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Studying customer behavior and anticipating future trends is a challenging task, as customer behavior is complex and constantly evolving. To effectively anticipate future trends, businesses need to analyze large amounts of data, use sophisticated analytical techniques, and stay up-to-date with the latest research and industry trends. In this paper, we propose a comprehensive framework to identify trends in consumer behavior using multiple layers of processing, including clustering, classification, and association rule learning. The aim is to help a major retailer in Saudi Arabia better understand customer behavior by utilizing the power of big data analysis. The proposed framework is presented as being generalized to gain insight into the generated big data and enable data-driven decision-making in other relevant domains. We developed this framework in collaboration with a large supermarket chain in Saudi Arabia, which provided us with over 1,000,000 sales transaction records belonging to around 30,000 of their loyal customers. In this study, we apply our proposed framework to those data as a case study and present our initial results of consumer clustering and association rules for each cluster. Moreover, we analyze our findings to figure out how we can further utilize intelligence to predict customer behavior in clustered groups. [ABSTRACT FROM AUTHOR]

There are multiple entities responsible for online training programs via the Taif Maharat platform at the university. There is a variation in objectives, content, trainers' efficiency, online interaction and participation patterns, training strategies, evaluation tools, and feedback techniques. In addition, there is a lower achievement of standards in dimensions such as planning and curriculum design, leadership, online learning and training, technology, assessment, student support, continuous improvement, and evaluation. Based on these justifications, the study proposed a Machine Learning (ML) approach, which predicts the student's performance to enhance the quality assurance of online training programs via the Maharat platform at Taif University based on the online learning training standards in the Kingdom of Saudi Arabia (KSA). The paper's primary objective is to forecast academic achievement by considering their participation in an online learning environment. The relevant feature was extracted using hybrid optimization, and following this classification process was carried out. For the predictions, the Support Vector Machine technique was applied. To identify and determine the degree of achievement of quality assurance of online training programs standards, we used a descriptive-analytical approach to analyze the sample opinions about quality assurance of online training via the Maharat platform at Taif University. [ABSTRACT FROM AUTHOR]

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%. [ABSTRACT FROM AUTHOR]

In Saudi Arabia, all high school graduates who want join local universities have to go through a preparatory year before selecting their specific specialization/major. One of the most concerning issues for those fresh undergraduate college students is the selection of their specialization. College specialization selection is critical for them, as their academic and career future will be affected by this decision. An unsuitable specialization selection will have unfortunate consequences, not only on the students' future but also on the university’s resources and budget. This paper suggests a solution to this problem by introducing a preliminary study of a recommender system (RS), which will recommend the appropriate specialization for the students based on various tests and grades during the preparatory year at King Abdulaziz University (KAU). The proposed system guides students through their specialization selection process based on their abilities. The collaborative filtering technique was used to build the RS and K-fold cross-validation was adopted to evaluate its accuracy and performance. The results showed the prediction of a specialization for each student with good accuracy ratio. These promising initial results provide a feasible solution to assess this issue further in future studies. [ABSTRACT FROM AUTHOR]

Chatbots have gained increasing importance in today research with a lot of available applications. They are flexible and can be applied in many application domains. In tourism, Chatbots can recommend sights, hotels, activities, or even full travel plans. Usually, users are required to visit plenty of websites and install different Apps into their mobile phones in order to navigate the city and planning for the trip which is inconvenient and time-consuming. To address this issue, this paper proposes "Smart Guidance", an AI text-based chatbot that is developed as a mobile application. It simulates a chat with users in a natural language, it also provides a two-way interaction and it is a single point of contact for all user communications. We provide a use case in the city of Jeddah, Saudi Arabia. Jeddah is the second largest city in Saudi Arabia and tourists have various choices when it comes to moving around the city. The final evaluation of the chatbot showed that the bot could understand meanings and users' requests. Moreover, users found that the interaction is effective, and the responses are provided immediately. Users were satisfied and like the idea that they could express their needs in their natural language. Furthermore, users enjoyed the interaction and felt engaged. [ABSTRACT FROM AUTHOR]