# An Artificial Intelligence (AI) Framework to Predict Operational Excellence: UAE Case Study
## 2 Materials and Methods
### AI Overview  
The second major component of this research's framework is artificial intelligence (AI). In this section, an AI overview is covered. AI is a field of computer science and technology that emphasizes creating systems and machines that can perform jobs requiring human intelligence. These tasks include reasoning, problem-solving, learning, perception, understanding language, and decision-making [20]. AI research began in the 1950s with the target of creating machines that can mimic human intelligence. Early AI systems were rule-based and relied on explicit programming to simulate human reasoning. However, these systems did not work well in complex real-world problems. Therefore, machine learning--a subset of AI--emerged in the 1970s. It introduced a paradigm shift by emphasizing the ability of machines to learn from data and enhance their performance without being explicitly programmed. Machine learning's fundamental concept is to allow computers to learn and adapt from data. This is achieved through various algorithms that can specify patterns, make predictions, and make decisions relative to the data they are provided [30]. Machine learning is achieved through training on large datasets, where the machine learns the underlying patterns and relationships in the data [20]. Machine learning is widely applied in terrorism prediction [31], cancer prediction [32], and sports result prediction [33].These applications leverage the ability of machine learning algorithms to find hidden patterns and correlations in data. Machine learning techniques are classified into categories: supervised learning [34], unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning, as shown in Figure 5 [30]. Recently, machine learning has spread over many applications and industries. It can be found in web searches, siri, big data [35], pricing prediction, weather prediction [36], marine work [37], aviation [38], manufacturing [39], transportation, crime prediction [40],  
Figure 4: EFQM papers classified by countries, publication year, and sector. Figure 5: Machine learning algorithms [30]. predicting COVID-19 cases [41], and healthcare [42]. Unfortunately, few papers have talked about using AI in predicting EFQM scores. Therefore, this paper provides a framework combining AI techniques with the EFQM model. In this framework, the k-means unsupervised machine learning technique is adopted. Artificial intelligence and machine learning are incorporated in different industries and in different countries, as Table 1 shows. Machine learning continues to advance rapidly and has become a basic tool in various fields, including healthcare [42], finance, natural language processing, computer vision, robotics [44], decision making [25; 45], human resources work [46], and autonomous systems. Its potential to extract valuable patterns from data makes it a powerful technology with a wide range of applications [47]. Unsupervised learning works by finding common input points in the data as previously trained. Clustering of inputs is a good description of this process where the inputs are correlated based on their statistical properties. Unsupervised data learning methods do not need any labeled output to train the algorithm. They are more subjective, since humans will not interfere as in supervised learning [48]. The main objective of unsupervised learning is to learn more about data by identifying patterns found in the data. In other words, it learns an input pattern by itself and compares it with the following input patterns as shown in Figure 6.  
K-means clustering is a simple and powerful unsupervised machine learning technique that works with most industries [49]. It groups similar inputs together to form meaningful