution based on record values when the shape parameter is known. After, we estimated scale parameter and reliability function of Weibull distribution by using maximum likelihood (ML) estimation and by several Bayes estimations. The Bayes estimates were obtained with respect to symmetric loss function (squared error loss (SEL)), asymmetric loss function (LINEX) and asymmetric loss function (WLINEX). The ML and the different Bayes estimates were compared via a Monte Carlo simulation study. The result of simulation mentioned that the proposed WLINEX loss function is promising and can be used in real environment especially at the case of underestimate where it revealed better performance than LINEX loss function for estimating scale parameter. [ABSTRACT FROM AUTHOR]