Recent years have witnessed the green growth as a renowned efficient approach to tracking the progress towards sustainable development. The paper is mainly aimed to propose an innovative extended approach based on Combined Compromise Solution (CoCoSo) and Interval‐Valued Intuitionistic Fuzzy Set (IVIFS); thus, it is called IVIF Combined Compromise Solution (IVIF‐CoCoSo). This study is the first effort that used the fuzzy sets theory and decision‐making technique (entitled IVIF‐CoCoSo) to rank and evaluate the indicators of green growth to sustainable manufacturing in the manufacturing sector. To reach the study objectives, a total of five indicators and 30 sub‐indicators for evaluation of green growth are collected from the existing literature review. Subsequently, this study has been extended using the IVIF‐CoCoSo approach for ranking, modeling, and evaluating the green growth indicators into sustainable manufacturing based on experts' opinions. The results of this study showed that the environmental and resource productivity of the economy indicators is more significant than the other indicators in the manufacturing sector. Moreover, findings confirmed the accuracy of the proposed method in solving the green growth problem, and comparison was also made between the results of the proposed method and some other Multi‐Criteria Decision Making (MCDM) methods.

The current work focuses on ranked set sampling and a simple random sample as sampling approaches for determining stress–strength reliability from the inverted Topp–Leone distribution. Asymptotic confidence intervals are established, along with a maximum likelihood estimator of the parameters and stress–strength reliability. The reliability of such a system is assessed using the Bayesian approach under symmetric and asymmetric loss functions. The highest posterior density credible interval is constructed successively. The results are extracted using Monte Carlo simulation to compare the proposed estimators performance with different sample sizes. Finally, by looking at waiting time data and failure times of insulating fluid, the usefulness of the suggested technique is demonstrated.

A new two-parameter model is proposed using the Kavya–Manoharan (KM) transformation family and Burr X (BX) distribution. The new model is called the Kavya–Manoharan–Burr X (KMBX) model. The statistical properties are obtained, involving the quantile (QU) function, moment (MOs), incomplete MOs, conditional MOs, MO-generating function, and entropy. Based on simple random sampling (SiRS) and ranked set sampling (RaSS), the model parameters are estimated via the maximum likelihood (MLL) method. A simulation experiment is used to compare these estimators based on the bias (BI), mean square error (MSER), and efficiency. The estimates conducted using RaSS tend to be more efficient than the estimates based on SiRS. The importance and applicability of the KMBX model are demonstrated using three different data sets. Some of the useful actuarial risk measures, such as the value at risk and conditional value at risk, are discussed.

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The forecasting of stock prices is an important area of research because of the benefits it provides for individuals, corporations, and governments. The purpose of this study is to investigate the application of a key of study to the prediction of the adjusted closing price of a particular firm. Estimating a stock’s volatility is one of the more difficult tasks that traders must undertake. Investors are able to mitigate the risks associated with their portfolios and investments to a greater extent when stock prices can be accurately predicted. Prices of stocks do not move in a linear fashion. We propose artificial intelligence (AI) for multilayer perceptron (MLP) and long short-term memory (LSTM) models to predict fluctuations on the Saudi Stock Exchange (Tadawul). This paper focuses on the future forecasting of the stock exchange in the communication, energy, financial, and industrial sectors. The historical records from Tadawul were used as a basis for data collection for these sectors, in time periods from 2018 to 2020. For the purpose of predicting the future values of various stock market sectors, the AI algorithms were applied over a period of 60 days. They demonstrated highly effective performance when simulated using input data, which was carried out to validate the proposed model. In addition, the correlation coefficient (R) of the LSTM and MLP models for predicting the stock market in four sectors in the Saudi Stock Exchange (Tadawul) was >0.9950, which indicates that the outcomes were in good agreement with the predicted values. The outcomes of the forecasts were provided for each method based on four different measures. Among all the algorithms utilized in this work, LSTM demonstrated the most accurate findings and had the best capacity for model fitting.