## 2 Related Work  
First, the authors in [7] restricts itself to detecting disruptive phone calls that are a major source of annoyance to users. To this end, they evaluated six types of learning algorithms, namely: SVM (support vector machine), NB (naive Bayes), KNN (K-nearest neighbors), RUSBoost, GP (genetic programming), and AR (association rule learning). The dataset used for the assessment was collected over a period of 16 weeks with the help of a mobile app. Similar to this work, we also used the NB, SVM, and KNN algorithms, and collected data from a mobile application to create the application scenario test. Following notification-related studies, the authors at [8] developed an architecture of an intelligent notification system that uses classification algorithms to manage the receipt of notifications according to contextual perception and user habits. The system consists of modules that monitor the environment and users, collecting information to send them to a DM mechanism. The primary relationships with this work are the comparison of the classification algorithms on the classification precision metric, the use of artificial dataset, and the system that implements a classification algorithm in the DM mechanism. The authors at [9] report in their article, a proposal for location verification and user confirmation in smart environments, in the context of notification control and management. User verification and notification control are performed based on parameters such as environment type, user profile type, location, time criteria, priority, and user preferences. The authors' work is also based on one of the modules of UBIPRI, being PRISER. Furthermore, this work is based on the modules PRIPRO and PRICRI. In the work of [10], a system has been developed to reduce manual user efforts by addressing and receiving relevant notifications by wireless communication in a university setting. In development, the _Knuth-Morris-Pratt_ (KMP) algorithm was applied to a real dataset with the following attributes:admin, dept, notice, notice, read-by, registration details, staff, and user table. Similar to this work, attributes related to the context was used to perform the notification management. The reference work [11] discusses in their article an assessment of an artificial dataset in a notification management system. In general, the set has the characteristics of the notification content, user context, and the receiving method, together with the synthetically entered data. In the evaluation, the fuzzy inference system (FIS) algorithm was used to verify the behavior of the generated artificial dataset. The primary relationship with this work is the generation and use of an artificial dataset for PRINM evaluation. Table 1 presents a synthesis of related work, pointing to the use of classification algorithms, artificial datasets, if the proposed comparison of algorithms on the classification precision metric are used, and if statistical hypothesis tests are used. Comparisons of literature with approaches are defined as: (i) yes, literature treats the approach; (ii) no, the literature does not address the approach; (iii) partial, the literature partially addresses the approach. The work presented in [12] does not have a direct relationship with the notification management context, but it is a part of a series of research that is related to UBIPRI. When compared to this work on the notification management approach, only the works [10; 11] do not use classification algorithms to manage notifications, but they still have a similarity in artificial dataset approaches. Using artificial datasets is not a good practice when it comes to using ML approaches. However, this practice is becoming increasingly utilized, as in the works [8; 11]. The works [7; 9] do not address the comparison of classification algorithms over the classification accuracy metric, since there is a range of algorithms to be studied and compared. Only the work [8] addresses this comparison.