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Currently, cloud computing provides the necessary infrastructure and software services to provide the requested services needed by users on the Internet. Due to the spectacular growth of cloud computing, the number of users and the number of demands are increasing rapidly, which creates a high workload on servers and computing resources. In this situation, for the optimal use of resources, the need for an efficient and effective management approach is fully felt. For this purpose, game theory has been used. The game structure is designed in such a way that the leader (leader) owns a large number of resources and plans the allocation of resources based on the request received from mobile users. The goal from the leader’s point of view is to minimize the cost of using the resources located in the fog nodes, on the other hand, the goal considered from the mobile users’ point of view is to minimize the cost of responding and transmitting the message to the desired fog node. For this purpose, the entire region is divided into regions and a fog node is considered for each region. The main goal of this research is to reduce the average delay in the provision of services related to Internet of Things applications in cloud computing platform. For this purpose, an attempt is made to provide a new method for allocating multiple tasks in mobile crowd sensing based on fog computing in Internet of Things using the inverse Stackelberg game theory with the help of fuzzy logic and deep reinforcement learning algorithm. [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]