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In recent years, large computational problems have beensolved by the distributed environment in which applications are executed in parallel. Also, lately, fog computing or edge computing as a new environment is applied to collect data from the devices and preprocessing is done before sending for main processing in cloud computing. Since one of the crucial issues in such systems is task scheduling, this issue is addressed by considering reducing energy consumption. In this study, an energy-aware method is introduced by using the Dynamic Voltage and Frequency Scaling (DVFS) technique to reduce energy consumption. In addition, in order to construct valid task sequences, a hybrid Invasive Weed Optimization and Culture (IWO-CA) evolutionary algorithm is applied. The experimental results revealed that the proposed algorithm improves some current algorithms in terms of energy consumption. • Proposing an energy-aware task scheduling approach to minimize energy consumption in fog computing. • Presenting a hybrid invasive weed optimization (IWO) along with the cultural evolution algorithm (CEA) for proposed task scheduling approach in fog computing. • Guaranteeing optimal solutions for proposed energy-aware task scheduling approach based on maximum level of QoS factors. [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]