## 2 Related Work  
DSS is a tool used to solve decision-making problems that Decision-Makers (DMs) face through collecting, analyzing, or organizing required information. They are used by both single or group of DMs when complex or unstructured scenarios are faced to ensure robust results [29]. While the authors of [30] stated that Information Systems (IS) are a basic component of DSS which improves the decision-making process and requires a high level of human-computer interaction to support the behavioral part of the decision-making process. The study in [31] tested the impact of using DSS on the Top Management (TM) level in developing countries' governmental institutions by applying Delphi methodology considering Saudi Arabia as a study case of developing nations. Results showed that DSSs improved the quality of decisions taken at the TM level with decreased cognitive bias. Furthermore, the work in [32] proposed a DSS which combined Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for evaluating government performance in Magelang Regency (Central Java, Indonesia). The study begins with criteria weights specified by AHP then performance ranking set by TOPSIS. Both models were chosen due to their efficiency and simple mathematical calculations. The system had a unique feature of dynamic selection for assessment criteria in which users can add or remove any condition from the available menu as per ongoing situation, regulation or condition. Results showed that 86.67% of users' ranking and opinions about the proposed system usage in village governmental organization. A massive number of scenarios were being solved through literature using intelligent systems. Yet, the complexity of these scenarios is increasing, and the need for better-performing DSS has increased as a response. It is the point where researchers combined intelligent models with DSS, in which Artificial Intelligence (AI) techniques were used to support the decision-making process. They were also referred to knowledge-based DSS and are classified into two categories as stated in [33]: rule-based expert DSS and ML-based DSS. An example of combining an intelligent system with DSS is the study conducted in [34] to support decisions made by governments on budgeting national Research and Development (R&D) programs. ML algorithms such as distributed random forest, generalized linear model, gradient boosting machine, deep learning, and stacked ensemble were used to estimate the R&D output per project. Then, a mathematical model was used to set the suitable budget for estimated outcomes from each R&D project. Moreover, the authors of [35] conducted a study on multi-criterion DSS for supporting the Indian Government's decisions during the pandemic conditions of COVID-19. An assumption-based approach was added into the DSS for multi-criterion decision support system (MCDSS) along with accurately specified risk factors of determining input parameters. Results were analyzed in terms of Precision, Recall, F-Score, and Other advanced metrics such as MCC (Matthews Correlation Coefficient), ROC (Receiver Operating Characteristics), and PRC (Precision Recall). The authors of [29] conducted a study in which they used fuzzy theory-based DSS to investigate re-opening strategies for post COVID-19 period. The system is examined using principles for multi-criteria decisions to decide the importance of 16 key indicators. The whole study focused on the critical pandemic conditions along with economic consequences and the vital role of decision-taking tools to be exploited in this regard giving the proposed system as an example. E-participation of citizens has a vital role in affecting policies and regulations taken by most governments around the world during the crisis of COVID-19 as stated in [3]. The study also observed that most studies during the pandemic condition have applied ML and DL for detecting, predicting, and identifying COVID-19.