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The purpose of this paper is to formulate a specialized artificial neural network algorithm utilizing radial basis function (RBF) for modeling of time to failure of aircraft engine turbines.

The model uses training failure data collected from operators of turboprop aircraft working in harsh desert conditions where sand erosion is a detrimental factor in reducing turbine life. Accordingly, the model is more suited to accurate prediction of life of critical components of such engines. The used RBF employs a closest neighbor type of classifier and the hidden unit’s activation is based on the displacement between the early prototype and the input vector.

The results of the algorithm are compared to earlier work utilizing Weibull regression modeling, as well as Feed Forward Back Propagation NN. The results show that the failure rates estimated by RBF more closely match actual failure data than the estimations by both other models. The trained model showed reasonable accuracy in predicting future failure events. Moreover, the technique is shown to have comparatively higher efficiency even with reduced number of neurons in each layer of ANN. This significantly decreases computation time with minimum effect on the accuracy of results.

Using RBF technique significantly decreases the computational time with minimum effect on the accuracy of results.

The study aims to evaluate and compare the quantity and quality levels of corporate social responsibility disclosure (CSRD) practices on the websites of the largest Islamic and conventional banks worldwide.

Two indices were developed: the quantity index, consisting of seven categories, and the quality index, which includes eight characteristics, to adopt the quantitative content analysis of the global leading 94 Islamic and 100 conventional banks’ websites.

The results show that conventional banks have higher levels of CSRD quantity and quality on their websites than those of Islamic banks. The study found that the products and services category is the most disclosed by largest Islamic and conventional banks worldwide, while environment and energy information is the lowest for Islamic banks and employee and human resource information is less common for conventional ones. The analysis reveals low levels of CSRD quantity and quality on Islamic banks’ websites (43.69% and 54.56%) and high levels on conventional banks’ websites (70.84% and 73.26%).

The study focuses on analyzing the quantity and quality of CSRD on English-accessible websites of the largest Islamic and conventional banks in 2022, focusing on English as a uniform language for data collection because it analyzed 194 websites from 48 countries with over 20 languages.

The results of this study are likely to be valuable to many interested parties because they inform investors about the status of CSRD practices on the largest Islamic and conventional banks’ websites worldwide and how they disclose such information. To ensure investor satisfaction and accurate investment decisions, these global banks should provide comprehensive and high-quality CSR information on their websites to show how they contribute to CSR activities. For scholars, its limitations may be helpful in their future research.

The originality of this paper derives from its focus on largest-world Islamic and non-Islamic banks, collecting primary data directly from their websites, and offering valuable theoretical, methodological and practical insights.