Nooruldeen Ayad Noori

* This paper presents a new statistical distribution, the Enhanced Gompertz Inverse Burr-X (EGIBX) distribution, which combines the properties of the Enhanced Gompertz family with those of the base Inverse Burr-X distribution. The general form of the probability density function (pdf ) and cumulative distribution function (CDF) are presented, along with other basic functions such as hazard and survival. Some mathematical properties of EGIBX are analyzed, including instantaneous values, skewness, and kurtosis, and the probabilistic behavior of functions is studied. Monte Carlo simulation is used to evaluate the performance of three parameter estimation methods and compare their effectiveness in small and large samples. The distribution is applied to real data, including breaking stress of carbon fibers (in GPa) and data from 40 leukemia patients from a Ministry of Health hospital in KSA. The efficiency and flexibility of EGIBX are verified by comparing the proposed distribution with six other distributions, proving its efficiency and superiority. This superiority was measured using four information criteria (AIC, BIC, CAIC, HQIC) and four Goodness-of-Fit measures. The outcomes indicated that the designed distribution maintains a robust degree of flexibility in its statistical representation and provides a greater accuracy of fit relative to the other competing distributions.

This paper proposes a new four-parameter statistical distribution based on the neutrosophic Gompertz (NGo-G) family and the extended Nadarajah-Haghighi distribution in neutrosophic logic called neutrosophic Gompertz Nadarajah-Haghighi (NGoNH) distribution to deal with uncertain and indeterminate data, known as neutrosophic data. The basic distribution functions and some properties are derived and the parameters of the proposed distribution are estimated using three different methods. To compare the performance of the different estimation methods, numerical simulations are performed using the evaluation criteria: MSE, RMSE, and bias. NGoNH distribution is applied to real data set representing the monthly minimum and maximum temperatures in Lahore, Pakistan, for the period 2016-2020. To verify the consistency of the data with the proposed distribution, the properties of the neutrosophic data are tested and the three components (True, Indeterminate, False) are plotted. In addition, the performance of the proposed distribution is compared with six other neutrosophic distributions using some information criteria and some goodness-of-fit tests. The extent to which the data fit the proposed distribution is plotted to demonstrate its effectiveness. The results indicate that the proposed distribution provides a better fit to neutrosophic data than other distributions, which enhances its effectiveness in analyzing data with uncertainty.

This paper presents a Neutrosophic extension of the New odd Weibull inverse Weibull (NNOWIW) distribution, aiming to develop a statistical model capable of handling ambiguous or imprecise data. The mathematical formulation of the proposed distribution was derived by combining Neutrosophic logic with the T-X method, specifying the NCDF, the NPDF and the survival and hazard functions. The statistical probability of the distribution was analyzed. To achieve optimal estimation of the distribution parameters, the maximum likelihood (MLE), least square (LSE), and weighted least squares (WLSE) methods were used, and their performance was evaluated using Monte Carlo simulations. The models efficiency was also tested using real battery life data and compared to competing distributions. The results show that the proposed distribution shows competitive and flexible performance in terms of information criteria (AIC, CAIC, BIC, HQIC) and goodness-of-fit tests suggesting its potential usefulness for modeling complex data. However, further validation on larger and more diverse datasets is required to generalize these findings.

The forecast of time series in financial applications is difficult to perform as time series forecasting is nonlinear in nature, seasonal, and has structural variability. Stock price series tend to follow a lot of nonlinear dynamics, which undermines the power of single-model approaches. Hybrid decomposition-based models have attracted increasing interest in order to gain accuracy by separating heterogeneous features from one another. In this work, we present a hybrid forecasting methodology that incorporates STD decomposition with RBFNN (Radial Basis Function Neural Network). The time series is decomposed, where trend, seasonal, and dispersion components are separately modeled using RBFNN with Gaussian basis functions. The predicted feature sets are then recombined to construct a forecast, to be evaluated with weekly Tesla stock price data and standard accuracy performance metrics. The experimental analysis of weekly Tesla stock price data presents that the STD–RBFNN structure results in lower forecast errors, compared to the comparison hybrid model discussed in this paper. The improvement seems to be realized by decomposing the original series into components before learning non-linearly, and then by reconstructing the final forecast from component-wise predictions. But this empirical work remains narrow to a single-asset case type and the benchmark set here. The proposed framework is therefore considered to be a prospective hybrid forecasting design that needs validation across additional assets and forecasting models to achieve wider generalizability.

* This paper presents a new probability distribution known as the “Neutrosophic New Odd Weibull-Weibull Distribution (NNOWW)” by combining the proposed New Odd Weibull-Weibull (NOWW) distribution with the neutrosophic logic (the direct method of this logic). The distribution is presented in two theoretical frameworks. The derivation of several main distribution functions, along with their properties, is presented, accompanied by both theoretical and graphical representations of these properties. Parameters are estimated using three different methods, with Monte Carlo simulations conducted for various sample sizes to determine the estimation efficiency and the most suitable sample size for applying the distribution to real data. An applied framework tests its efficiency on real data representing the heart rate of 50 patients. The model was developed within this logic framework to define truth, falsity, and uncertainty in the data. The model was compared with five other neutrosophic distributions using criteria such as AIC, BIC, CAIC, and HQIC. The results showed a clear superiority of the NNOWW model across all indicators, confirming its accuracy and ability to represent uncertain data. Analytical graphs support this superiority. This model is recommended for use in medical fields and for analyzing Neutrosophic data.

This paper presents a Neutrosophic extension of the New odd Weibull inverse Weibull (NNOWIW) distribution, aiming to develop a statistical model capable of handling ambiguous or imprecise data. The mathematical formulation of the proposed distribution was derived by combining Neutrosophic logic with the T-X method, specifying the NCDF, the NPDF and the survival and hazard functions. The statistical probability of the distribution was analyzed. To achieve optimal estimation of the distribution parameters, the maximum likelihood (MLE), least square (LSE), and weighted least squares (WLSE) methods were used, and their performance was evaluated using Monte Carlo simulations. The models efficiency was also tested using real battery life data and compared to competing distributions. The results show that the proposed distribution shows competitive and flexible performance in terms of information criteria (AIC, CAIC, BIC, HQIC) and goodness-of-fit tests suggesting its potential usefulness for modeling complex data. However, further validation on larger and more diverse datasets is required to generalize these findings.

* This paper presents a Neutrosophic extension of the New odd Weibull inverse Weibull (NNOWIW) distribution, aiming to develop a statistical model capable of handling ambiguous or imprecise data. The mathematical formulation of the proposed distribution was derived by combining Neutrosophic logic with the T-X method, specifying the NCDF, the NPDF and the survival and hazard functions. The statistical probability of the distribution was analyzed. To achieve optimal estimation of the distribution parameters, the maximum likelihood (MLE), least square (LSE), and weighted least squares (WLSE) methods were used, and their performance was evaluated using Monte Carlo simulations. The models efficiency was also tested using real battery life data and compared to competing distributions. The results show that the proposed distribution shows competitive and flexible performance in terms of information criteria (AIC, CAIC, BIC, HQIC) and goodness-of-fit tests suggesting its potential usefulness for modeling complex data. However, further validation on larger and more diverse datasets is required to generalize these findings.

* The forecast of time series in financial applications is difficult to perform as time series forecasting is nonlinear in nature, seasonal, and has structural variability. Stock price series tend to follow a lot of nonlinear dynamics, which undermines the power of single-model approaches. Hybrid decomposition-based models have attracted increasing interest in order to gain accuracy by separating heterogeneous features from one another. In this work, we present a hybrid forecasting methodology that incorporates STD decomposition with RBFNN (Radial Basis Function Neural Network). The time series is decomposed, where trend, seasonal, and dispersion components are separately modeled using RBFNN with Gaussian basis functions. The predicted feature sets are then recombined to construct a forecast, to be evaluated with weekly Tesla stock price data and standard accuracy performance metrics. The STD–RBFNN model gives very low forecasting errors under different variables and a high coefficient of determination. It shows superiority compared with an alternative hybrid neural network model, especially in modeling nonlinear variation under similar experimental conditions. This results in substantially greater forecasting accuracy because trend, seasonal, and dispersion components separate before neural modeling. The proposed STD and RBFNN pipeline is a good and highly flexible method to forecast complex nonlinear and seasonal financial time series.

* This paper presents a new probability distribution known as the “Neutrosophic New Odd Weibull-Weibull Distribution (NNOWW)” by combining the proposed New Odd Weibull-Weibull (NOWW) distribution with the neutrosophic logic (the direct method of this logic). The distribution is presented in two theoretical frameworks. The derivation of several main distribution functions, along with their properties, is presented, accompanied by both theoretical and graphical representations of these properties. Parameters are estimated using three different methods, with Monte Carlo simulations conducted for various sample sizes to determine the estimation efficiency and the most suitable sample size for applying the distribution to real data. An applied framework tests its efficiency on real data representing the heart rate of 50 patients. The model was developed within this logic framework to define truth, falsity, and uncertainty in the data. The model was compared with five other neutrosophic distributions using criteria such as AIC, BIC, CAIC, and HQIC. The results showed a clear superiority of the NNOWW model across all indicators, confirming its accuracy and ability to represent uncertain data. Analytical graphs support this superiority. This model is recommended for use in medical fields and for analyzing Neutrosophic data.

* This paper presents a Neutrosophic extension of the New odd Weibull inverse Weibull (NNOWIW) distribution, aiming to develop a statistical model capable of handling ambiguous or imprecise data. The mathematical formulation of the proposed distribution was derived by combining Neutrosophic logic with the T-X method, specifying the NCDF, the NPDF and the survival and hazard functions. The statistical probability of the distribution was analyzed. To achieve optimal estimation of the distribution parameters, the maximum likelihood (MLE), least square (LSE), and weighted least squares (WLSE) methods were used, and their performance was evaluated using Monte Carlo simulations. The models efficiency was also tested using real battery life data and compared to competing distributions. The results show that the proposed distribution shows competitive and flexible performance in terms of information criteria (AIC, CAIC, BIC, HQIC) and goodness-of-fit tests suggesting its potential usefulness for modeling complex data. However, further validation on larger and more diverse datasets is required to generalize these findings.