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In the IoT-based users monitor tasks in the network environment by participating in the data collection process by smart devices. Users monitor their data in the form of fog computing (mobile mass monitoring). Service providers are required to pay user rewards without increasing platform costs. One of the NP-Hard methods to maximise the coverage rate and reduce the platform costs (reward) is the Cooperative Based Method for Smart Sensing Tasks (CMST). This article uses chaos theory and fuzzy parameter setting in the forest optimisation algorithm. The proposed method is implemented with MATLAB. The average findings show that the network coverage rate is 31% and the monitoring cost is 11% optimised compared to the CMST scheme and the mapping of the mobile mass monitoring problem to meta-heuristic algorithms. And using the improved forest optimisation algorithm can reduce the costs of the mobile crowd monitoring platform and has a better coverage rate. [ABSTRACT FROM AUTHOR]

Most web-based educational systems contain some drawbacks, as compared to traditional classrooms. Particularly, it becomes difficult for teachers to guide students to choose an appropriate learning resource due to the large number of online learning resources. Meanwhile, student decisions make it more difficult to choose educational resources according to their circumstances. In this matter, the resource recommender system can be employed as an educational environment to recommend the educational resource advice for students, so that these recommendations can be coordinated to each student's preferences and needs. This paper presents the resource recommender system as a combination of MLP, BiLSTM, and LSTM improved deep learning networks using the attention method. Compared to similar studies conducted using DBN networks and focus only on the near past interests and preferences of users, the proposed system provides higher accuracy and more appropriate recommendations considering current interests, in addition to the user's long-term past interests. The proposed recommender system with accuracy of 0.96 and a loss of 0.0822 contains a better performance to recommend resources to students compared to other methods. [ABSTRACT FROM AUTHOR]

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Copyright of Revista Latinoamericana de Hipertension is the property of Revista Latinoamericana de Hipertension and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)