## 1 Introduction
This strategy ensures that expert knowledge and priorities are accurately represented in our review process, which is crucial for a comprehensive assessment of MLPs. Additionally, FAHP is used in this work to assign weights to risk factors that cannot be evaluated by FMEA or GRA-FMEA techniques alone, such as occurrence, severity, and detection. Popular MCDM method TOPSIS identifies choices that are closest to the ideal solution; however, it performs best with precise data and may not perform as well with ambiguous or subjective information. In our research, fuzzy data are used to capture the ambiguity and vagueness present in expert assessments, making FAHP a superior choice. Furthermore, normalizing data is necessary for TOPSIS, which could make comparing criteria with various scales and units more difficult. Another MCDM method that focuses on outranking relationships between options is called ELECTRE. It is frequently more appropriate in situations where there are obvious differences in the performance levels of the available options and can be computationally complex. In contrast, our study addresses platforms that might exhibit similar performance across several criteria, necessitating the use of a method such as GRA that can handle these nuances efficiently and offer a more nuanced ranking based on relative closeness. MCDM is a mechanism for the determination of the best option from several options, given that multiple alternatives are, in most cases, conflicting decision criteria [8]. This study addresses the essential problem of difficulty in choosing the most suitable mobile learning platform since there are abundant learning platforms readily available. For this purpose, the study proposes an efficient way to rank and choose the best quality mobile learning platforms by integrating risk analysis and multi-criteria decision-making methods. This research is believed to be the only study applying an integrated FMEA-FAHP approach to evaluate the risks and quality of mobile learning platforms. What makes this study unique in comparison to other relevant studies is the fact that it implements failure mode effects analysis (FMEA) in collecting and evaluating data collected from various mobile learning platforms, which are then weighted using the fuzzy inference system (FIS) and finally ranked using the fuzzy analytic hierarchy process (FAHP). The significant contributions of this study toward selecting a suitable mobile learning platform are given below:  
* The proposed method uses unique, integrated risk analysis and multi-criteria decision-making approaches to overcome the shortcomings of the traditional FMEA approach. An integrated FMEA-GRA-FAHP method is a combination of risk analysis and multi-criteria decision-making methods for the suitability of mobile learning platforms. The initial implementation of Grey relational analysis involved assuming that risk factors carried equal weights. In the subsequent application, distinct weights were assigned to risk factors through the utilization of Fuzzy AHP, as determined by evaluations from experts. This represents a significant advancement in the realm of failure mode and effect analysis (FMEA), as the introduction of varied weights through Fuzzy AHP introduces a novel approach to the FMEA methodology. * To researchers' knowledge, this is the only known study to present the FMEA-GRA-FAHP technique for choosing the top mobile learning platform. * The evaluation's results indicate that the proposed approach is an effective way to find the most suitable mobile learning platform to use. Therefore, this study aims to employ a multi-criteria method such as FMEA-GRA-FAHP to evaluate and rank the five selected MLPs. Furthermore, the study adopts the technical requirements concerning the ISO/IEC 9126 model to generate a suitable framework for the effective evaluation of the selected MLPs using such techniques.