Corporate bankruptcy prediction is an important task in the determination of corporate solvency, that is, whether a company can meet up to its financial obligations or not. It is widely studied as it has a significant effect on employees, customers, management, stockholders, bank lending assessments, and profitability. In recent years, machine learning techniques, particularly Artificial Neural Network (ANN), have widely been studied for bankruptcy prediction since they have proven to be a good predictor, especially in financial applications. A critical process in learning a network is weight training. Although the ANN is mathematically efficient, it has a complex weight training process, especially in computation time when involving a large training data. Many studies improved ANN’s weight training using metaheuristic algorithms such as Evolutionary Algorithms (EA), and Swarm Intelligence (SI) approaches for bankruptcy prediction. In this study, two metaheuristics algorithms, Magnetic Optimization Algorithm (MOA) and Particle Swarm Optimization (PSO), have been enhanced through hybridization to propose a new method MOA-PSO. Hybrid algorithms have been proven to be capable of solving optimization problems faster, with better accuracy. The MOA-PSO was used in training ANN to improve the performance of the ANN in bankruptcy prediction. The performance of the hybrid MOA-PSO was compared with that of four existing algorithms. The proposed hybrid MOA-PSO algorithm exhibits promising results with a faster and more accurate prediction, with 99.7% accuracy.

The role of information and communication technology (ICT) in education continues to serve the development of teaching and learning for most subjects, and the subject of Islamic studies is no exception. However, to date, a systematic literature review (SLR) on the role of ICT for this particular subject is lacking. Therefore, to facilitate the implementation and adoption of ICT, we focus on e-learning for the majority of subjects, including Islamic education and ICT applications, e.g. educational software and the Internet, which are gaining exponential importance with time. This SLR considered research works in the literature that have been published in the temporal range of June 2007 to June 2020. We derived 41 publications from an initial 301 candidate publications, by applying the inclusion and exclusion criteria in two distinct rounds. This rigorous review explored teachers' and students' opinions on the application of technology, and shed light on the challenges to date, as well as the potential opportunities and future research directions. We also performed Tweet analysis in an example of public opinion available on social media, e.g. Twitter, for learning education through ICT-based methods. This study delivers several implications for researchers and practitioners, and provides insight on state-of-the-art. Our findings suggest that ICT-based teaching methods for learning Islamic studies as a research context require more attention. Further empirical investigation is crucial to better understand the impact of ICT practices and use, especially in the context of Islamic studies.