Mobile learning is considered a new phase of e-learning which gives the opportunity to learn more effectively and efficiently. In addition, the use of mobile devices for leaning is more sophisticated and more useful. The m-learning has become available anywhere and anytime for all students and professors. Moreover, the features of these mobile devices include ease of use in every place and time, very reasonable cost for students, and the ability to communicate through the internet or mobile networks, encouraging the development of many kinds of methods and systems. Now, a huge number of applications, in several domains, are oriented to this kind of mobile device. Researchers have been exploiting this technology to enhance the knowledge of learner (especially foreign languages learning). In this paper, the author proposed an educational system that provides the opportunity for students to learn English language outside the classroom and encourages them to get actively involved in their own learning processes.
In Reconfigurable Manufacturing Systems (RMSs), the structure of the system can be changed during execution of the system. This reconfiguration can be motivated by a new requirement in the production process, or to avoid some problems caused by machines breakdowns. These systems offer a high flexibility leading to more productivity and efficiency. However, their design is more complicated implying new techniques and paradigms. The use of formal high level Petri Nets offers the ability to design these systems and to analyse or prove their properties. In this paper, we apply Reconfigurable Object Nets (RONs) for the modelling, simulation and analysis of reconfigurable manufacturing systems. We propose a formal approach, where the reconfiguration is specified as graph transformations, the simulation is realized using the RON-tool, and the analysis exploits some software tools such as TINA-tool and PIPE-tool.
PurposeThe purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.Design/methodology/approachWe have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.FindingsWe apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.Research limitations/implicationsThis research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.Practical implicationsFindings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.Originality/valueThe findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.
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