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 Benoitivacy exposure and storage capacity issues. To tackle these issues, we propose a Long Short-Term Memory (LSTM) architecture for HAR taking accelerometer data acquired from smartphones as its input values. The experimental results showed the outstanding performance of the proposed model against a con

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Philipp Christen,Rolf Jaussi,Roger Benoit discovery. Modeling the properties and functions of protein sequences is an important, but challenging at the same time. This work, is presented developed method as representation learning for compounds and proteins, which integrate the representations, and develop a new protein-compound prediction

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https://doi.org/10.1007/978-3-662-06058-2surrounded by a tremendous number of sensors and connected objects that produce data regularly. These data represent the fuel for a DT as they are used to represent the most accurate digital model of a system or an object and to predict and simulate all the possible scenarios. Recently, DT has been

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https://doi.org/10.1007/978-3-662-06058-2ferent teaching approaches, which can satisfy the needs of both full-time and part-time students. The purpose of this article is to describe the effectiveness of the BL approach in teaching English as a foreign language (EFL) to part-time students in their third year of study at the Faculty of Infor

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Kohlenhydrate, Lipide und Aminos?urenes. To solve these problems, artificial intelligence based malicious file detection methods have been proposed. However, artificial intelligence based malicious file detection method has a disadvantage that takes long time because it requires dynamic analysis. We can use similarity hashes to solve t

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https://doi.org/10.1007/978-3-662-06058-2h training from data. However, overfitting problems in training models have a significant impact on the performance of a neural network. In this paper, the effect of dropout to reduce overfitting in neural networks is analyzed through experiments. It also analyzes the effect on the performance of ne

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Lecture Notes in Electrical Engineeringhttp://image.papertrans.cn/a/image/146016.jpg