Titlebook: Advanced Multimedia and Ubiquitous Engineering; MUE-FutureTech 2020 James J. Park,Vincenzo Loia,Yunsick Sung Conference proceedings 2021 Th

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Philipp Christen,Rolf Jaussi,Roger Benoitironment. In this paper, we propose a method of applying cluster-based LEACH (low energy adaptive clustering hierarchy) and chain-based PEGASIS (Power-Efficient Gathering in Sensor Information Systems) to MEC environments among typical WSN routing protocols. Simulation results show that the proposed

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Philipp Christen,Rolf Jaussi,Roger Benoiteral metrics. The results have outperformed other similar work by achieving 97% in accuracy in pre-diction. Furthermore, the combination of Convolution and Bi-directional LSTM tends to provide a more reliable result for the prediction.

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https://doi.org/10.1007/978-3-662-06058-2-face sessions and online classes. In fact, the success of teaching and learning depends on a variety of methods used, which are targeted at meeting students’ needs. In this study, the BL approach was aimed at students’ language, working and family needs in order to enhance their language skills, as

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Research on Optimization of Knapsack Problem in Logistics Distribution,samples and sampling strategy, context, time) and research procedures (from entry to access and completion of the research project) with resources planning. In addition, the book also addresses data analysis an978-981-13-6141-8978-981-13-6142-5

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1876-1100 International Conference on Future Information Technology (Future Tech 2020). And this book presents the latest developments in the field of ubiquitous computing technologies..It also discusses the state of the art in the development of computational methods, involving theory, algorithms, numerical

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Philipp Christen,Rolf Jaussi,Roger Benoity module, an FPGA design for driving the modules, and an artificial neural network algorithm were developed. The pointing accuracy of the developed button device showed 99% accuracy in the recognition test in the laboratory.

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Transkription: Biosynthese der RNAected execution time based on trained deep learning model. Based on the experimental results, our scheme can select the computing device dynamically to reduce the execution time and maximize the computing efficiency.