## 2 Related Research
However, they also used FAHP to assign weights to the identified sub-dimensions, which is a further indication that the traditional FMEA process is not sufficient for ranking in an MCDM process. Due to the drawbacks associated with the traditional FMEA process, the traditional RPN calculation in the FMEA process does not consider the significance of individual risk factors to the peculiarity of the processor type of software being used. FAHP is therefore employed to enable us to assign priority to individual risk factors (severity occurrence and detection). Various studies have employed the use of GRA- FAHP-FMEA in different aspects of evaluation research. However, before this study, no research has employed this approach in evaluating mobile learning platforms. Researchers in [15] proposed a model that involves the combination of the fuzzy analytic hierarchy process (FAHP) with FMEA to obtain accurate and timely results when carrying out maintenance, particularly in the paper industry. The model was applied to a numerical sample to show its efficiency. Researchers in [16] also proposed a method for analyzing the risks of green components in the electronic manufacturing process in a bid to improve quality control. They used FMEA to analyze the failure modes and effects of green components and used FAHP to determine the relative weightings of four factors. This helped them to calculate a green component risk priority number (GC-RPN) for each of the components that a supplier provided to identify potential risks and manage them appropriately. The most frequently used MCDM methods were summarized as TOPSIS, ELECTRE, and FAHP in [17]. The core features in the mobile learning platform must be for the MLPs to be able to offer learning irrespective of location and time. Likewise, knowing the best method for the selection criteria is another problem, and manual selections among several MLPs are both tiresome and time-consuming, which leads to poor choices. Therefore, there is a need to devise a means of independently selecting and evaluating MLPs. For this purpose, utilizing FMEA-FAHP approaches in the selection and evaluation of MLPs provides time efficiency, optimum results, and the best technique for future study. Hence, locating the most suitable mobile learning platforms using a uniquely developed framework is proposed from the standardized model of ISO/IEC 9126. The number of studies located in the context of mobile learning literature that applied either multi-criteria decision-making or machine learning approaches is limited. There are no located studies that apply grey relational analysis or FMEA techniques in the context of mobile learning. A study used the analytical hierarchy process (AHP) to determine the critical success factors for the successful implementation of cloud-based mobile learning as Pedagogy, Mobile Compatibility, Data Security, Learning Management, Content Quality, Portability, and Support for Teachers [18]. Another study evaluated and ranked the several sub-factors of cloud-based mobile learning through the fuzzy analytic hierarchy process (FAHP) and analytical hierarchy process-group decision-making (AHP-GDM) [19]. Another study employed a machine learning-based algorithm to select the most suitable mobile learning application for children by using textual analysis of app reviews [20]. Another study uses fuzzy-TOPSIS group decision-making with 25 criteria and 125 students as decision-makers to rank educational applications [21]. The diversity and ease of availability of mobile learning applications encouraged researchers and educators to use mobile learning platforms to enhance teaching and learning [22]. The mobile learning platforms are another perspective of implementing e-learning, which uses both the technical and non-technical ability of mobile devices to deliver content to users.